Journal articles on the topic 'Etichetta Multiple Traffic Light'
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1
Hosseinyalmdary, S., and A. Yilmaz. "TRAFFIC LIGHT DETECTION USING CONIC SECTION GEOMETRY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 2, 2016): 191–200. http://dx.doi.org/10.5194/isprsannals-iii-1-191-2016.
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Traffic lights detection and their state recognition is a crucial task that autonomous vehicles must reliably fulfill. Despite scientific endeavors, it still is an open problem due to the variations of traffic lights and their perception in image form. Unlike previous studies, this paper investigates the use of inaccurate and publicly available GIS databases such as OpenStreetMap. In addition, we are the first to exploit conic section geometry to improve the shape cue of the traffic lights in images. Conic section also enables us to estimate the pose of the traffic lights with respect to the camera. Our approach can detect multiple traffic lights in the scene, it also is able to detect the traffic lights in the absence of prior knowledge, and detect the traffics lights as far as 70 meters. The proposed approach has been evaluated for different scenarios and the results show that the use of stereo cameras significantly improves the accuracy of the traffic lights detection and pose estimation.
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Hosseinyalmdary, S., and A. Yilmaz. "TRAFFIC LIGHT DETECTION USING CONIC SECTION GEOMETRY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 2, 2016): 191–200. http://dx.doi.org/10.5194/isprs-annals-iii-1-191-2016.
Full textAbstract:
Traffic lights detection and their state recognition is a crucial task that autonomous vehicles must reliably fulfill. Despite scientific endeavors, it still is an open problem due to the variations of traffic lights and their perception in image form. Unlike previous studies, this paper investigates the use of inaccurate and publicly available GIS databases such as OpenStreetMap. In addition, we are the first to exploit conic section geometry to improve the shape cue of the traffic lights in images. Conic section also enables us to estimate the pose of the traffic lights with respect to the camera. Our approach can detect multiple traffic lights in the scene, it also is able to detect the traffic lights in the absence of prior knowledge, and detect the traffics lights as far as 70 meters. The proposed approach has been evaluated for different scenarios and the results show that the use of stereo cameras significantly improves the accuracy of the traffic lights detection and pose estimation.
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Yeh, Tien-Wen, Huei-Yung Lin, and Chin-Chen Chang. "Traffic Light and Arrow Signal Recognition Based on a Unified Network." Applied Sciences 11, no. 17 (August 31, 2021): 8066. http://dx.doi.org/10.3390/app11178066.
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We present a traffic light detection and recognition approach for traffic lights that utilizes convolutional neural networks. We also introduce a technique for identifying arrow signal lights in multiple urban traffic environments. For detection, we use map data and two different focal length cameras for traffic light detection at various distances. For recognition, we propose a new algorithm that combines object detection and classification to recognize the light state classes of traffic lights. Furthermore, we use a unified network by sharing features to decrease computation time. The results reveal that the proposed approach enables high-performance traffic light detection and recognition.
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SREEVARDHAN, V., V. SANTOSH, and E. RAHUL. "AUTOMATIC SYNCHRONISED FSM BASED TRAFFIC LIGHT CONTROLLER." International Journal of Computer Science and Mobile Computing 11, no. 1 (January 30, 2022): 214–20. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.029.
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Traffic light controller is designed to show the behavior of the traffic lights system and also a kind of good sequential circuit. Xilinx vivado is a tool which is being used to Annalise the heavy traffic by analyzing it through the various tools such as NEXYS-4, ARTIX -7, FPGA Board. The approach can be success by allowing a proper access to the areas shared through the multiple intersections and allocating effective time between various users, on or off the peaking hours. Theoretically the waiting times for drivers during peak hours has been reduced further, therefore this is system is better than the one which is being used at the moment. Further improvements include addition of other function to the designed circuit which suits to the various upcoming traffic conditions at different location. Traffic is the one of the biggest problem in this modern world due to this problem so many people are suffering even ambulances are not getting out of traffic in cities. This is our small initiation to make easy flow and control of traffic.
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Iqbal, Masab, Luis Velasco, Marc Ruiz, Nelson Costa, Antonio Napoli, Joao Pedro, and Jaume Comellas. "Supporting Heterogenous Traffic on Top of Point-to-Multipoint Light-Trees." Sensors 23, no. 5 (February 23, 2023): 2500. http://dx.doi.org/10.3390/s23052500.
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New 5 G and beyond services demand innovative solutions in optical transport to increase efficiency and flexibility and reduce capital (CAPEX) and operational (OPEX) expenditures to support heterogeneous and dynamic traffic. In this context, optical point-to-multipoint (P2MP) connectivity is seen as an alternative to provide connectivity to multiple sites from a single source, thus potentially both reducing CAPEX and OPEX. Digital subcarrier multiplexing (DSCM) has been shown as a feasible candidate for optical P2MP in view of its ability to generate multiple subcarriers (SC) in the frequency domain that can be used to serve several destinations. This paper proposes a different technology, named optical constellation slicing (OCS), that enables a source to communicate with multiple destinations by focusing on the time domain. OCS is described in detail and compared to DSCM by simulation, where the results show that both OCS and DSCM provide a good performance in terms of the bit error rate (BER) for access/metro applications. An exhaustive quantitative study is afterwards carried out to compare OCS and DSCM considering its support to dynamic packet layer P2P traffic only and mixed P2P and P2MP traffic; throughput, efficiency, and cost are used here as the metrics. As a baseline for comparison, the traditional optical P2P solution is also considered in this study. Numerical results show that OCS and DSCM provide a better efficiency and cost savings than traditional optical P2P connectivity. For P2P only traffic, OCS and DSCM are utmost 14.6% more efficient than the traditional lightpath solution, whereas for heterogeneous P2P + P2MP traffic, a 25% efficiency improvement is achieved, making OCS 12% more efficient than DSCM. Interestingly, the results show that for P2P only traffic, DSCM provides more savings of up to 12% than OCS, whereas for heterogeneous traffic, OCS can save up to 24.6% more than DSCM.
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Poythress, Vern. "A simple traffic-light semiotic model for tagmemic theory." Semiotica 2018, no. 225 (November 6, 2018): 253–67. http://dx.doi.org/10.1515/sem-2017-0025.
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AbstractThe complexity and flexibility of tagmemic theory, as a semiotic theory developed by Kenneth L. Pike, can be better understood by examining how it applies to a simple semiotic system like traffic lights. We can then compare the result with how it functions in analyzing a piece of natural language. Tagmemic theory introduces three observer viewpoints – the particle view, the wave view, and the field view. Each view generates a suite of questions to answer. Any one of the views results in a “complete” description of traffic lights, from which the information about the other views can be inferred. And yet each view is distinct in texture from the others, and the existence of such multiple views – each with a claim to emic integrity and each serving as a perspective on the whole – has to be accounted for in a robust semiotic approach. The same phenomena occur when we apply the three views to the analysis of meaning in natural language. The chief illustration is to analyze the meaning of the word dog in multiple ways. The multi-dimensional potential for semiotic analysis highlights the limitations of Aristotelian logic and symbolic logic, both of which simplify for the sake of rigor.
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Jafari, Sadiqa, Zeinab Shahbazi, and Yung-Cheol Byun. "Improving the Road and Traffic Control Prediction Based on Fuzzy Logic Approach in Multiple Intersections." Mathematics 10, no. 16 (August 9, 2022): 2832. http://dx.doi.org/10.3390/math10162832.
