Journal articles on the topic 'Connected Vehicles (CVs)'
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1
Liang, Xiao (Joyce), S. Ilgin Guler, and Vikash V. Gayah. "Signal Timing Optimization with Connected Vehicle Technology: Platooning to Improve Computational Efficiency." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 18 (July 16, 2018): 81–92. http://dx.doi.org/10.1177/0361198118786842.
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This paper develops a real-time traffic signal optimization algorithm in the presence of connected and autonomous vehicles (CAVs). The proposed algorithm leverages information from connected vehicles (CVs) arriving at an intersection to identify naturally occurring platoons that consist of both CVs and non-CVs. Signal timings are then selected to optimize the sequence at which these platoons are allowed to discharge through the intersection to minimize total vehicle delay. Longitudinal trajectory guidance that explicitly accounts for vehicle acceleration and deceleration behavior is provided to the lead autonomous vehicle (AV) in any platoon to minimize the total number of stopping maneuvers performed by all vehicles. Simulation tests reveal that the proposed platoon-based algorithm provides superior computational savings (over 95%) compared with a previously developed algorithm that focuses on optimizing departure sequences of individual vehicles, with negligible changes in operational performance. The computational savings allow the platoon-based algorithm to accommodate intersections with four multi-lane approaches and left turns, whereas large computational costs limited the previous vehicle-based algorithm to only two single-lane approaches without conflicting left turns. Additional simulation tests of the platoon-based algorithm on these more realistic intersection configurations show that intersection performance increases as the penetration rate of CAVs in the vehicle fleet increases. However, the marginal benefits decrease rapidly after the fleet is composed of 40% CAVs.
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Ahmed, Hafiz Usman, Ying Huang, Pan Lu, and Raj Bridgelall. "Technology Developments and Impacts of Connected and Autonomous Vehicles: An Overview." Smart Cities 5, no. 1 (March 17, 2022): 382–404. http://dx.doi.org/10.3390/smartcities5010022.
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The scientific advancements in the vehicle and infrastructure automation industry are progressively improving nowadays to provide benefits for the end-users in terms of traffic congestion reduction, safety enhancements, stress-free travels, fuel cost savings, and smart parking, etc. The advances in connected, autonomous, and connected autonomous vehicles (CV, AV, and CAV) depend on the continuous technology developments in the advanced driving assistance systems (ADAS). A clear view of the technology developments related to the AVs will give the users insights on the evolution of the technology and predict future research needs. In this paper, firstly, a review is performed on the available ADAS technologies, their functions, and the expected benefits in the context of CVs, AVs, and CAVs such as the sensors deployed on the partial or fully automated vehicles (Radar, LiDAR, etc.), the communication systems for vehicle-to-vehicle and vehicle-to-infrastructure networking, and the adaptive and cooperative adaptive cruise control technology (ACC/CACC). Secondly, for any technologies to be applied in practical AVs related applications, this study also includes a detailed review in the state/federal guidance, legislation, and regulations toward AVs related applications. Last but not least, the impacts of CVs, AVs, and CAVs on traffic are also reviewed to evaluate the potential benefits as the AV related technologies penetrating in the market. Based on the extensive reviews in this paper, the future related research gaps in technology development and impact analysis are also discussed.
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Du, Mengxiao, Shiyao Yang, and Qun Chen. "Impacts of vehicle-to-infrastructure communication on traffic flows with mixed connected vehicles and human-driven vehicles." International Journal of Modern Physics B 35, no. 06 (March 10, 2021): 2150091. http://dx.doi.org/10.1142/s0217979221500910.
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This paper explored the impacts of vehicle-to-infrastructure (V2I) communication on the mixed traffic flow consisting of connected vehicles (CVs) and human-driven vehicles (HVs). We developed a cellular automaton model for mixed flow at the signalized intersection. In addition to considering the motion characteristics of CVs and the influence of HVs on the motion behavior of CVs, the model also considered the influence of signal lights. CVs determine their velocities via V2I communication in order to pass the signal light with less delay and avoid stopping. Through simulations, we found that the presence, frequency and range of V2I communication all make a difference in the mixed flow. Also, 1-Hz communication reduces the number of vehicles within 300 m before the red light from 36 to 26, and the 10-Hz communication reduces one more; 1-Hz communication increases the number of accelerations, but when the frequency increases to 10 Hz, the number of accelerations decreases to the same value as without V2I communication, but the value of number of accelerations increases monotonously with the frequency; traffic delay decreases and capacity increases as the frequency increases. However, as the communication range increases, except that the number of accelerations first decreases and then increases, other traffic characteristics remain unchanged. The number of accelerations reaches a minimum at about 500 m.
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Sahebi, Sina, and Habibollah Nassiri. "Assessing Public Acceptance of Connected Vehicle Systems in a New Scheme of Usage-Based Insurance." Transportation Research Record: Journal of the Transportation Research Board 2625, no. 1 (January 2017): 62–69. http://dx.doi.org/10.3141/2625-07.
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The consequences of road accidents are significant for drivers and society. The connected vehicle system (CVS) is a promising technology that can improve road safety by warning drivers of traffic hazards. Broad implementation of the CVS could mitigate the harmful consequences of road accidents. Widespread implementation requires schemes that can promote the pervasive adoption of the system by drivers. This study proposes the innovative idea of implementing the CVS in usage-based insurance (UBI) as a measurement probe and modeling drivers’ acceptance of the new UBI scheme. This study developed a random effect logit model demonstrating that the drivers of cheaper vehicles and middle-age drivers (30 to 60) were more inclined to accept the new UBI scheme and use the CVS in their vehicles. Risk-averse drivers were more likely to accept the scheme than were other drivers. The pervasive implementation of the CVS can be costly, but it can improve traffic safety. Because of the two-way spectrum of the costs and benefits of the CVS, providing comprehensive projects to develop the system is important for CVS investors and developers.
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Mohammadi, Roozbeh, and Claudio Roncoli. "Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment." Sensors 21, no. 24 (December 19, 2021): 8477. http://dx.doi.org/10.3390/s21248477.
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Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.
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Wang, Zhendong, Haoran Wei, Jianda Wang, Xiaoming Zeng, and Yuchao Chang. "Security Issues and Solutions for Connected and Autonomous Vehicles in a Sustainable City: A Survey." Sustainability 14, no. 19 (September 29, 2022): 12409. http://dx.doi.org/10.3390/su141912409.
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Connected and Autonomous Vehicles (CAVs) combine technologies of autonomous vehicles (AVs) and connected vehicles (CVs) to develop quicker, more reliable, and safer traffic. Artificial Intelligence (AI)-based CAV solutions play significant roles in sustainable cities. The convergence imposes stringent security requirements for CAV safety and reliability. In practice, vehicles are developed with increased automation and connectivity. Increased automation increases the reliance on the sensor-based technologies and decreases the reliance on the driver; increased connectivity increases the exposures of vehicles’ vulnerability and increases the risk for an adversary to implement a cyber-attack. Much work has been dedicated to identifying the security vulnerabilities and recommending mitigation techniques associated with different sensors, controllers, and connection mechanisms, respectively. However, there is an absence of comprehensive and in-depth studies to identify how the cyber-attacks exploit the vehicles’ vulnerabilities to negatively impact the performance and operations of CAVs. In this survey, we set out to thoroughly review the security issues introduced by AV and CV technologies, analyze how the cyber-attacks impact the performance of CAVs, and summarize the solutions correspondingly. The impact of cyber-attacks on the performance of CAVs is elaborated from both viewpoints of intra-vehicle systems and inter-vehicle systems. We pointed out that securing the perception and operations of CAVs would be the top requirement to enable CAVs to be applied safely and reliably in practice. Additionally, we suggested to utilize cloud and new AI methods to defend against smart cyber-attacks on CAVs.
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Yu, Bin, Miyi Wu, Shuyi Wang, and Wen Zhou. "Traffic Simulation Analysis on Running Speed in a Connected Vehicles Environment." International Journal of Environmental Research and Public Health 16, no. 22 (November 8, 2019): 4373. http://dx.doi.org/10.3390/ijerph16224373.
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Connected vehicles (CVs) exchange a variety of information instantly with surrounding vehicles and traffic facilities, which could smooth traffic flow significantly. The objective of this paper is to analyze the effect of CVs on running speed. This study compared the delay time, travel time, and running speed in the normal and the connected states, respectively, through VISSIM (a traffic simulation software developed by PTV company in German). The optimization speed model was established to simulate the decision-makings of CVs in MATLAB, considering the parameters of vehicle distance, average speed, and acceleration, etc. After the simulation, the vehicle information including speed, travel time, and delay time under the normal and the connected states were compared and evaluated, and the influence of different CV rates on the results was analyzed. In a two-lane arterial road, running speed in the connected state increase by 4 km/h, and the total travel time and delay time decrease by 5.34% and 16.76%, respectively, compared to those in the normal state. The optimal CV market penetration rate related to running speed and delay time is 60%. This simulation-based study applies user-defined lane change and lateral behavior rules, and takes different CV rates into consideration, which is more reliable and practical to estimate the impact of CV on road traffic characteristics.