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Traffic congestion is a significant issue in many countries today. The suggested method is a novel control method based on multiple intersections considering the kind of traffic light and the duration of the green phase to determine the optimal balance at intersections by using fuzzy logic control, for which the balance should be adaptable to the unchanging behavior of time. It should reduce traffic volume in transport, average waits for each vehicle, and collisions between cars by controlling this balance in response to the typical behavior of time and randomness in traffic conditions. The proposed method is investigated at intersections using a sampling multi-agent system to set traffic light timings appropriately. The program is provided with many intersections, each of which is an independent entity exchanging information with the others. The stability per entity is proven separately. Simulation results show that Takagi–Sugeno (TS) fuzzy modeling performs better than Takagi–Sugeno (TS) fixed-time scheduling in decreasing the length of queueing times for vehicles.
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Shunu, Daniel. "IMPROVING TRAFFIC FLOW AT INTERSECTION USING INTELLIGENT TRAFFIC MANAGEMENT SYSTEM." Computer Science & IT Research Journal 1, no. 2 (April 18, 2020): 65–70. http://dx.doi.org/10.51594/csitrj.v1i2.137.
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In this study, a proposed intelligent traffic management system is presented making use of the wireless sensor network for improving traffic flow. By making use of the clustering algorithm, VANET environment is utilized for the proposed system. The components of the proposed system include sensor node hardware, vehicle detection system through magnetometer, and UDP protocol for communication between the nodes. The intersection control agent receives the information about the vehicles and by making use of its algorithm, it dynamically changes the traffic light timings. By making use of the greedy algorithm, the system can be enhanced to a wider area by connecting multiple intersections.
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KRISHNAN, SUREN, RAJAN THANGAVELOO, SHAPI-EE BIN ABD RAHMAN, and SIVA RAJA SINDIRAMUTTY. "Smart Ambulance Traffic Control System." Trends in Undergraduate Research 4, no. 1 (June 29, 2021): c28–34. http://dx.doi.org/10.33736/tur.2831.2021.
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The traffic lights control system is broadly implemented to track and control the flow of vehicles through the intersection of multiple roads. Nevertheless, the synchronization of traffic light system at adjacent junctions is an intricate issue given the different parameters involved. Existing traffic light control systems do not control many flows approaching the same junctions. This results in traffic jams and congestion at urban areas or major cities with high volume traffic consisting of various types of vehicles. This includes emergency ambulances travelling on the same traffic junction during peak hour traffic. Thus, an enhanced traffic light control system is imperative to provide a smooth and free flow for an ambulance on the way to its destination. The Smart Ambulance Traffic Control System proposed in this paper is an integrated system of traffic light control for emergency ambulance service. The traffic lights can be controlled in a timely and efficient manner every time an emergency ambulance is approaching. The Radio-Frequency Identification (RFID) is used as an instrument to communicate with traffic lights during traffic congestion. The emergency ambulance driver needs to activate the RFID tag to allow the detection of RFID readers to control the traffic light operation at the upcoming traffic light junctions. The traffic lights in the path of the ambulance are forced to be green to allow the emergency ambulance to pass through the junction with top priority. Immediately after the ambulance has passed the junction, the control system will reset and return to normal operations.
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Guo, Jian, and István Harmati. "Comparison of Game Theoretical Strategy and Reinforcement Learning in Traffic Light Control." Periodica Polytechnica Transportation Engineering 48, no. 4 (June 8, 2020): 313–19. http://dx.doi.org/10.3311/pptr.15923.
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Many traffic models and control methods have already been utilized in the public transportation system due to the increasing traffic congestion. Thus, an intelligent traffic model is formalized and presented to control multiple traffic light simultaneously and efficiently according to the distribution of vehicles from each incoming link (i.e. sections) in this paper. Compared with constant strategy, two methods are proposed for traffic light control, i.e., game theoretical strategy and reinforcement learning methods. Game theoretical strategy is generated in a game theoretical framework where incoming links are regarded as players and the combination of the status of traffic lights can be regarded as decisions made by these players. The cost function is evaluated and the strategy is produced with Nash equilibrium for passing maximum vehicles in an intersection. The other one is Single-Agent Reinforcement Learning (SARL), specifically with the Q-learning algorithm in this case, which is usually used in such a dynamic environment to control traffic flow so the traffic problem could be improved. Generally, the intersection is regarded as the centralized agent and controlling signal status is considered as the actions of the agent. The performance of these two methods is compared after simulated and implemented in a junction.
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Nimac, Peter, Andrej Krpič, Boštjan Batagelj, and Andrej Gams. "Pedestrian Traffic Light Control with Crosswalk FMCW Radar and Group Tracking Algorithm." Sensors 22, no. 5 (February 23, 2022): 1754. http://dx.doi.org/10.3390/s22051754.
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The increased mobility requirements of modern lifestyles put more stress on existing traffic infrastructure, which causes reduced traffic flow, especially in peak traffic hours. This calls for new and advanced solutions in traffic flow regulation and management. One approach towards optimisation is a transition from static to dynamic traffic light intervals, especially in spots where pedestrian crossing cause stops in road traffic flow. In this paper, we propose a smart pedestrian traffic light triggering mechanism that uses a Frequency-modulated continuous-wave (FMCW) radar for pedestrian detection. Compared to, for example, camera-surveillance systems, radars have advantages in the ability to reliably detect pedestrians in low-visibility conditions and in maintaining privacy. Objects within a radar’s detection range are represented in a point cloud structure, in which pedestrians form clusters where they lose all identifiable features. Pedestrian detection and tracking are completed with a group tracking (GTRACK) algorithm that we modified to run on an external processor and not integrated into the used FMCW radar itself. The proposed prototype has been tested in multiple scenarios, where we focused on removing the call button from a conventional pedestrian traffic light. The prototype responded correctly in practically all cases by triggering the change in traffic signalization only when pedestrians were standing in the pavement area directly in front of the zebra crossing.
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Trinkaus, John. "An Informal Look at Left-Turning Traffic." Perceptual and Motor Skills 87, no. 2 (October 1998): 701–2. http://dx.doi.org/10.2466/pms.1998.87.2.701.
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Based on a total of 56 hours of observation of the traffic-light controlled left-turn lanes of a relatively busy road intersection, analysis showed lead vehicles of multiple vehicle queues generally took approximately 0.8 sec. to move out on a green arrow as contrasted with 0.5 sec. when the first vehicle was the only vehicle in the que.
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Sun, D. H., M. Zhang, and T. Chuan. "Multiple optimal current difference effect in the lattice traffic flow model." Modern Physics Letters B 28, no. 11 (May 9, 2014): 1450091. http://dx.doi.org/10.1142/s0217984914500912.
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Kerner and Konhäuser study moving jam dynamics first discovered in 1993 in Ref. 1. In light of their previous work, a new lattice hydrodynamic model is presented with consideration of the effect of multiple optimal current difference. To investigate the influences of new consideration on traffic jams, the linear stability analysis of the new model is conducted by employing the linear stability theory. Theoretical analysis result shows that the new consideration can stabilize traffic flow. By means of nonlinear analysis method, a modified Korteweg–deVries (mKdV) equation near the critical point is constructed and solved. The propagation behavior of traffic jam can thus be described by the kink–antikink soliton solution for the mKdV equation. Numerical simulation result shows that the effect of the multiple optimal current differences can suppress the emergence of traffic jams and the result is in good agreement with the analytical results.
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Wu, Qiang, Jianqing Wu, Jun Shen, Binbin Yong, and Qingguo Zhou. "An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control." Sensors 20, no. 15 (July 31, 2020): 4291. http://dx.doi.org/10.3390/s20154291.
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With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment.
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Mulung, Bibi Rawiyah, and Andino Maseleno. "Proposed SMART Traffic Control Signal in Brunei Darussalam." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (August 1, 2015): 277. http://dx.doi.org/10.11591/tijee.v15i2.1540.
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This paper presents proposed SMART (Systematic Monitoring of Arterial Road Traffic Signals) traffic control signal in Brunei Darussalam. Traffic congestion due to stops and delays at traffic light signals has much been complained about in Brunei Darussalam as well as across the world during the recent years. There are primarily two types of traffic signal controls in Brunei Darussalam. The most common one is the fixed or pre-timed signal operation traffic light and the other one is the actuated signal operation traffic light. Although the actuated signal control is more efficient than the fixed or pre-fixed signal control in the sense that it provides fewer stops and delays to traffic on the major arteries, the best option for Brunei Darussalam would be to introduce smart traffic control signal. This type of traffic signal uses artificial intelligence to take the appropriate action by adjusting the times in real time to minimise the delay in the intersection while also coordinating with intersections in the neighbourhood. SMART Signal simultaneously collects event-based high-resolution traffic data from multiple intersections and generates real-time signal performance measures, including arterial travel time, number of stops, queue length, intersection delay, and level of service. In Brunei Darussalam, where we have numerous intersections where several arterial roads are linked to one another, The SMART signal traffic control method should be implemented.
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Setyowati, Rustiana, and Siti Maria Ulfa. "Hubungan Beban Kerja Dan Lingkungan Kerja Terhadap Stres Kerja Pada Polisi Satlantas Polres Bantul." Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo 6, no. 2 (November 10, 2020): 169. http://dx.doi.org/10.29241/jmk.v6i2.338.
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In Indonesia cases of stress due to workload in the work environment of the police have occurred in several areas and have had quite serious impacts. The Research was conducted at the traffic police Special Region of Yogyakarta in 2019. This Research is a quantitative non experimental research with correlational descriptive. Data analysis techniques using multiple linier regression analysis. Respondents as many as 50 police Bantul Police Traffic Unit, the sample uses a saturated sample.The Study aims to determine the relationship of workload and work environment to work stress of Bantul Police Traffic Unit. The results of the study stated that the traffic police unit that has a light workload of 3 people (6%) moderate 39 people (78%) and weighs 8 people(16%) while the traffic unit police who have a light work environment are 6 people (12%) moderate work environment as many as 31 people (62%) and high work environment as many as 13 people (26%). While the traffic police who have low work stress are 6 people (12%) moderate are 34 people (68%) and heavy are 10 people (20%). Data Analysis using multiple linier regression with a coefficient of deternination. 1,907 and F calculated 49,41 and F table 3,23. The results of the study concluded that there was a relationship between workload and worke nvironment on the work stress of the traffic police at Bantul Region Police Station in Yogyakarta Keywords : Load, environment, Stress, Traffic Police unit
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Andronov, R. V., E. Eh Leverents, D. A. Genze, and E. N. Legostaeva. "The influence of traffic management at a regulated intersection on the uniformity of traffic capacity." Вестник гражданских инженеров 17, no. 6 (2020): 179–85. http://dx.doi.org/10.23968/1999-5571-2020-17-6-179-185.
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The paper deals with the issue of uniformity indicator of the traffic capacity at the regulated intersections from the standpoint of the traffic management, emphasizing the importance of accounting the value of uniformity, in addition to the value on the traffic performance. The schemes of traffic management are provided, where the interferences, due to turning vehicles and the movement of pedestrians in one phase, reduce the uniformity of the traffic capacity. This reduces the efficiency of traffic light regulation and increases the queue length and overall vehicle delays. The conclusions of the study are confirmed by carrying out a multiple-factor testing with the search for statistically significant coefficients.
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Maczyński, Andrzej, Krzysztof Brzozowski, and Artur Ryguła. "Analysis and Prediction of Vehicles Speed in Free-Flow Traffic." Transport and Telecommunication Journal 22, no. 3 (June 1, 2021): 266–77. http://dx.doi.org/10.2478/ttj-2021-0020.
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Abstract Speed is a crucial factor in the frequency and severity of road accidents. Light and heavy vehicles speed in free-flow traffic at six locations on Poland’s national road network was analyzed. The results were used to formulate two models predicting the mean speed in free-flow traffic for both light and heavy vehicles. The first one is a multiple linear regression model, the second is based on an artificial neural network with a radial type of neuron function. A set of the following input parameters is used: average hourly traffic, the percentage of vehicles in free-flow traffic, geometric parameters of the road section (lane and hard shoulder width), type of day and time (hour). The ANN model was found to be more effective for predicting the mean free-flow speed of vehicles. Assuming a 5% acceptable error of indication, the ANN model predicted the mean free-flow speed correctly in 84% of cases for light and 89% for heavy vehicles.
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Yan, Xuedong, Essam Radwan, and Elizabeth Birriel. "Analysis of Red Light Running Crashes Based on Quasi-Induced Exposure and Multiple Logistic Regression Method." Transportation Research Record: Journal of the Transportation Research Board 1908, no. 1 (January 2005): 70–79. http://dx.doi.org/10.1177/0361198105190800109.
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According to recent national statistics, red light running crashes represent a significant safety problem at signalized intersections. To examine the overall characteristics of red light running crashes, this study used the 1999 to 2001 Florida crash database to investigate the crash propensity related to traffic environments, driver characteristics, and vehicle types. The quasi-induced exposure concept and multiple logistic regression technique were used to perform this analysis. The results showed that traffic factors including number of lanes, crash time, weather, highway character, day of week, urban or rural location, speed limit, driver age, alcohol or drug use, physical defect, driver residence, and vehicle type were significantly associated with the risk of red light running crashes. Furthermore, it confirmed that there were significant interaction effects between the risk factors, including crash time and highway character, number of lanes and urban or rural location, weather condition and driver age, driver age and gender, alcohol or drug use and gender, and type of vehicle and gender.
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Zhou, Xingyu, Ness Shroff, and Adam Wierman. "Asymptotically Optimal Load Balancing in Large-scale Heterogeneous Systems with Multiple Dispatchers." ACM SIGMETRICS Performance Evaluation Review 48, no. 3 (March 5, 2021): 57–58. http://dx.doi.org/10.1145/3453953.3453965.
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We consider the load balancing problem in large-scale heterogeneous systems with multiple dispatchers. We introduce a general framework called Local-Estimation-Driven (LED). Under this framework, each dispatcher keeps local (possibly outdated) estimates of the queue lengths for all the servers, and the dispatching decision is made purely based on these local estimates. The local estimates are updated via infrequent communications between dispatchers and servers. We derive sufficient conditions for LED policies to achieve throughput optimality and delay optimality in heavy-traffic, respectively. These conditions directly imply delay optimality for many previous local-memory based policies in heavy traffic. Moreover, the results enable us to design new delay optimal policies for heterogeneous systems with multiple dispatchers. Finally, the heavy-traffic delay optimality of the LED framework also sheds light on a recent open question on how to design optimal load balancing schemes using delayed information.
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Su, Baofeng, Jiangbi Hu, Juncheng Zeng, and Ronghua Wang. "Traffic Safety Improvement via Optimizing Light Environment in Highway Tunnels." International Journal of Environmental Research and Public Health 19, no. 14 (July 12, 2022): 8517. http://dx.doi.org/10.3390/ijerph19148517.
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Driving in tunnel areas depends more heavily on light conditions than that on open roadways. Traditional lighting systems in highway tunnels adjust lighting parameters only caring about outside light luminance, and focus is usually on energy conservation; however, little concern is about drivers’ actual physical and psychological needs. How to leverage the enormous research progress of traffic safety, light environment, human factors engineering, and modern lighting sources to create an ideal tunnel light environment that aids with ensuring driving safety and lower interference effects caused by the change of light environment will greatly improve safety level and reduce adverse influence on drivers’ visual health in a tunnel area. An intelligent lighting control system designed with multiple influence factors are systematically considered. Based on sensor data from outside natural light conditions, target lighting parameters are determined per each lighting zone requires; then, lighting commands will be transferred and parsed by adaptive lighting controllers and modules, eventually LED lighting properties are altered step by step. This system helps a lot with optimizing tunnel lighting quality and improving drivers’ visual performance; as a result, it contributes to lower the fluctuation of drivers’ workload and get a smooth traffic flow, and ultimately this technically ensures physical and mental health of drivers in a tunnel area.