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Mishra, Rohit, Yiqi Zhang, Fenglong Ma, and Anlong Li. "The Prediction of Collisions in Connected Vehicle Systems with A Long Short-Term Memory Model." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 775–79. http://dx.doi.org/10.1177/1071181320641178.
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The advances in connected vehicle systems (CVS) allow vehicles to communicate with each other and with infrastructures via wireless communication networks. This technology enables vehicles to detect potential hazards on the road, generate warnings, and assist the driver in taking preventive actions. To date, few mathematical models have been developed to predict the collision rates in connected vehicle systems. In this work, a Long Short-Term Memory model (LSTM) using time-series data of human drivers was developed to predict the collision rates in CVS by quantifying warning parameters and hazard scenario features. The model was validated with the driving performance data before and after warnings from thirty-two drivers in a behavioral experiment. The results indicated the LSTM model showed a prediction accuracy of 74% higher than SVM and logistic regression models. The LSTM model showed the potential to help optimize the warning algorithm in the connected vehicle systems to improve driver safety.
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Park, Hyung Geun, Sunghoon Kim, and Taehyung Kim. "Traffic-Responsive Signal Control at Intersections Using Real-Time Data of Vehicles Connected via V2X Communication." Journal of Advanced Transportation 2023 (January 10, 2023): 1–18. http://dx.doi.org/10.1155/2023/4025210.
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The positive effect of traffic-responsive signal control can be assured when real-time traffic data is reliable, but data reliability may be an issue that depends on the number of probe vehicles equipped with navigation devices or smartphones. However, there is a high chance of improving reliability with the recent deployment of connected vehicles (CVs) that use the vehicle-to-everything (V2X) communication data. Therefore, this paper proposes a traffic signal control strategy that utilizes V2X communication data obtained from CV operations, which is called the capacity waste reduction (CWR) strategy. In this strategy, vehicle queues on each road lane as an intersection approaches are initially estimated using V2X data. Then, the signal control algorithm determines the duration of the green signal for the currently applied phase based on the estimated vehicle queues. Furthermore, the strategy includes an algorithm for active priority signal control for the vehicles of bus rapid transit systems. The efficiency of the provided control strategy is tested with the VISSIM microsimulation program at different levels of the market penetration rate (MPR) of CVs. Based on the results of the experiment, the proposed strategy shows positive effects in both decreasing travel delay and increasing traffic flow even at the low levels of MPR of CVs. The results of the proposed strategy can be used as the base data for the development of smart intersection operations.
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Yang, Xianfeng (Terry), Gang-Len Chang, Zhao Zhang, and Pengfei (Taylor) Li. "Smart Signal Control System for Accident Prevention and Arterial Speed Harmonization under Connected Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (March 27, 2019): 61–71. http://dx.doi.org/10.1177/0361198119837242.
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The intent of this paper is to develop a system that can integrate connected vehicle (CV) data and traffic sensor information to concurrently address the need to improve urban arterial safety and mobility. Under the mixed traffic pattern of CVs and human-driven vehicles (HVs), the system aims to achieve three primary objectives: proactively preventing rear-end collision, reactively protecting side-street traffic from red-light-running vehicles, and effectively facilitating speed harmonization along local arterials. The embedded safety function will integrate CV and roadside sensor data to compute the distribution of dilemma zones for vehicles of different approaching speeds in real-time. Such data fusion will enable the proposed system to offer the advice of either “stop” or “go” to both CVs and HVs so as to prevent rear-end collisions and side-angled crashes. Given the locations and speeds of CVs, and the number of vehicles monitored by sensors, the proposed system can further compute the time-varying intersection queue length. Then the embedded mobility function will optimize the arterial signal plan in real-time and produce the speed advisory for approaching vehicles to facilitate their progression through intersections. Results from extensive simulation experiments confirm the effectiveness of the proposed system in both reducing potential intersection crash rates and improving arterial progression efficiency. The proposed control framework also proves the effectiveness of using dilemma zone protection sensors for traffic mobility improvement.
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Wang, Xingmin, Shengyin Shen, Debra Bezzina, James R. Sayer, Henry X. Liu, and Yiheng Feng. "Data Infrastructure for Connected Vehicle Applications." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 5 (April 9, 2020): 85–96. http://dx.doi.org/10.1177/0361198120912424.
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Ann Arbor Connected Vehicle Test Environment (AACVTE) is the world’s largest operational, real-world deployment of connected vehicles (CVs) and connected infrastructure, with over 2,500 vehicles and 74 infrastructure sites, including intersections, midblocks, and highway ramps. The AACVTE generates a massive amount of data on a scale not seen in the traditional transportation systems, which provides a unique opportunity for developing a wide range of connected vehicle (CV) applications. This paper introduces a data infrastructure that processes the CV data and provides interfaces to support real-time or near real-time CV applications. There are three major components of the data infrastructure: data receiving, data pre-processing, and visualization including the performance measurements generation. The data processing algorithms include signal phasing and timing (SPaT) data compression, lane phase mapping identification, trajectory data map matching, and global positioning system (GPS) coordinates conversion. Simple performance measures are derived from the processed data, including the time–space diagram, vehicle delay, and observed queue length. Finally, a web-based interface is designed to visualize the data. A list of potential CV applications including traffic state estimation, traffic control, and safety, which can be built on this connected data infrastructure is discussed.
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Guo, Xiaoyu, Yongxin Peng, Sruthi Ashraf, and Mark W. Burris. "Performance Analyses of Information-Based Managed Lane Choice Decisions in a Connected Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (August 20, 2020): 120–33. http://dx.doi.org/10.1177/0361198120940311.
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Connected vehicle (CV) technology can connect, communicate, and share information between vehicles, infrastructure, and other traffic management systems. Recent research has examined and promoted CV and connected automated vehicle (CAV) technology on managed lane systems to increase capacity and reduce congestion, as managed lane systems could be equipped with advanced infrastructure relatively quickly. However, the effect on travel considering, information-based managed lane choice decisions in a CV environment is not clear. Therefore, this research analyzed the potential effects on a managed lane system with connected vehicles considering several travel behavior elements, including drivers’ willingness to reroute and their choice of managed lanes based on individual travel time savings. This study analyzed the potential effects on a managed lane system by assigning different market penetration rates (0%, 10%, 50%, 100%) of CVs and informing CV drivers about travel time savings for a 10-mi stretch at 5-min intervals. How the traffic performance measurements (i.e., throughput, travel time saving, average speed and average travel time) vary under different market penetration rates of CVs is then investigated. Two major conclusions are reached: (i) although information exchange was assumed to be instantaneous between vehicles and the system, there existed a response time (or time delay) in the macroscopic traffic reflection; (ii) managed lane use may decrease, when travel time information becomes available, since drivers perceive they are saving more travel time than they actually do save.
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Rahman, Md Sharikur, Mohamed Abdel-Aty, Ling Wang, and Jaeyoung Lee. "Understanding the Highway Safety Benefits of Different Approaches of Connected Vehicles in Reduced Visibility Conditions." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 19 (June 11, 2018): 91–101. http://dx.doi.org/10.1177/0361198118776113.
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This study evaluated the effectiveness of connected vehicle (CV) technologies in adverse visibility conditions using microscopic traffic simulation. Traffic flow characteristics deteriorate significantly in reduced visibility conditions resulting in high crash risks. This study applied CV technologies on a segment of Interstate I-4 in Florida to improve traffic safety under fog conditions. Two types of CV approaches (i.e., connected vehicles without platooning (CVWPL) and connected vehicles with platooning (CVPL) were applied to reduce the crash risk in terms of three surrogate measures of safety: the standard deviation of speed, the standard deviation of headway, and rear-end crash risk index (RCRI). This study implemented vehicle-to-vehicle (V2V) communication technologies of CVs to acquire real-time traffic data using the microsimulation software VISSIM. A car-following model for both CV approaches was used with an assumption that the CVs would follow this car-following behavior in fog conditions. The model performances were evaluated under different CV market penetration rates (MPRs). The results showed that both CV approaches improved safety significantly in fog conditions as MPRs increase. To be more specific, the minimum MPR should be 30% to provide significant safety benefits in terms of surrogate measures of safety for both CV approaches over the base scenario (non-CV scenario). In terms of surrogate safety measures, CVPL significantly outperformed CVWPL when MPRs were equal to or higher than 50%. The results also indicated a significant improvement in the traffic operation characteristics in terms of average speed.
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Nezafat, Reza Vatani, Ehsan Beheshtitabar, Mecit Cetin, Elizabeth Williams, and George F. List. "Modeling and Evaluating Traffic Flow at Sag Curves When Imposing Variable Speed Limits on Connected Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 20 (July 11, 2018): 193–202. http://dx.doi.org/10.1177/0361198118784169.