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Wang, Yanhong. "A unified model for two-lane lattice traffic flow." International Journal of Modern Physics B 30, no. 31 (December 5, 2016): 1650227. http://dx.doi.org/10.1142/s0217979216502271.
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In this paper, a unified model is presented for two-lane lattice traffic flow, with comparing different effects in the various lattice hydrodynamic models. Results of linear and nonlinear analysis show that multiple density difference effect (MDDE) is the strongest to enlarge the stable region in two-lane systems. Followed by density difference effect (DDE), multiple flux difference effect (MFDE), and finally flux difference effect (FDE). But when density is around 0.25, MFDE is better to enlarge the stable region than DDE. The reason is that a small flow-rate value might correspond to either a light traffic or a heavy traffic. Also energy consumption and traffic emissions are analyzed and shown to be the same marshaling sequence as linear and nonlinear analysis results. Numerical simulations validate theoretical analysis. And this is consistent with the realistic.
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Qu, Dayi, Kun Chen, Shaojie Wang, and Qikun Wang. "A Two-Stage Decomposition-Reinforcement Learning Optimal Combined Short-Time Traffic Flow Prediction Model Considering Multiple Factors." Applied Sciences 12, no. 16 (August 9, 2022): 7978. http://dx.doi.org/10.3390/app12167978.
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Accurate short-term traffic flow prediction is a prerequisite for achieving an intelligent transportation system to proactively alleviate traffic congestion. Considering the complex and variable traffic environment, so that the traffic flow contains a large number of non-linear characteristics, which makes it difficult to improve the prediction accuracy, a combined prediction model that reduces the unsteadiness of traffic flow and fully extracts the traffic flow features is proposed. Firstly, decompose the traffic flow data into multiple components by the seasonal and trend decomposition using loess (STL); these components contain different features, and the optimized variational modal decomposition (VMD) is used for the second decomposition of the component with large fluctuation frequencies, and then the components are reconstructed according to the fuzzy entropy and Lempel-Ziv complexity index and the Pearson correlation coefficient is used to filter the traffic flow features. Then light gradient boosting machine (LightGBM), long short-term memory with attention mechanism (LA), and kernel extreme learning machine with genetic algorithm optimization (GA-KELM) are built for prediction. Finally, we use reinforcement learning to integrate the advantages of each model, and the weights of each model are determined to obtain the best prediction results. The case study shows that the model established in this paper is better than other models in predicting urban road traffic flow, with an average absolute error of 2.622 and a root mean square error of 3.479, both of which are lower than the prediction errors of other models, indicating that the model can fully extract the features in complex traffic flow.
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Jiang, Ze-Hao, Xiao-Guang Yang, Tuo Sun, Tao Wang, and Zheng Yang. "Investigating the Relationship between Traffic Violations and Crashes at Signalized Intersections: An Empirical Study in China." Journal of Advanced Transportation 2021 (April 16, 2021): 1–8. http://dx.doi.org/10.1155/2021/4317214.
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About 90% of traffic crashes are caused by human factors, within which traffic violations are one of the most typical and common causes. In order to investigate the relationship between traffic violations and traffic crashes, this research targets signalized intersections in two Chinese cities: Yinchuan and Suqian. Thirty-one intersections are selected as the research sites, and additionally, the traffic volume, traffic violation, and traffic crash data of each intersection are collected for one year. A White’s test is conducted to test the homoscedasticity of the data and a multiple linear regression model is employed to investigate the relationship between traffic crashes and violations. The results show the following: (1) although the research sites are located in two different cities, the data is homoscedastic, which suggests that the above result may be statistically stable between different cities. (2) There is a significant multiple linear regression relationship (R2 = 0.782, adjusted R2 = 0.716) between the total number of traffic crashes and traffic violations. Among the chosen 7 independent variables, four are significantly related to the dependent variable, namely, driving commercial vehicle during internship, wrong-way entry, speeding, and traffic-light violation. (3) With the increase of annual average daily traffic (AADT), the number of total crashes goes up; however, the injury-or-fatality rate decreases, which means that intersections with smaller traffic volumes tend to have higher traffic crash severity. Based on the above conclusions, it is possible to conduct more targeted enforcement to improve the safety of intersections.
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Iyer, Parinith R., Shrutheesh Raman Iyer, Raghavendran Ramesh, Anala M.R., and K. N. Subramanya. "Adaptive real time traffic prediction using deep neural networks." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 2 (June 1, 2019): 107. http://dx.doi.org/10.11591/ijai.v8.i2.pp107-119.
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<span lang="EN-US">The ever-increasing sale of vehicles and the steady increase in population density in metropolitan cities have raised many growing concerns, most importantly commute time, air and noise pollution levels. Traffic congestion can be alleviated by opting using adaptive traffic light systems, instead of fixed-time traffic signals. In this paper, a system is proposed which can detect, classify and count vehicles passing through any traffic junction using a single camera (as opposed to multi-sensor approaches). The detection and classification are done using SSD Neural Network object detection algorithm. The count of each class (2-wheelers, cars, trucks, buses etc.) is used to predict the signal green-time for the next cycle. The model self-adjusts every cycle by utilizing weighted moving averages. This system works well because the change in the density of traffic on any given road is gradual, spanning multiple traffic stops throughout the day.</span>
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Pádua, Luís, José Sousa, Jakub Vanko, Jonáš Hruška, Telmo Adão, Emanuel Peres, António Sousa, and Joaquim J. Sousa. "Digital Reconstitution of Road Traffic Accidents: A Flexible Methodology Relying on UAV Surveying and Complementary Strategies to Support Multiple Scenarios." International Journal of Environmental Research and Public Health 17, no. 6 (March 13, 2020): 1868. http://dx.doi.org/10.3390/ijerph17061868.
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The reconstitution of road traffic accidents scenes is a contemporary and important issue, addressed both by private and public entities in different countries around the world. However, the task of collecting data on site is not generally focused on with the same orientation and relevance. Addressing this type of accident scenario requires a balance between two fundamental yet competing concerns: (1) information collecting, which is a thorough and lengthy process and (2) the need to allow traffic to flow again as quickly as possible. This technical note proposes a novel methodology that aims to support road traffic authorities/professionals in activities involving the collection of data/evidences of motor vehicle collision scenarios by exploring the potential of using low-cost, small-sized and light-weight unmanned aerial vehicles (UAV). A high number of experimental tests and evaluations were conducted in various working conditions and in cooperation with the Portuguese law enforcement authorities responsible for investigating road traffic accidents. The tests allowed for concluding that the proposed method gathers all the conditions to be adopted as a near future approach for reconstituting road traffic accidents and proved to be: faster, more rigorous and safer than the current manual methodologies used not only in Portugal but also in many countries worldwide.
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Babio, Nancy, Paloma Vicent, Leonor López, Anna Benito, Julio Basulto, and Jordi Salas-Salvadó. "Adolescents’ ability to select healthy food using two different front-of-pack food labels: a cross-over study." Public Health Nutrition 17, no. 6 (May 17, 2013): 1403–9. http://dx.doi.org/10.1017/s1368980013001274.