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Sag curves, road segments where the gradient changes from downwards to upwards, generally reduce the roadway capacity and cause congestion. This results from a change in longitudinal driving behavior when entering a sag curve as drivers tend to reduce speeds or increase headways as vehicles reach the uphill section. In this research, a control strategy is investigated through manipulating the speed of connected vehicles (CVs) in the upstream of the sag curve to avoid the formation of bottlenecks caused by the change in driver behavior. Traffic flow along a sag curve is simulated using the intelligent driver model (IDM), a time-continuous car-following model. A feedback control algorithm is developed for adjusting the approach speeds of CVs so that the throughput of the sag curve is maximized. Depending on the traffic density at the sag curve, adjustments are made for the speeds of the CVs. A simulation-based optimization method using a meta-heuristic algorithm is employed to determine the critical control parameters. Various market penetration rates for CVs are also considered in the simulations. Even at relatively low market penetration rates (e.g., 5–10%), significant improvements in travel times and throughput are observed.
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Do Nascimento, Douglas Aguiar, Yuzo Iano, Hermes José Loschi, Navid Razmjooy, Robert Sroufe, Vlademir De Jesus Silva Oliveira, Diego Arturo Pajuelo Castro, and Matheus Montagner. "Sustainable Adoption of Connected Vehicles in the Brazilian Landscape: Policies, Technical Specifications and Challenges." Transactions on Environment and Electrical Engineering 3, no. 1 (March 18, 2019): 44. http://dx.doi.org/10.22149/teee.v3i1.130.
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This review addresses the intervehicular communication in Connected Vehicles (CV) by emphasizing V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communications in terms of evolution, current standards, state-of-the-art studies, embedded devices, simulation, trends, challenges, and relevant legislation. This review is based on studies conducted from 2009 to 2019, government reports about the sustainable deployment of these technologies and their adoption in the Brazilian automotive market. Moreover, WAVE (Wireless Access in Vehicular Environment) and DSRC (Dedicated Short-range Communication) standards, the performance analysis of communication parameters and intervehicular available at the market are also described. The current status of ITS (Intelligent Transportation System) development in Brazil was reviewed, as well as the research institutes and governmental actions focused on introducing the concept of connected vehicles into the society. The Brazilian outlook for technological adoption concerning CVs was also discussed. Moreover, challenges related to technical aspects, safety and environmental issues, and the standardization for vehicle communication are also described. Finally, this review highlights the challenges and proposals from available technologies devoted to the roads and vehicular infrastructure communication, their evolution and upcoming trends.
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Mutasem, Alzoubaidi, and Milan Zlatkovic. "Safety Performance Evaluation of Continuous Flow Intersections in the Era of Connected Vehicles: A Microsimulation Modelling Approach." Put i saobraćaj 68, no. 4 (December 17, 2022): 1–10. http://dx.doi.org/10.31075/pis.68.04.01.
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This study employed Federal Highway Administration’s Surrogate Safety Assessment Model (SSAM) to investigate the safety of implementing Connected Vehicles (CVs) at the Continuous Flow Intersection (CFI), by reproducing a real-world corridor, that has multiple successive implementations of CFIs, in VISSIM. Econolite’s ASC/3 Software-in-the-Loop signal controllers and Python-programmed Vehicle to Infrastructure (V2I) communications were embedded in VISSIM. Additionally, the effect of CV-Market Penetration Rate (CV-MPR) on safety is taken into consideration. The study shows that CV deployments at partial and full CFIs leads to notable reductions in crash likelihoods and severities. The total number of conflicts, rear-end and lane change conflicts dropped by 23.8%, 23.6% and 24.4%, respectively at full CFIs and 100% MPR, whereas those were reduced by 6.4%, 4.8% and 17.9%, respectively at partial CFIs and 100% MPR. It was also found that at least a 50% MPR of CVs is required for safety improvements to be influential.
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Tišljarić, Leo, Filip Vrbanić, Edouard Ivanjko, and Tonči Carić. "Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices." Sensors 22, no. 7 (April 6, 2022): 2807. http://dx.doi.org/10.3390/s22072807.
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Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that supports Connected Vehicles (CVs), which act as an active mobile sensor for collecting traffic data, provides an opportunity to harness the large datasets to develop novel methods regarding traffic bottlenecks detection. This paper presents a speed transition matrix based model for bottleneck probability estimation on motorways. The method is based on the computation of the speed at the vehicle transition point between consecutive motorway segments, which forms a traffic pattern that is represented using transition matrices. The main feature extracted from the traffic patterns was the center of mass, whose position is used as an input to the fuzzy-based system for bottleneck probability estimation. The proposed method is evaluated on four different simulated motorway traffic scenarios: (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms.
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Emami, Azadeh, Majid Sarvi, and Saeed Asadi Bagloee. "A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles." Journal of Modern Transportation 27, no. 4 (November 24, 2019): 341–54. http://dx.doi.org/10.1007/s40534-019-00200-y.
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AbstractThis paper presents a novel method to estimate queue length at signalised intersections using connected vehicle (CV) data. The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers. The model is applicable for real-time applications when there are sufficient training data available to train the estimation model. To this end, we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication (the communication platform used in CV system). The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems. Furthermore, a linear regression model is applied to weigh the importance of input variables to be used in a neural network model. Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions, a varying number of CVs (i.e. CVs’ market penetration rate) as well as various traffic signal control scenarios. As it is expected, when the market penetration rate increases, the accuracy of the model enhances consequently. In a congested traffic condition (saturated flow), the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates. Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters, the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information. The proposed algorithm is also tested using large size data from a CV test bed (i.e. Australian Integrated Multimodal Ecosystem) currently underway in Melbourne, Australia. The simulation results show that the model can perform well irrespective of the intersection layouts, traffic signal plans and arrival patterns of vehicles. Based on the numerical results, 20% penetration rate of CVs is a critical threshold. For penetration rates below 20%, prediction algorithms fail to produce reliable outcomes.
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Alzoubaidi, Mutasem, Adli Al-Balbissi, Abdel Rahman Alzoubaidi, Amr Alzoubaidi, Baha Azzeh, Ahmed Al-Mansour, and Ahmed Farid. "Connected Vehicles Versus Conventional Traffic Congestion Mitigation Measures: An Operational Economic Analysis." Azerbaijan Journal of High Performance Computing 4, no. 2 (December 31, 2021): 155–69. http://dx.doi.org/10.32010/26166127.2021.4.2.155.169.
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This paper conducted an operational, economic analysis to assess alternative solutions to traffic congestion. They involved integrating adaptive traffic signal control (ATSC) with connected vehicle technology (ATSC-CV) and the application of various conventional and unconventional solutions. The studied conventional scenarios include signal timing optimization, signal actuation, and upgrading existing intersections to interchanges. There were unconventional scenarios involving converting two intersections to interchanges and the third to a continuous green-T intersection (CGTI). Different unconventional alternatives involved deploying ATSC-CV-based systems assuming varying market penetration rates (MPRs). The operational performance of each alternative was analyzed using VISSIM microsimulation software. To model the driving behavior of CVs, Python programming language was used through the COM interface in VISSIM. One-way analysis of variance (ANOVA) and post-hoc testing results indicate that implementing any suggested alternative would substantially decrease the mean vehicular travel time compared to the fixed signal control strategy currently implemented. Specifically, the ATSC-CVbased systems yielded notable travel time reductions ranging from 9.5% to 21.3%. Also, ANOVA results revealed that the highest benefit-to-cost ratio among all alternatives belonged to scenarios in which the MPRs of CVs were 100%. It was also found that ATSC-CV-based systems with MPRs of 25% and 50% would be as feasible as converting signalized intersections to underpass interchanges.
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Severino, Alessandro, Giuseppina Pappalardo, Salvatore Curto, Salvatore Trubia, and Isaac Oyeyemi Olayode. "Safety Evaluation of Flower Roundabout Considering Autonomous Vehicles Operation." Sustainability 13, no. 18 (September 9, 2021): 10120. http://dx.doi.org/10.3390/su131810120.
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With the significant technological growth that affected autonomous vehicles in the last decade, several consequences occurred as: human factor exclusion, entry and exit manoeuvres precision from roundabouts, and headway reduction. In this paper, it was carried out a microsimulation approach study that aims to evaluate benefits in terms of safety obtained with flower roundabouts in a scenario where traffic is characterized by conventional vehicles “CVs” and Connected Autonomous Vehicles “CAVs”. This study focused on the evaluation of CAVs and CVs operation with the presence of the so called “weak users” or rather, pedestrians and bikes. Then, simulated scenarios were characterized by the presence of zebra-crossings in main roads, positioned at 20 m from circulatory carriageway edges. Micro simulation choice is due to the absence of survey data collection because the presence of CAVs in ordinary traffic is still minimal. The micro simulation was carried out through VISSIM, so it was operated with a specific methodological path, consisting, in the application, of O–D matrix based on real cases, in order to achieve an assessment of potential conflicts in relation with the increase in CAVs. Simulation results showed that higher safety levels were achieved for special cases of O–D distribution and with CAVs present. Finally, considering crash absence in results related to CAVs presence, safety interventions of such roundabout types have to be thorough. There were 10 O/D matrices analysed through VISSIM considering parameters as: average tail length, maximum tail length, average speed, vehicles, and number of stops quantity. As reported in the conclusion section, O/D matrices that showed minimum conflicts and maximum dynamic performances were identified.
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Trinh, Hung Tuan, Sang-Hoon Bae, and Duy Quang Tran. "Deep Reinforcement Learning for Vehicle Platooning at a Signalized Intersection in Mixed Traffic with Partial Detection." Applied Sciences 12, no. 19 (October 9, 2022): 10145. http://dx.doi.org/10.3390/app121910145.
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The intersection management system can increase traffic capacity, vehicle safety, and the smoothness of all vehicle movement. Platoons of connected vehicles (CVs) use communication technologies to share information with each other and with infrastructures. In this paper, we proposed a deep reinforcement learning (DRL) model that applies to vehicle platooning at an isolated signalized intersection with partial detection. Moreover, we identified hyperparameters and tested the system with different numbers of vehicles (1, 2, and 3) in the platoon. To compare the effectiveness of the proposed model, we implemented two benchmark options, actuated traffic signal control (ATSC) and max pressure (MP). The experimental results demonstrated that the DRL model has many outstanding advantages compared to other models. Through the learning process, the average waiting time of vehicles in the DRL method was improved by 20% and 28% compared with the ATSC and MP options. The results also suggested that the DRL model is effective when the CV penetration rate is over 20%.
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Khazraeian, Samaneh, Mohammed Hadi, and Yan Xiao. "Safety Impacts of Queue Warning in a Connected Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2621, no. 1 (January 2017): 31–37. http://dx.doi.org/10.3141/2621-04.
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Queue warning systems (QWSs) have been implemented to increase traffic safety by informing drivers about queued traffic ahead so that they can react in a timely manner to the queue. Existing QWSs rely on fixed traffic sensors to detect the back of a queue. It is expected that if the transmitted messages from connected vehicles (CVs) are used for this purpose, detection can be faster and more accurate. In addition, with CVs, delivery of the messages can be done with onboard units instead of dynamic message signs and provide more flexibility on how far upstream of the queue the messages are delivered. This study investigates the accuracy and benefits of the QWS on the basis of CV data. The study evaluated the safety benefits of the QWS under different market penetrations of CVs in future years. Surrogate safety measures were estimated with simulation modeling combined with the surrogate safety assessment model tool. Results from this study indicate that a relatively low market penetration—about 3% to 6%—for the congested freeway examined in this study was sufficient for an accurate and reliable estimation of the queue length. Even at 3% market penetration, the CV-based estimation of back-of-queue identification was significantly more accurate than that based on detector measurements. The results also found that CV data allowed faster detection of the bottleneck and queue formation. Further, the QWS improved the safety conditions of the network by reducing the number of rear-end conflicts. Safety effects become significant when the compliance percentage with the queue warning messages is more than 15%.
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Asano, Shinka, and Susumu Ishihara. "Safe, Smooth, and Fair Rule-Based Cooperative Lane Change Control for Sudden Obstacle Avoidance on a Multi-Lane Road." Applied Sciences 12, no. 17 (August 26, 2022): 8528. http://dx.doi.org/10.3390/app12178528.
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When an unexpected obstacle occupies some of the lanes on a multi-lane highway, connected vehicles (CVs) may be able to avoid it cooperatively. For example, a CV that detects the obstacle first can immediately notify the following vehicles of the obstacle by using vehicle-to-vehicle (V2V) communication. In turn, the following vehicles can take action to avoid the obstacle smoothly using wide range behind the obstacle without sacrificing safety and ride comfort. In this study, we propose a method to realize safe, smooth, and fair wide-range cooperative lane changing, reacting to a sudden obstacle on the road. The proposed method is based on the authors’ previous work, which utilizes multi-hop communication to share the obstacle position and controls the inter-vehicular distance of vehicles away from the obstacle to assist in a smooth lane changing operation, while existing lane-changing methods for CVs focus on microscopic operation around the obstacle. Though the previous work treats only a two-lane road, the proposed method is extended to work on a three- or more lane road assuming only one lane is blocked. In the proposed scheme, each vehicle approaching the obstacle selects a lane to change to in accordance with the obstacle’s location and the vehicle density in each lane estimated from the beacon messages broadcast by each CV, thereby improving traffic fairness among all lanes without degrading safety or ride comfort. We confirmed the effectiveness of the proposed scheme on realizing fairness among lanes, safety, ride comfort, and traffic throughput through comprehensive simulations of a two-lane road and a three-lane road with various traffic scenarios.
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Abdelkader, Ghadeer, Khalid Elgazzar, and Alaa Khamis. "Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities." Sensors 21, no. 22 (November 19, 2021): 7712. http://dx.doi.org/10.3390/s21227712.
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In an effort to reach accident-free milestones or drastically reduce/eliminate road fatalities rates and traffic congestion and to create disruptive, transformational mobility systems and services, different parties (e.g., automakers, universities, governments, and road traffic regulators) have collaborated to research, develop, and test connected vehicle (CV) technologies. CVs create new data-rich environments and are considered key enablers for many applications and services that will make our roads safer, less congested, and more eco-friendly. A deeper understanding of the CV technologies will pave the way to avoid setbacks and will help in developing more innovative applications and breakthroughs. In the CV paradigm, vehicles become smarter by communicating with nearby vehicles, connected infrastructure, and the surroundings. This connectivity will be substantial to support different features and systems, such as adaptive routing, real-time navigation, and slow and near real-time infrastructure. Further examples include environmental sensing, advanced driver-assistance systems, automated driving systems, mobility on demand, and mobility as a service. This article provides a comprehensive review on CV technologies including fundamental challenges, state-of-the-art enabling technologies, innovative applications, and potential opportunities that can benefit automakers, customers, and businesses. The current standardization efforts of the forefront enabling technologies, such as Wi-Fi 6 and 5G-cellular technologies are also reviewed. Different challenges in terms of cooperative computation, privacy/security, and over-the-air updates are discussed. Safety and non-safety applications are described and possible future opportunities that CV technology brings to our life are also highlighted.
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Pisu, Pierluigi, Jim Martin, and Zoleikha Abdollahi Biron. "A Control Oriented Perspective for Security in Connected and Automated Vehicles." Mechanical Engineering 139, no. 12 (December 1, 2017): S17—S20. http://dx.doi.org/10.1115/1.2017-dec-10.
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This article provides an overview of the potential attacks that can impact connected vehicles (CV) technologies and highlights how a resilient control scheme can be effective to mitigate the effect of these attacks by allowing the system to safely operate with reduced performance. CVs endure several challenges that can occur due to cyberattacks with purposes of disrupting the performance of the connected vehicles system. To improve safety and security, advanced vehicular control systems must be designed to be resilient to cyberattacks. The attack detection and switching strategy is formulated as an MPC-like optimization problem, where the control variable is constrained to a specific strategy and applied in a receding horizon fashion. The choice of the cost function plays an important role in the performance of the system. The results of the switching strategy show that in comparison with the perfect case—in which the attacks are perfectly identified and the correct strategy selected immediately—there is approximately a 22 percent strategy improvement that could still be achieved by changing the switching strategy.
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Xu, Qing, Jiangfeng Wang, Botong Wang, and Xuedong Yan. "Modeling and simulation of intersection quasi-moving block speed guidance based on connected vehicles." Journal of Intelligent and Connected Vehicles 3, no. 2 (August 10, 2020): 67–78. http://dx.doi.org/10.1108/jicv-01-2020-0002.
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Purpose This study aims to propose a speed guidance model of the CV environment to alleviate traffic congestion at intersections and improve traffic efficiency. By introducing the theory of moving block section for high-speed train control, a speed guidance model based on the quasi-moving block speed guidance (QMBSG) is proposed to direct platoon including human-driven vehicles and connected vehicles (CV) through the intersection coordinately. Design/methodology/approach In this model, the green time of the intersection is divided into multiple block intervals according to the minimal safety headway. Connected vehicles can pass through the intersection by following the block interval using the QMBSG model. The block interval is assigned dynamically according to the traveling relation of HV and CV, when entering the communication range of the intersection. To validate the comprehensive guidance effect of the proposed model, a general evaluation function (GEF) is established. Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement. Findings Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement. Also, compared to the single intersection speed guidance model, the GEF value of the QMBSG model improves over 17.1%. To further explore the guidance effect, the impact of sensitivity factors of the CVs’ environment, such as intersection environment, communication range and penetration rate (PR) is analyzed. When the PR reaches 75.0%, the GEF value will change suddenly and the model guidance effect will be significantly improved. This paper also analyzes the impact of the length of block interval under different PR and traffic demands. It is found that the proposed model has a better guidance effect when the length of the block section is 2 s, which facilitates traffic congestion alleviation of the intersection in practice. Originality/value Based on the aforementioned discussion, the contributions of this paper are three-fold. Based on the traveling information of HV/CV and the signal phase and timing plans, the QMBSG model is proposed to direct platoon consisting of HV and CV through the intersection coordinately, by following the block interval assigned dynamically. Considering comprehensively the indexes of mobility, safety and environment, a GEF is provided to evaluate the guidance effect of vehicles through the intersection. Sensitivity analysis is carried out on the QMBSG model. The key communication and traffic parameters of the CV environment are analyzed, such as path attenuation, PR, etc. Finally, the effect of the length of block interval is explored.
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Zhang, Yiqi, Changxu Wu, Chunming Qiao, and Yunfei Hou. "Effects of Warning Characteristics on Driver Performance in Connected Vehicle Systems with Missing Warnings." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1827. http://dx.doi.org/10.1177/1541931218621415.