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AbstractObjectiveTo compare, in adolescents, two models of front-of-pack Guideline Daily Amounts (GDA) labels in terms of (i) friendliness and acceptance and (ii) the ability to choose a diet that closely follows the nutritional recommendations.DesignA randomized cross-over study was designed to compare two simplified front-of-pack GDA nutrition labels.SettingA Spanish secondary school.SubjectsEighty-one healthy adolescents aged between 14 and 16 years were recruited. Participants were randomly exposed to two experimental non-real food-choice conditions using multiple-traffic-light or monochrome nutritional labels. Participants had to choose options from a closed menu for 5 d on the basis of the experimental front-of-pack labelling. For each meal, three food options with different nutritional compositions were given to the participants. The contents of total energy and fat, saturated fat, sugar and salt of the chosen options were calculated.ResultsThere were no significant differences in baseline sociodemographic and anthropometric characteristics between participants regardless of the experimental condition in which they started. There were no carry-over effects between the experimental sequences. It was observed that when participants used the multiple-traffic-light GDA system they chose significantly less total energy (mean –123·1 (sd 211·0) kJ (−29·4 (sd 50·4) kcal), P < 0·001), sugar (−4·5 (sd 4·6) g, P < 0·001), fat (−2·1 (sd 4·5) g, P = 0·006), saturated fat (−1·0 (sd 1·9) g, P = 0·002) and salt (−0·4 (sd 0·5) g, P < 0·001) than when they used the monochrome GDA system.ConclusionsCompared with the monochrome GDA front-of-pack nutritional label, the multiple-traffic-light system helped adolescents to differentiate between healthier and less healthy food, theoretically making it possible for them to choose a diet closer to dietary recommendations.
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Yang, Yanqun, Danni Yin, Said M. Easa, and Jiang Liu. "Attitudes toward Applying Facial Recognition Technology for Red-Light Running by E-Bikers: A Case Study in Fuzhou, China." Applied Sciences 12, no. 1 (December 26, 2021): 211. http://dx.doi.org/10.3390/app12010211.
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The application of facial recognition technology (FRT) can effectively reduce the red-light running behavior of e-bikers. However, the privacy issues involved in FRT have also attracted widespread attention from society. This research aims to explore the public and traffic police’s attitudes toward FRT to optimize the use and implementation of FRT. A structured questionnaire survey of 270 people and 94 traffic police in Fuzhou, China, was used. In the research, we use several methods to analyze the investigation data, including Mann–Whitney U test, Kruskal–Wallis test, and multiple correspondence analysis. The survey results indicate that the application of FRT has a significant effect on reducing red-light running behavior. The public’s educational level and driving license status are the most influential factors related to their attitudes to FRT (p < 0.001). Public members with these attributes show more supportive attitudes to FRT and more concerns about privacy invasion. There are significant differences between the public and traffic police in attitudes toward FRT (p < 0.001). Compared with the public, traffic police officers showed more supportive attitudes to FRT. This research contributes to promoting the application of FRT legitimately and alleviating people’s concerns about the technology.
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Wali, Aamir, Khansa Tanveer, Samreen Fatima, Ayeza Tanveer, and Sara Iftikhar. "An Efficient Cloud-Based Traffic Signal Manipulation Algorithm for Path Clearance." International Journal of Distributed Systems and Technologies 11, no. 2 (April 2020): 32–44. http://dx.doi.org/10.4018/ijdst.2020040103.
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Beside many challenges that urban cities have to face, one of them is increasing traffic. Unfortunately, in developing countries like, for example, Pakistan, the traffic management infrastructure does not scale accordingly. This leads to two types of problems: congestion and long queues at traffic signals. This makes it difficult for emergency vehicles (EV) such as ambulances to reach their destination on time. Therefore, in this article, the authors have developed an intelligent path clearance system for emergency vehicles. The particular focus is on long queues at traffic signals. Given the GPS coordinates of an EV, a destination, a map, and the traffic light grid system, our system provides a signal free corridor to the priority vehicle by automatically manipulating traffic signals that fall in its path using cloud computing. The idea is to clear the path of the vehicle. The proposed system also makes decision based on the time of the day and current traffic conditions in real time. In case of multiple options, it also calculates the shortest path to the destination.
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Paavani, R. Krishna, V. Indraja, V. Neelimajyothi, S. Sai, and Mr M. Sriramulu. "Traffic Sign Board Detection Using Single Shot Detection (SSD)." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4095–97. http://dx.doi.org/10.22214/ijraset.2022.43336.
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Abstract: Traffic sign board detection (TSBD) is a significant portion of intelligent transportation system (ITS). Being able to identify traffic signals more accurately and effectively can improve safe driving .Due to increase in technology there are autonomous vehicles . The traffic sign recognition process includes two parts: detection and classification. In this paper, we use an object detection algorithm called SSD to detect the traffic signs. This convolutional neural network uses multiple feature maps to detect objects. For the traffic sign is very small to the whole picture, the SSD model has been improved to have a better detection result of traffic signs. In the experiments, the model has been simplified and the size of the prior box has been modified. The improved network has a good detection effect on small targets. The results on the test data set show that the proposed algorithm performs well for single-target, multi-target and dark-light images. The precision and recall on the test data set are 91.09%, and 88.06%. Keywords: Automatic traffic sign board Detection, SSD, Image processing, Text alert.
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Alsaawy, Yazed, Ahmad Alkhodre, Adnan Abi Sen, Abdullah Alshanqiti, Wasim Ahmad Bhat, and Nour Mahmoud Bahbouh. "A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics." Applied Sciences 12, no. 4 (February 16, 2022): 2043. http://dx.doi.org/10.3390/app12042043.
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Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy.
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Kostermans, Evelien, Renske Spijkerman, Rutger C. M. E. Engels, Harold Bekkering, and Ellen R. A. de Bruijn. "To Cross or Not To Cross." Journal of Psychophysiology 27, no. 3 (July 1, 2013): 113–23. http://dx.doi.org/10.1027/0269-8803/a000096.
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Different theoretical accounts have attempted to integrate anterior cingulate cortex involvement in relation to conflict detection, error-likelihood predictions, and error monitoring. Regarding the latter, event-related potential studies have identified the feedback-related negativity (FRN) component in relation to processing feedback which indicates that a particular outcome was worse than expected. According to the conflict-monitoring theory the stimulus-locked N2 reflects pre-response conflict. Assumptions of these theories have been made on the basis of relatively simple response-mapping tasks, rather than more complex decision-making processes associated with everyday situations. The question remains whether expectancies and conflicts induced by everyday knowledge similarly affect decision-making processes. To answer this question, electroencephalogram and behavioral measurements were obtained while participants performed a simulated traffic task that varied high and low ambiguous situations at an intersection by presenting multiple varying traffic light combinations. Although feedback was kept constant for the different conditions, the tendency to cross was more pronounced for traffic light combinations that in reallife are associated with proceeding, as opposed to more ambiguous traffic light combinations not uniquely associated with a specific response. On a neurophysiological level, the stimulus-locked N2 was enhanced on trials that induced experience-based conflict and the FRN was more pronounced for negative as compared to positive feedback, but did not differ as a function of everyday expectancies related to traffic rules. The current study shows that well-learned everyday rules may influence decision-making processes in situations that are associated with the application of these rules, even if responding accordingly does not lead to the intended outcomes.
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Radwan, Noha, Wolfram Burgard, and Abhinav Valada. "Multimodal interaction-aware motion prediction for autonomous street crossing." International Journal of Robotics Research 39, no. 13 (October 1, 2020): 1567–98. http://dx.doi.org/10.1177/0278364920961809.