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The connected vehicle systems (CVS) aim to provide drivers with information in a timely and reliable way to improve transportation safety. With the emerging wireless communication technologies, the vehicles will be equipped with the ability to communicate with each other about the surrounding traffic situations by exchanging vehicle status and motion data via Dedicated Short-Range Communications (DSRC) network (Kenney, 2011). With the assistance of the cooperative collision warnings, the impact of designed warning parameters on driver performance is increasingly important. Existing empirical studies have studied the warning timing and warning reliability in determining the effectiveness of the collision warning systems in advanced driver assistance systems (ADAS). In terms of warning timing, the studies in reached consistent conclusions that early warnings induced more timely braking and longer braking process, resulted in higher trust of the warning systems, and reduced collision rates (for example, Abe & Richardson, 2006a; Lee, McGehee, Brown, & Reyes, 2002; Yan, Xue, Ma, & Xu, 2014; Yan, Zhang, & Ma, 2015; Wan, Wu, and Zhang, 2016). In terms of the warning reliability, research has shown that warnings with a higher reliability increased driver’s trust of the warning systems, led to higher frequency in warning responses, and reduced crash rates (for example, Abe, Itoh, & Yamamura, 2009; Bliss & Acton, 2003; Maltz & Shinar, 2007; Sullivan, Tsimhoni, & Bogard, 2008). However, the interaction effects of warning lead time and warning reliability on driver performance was not examined especially under the connected vehicle settings. The current research investigated the interaction effects of warning lead time (2.5s vs. 4.5s), warning reliability (73% vs. 89%), and speech warning style (command vs. notification) on driver performance and subjective evaluation of warnings in CVS. A driving simulator study with thirty-two participants was conducted to simulate a connected vehicle environment with missing warnings due to the failures in the data transmission within the communication network of the CVS. The results showed command warnings led to a smaller collision rate compared to notification warnings with the warning lead time of 2.5s, whereas notification warnings resulted in a smaller collision rate compared to command warning with the warning lead time of 4.5s. These results suggested notification warnings should be selected when warning lead time is longer and warning reliability is higher, which resulted in higher safety benefits and higher subjective ratings. Command warnings could be selected when warning lead time is shorter since they led to more safety benefits. However, such selection has to be made with caution since command warnings may limit drivers’ response type and were perceived as less helpful than notification warnings.
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Alzoubaidi, Mutasem, and Milan Zlatkovic. "Conventional Diamond, Diverging Diamond, and Single Point Diamond Interchanges: A Comparative Operational Performance Evaluation in the Era of Connected Vehicles." Put i saobraćaj 68, no. 3 (October 3, 2022): 1–9. http://dx.doi.org/10.31075/pis.68.03.01.
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This study evaluates the mobility impact of implementing Connected Vehicle (CV) technology at the Conventional Diamond Interchange (CDI), Diverging Diamond Interchange (DDI), and Single Point Diamond Interchange (SPDI). The evaluation is based on a microsimulation environment created in VISSIM combined with ASC/3 Software-in-the-Loop signal controllers and Python-programmed Vehicle to Infrastructure (V2I) communication algorithms. The effect of varying CV-Market Penetration Rates (CV-MPRs) on traffic operations is taken into consideration. The study shows that the interchange design has a higher impact on traffic operations than does the CV-MPR. Particularly, a 100% CV-MPR has led to 6.4% reductions in delays compared to the 0% CV-MPR, without considering the effect of interchange design. Contrarily, the CDI would increase delays as high as 24.0% as opposed to the SPDI, without considering the effect of CVs. Similarly, the DDI would reduce delays by up to 60.6% compared to the SPDI, without considering the effect of CVs.
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Khan, Junaid Ahmed, Kavyashree Umesh Bangalore, Abdullah Kurkcu, and Kaan Ozbay. "TREAD: Privacy Preserving Incentivized Connected Vehicle Mobility Data Storage on InterPlanetary-File-System-Enabled Blockchain." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 2 (October 21, 2021): 680–91. http://dx.doi.org/10.1177/03611981211045074.
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Trajectory data from connected vehicles (CVs) and other micromobility sources such as e-scooters, bikes, and pedestrians is important for researchers, policy makers, and other stakeholders for leveraging the location, speed, and heading, along with other mobility data, to improve safety and bolster technology development toward innovative location-based applications for citizens. Such raw data needs to be stored and accessed from a non-proprietary database while the obfuscation and encryption techniques on current cloud-based proprietary solutions incur data losses that are deemed inefficient for accurate usage, particularly in time-sensitive real-time operations. In this paper, we target the problem of scalably storing and retrieving potentially sensitive data generated by vehicles and propose TREAD, a blockchain-based system comprising smart contracts to store this mobility data on a distributed ledger such that multiple peers can access and utilize it in different location-based applications while not revealing users’ sensitive personal information. It is, however, challenging to scalably store large amounts of constantly generated trajectories, and to achieve scalability we leverage InterPlanetary File System (IPFS), a scalable distributed peer-to-peer data storage system. To avoid users injecting malicious/fake trajectories into the ledger, we develop efficient consensus algorithms for the stakeholders to validate the storage and retrieval process in a distributed manner. We implemented TREAD on the open-source Hyperledger Fabric blockchain platform using trajectory data generated for 700 vehicles in a simulation environment well calibrated with vehicle trajectories from a real-world test-bed in New York City. Results show that TREAD scalably stores trajectory data with lower delay and overhead.
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Liang, Xiao (Joyce), S. Ilgin Guler, and Vikash V. Gayah. "Joint Optimization of Signal Phasing and Timing and Vehicle Speed Guidance in a Connected and Autonomous Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (April 2019): 70–83. http://dx.doi.org/10.1177/0361198119841285.
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A joint traffic signal optimization algorithm is proposed which utilizes connected vehicle (CV) information to identify optimum signal timing and phasing plans while also providing speed guidance to individual vehicles to minimize total number of stopping maneuvers. The contribution of this paper is provision of speed guidance to both autonomous (AVs) and human-driven speed guidance-enabled vehicles (SGVs), recognizing that the latter may not fully comply with the speed guidance and would require some delay (i.e., reaction time) to implement it. The control algorithm is triggered at regular discrete time intervals during which CV information is used to identify the presence of non-CVs and incorporate them into signal timing decision-making. Optimal speeds are determined for any AVs or SGVs so that they can travel through the intersection at the expected departure time without stopping, considering both acceleration/deceleration and human reaction times. Simulation tests are performed under different CV, AV, and SGV penetration rates, while explicitly modeling the potential human errors and varying acceptance levels. The results suggest that average delay and number of stops decrease with higher CV penetration rate. Furthermore, the number of stops decreases as the ratio of both AVs and SGVs increases. While AVs are about 10% more efficient than SGVs, human-driven vehicles still provide a benefit even when they do not fully comply with speed guidance information. Sensitivity tests suggest that operation is not significantly affected by the range of human driver errors in speed compliance or range of reaction times.
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Feng, Yiheng, Jianfeng Zheng, and Henry X. Liu. "Real-Time Detector-Free Adaptive Signal Control with Low Penetration of Connected Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 18 (August 10, 2018): 35–44. http://dx.doi.org/10.1177/0361198118790860.
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Most of the existing connected vehicle (CV)-based traffic control models require a critical penetration rate. If the critical penetration rate cannot be reached, then data from traditional sources (e.g., loop detectors) need to be added to improve the performance. However, it can be expected that over the next 10 years or longer, the CV penetration will remain at a low level. This paper presents a real-time detector-free adaptive signal control with low penetration of CVs ([Formula: see text]10%). A probabilistic delay estimation model is proposed, which only requires a few critical CV trajectories. An adaptive signal control algorithm based on dynamic programming is implemented utilizing estimated delay to calculate the performance function. If no CV is observed during one signal cycle, historical traffic volume is used to generate signal timing plans. The proposed model is evaluated at a real-world intersection in VISSIM with different demand levels and CV penetration rates. Results show that the new model outperforms well-tuned actuated control regarding delay reduction, in all scenarios under only 10% penetrate rate. The results also suggest that the accuracy of historical traffic volume plays an important role in the performance of the algorithm.
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Zhang, Yiqi, and Changxu Wu. "Modeling the Effects of Warning Lead Time, Warning Reliability and Warning Style on Human Performance Under Connected Vehicle Settings." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 701. http://dx.doi.org/10.1177/1541931218621158.