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For mobile robots navigating on sidewalks, the ability to safely cross street intersections is essential. Most existing approaches rely on the recognition of the traffic light signal to make an informed crossing decision. Although these approaches have been crucial enablers for urban navigation, the capabilities of robots employing such approaches are still limited to navigating only on streets that contain signalized intersections. In this article, we address this challenge and propose a multimodal convolutional neural network framework to predict the safety of a street intersection for crossing. Our architecture consists of two subnetworks: an interaction-aware trajectory estimation stream ( interaction-aware temporal convolutional neural network (IA-TCNN)), that predicts the future states of all observed traffic participants in the scene; and a traffic light recognition stream AtteNet. Our IA-TCNN utilizes dilated causal convolutions to model the behavior of all the observable dynamic agents in the scene without explicitly assigning priorities to the interactions among them, whereas AtteNet utilizes squeeze-excitation blocks to learn a content-aware mechanism for selecting the relevant features from the data, thereby improving the noise robustness. Learned representations from the traffic light recognition stream are fused with the estimated trajectories from the motion prediction stream to learn the crossing decision. Incorporating the uncertainty information from both modules enables our architecture to learn a likelihood function that is robust to noise and mispredictions from either subnetworks. Simultaneously, by learning to estimate motion trajectories of the surrounding traffic participants and incorporating knowledge of the traffic light signal, our network learns a robust crossing procedure that is invariant to the type of street intersection. Furthermore, we extend our previously introduced Freiburg Street Crossing dataset with sequences captured at multiple intersections of varying types, demonstrating complex interactions among the traffic participants as well as various lighting and weather conditions. We perform comprehensive experimental evaluations on public datasets as well as our Freiburg Street Crossing dataset, which demonstrate that our network achieves state-of-the-art performance for each of the subtasks, as well as for the crossing safety prediction. Moreover, we deploy the proposed architectural framework on a robotic platform and conduct real-world experiments that demonstrate the suitability of the approach for real-time deployment and robustness to various environments.
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Zhang, Zheyuan, Jianying Zheng, Yanyun Tao, Yang Xiao, Shumei Yu, Sultan Asiri, Jiacheng Li, and Tieshan Li. "Traffic Sign Based Point Cloud Data Registration with Roadside LiDARs in Complex Traffic Environments." Electronics 11, no. 10 (May 13, 2022): 1559. http://dx.doi.org/10.3390/electronics11101559.
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The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of intelligent roads. Furthermore, a fusion of multiple LiDARs has become a current hot spot to extend the data collection range and improve detection accuracy. This paper focuses on point cloud registration in a complex traffic environment and proposes a three-dimensional (3D) registration method based on traffic signs and prior knowledge of traffic scenes. Traffic signs with their reflective films are used as reference targets to register 3D point cloud data from roadside LiDARs. The proposed method consists of a vertical registration and a horizontal registration. For the vertical registration, we propose a panel rotation algorithm to rotate the initial point cloud to register it vertically, converting the 3D point cloud registration into a two-dimensional (2D) rigid body transformation. For the vertical registration, our system registers traffic signs from different LiDARs. Our method has been verified in some actual scenarios. Compared with previous methods, the proposed method is automatic and does not need to search reference targets manually. Furthermore, it is suitable for actual engineering use and can be applied to sparse point cloud data from LiDAR with few beams, realizing point cloud registration of large disparity.
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Wang, Zhaocheng, and Jiaxuan Chen. "Networked multiple-input-multiple-output for optical wireless communication systems." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2169 (March 2, 2020): 20190189. http://dx.doi.org/10.1098/rsta.2019.0189.
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With the escalation of heterogeneous data traffic, the research on optical wireless communication (OWC) has attracted much attention, owing to its advantages such as wide spectrum, low power consumption and high security. Ubiquitous optical devices, e.g. light-emitting diodes (LEDs) and cameras, are employed to support optical wireless links. Since the distribution of these optical devices is usually dense, multiple-input-multiple-output (MIMO) can be naturally adopted to attain spatial diversity gain or spatial multiplexing gain. As the scale of OWC networks enlarges, optical MIMO can also collaborate with network-level operations, like user/AP grouping, to enhance the network throughput. Since OWC is preferred for short-range communications and is sensitive to the directions/rotations of transceivers, optical MIMO links vary frequently and sharply in outdoor scenarios when considering the mobility of optical devices, raising new challenges to network design. In this work, we present an overview of optical MIMO techniques, as well as the cooperation of MIMO and user/AP grouping in OWC networks. In consideration of the challenges for outdoor OWC, key technologies are then proposed to facilitate the adoption of optical MIMO in outdoor scenarios, especially in vehicular ad hoc networks. Lastly, future applications of MIMO in OWC networks are discussed. This article is part of the theme issue ‘Optical wireless communication’.
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Jereb, Borut, Ondrej Stopka, and Tomáš Skrúcaný. "Methodology for Estimating the Effect of Traffic Flow Management on Fuel Consumption and CO2 Production: A Case Study of Celje, Slovenia." Energies 14, no. 6 (March 17, 2021): 1673. http://dx.doi.org/10.3390/en14061673.
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The manuscript discusses the investigation of vehicle flow in a predesignated junction by an appropriate traffic flow management with an effort to minimize fuel consumption, the production of CO2, an essential greenhouse gas (hereinafter referred to as GHG), and related transport costs. The particular research study was undertaken in a frequented junction in the city of Celje, located in the eastern part of Slovenia. The results obtained summarize data on consumed fuel and produced CO2 amounts depending on the type of vehicle, traffic flow mixture, traffic light signal plan, and actual vehicle velocity. These values were calculated separately for three different conditions of traffic flow management. Amounts of fuel consumed were experimentally investigated in real traffic situations, whereas CO2 production was calculated by applying the actual European standard entitled EN 16258:2012 associated with a guideline for measuring emission values, as well as by examining specific traffic flow parameters. The key objective of the manuscript is to present multiple scenarios towards striving to minimize environmental impacts and improve transport operation’s economic consequences when implementing proper traffic flow management. As for crucial findings, we quantified fuel consumption and CO2 emissions based on real data on the number and type of vehicles crossing the examined intersection and traffic light switching intervals. The results show that most of the CO2 was produced while waiting and in the accelerating phase in front of traffic lights, whereby in the running phase through the intersection, significantly less fuel was used. This study represents a mosaic fragment of research addressing endeavors to reduce CO2 production in urban transport. Following the experiments conducted, we can see a notable contribution towards reducing CO2 production with known and tested interventions in the existing transport infrastructure. A procedure embracing individual research steps may be deemed as an approach methodology dealing with traffic flow management with an aim to decrease the environmental and economic impacts of traffic and transport operation; this is where the novelty of the research lies.
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Yang, Hong, Yuzhong Shen, Mahmud Hasan, Daniel Perez, and John Shull. "Framework for Interactive M3 Visualization of Microscopic Traffic Simulation." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 44 (July 28, 2018): 62–71. http://dx.doi.org/10.1177/0361198118787088.
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Full comprehension of microscopic traffic simulation (MTS) models has necessitated the development of proper visualizations. Existing MTS models only provide limited capability of two- and/or three-dimensional displays that often restrict users’ viewpoint to a flat screen. Their downscaled scenes neither provide a realistic representation of the environment nor allow different users to simultaneously experience the simulation model from different perspectives. This largely prevents analysts from effectively demonstrating and disseminating their simulation results to various stakeholders of different background and knowledge. In light of these issues, this paper aims to develop a framework that enables a multi-user, multi-perspective, and multi-mode ( M3) visualization architecture for microscopic traffic simulation. The proposed framework is empowered by the latest advances in cloud computing and virtual reality (VR) to support interactive and immersive visualization for simulated traffic environments. A client-server architecture allows multiple users at distributed physical locations to view the same simulation from multiple perspectives simultaneously and supports a variety of virtual/augmented reality devices. A prototype of the proposed M3 visualization framework is implemented and demonstrated by simulating and visualizing a model of typical traffic operations in a high-density urban area. The promising capability of the M3 visualization framework is attested. Potential improvements over the present study to further excel current visualization framework are also discussed.
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Delgado-Fernández, Vicente Joaquín, María del Carmen Rey-Merchán, Antonio López-Arquillos, and Sang D. Choi. "Occupational Traffic Accidents among Teachers in Spain." International Journal of Environmental Research and Public Health 19, no. 9 (April 24, 2022): 5175. http://dx.doi.org/10.3390/ijerph19095175.
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Occupational traffic accidents are a leading cause of injuries or deaths among workers. Teachers in Spain are especially concerned about the problem of commuting due to their particular labor conditions. Multiple work-related factors are associated with the risk and severity of occupational traffic-related motor vehicle crashes. The objective of this research is to analyze the influence of the variables associated with the severity of occupational traffic accidents among teachers in Spain. A logistic regression model was used for the current study. The odds ratio (OR) and confidence interval (CI) were calculated for the injured worker on a sample of 20,190 occupational traffic accidents suffered by teachers. The results showed that women, Spanish nationality, younger than 55 years, and those driving a car were more likely to suffer a light crash. In contrast, men, foreign nationalities, older than 55 years, and those riding a motorbike were more likely to suffer a serious crash. Based on these findings, motor vehicle safety training could be designed and adapted to the riskiest profiles. Additionally, effective mobility plans for commuting could help reduce work-related traffic accidents.
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Gill, Sanjeev, and Rajeev Kumar. "VARIOUS TYPES OF SPACES PARKING AND LIGHT WEIGHT FOUR WHEELER PARKING." International Journal of Research -GRANTHAALAYAH 5, no. 3 (March 31, 2017): 161–66. http://dx.doi.org/10.29121/granthaalayah.v5.i3.2017.1763.
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The unscrupulous growing population has created many problems in country like India. Parking is one of the serious problems that confront the urban planner and the traffic engineer. One of the problems created by road traffic is parking. Not only do vehicles require street space to move about, but also do they require space to park where the occupants can be loaded and unloaded. It is roughly estimated that out of 8,760 hours in a year, the car runs on an average for only 400 hours, leaving 8,360 hours when it is parked. Besides the problem of space for cars moving on the road, greater is the problem of space for a parked vehicle considering that private vehicles remain parked for most of their time. Besides the problem of space for cars moving on the road, greater is the problem of space for a parked vehicle considering that private vehicles remain parked for most of their time. While residential projects still escape with designated parking, the real problem lies with commercial spaces many a time which is overcome by taking extra open spaces to park. Multi-level Parking systems for some time have provided relief since they come with a number of advantages – Optimal utilization of space, lower maintenance and operational cost, lower construction cost, secure and environment-friendly nature, comfortable for the drivers, cost saving for builders by saving height or depth. Multiple Level Car Parking Systems are much in vogue a method of automatically parking and retrieving cars that typically use a system of pallets and lifts and signaling devices for retrieval. They serve advantages like safety, saving of space, time and fuel space but also need to have an extra and a very detailed assessment of the parking required, space availability and traffic flow.
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Kumar, Harish, Yassine Aoudni, Geovanny Genaro Reivan Ortiz, Latika Jindal, Shahajan Miah, and Rohit Tripathi. "Light Weighted CNN Model to Detect DDoS Attack over Distributed Scenario." Security and Communication Networks 2022 (June 13, 2022): 1–10. http://dx.doi.org/10.1155/2022/7585457.
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The minimal-degree distributed denial-of-service attack takes advantage of flaws in the adaptive mechanisms of network protocols, which could have a big impact on network service quality. It is very hard to find, has a low attack rate, and comes at a set time. Detection methods that have been used before have problems because they only use one type of detection and are not very good at identifying the object. In the end, a way to detect many sorts of minimal DDoS assaults that use deep hybrid learning is suggested. To construct multi-type limited DDoS threat data sets and mimic diverse sorts DDoS assaults and legitimate traffic in varying situations in the 5G setting, collect congestion at the networking entry and extract flow feature info are considered. From a statistical threshold and feature engineering point of view, these data sets show how many sorts of minimal DDoS assaults are there. This study aims to develop a deep hybrid learning-based multi-type low-rate DDoS attack detection solution for 5G networks which is the novel model that is recently deployed, and a hybrid deep learning algorithm was used to train the algorithm offline, and the algorithm’s performance was compared to that of the LSTM-Light GBM and LSTM-RF algorithms. The CNN-RF revealing model was then used to detect minimal DDoS assaults at the gateway, so that multiple attacks could be detected at the same time. It can identify 4 sorts of low-rate DDoS assaults like Slow-Headers, Slow-Body, Slow-Read, and Shrew assaults, in a 120-second window. The false intercept rate is 11.03 percent. This means that 96.22 percent of traffic could be found. Using the strategy suggested can help cut down on the traffic concentration of minimal DDoS attacks at the net ingress. It can also be used in real-world situations.
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Zhong, Nan, Kaifeng Liu, and Yurong Li. "Deep Q-Learning Network Model for Optimizing Transit Bus Priority at Multiphase Traffic Signal Controlled Intersection." Mathematical Problems in Engineering 2023 (February 23, 2023): 1–14. http://dx.doi.org/10.1155/2023/9137889.
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When multiple bus vehicles send priority requests at a single intersection, the existing fixed-phase sequence control methods cannot provide priority traffic request services for multiphase bus vehicles. In view of the conflict of multiphase bus priority requests at intersections, the priority vehicle traffic sequence is determined, which is the focus of this study. In this paper, a connected vehicle-enabled transit signal priority system (CV-TSPS) has been proposed, which uses vehicle-infrastructure communication function (V2I) technology to obtain real-time vehicle movement, road traffic states, and traffic signal light phase information. By developing a deep Q-learning neural network (DQNN), especially for optimizing traffic signal control strategy, the public transit vehicles will be prioritized to improve their travel efficiency, while the overall delay of road traffic flow will be balanced to ensure the safe and orderly passage of intersections. In order to verify the validity of the model, the SUMO traffic analysis software has been applied to simulate real-time traffic control, and the experimental results show that compared with the traditional timing signal control, the loss time of vehicles is reduced by nearly 40%, and the cumulative loss time per capita is reduced by nearly 43.5%, and a good control effect is achieved. In the case of medium and low densities, it is better than the solid scheduled traffic control scheme.
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Al-Sanjary, Omar Ismael, Muhammad Aiman Bin Roslan, Rabab Alayham Abbas Helmi, and Ahmed Abdullah Ahmed. "Comparison and Detection Analysis of Network Traffic Datasets Using K-Means Clustering Algorithm." Journal of Information & Knowledge Management 19, no. 03 (August 3, 2020): 2050026. http://dx.doi.org/10.1142/s0219649220500264.
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Anomaly detection in specific datasets involves the detection of circumstances that are common in a homogeneous data. When looking at network traffic data, it is generally difficult to determine the type of attack without proper analysis and this holds true when simply viewing a record of network traffic with thousands of internet users to detect malicious activity. However, there are different types of datasets in light of the way they record or acquire data and facts. The paper aims to compare and analyse multiple datasets including NSL-KDD and MAWI by using K-means clustering algorithm. Specifically, the paper analyses the blind-Spots of the datasets and evaluates the most suitable dataset for K-means clustering algorithm. This paper’s quantitative data analysis results are helpful in evaluating weaknesses of each dataset of traffic data, when using K-means clustering algorithm.
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Zarbakhsh, N., and G. McArdle. "PREDICTING TRAFFIC CONGESTION DURING COVID19 USING HUMAN MOBILITY AND STREET-WASTE FEATURES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W3-2022 (October 14, 2022): 301–8. http://dx.doi.org/10.5194/isprs-annals-x-4-w3-2022-301-2022.