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Deaths and injuries resulted from traffic accidents is still a major public health problem. Recent advances in connected vehicle technology support a connected driving environment in which vehicles are enabled to communicate with each other and with roadside infrastructures via Dedicated Short Range Communication (DSRC). Connected vehicle safety applications supported by this technology allow drivers to learn about the traffic situations out of their sight and ahead of time so that drivers are warned early enough to make proper responses. As the connected vehicle systems (CVS) are designed with an aim to improving driver safety, the effectiveness of the CVS can not be achieved without drivers making proper responses in responding to the wireless warnings. Therefore, it is essential to understand and model the mechanism for human processing and responding to warnings from connected vehicle systems, and apply the driver model to optimize the design the CVS at the interface level and the communication level. Queuing Network-Model Human Processor (QN-MHP) is a computational framework that integrates three discrete serial stages of human information processing (i.e., perceptual, cognitive, and motor processing) into three continuous subnetworks. Each subnetwork is constructed of multiple servers and links among these servers. Each individual server is an abstraction of a brain area with specific functions, and links among servers represent neural pathways among functional brain areas. The neurological processing of stimuli is illustrated in the transformation of entities passing through routes in QN-MHP. Since this architecture was established, QN-MHP has been applied to quantify various aspects of aspects of driver behavior and performance, including speed control (Bi & Liu, 2009; Zhao & Wu, 2013b), lateral control (Bi et al., 2012; Bi et al., 2013), driver distraction (Bi et al., 2012; Fuller, Reed & Liu, 2012; Liu, Feyen & Tsimhoni, 2006), and driver workload (Wu & Liu, 2007; Wu et al., 2008). Most of the driver model built upon QN-MHP focused on the modeling of driver performance in normal driving situation. In a previous work of authors, a mathematical model was developed to predict the effects of warning loudness, word choice, and lead time on drivers’ warning reaction time (Zhang, Wu, & Wan, 2016). The current research focused on the development of a mathematical model based on QN-MHP to quantify and predict driver performance in responding to warnings from connected vehicle systems, including warning response time and the selection of warning response type. The model also quantified the effects of important warning characteristics in connected vehicle systems, including warning reliability, warning lead time, and speech warning style. The model was validated via an experimental study indicating its good predictability of driver behavior and performance in connected vehicle systems. In particular, the model was able to explain 68.83% of the warning response type in the initial trial of the experiment with a root mean square error (RMSE) of 0.18. By adding the warning effect on the probability of a response type through trials, the model was able to explain 65.13% of the warning response type in the initial trial of the experiment with a root mean square error (RMSE) of 0.16. In terms of warning response time, the model prediction of warning response time under different warning reliability, style and lead time were very similar to the response time results from the experiments. The model was able to explain 88.30% of the experimental response time in average with a root mean square error (RMSE) of 0.16s. The developed driver model could be applied to optimize the design of the connected vehicle systems based on driver
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Tibor Petrov, Ilya Finkelberg, Nina Zarkhin, Peter Počta, Ľuboš Buzna, Ayelet Gal-Tzur, Tatiana Kováčiková, Tomer Toledo, and Milan Dado. "A Framework Coupling VISSIM and OMNeT++ to Simulate Future Intelligent Transportation Systems." Communications - Scientific letters of the University of Zilina 23, no. 2 (April 1, 2021): C23—C29. http://dx.doi.org/10.26552/com.c.2021.2.c23-c29.
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With the spread of connected vehicles (CVs), a growth of novel information services exploiting data transmitted by CVs is expected. Wireless communication systems, in particular in vehicular applications, operate with a varying level of transmission reliability, which may affect the quality of V2X-data-driven intelligent transport systems (ITS). Therefore, the performance of ITS should be evaluated in a variety of conditions and the configuration of parameters should be fine-tuned in a safe testbed, using computer simulations. A simple framework is presented, which couples VISSIM traffic simulation and OMNeT++ communication networks simulation in real time, enabling an assessment of the relationship between a communication reliability and transport service quality. A functionality of the framework is demonstrated by applying it to a scheme controlling signalized intersections while estimating traffic flows from the V2I data.
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Chowdhury, Mashrur, Mizanur Rahman, Anjan Rayamajhi, Sakib Mahmud Khan, Mhafuzul Islam, Zadid Khan, and James Martin. "Lessons Learned from the Real-World Deployment of a Connected Vehicle Testbed." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 22 (October 6, 2018): 10–23. http://dx.doi.org/10.1177/0361198118799034.
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The connected vehicle (CV) system promises unprecedented safety, mobility, environmental, economic, and social benefits, which can be unlocked using the enormous amount of data shared between vehicles and infrastructure (e.g., traffic signals, centers). Real-world CV deployments, including pilot deployments, help solve technical issues and observe potential benefits, both of which support the broader adoption of the CV system. This study focused on the Clemson University Connected Vehicle Testbed (CU-CVT) with the goal of sharing the lessons learned from the CU-CVT deployment. The motivation of this study was to enhance early CV deployments with the objective of depicting the lessons learned from the CU-CVT testbed, which includes unique features to support multiple CV applications running simultaneously. The lessons learned in the CU-CVT testbed are described at three different levels: (i) the development of system architecture and prototyping in a controlled environment, (ii) the deployment of the CU-CVT testbed, and (iii) the validation of the CV application experiments in the CU-CVT. Field experiments with CV applications validated the functionalities needed for running multiple diverse CV applications simultaneously using heterogeneous wireless networking, and meeting real-time and non-real-time application requirements. The unique deployment experiences, related to heterogeneous wireless networks, real-time data aggregation, data dissemination and processing using a broker system, and data archiving with big data management tools, gained from the CU-CVT testbed, could be used to advance CV research and guide public and private agencies for the deployment of CVs in the real world.
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Ding, Fan, Yongyi Zhang, Rui Chen, Zhanwen Liu, and Huachun Tan. "A Deep Learning Based Traffic State Estimation Method for Mixed Traffic Flow Environment." Journal of Advanced Transportation 2022 (April 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/2166345.
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Traffic state estimation plays a fundamental role in traffic control and management. In the connected vehicles (CVs) environment, more traffic-related data perceived and interacted by CVs can be used to estimate traffic state. However, when there is a low penetration rate of CVs, the data collected from CVs would be inadequate. Meanwhile, the representativeness of the collected data is positively correlated with the penetration rate. This article presents a traffic state estimation method based on a deep learning algorithm under a low and dynamic CVs penetration rate environment. Specifically, we design a K-Nearest Neighbor (KNN) data filling model integrating acceleration data to solve the problem of insufficient data. This method can fuse the time feature of speed by acceleration modification and mine the distribution features of speed by KNN. In addition, to reduce the estimation error caused by penetration rate, we design a Long Short-Term Memory (LSTM) model, which uses penetration rate estimated by Macroscopic Fundamental Diagram (MFD) as one of the input factors. Finally, we use the concept of operational efficiency for reference, dividing traffic state into three categories according to the estimated speed: free flow, optimal flow, and congestion. SUMO is used to simulate traffic cases under different penetration rates to evaluate our scheme. The results suggest that our data filling model can significantly improve filling accuracy under a low penetration rate; there is also a better performance of our estimation model than that of other comparison models in both low and dynamic penetration rates.
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Hao, Ruochen, Ling Wang, Wanjing Ma, and Chunhui Yu. "Estimating Signal Timing of Actuated Signal Control Using Pattern Recognition under Connected Vehicle Environment." Promet - Traffic&Transportation 33, no. 1 (February 5, 2021): 153–63. http://dx.doi.org/10.7307/ptt.v33i1.3555.
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The Signal Phase and Timing (SPaT) message is an important input for research and applications of Connected Vehicles (CVs). However, the actuated signal controllers are not able to directly give the SPaT information since the SPaT is influenced by both signal control logic and real-time traffic demand. This study elaborates an estimation method which is proposed according to the idea that an actuated signal controller would provide similar signal timing for similar traffic states. Thus, the quantitative description of traffic states is important. The traffic flow at each approaching lane has been compared to fluids. The state of fluids can be indicated by state parameters, e.g. speed or height, and its energy, which includes kinetic energy and potential energy. Similar to the fluids, this paper has proposed an energy model for traffic flow, and it has also added the queue length as an additional state parameter. Based on that, the traffic state of intersections can be descripted. Then, a pattern recognition algorithm was developed to identify the most similar historical states and also their corresponding SPaTs, whose average is the estimated SPaT of this second. The result shows that the average error is 3.1 seconds.
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Adomah, Eric, Arash Khoda Bakhshi, and Mohamed M. Ahmed. "Safety Impact of Connected Vehicles on Driver Behavior in Rural Work Zones under Foggy Weather Conditions." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 3 (October 21, 2021): 88–107. http://dx.doi.org/10.1177/03611981211049147.
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Work zone safety is one of the paramount goals of the safety community. Safety in WZs is a particular concern under foggy conditions as they represent an exogenous factor contributing to high variability in driver behavior. In line with the Connected Vehicle (CV) Pilot Deployment Program on Interstate-80 (I-80) in Wyoming, this study investigates the safety benefits of CV Work Zone Warning (WZW) applications on driver behavior during foggy weather conditions. A work zone (WZ) was simulated using VISSIM in four sequential areas, including the advance warning, transition, activity, and termination area. The effect of drivers’ increased situational awareness under the effect of WZW was calibrated in VISSIM based on the results of a high-fidelity driving simulator experiment. Various Surrogate Measures of Safety (SMoS), including Time-To-Collision (TTC), Time Exposed Time-to-collision (TET), Time-Integrated Time-to-collision (TIT), and Modified Deceleration Rate to Avoid Crash (MDRAC), were employed to quantify the safety performance of CVs under varying CV Market Penetration Rates (MPRs). According to the results of TTC and MDRAC, it was found that an increase in CV-MPR enhances the safety performance of the WZ area. Findings showed that, under foggy weather conditions, the advance warning area had the highest TIT and TET values. Furthermore, it was revealed that an increase in MPR of up to 60% on I-80 would reduce mean speeds and the standard deviation of speed at each of the WZ areas, leading to more speed harmonization and minimizing the crash risk in WZs.
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Wu, Lina, Yusheng Ci, Yichen Sun, and Wei Qi. "Research on Joint Control of On-Ramp Metering and Mainline Speed Guidance in the Urban Expressway Based on MPC and Connected Vehicles." Journal of Advanced Transportation 2020 (March 19, 2020): 1–8. http://dx.doi.org/10.1155/2020/7518087.
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The traffic operational efficiency of the urban expressway system will affect one of the entire cities. Moreover, the idea that traffic control can improve the traffic operational efficiency of the urban expressway system has been fully confirmed. At present, the main control methods include on-ramp metering and speed guidance control. However, there is a gap in using these two control methods together, such as unclear application conditions and unsystematic methods. In this paper, on-ramp metering and speed guidance control are combined effectively. Based on the research of METANET macroscopic traffic flow model and model predictive control (MPC), a novel joint control method based on MPC and connected vehicles (CVs) for on-ramp metering and speed guidance control of the urban expressway is proposed. Finally, the simulation results show that the proposed control method can effectively improve the traffic efficiency and traffic safety.
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Hong, Gaofeng, Bin Yang, Wei Su, Qili Wen, Xindi Hou, and Haoru Li. "Decentralized Vehicular Mobility Management Study for 5G Identifier/Locator Split Networks." Wireless Communications and Mobile Computing 2022 (July 15, 2022): 1–14. http://dx.doi.org/10.1155/2022/6300715.
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The identifier/locator split (ILS) architectures are highly promising to reduce the signaling latency of frequent handovers in fifth generation (5G) networks, while decentralized vehicular mobility management holds greater potential than the traditional centralized management to enhance the critical performance of highly dynamic and dense cell networks. By carefully exploiting ILS, dual connectivity, and multiaccess edge computing (MEC) concepts, this paper proposes a decentralized vehicular mobility management mechanism in the network with dense 5G Non-Standalone deployment. Under such a mechanism, we design an ILS-based local anchor handover management architecture to reduce signaling costs and handover latency. Specifically, we propose a quality of service- (QoS-) based handover decision algorithm using a long short-term memory- (LSTM-) based trajectory prediction method to obtain the cell sojourn time of connected vehicles (CVs) in predefined QoS coverage areas. Combining a built-in dynamic handover trigger condition, this algorithm can ensure a flexible load balance as well as low handover times. Extensive simulation results are presented to verify the effectiveness of the proposed mechanism in improving network performance.
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Wang, ShiHui, Min Zhao, DiHua Sun, and Xiaoyu Liu. "Merging Sequence Optimization Based on Reverse Auction Theory and Merging Strategy with Active Trajectory Adjustment of Heterogeneous Vehicles." Journal of Advanced Transportation 2022 (May 26, 2022): 1–20. http://dx.doi.org/10.1155/2022/3926976.
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This paper investigates the optimized merging sequence (MS) and on-ramp merging strategy in mixed traffic with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). To this end, a cooperative merging sequence optimization method is first proposed based on the reverse auction. In the method, a hybrid optimized computing structure is proposed to provide the foundation for connected and human-driving vehicles (CHVs) and connected and automated vehicles (CAVs) to obtain the MS more efficiently. And a cooperative merging strategy based on all cooperative merging vehicles under mixed traffic conditions is proposed. In particular, the downstream vehicles in the strategy can change their original velocities to actively participate in the cooperative merging process according to the merging requirements of on-ramp vehicles. And the vehicles in this strategy are all subject to state constraints to avoid the adverse effects of cooperative merging behavior on the following traffic on the main road. Results of numerical experiments illustrate that the merging sequence optimization method can reduce the time to obtain the optimal MS, and the increased computational efficiency is affected by CAV penetration. In addition, in mixed traffic conditions, the cooperative merging strategy can reduce fuel consumption and the time required for merging.
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Bang, Sookyuk, and Soyoung Ahn. "Analysis and Control of Heterogeneous Connected and Autonomous Vehicles using a Spring-Mass-Damper System." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 8 (June 20, 2020): 309–18. http://dx.doi.org/10.1177/0361198120927696.
Full textAbstract:
This study analyzes the behavior of heterogeneous connected and autonomous vehicles (CAVs) and proposes the best vehicle sequence for optimal platoon throughput and platoon formation. A spring-mass-damper (SMD) system is adopted for control of CAVs, and the control parameters are formulated in relation to the physical capabilities of vehicles. To gain insight, we consider three types of vehicle: passenger cars, mini-vans, and heavy-duty vehicles. For each type, we investigate the maximum platoon throughput and the clustering time, defined as the time to reach the target equilibrium state. We further investigate different sequences of vehicle types in a platoon to identify the optimal vehicle order that maximizes the throughput and minimizes clustering time. Findings suggest that the highest performance vehicle (in relation to acceleration capability) should be placed as the leader of a platoon and that the number of passenger cars behind heavy vehicles (e.g., semi-trailers) should be minimized in the platoon. In addition, we examine how the proportions of lower performance vehicles affect throughput and clustering times. The result suggests that the higher the proportions, the lower the throughput and the longer the clustering time. The lowest performance vehicle had the greatest effect.
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Do, Wooseok, Omid M. Rouhani, and Luis Miranda-Moreno. "Simulation-Based Connected and Automated Vehicle Models on Highway Sections: A Literature Review." Journal of Advanced Transportation 2019 (June 26, 2019): 1–14. http://dx.doi.org/10.1155/2019/9343705.
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This study provides a literature review of the simulation-based connected and automated intelligent-vehicle studies. Media and car-manufacturing companies predict that connected and automated vehicles (CAVs) would be available in the near future. However, society and transportation systems might not be completely ready for their implementation in various aspects, e.g., public acceptance, technology, infrastructure, and/or policy. Since the empirical field data for CAVs are not available at present, many researchers develop micro or macro simulation models to evaluate the CAV impacts. This study classifies the most commonly used intelligent-vehicle types into four categories (i.e., adaptive cruise control, ACC; cooperative adaptive cruise control, CACC; automated vehicle, AV; CAV) and summarizes the intelligent-vehicle car-following models (i.e., Intelligent Driver Model, IDM; MICroscopic Model for Simulation of Intelligent Cruise Control, MIXIC). The review results offer new insights for future intelligent-vehicle analyses: (i) the increase in the market-penetration rate of intelligent vehicles has a significant impact on traffic flow conditions; (ii) without vehicle connections, such as the ACC vehicles, the roadway-capacity increase would be marginal; (iii) none of the parameters in the AV or CAV models is calibrated by the actual field data; (iv) both longitudinal and lateral movements of intelligent vehicles can reduce energy consumption and environmental costs compared to human-driven vehicles; (v) research gap exists in studying the car-following models for newly developed intelligent vehicles; and (vi) the estimated impacts are not converted into a unified metric (i.e., welfare economic impact on users or society) which is essential to evaluate intelligent vehicles from an overall societal perspective.
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Zhang, Junjie, Can Yang, Haiyang Yu, Jun Zhang, and Zixiao Wang. "String Stability Control Strategy Analysis of Mixed Traffic Flow with the CIVs and NCVs." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012084. http://dx.doi.org/10.1088/1742-6596/2025/1/012084.
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Abstract With the development of vehicle-to-infrastructure cooperation system, a mixed traffic phenomenon with non-connected vehicles (NCVs) and connected and intelligent vehicles (CIVs) will exist over a long period of time. Therefore, the mixed traffic flow stability control has become a hot topic in the future. In order to improve the string stability in the complex and changeable internet of vehicles environment, it is necessary to propose the optimal control method of string stability in the mixed traffic flow. In this paper, NCV and CIV car-following modes are employed to propose a local platoon control method of the connected vehicle, which can achieve the purpose of optimizing the mixed traffic flow stability. Two types of local mixed platoon are considered when the effective communication distance with two vehicles in the vehicle-to-vehicle (V2V) communication. Numerical simulations results show that our proposed string stability control strategy has the effectiveness in the improvement of the mixed traffic flow stability.
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Jiang, Yangsheng, Bin Zhao, Meng Liu, and Zhihong Yao. "A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment." Journal of Advanced Transportation 2021 (April 26, 2021): 1–13. http://dx.doi.org/10.1155/2021/9945398.
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Connected and automated vehicles (CAVs) trajectories not only provide more real-time information by vehicles to infrastructure but also can be controlled and optimized, to further save travel time and gasoline consumption. This paper proposes a two-level model for traffic signal timing and trajectories planning of multiple connected automated vehicles considering the random arrival of vehicles. The proposed method contains two levels, i.e., CAVs’ arrival time and traffic signals optimization, and multiple CAVs trajectories planning. The former optimizes CAVs’ arrival time and traffic signals in a random environment, to minimize the average vehicle’s delay. The latter designs multiple CAVs trajectories considering average gasoline consumption. The dynamic programming (DP) and the General Pseudospectral Optimal Control Software (GPOPS) are applied to solve the two-level optimization problem. Numerical simulation is conducted to compare the proposed method with a fixed-time traffic signal. Results show that the proposed method reduces both average vehicle’s delay and gasoline consumption under different traffic demand significantly. The average reduction of vehicle’s delay and gasoline consumption are 26.91% and 10.38%, respectively, for a two-phase signalized intersection. In addition, sensitivity analysis indicates that the minimum green time and free-flow speed have a noticeable effect on the average vehicle’s delay and gasoline consumption.
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Park, Suyong, Sanghyeon Nam, Gokul S. Sankar, and Kyoungseok Han. "Evaluating the Efficiency of Connected and Automated Buses Platooning in Mixed Traffic Environment." Electronics 11, no. 19 (October 8, 2022): 3231. http://dx.doi.org/10.3390/electronics11193231.
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Due to the battery capacity limitation of battery electric vehicles (BEVs), the importance of minimizing energy consumption has been increasing in recent years. In the mean time, for improving vehicle energy efficiency, platooning has attracted attention of several automakers. Using the connected and automated vehicles (CAVs) technology, platooning can achieve a longer driving range while preserving a closer distance from the preceding vehicle, resulting in the minimization of the aerodynamic force. However, undesired behaviors of human-driven vehicles (HVs) in the platooning group can prohibit the maximization of the energy efficiency. In this paper, we developed a speed planner based on the model predictive control (MPC) to minimize the total platooning energy consumption, and HVs were programmed to maintain a long enough distance from the preceding vehicle to avoid collision. The simulations were performed to determine how HV influences the efficiencies of the platooning group, which is composed of CAVs and HVs together, in several scenarios including the different positions and numbers of the HVs. Test results show that the CAVs planned by our approach reduces energy consumption by about 4% or more than 4% compared to that of the HVs.
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Wang, Faan, Liwei Xu, Xianjian Jin, Guodong Yin, and Ying Liu. "A Cooperative Positioning Method of Connected and Automated Vehicles with Direction-of-Arrival and Relative Distance Fusion." Mathematical Problems in Engineering 2022 (January 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/5340693.
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The rapid development of science and technology has created favorable conditions for Connected and Automated Vehicles (CAVs). Accurate localization is one of the fundamental functions of CAV to realize some advanced operations such as vehicle platooning. However, complicated urban traffic environments, such as the flyover, significantly influence vehicular positioning accuracy. The inability of CAV to accurately perceive self-localization information has become an urgent issue to be addressed. This paper proposed a novel cooperative localization method by introducing the relative Direction-of-Arrival (DOA) and Relative Distance (RD) into CAV to improve the localization accuracy of CAV in the multivehicle environment. First, the three-dimensional positioning error model of the host vehicle concerning adjacent vehicles in azimuth angle and pitch angle and intervehicle distances under the vehicle-to-vehicle communication was established. Second, two least-squares estimation algorithms, linear and nonlinear, are established to decrease the position errors by combining relative DOA and RD measurement information. To verify the proposed algorithm's effect, the PreScan-Simulink joint simulation is carried out. The results show that the host vehicle's localization accuracy by the proposed method can be improved by 25% compared with direct linearization. Besides, by combining relative DOA and relative RD measurement, the locating capability of the least-square-based nonlinear optimization method can be enhanced by 22%.
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Wang, Yunze, Ranran Xu, and Ke Zhang. "A Car-Following Model for Mixed Traffic Flows in Intelligent Connected Vehicle Environment Considering Driver Response Characteristics." Sustainability 14, no. 17 (September 3, 2022): 11010. http://dx.doi.org/10.3390/su141711010.
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Autonomous driving technology and vehicle-to-vehicle communication technology make the hybrid driving of connected and automated vehicles (CAVs) and regular vehicles (RVs) a long-existing phenomenon in the coming future. Among the existing studies, IDM models are mostly used to study the performance of homogeneous traffic flow. To explore the stability of mixed traffic flow, an extended intelligent driver model (IDM) based car-following model was proposed for mixed traffic flow (MTF) with both CAVs and RVs, considering the headway, the speed and acceleration of multiple front vehicles, as well as the response characteristics of RV drivers. Through the linear stability analysis, the criterion for the stability of MTFs was derived, and the relationship among the penetration rate of CAVs, equilibrium velocity and traffic stability in MTF are discussed. Based on the above theoretical model, a numerical simulation was conducted in two typical scenarios of starting and braking. The results showed that, at the microscopic scale, the vehicle in the Cooperative Adaptive Cruise Control (CACC) mode could significantly decelerate in response to the interference from other vehicles in the same traffic environment. At the macroscopic scale, as the penetration rate of CAVs increased, the overall acceleration fluctuation of the traffic flow decreased. At the same penetration rate of CAVs, the higher density of CAVs coincided with the higher stability of the MTF. When the penetration rate of CAVs was 50%, the degree of distribution had the greatest impact on the MTF. When the penetration rate of CAVs exceeded 70%, the degree of distribution had little impact on the MTF. This research can provide basic theoretical support for the management and control of MTF in the future.
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Lee, Geonil, and Jae-il Jung. "Decentralized Platoon Join-in-Middle Protocol Considering Communication Delay for Connected and Automated Vehicle." Sensors 21, no. 21 (October 27, 2021): 7126. http://dx.doi.org/10.3390/s21217126.
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Cooperative driving is an essential component of intelligent transport systems (ITSs). It promises greater safety, reduced accidents, efficient traffic flow, and fuel consumption reduction. Vehicle platooning is a representative service model for ITS. The principal sub-systems of platooning systems for connected and automated vehicles (CAVs) are cooperative adaptive cruise control (CACC) systems and platoon management systems. Based on vehicle state information received through vehicle-to-vehicle (V2V) communication, the CACC system allows platoon vehicles to maintain a narrower safety distance. In addition, the platoon management system using V2V communications allows vehicles to perform platoon maneuvers reliably and accurately. In this paper, we propose a CACC system with a variable time headway and a decentralized platoon join-in-middle maneuver protocol with a trajectory planning system considering the V2V communication delay for CAVs. The platoon join-in-middle maneuver is a challenging research subject as the research must consider the requirement of a more precise management protocol and lateral control for platoon safety and string stability. These CACC systems and protocols are implemented on a simulator for a connected and automated vehicle system, PreScan, and we validated our approach using a realistic control system and V2V communication system provided by PreScan.
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Chityala, Sneha, John O. Sobanjo, Eren Erman Ozguven, Thobias Sando, and Richard Twumasi-Boakye. "Driver Behavior at a Freeway Merge to Mixed Traffic of Conventional and Connected Autonomous Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (September 16, 2020): 867–74. http://dx.doi.org/10.1177/0361198120950721.
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Freeway merge ramps serve as one of the most challenging areas in traffic operations. This paper primarily focuses on creating a mixed traffic of conventional and connected/autonomous vehicles (CAVs) on freeways, and capturing driver behaviors both for the merging vehicle on the ramp and the freeway vehicles. The mixed distribution of vehicle headways of the freeway vehicles, developed based on various market penetration rates of the CAVs, was used to randomly generate vehicles through Monte Carlo simulation, and assigned as headways in a driving simulator. Based on perception, young drivers on the merge ramp were observed to choose critical headway gaps of 2.9 s, 1.8 s, and 1.7 s for freeway traffic of 0%, 50%, 75% penetration rates, respectively. For similar CAV penetration rates, the critical gaps observed for elderly drivers were 3.5 s, 2.0 s, and 1.9 s, respectively. When actually driving in the simulator, for the scenarios of 0% CAVs and 50% CAVs on the freeway, the values of average headway gaps accepted by young drivers were estimated as 2.36 s and 1.53 s, respectively. For the elderly drivers driving the simulator, the average headway gap values accepted were estimated as 2.72 s and 1.55 s, respectively, in the 0% and 50% penetration rates on the freeway traffic. Analyses of the speed profiles of the vehicles showed the effects of the acceleration/deceleration of merging vehicles, for both young and older drivers, on the freeway vehicles, including a few cases of collision. Overall, it was observed that the subject drivers accepted shorter headway gaps for increased CAV penetration levels.
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Liu, Pengfei, and Wei Fan. "Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment." Promet - Traffic&Transportation 33, no. 5 (October 8, 2021): 767–74. http://dx.doi.org/10.7307/ptt.v33i5.3779.
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Connected and autonomous vehicles (CAVs) have the ability to receive information on their leading vehicles through multiple sensors and vehicle-to-vehicle (V2V) technology and then predict their future behaviour thus to improve roadway safety and mobility. This study presents an innovative algorithm for connected and autonomous vehicles to determine their trajectory considering surrounding vehicles. For the first time, the XGBoost model is developed to predict the acceleration rate that the object vehicle should take based on the current status of both the object vehicle and its leading vehicle. Next Generation Simulation (NGSIM) datasets are utilised for training the proposed model. The XGBoost model is compared with the Intelligent Driver Model (IDM), which is a prior state-of-the-art model. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are applied to evaluate the two models. The results show that the XGBoost model outperforms the IDM in terms of prediction errors. The analysis of the feature importance reveals that the longitudinal position has the greatest influence on vehicle trajectory prediction results.