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Abstract. With COVID-19’s prevalence and government efforts to curb its spread, urban travel behaviour has significantly altered, resulting in a significant shift in traffic congestion. Rather than predicting traffic congestion based on historical data, we aim to model the correlation between travel behaviour and external mobility-related urban features and use Dublin in Ireland as a case study. This study incorporates four categories of urban data, including 1) Mobility-based features, including the government’s interventions and mobility pattern changes in different locations, 2) Environmental features such as weather and urban street-waste, 3) COVID-19- related features such as the positivity and vaccination rates, and 4) Time-related features such as public holidays. First, we examine the impact of COVID-19 on traffic congestion and street-waste to understand the city’s dynamic. Then, multiple machine learning (ML) models, such as random forests, support vector regression, light gradient boosting machine, and multiple linear regression are trained, and their performance optimized to predict traffic congestion changes. We compare the outcomes of the models with several evaluation metrics and interpret the best performing model. The results indicate that mobility changes in grocery and pharmacy, retail and recreation, workplaces sectors, and the amount of urban street-waste significantly contribute to the model outcomes. Findings could predict traffic dynamics in times of crisis and allow authorities to comprehend the effects of their intervention measures on mobility, which would ultimately benefit developing smart cities and intelligent transportation systems.
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Yılmaz Sönmez, Hazal, and Zübeyde Öztürk. "Effects of Traffic Loads and Track Parameters on Rail Wear: A Case Study for Yenikapi–Ataturk Airport Light Rail Transit Line." Urban Rail Transit 6, no. 4 (October 28, 2020): 244–64. http://dx.doi.org/10.1007/s40864-020-00136-1.
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AbstractThe aim of this study is to investigate the effects of traffic loads and track parameters, including track curvature, superelevation, and train speed, on vertical and lateral rail wear. The Yenikapi–Ataturk Airport Light Rail Transit (LRT) line in Istanbul was selected as a case study, and rail wear measurements were carried out accordingly. Passenger counts were performed in all wagons of the train on different days and time intervals to calculate the number of passengers carried in track sections between stations regarding traffic loads on the LRT line. Values of traffic load, track curvature, superelevation, and speed were determined for each kilometer where measurements of rail wear were conducted. A multiple linear regression analysis (MLRA) method was used to identify effective parameters on rail wear. Independent variables in MLRA for both vertical and lateral wear include traffic load, track curvature, superelevation, and train speed. The dependent variables in MLRA for vertical and lateral wear are the amount of vertical and lateral wear, respectively. The correlation matrix of the dependent and independent variables was analyzed before performing MLRA. Multicollinearity tests and cross-validation analyses were conducted. According to the results of MLRA for vertical and lateral wear, the obtained coefficients of determination indicate that a high proportion of variance in the dependent variables can be explained by the independent variables. Traffic load has a statistically significant effect on the amount of vertical and lateral rail wear. However, track curvature, superelevation, and train speed do not have a statistically significant effect on the amount of vertical or lateral rail wear.
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Ahluwalia, Raju, Erika Vainieri, Joseph Tam, Saif Sait, Aaditya Sinha, Chris Adusei Manu, Ines Reichert, Venu Kavarthapu, Michael Edmonds, and Prashant Vas. "Surgical Diabetic Foot Debridement: Improving Training and Practice Utilizing the Traffic Light Principle." International Journal of Lower Extremity Wounds 18, no. 3 (June 25, 2019): 279–86. http://dx.doi.org/10.1177/1534734619853657.
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Comprehensive management of a severe diabetic foot infection focus on clear treatment pathways. Including rapid, radical debridement of all infection in addition to intravenous antibiotics and supportive measures. However, inexperienced surgeons can often underestimate the extent of infection, risking inadequate debridement, repeated theatre episodes, higher hospital morbidity, and hospital length of stay (LOS). This study aims to assess protocolized diabetic-foot-debridement: Red-Amber-Green (RAG) model as part of a value-based driven intervention. The model highlights necrotic/infected tissue (red-zone, nonviable), followed by areas of moderate damage (amber-zone), healthy tissue (green-zone, viable). Sequential training of orthopedic surgeons supporting our emergency service was undertaken prior to introduction. We compared outcomes before/after RAG introduction (pre-RAG, n = 48; post- RAG, n = 35). Outcomes measured included: impact on number of debridement/individual admission, percentage of individuals requiring multiple debridement, and length-of-hospital-stay as a function-of-cost. All-patients fulfilled grade 2/3, stage-B, of the Texas-Wound-Classification. Those with evidence of ischemia were excluded. The pre-RAG-group were younger (53.8 ± 11.0 years vs 60.3 ± 9.2 years, P = .01); otherwise the 2-groups were matched: HbA1c, white blood cell count, and C-reactive protein. The post-RAG-group underwent significantly lower numbers of debridement’s (1.1 ± 0.3 vs 1.5 ± 0.6/individual admission, P = .003); equired fewer visits to theatre (8.6% vs 38%, P = .003), their LOS was reduced (median LOS pre-RAG 36.0 vs post-RAG 21.5 days, P = .02). RAG facilitates infection clearance, fewer theatre-episodes, and shorter LOS. This protocolized-management-tools in acute severely infected diabetic foot infection offers benefits to patients and health-care-gain.
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Guo, Lin, Jincai Huang, Wei Ma, Longzhi Sun, Lianjie Zhou, Jianping Pan, and Wentao Yang. "Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors." Applied Sciences 12, no. 13 (June 27, 2022): 6511. http://dx.doi.org/10.3390/app12136511.
Full textAbstract:
Nowadays, large-scale human mobility has led to increasingly severe traffic congestion in cities, how to accurately identify people’s travel mode has become particularly important for urban traffic planning and management. However, traditional methods are based on telephone interviews or questionnaires, which makes it difficult to obtain accurate and effective data. Nowadays, numbers of smartphones are equipped with various sensors, including accelerometers, gyroscopes, and GPS, providing a novel social sensing data source to detect people’s travel modes. The fusion of multiple sensor data is a promising way for travel mode detection. However, how to use these sensor data to accurately detect travel mode is still a challenging task. In this paper, we presented a light-weight method for travel mode detection based on four types of smartphone sensor data collected from an accelerometer, gyroscope, magnetometer, and barometer, and a prototype application was developed. Then, a novel convolutional neural network (CNN) was designed to identify five representative travel modes (walk, bicycle, bus, car, and metro). We compared the overall performance of the proposed method via different hyperparameters, and the experimental results show that the F value of the proposed method reaches 97%, which verified the effectiveness of the proposed method for travel mode classification.
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Gargoum, Suliman, Karim El-Basyouny, Joseph Sabbagh, and Kenneth Froese. "Automated Highway Sign Extraction Using Lidar Data." Transportation Research Record: Journal of the Transportation Research Board 2643, no. 1 (January 2017): 1–8. http://dx.doi.org/10.3141/2643-01.
Full textAbstract:
Traffic signs are integral elements of any transportation network; however, keeping records of those signs and their condition is a tedious, time-consuming, and labor-intensive process. As a result, many agencies worldwide have been working toward automating the process. One form of automation uses remote sensing techniques to extract traffic sign information. An algorithm is proposed that can automatically extract traffic signs from mobile light detection and ranging data. After the number of signs on a road segment has been determined, the coordinates of those signs are mapped onto the road segment. The sign extraction procedure involves applying multiple filters to the point cloud data and clustering the data into traffic signs. The proposed algorithm was tested on three highways located in different regions of the province of Alberta, Canada. The segments on which the algorithm was tested include a two-lane undivided rural road and four-lane divided highways. The highway geometry varied, as did vegetation and tree density. Success rates ranged from 93% to 100%, and the algorithm performed better on highways without overhead signs. Results indicate that the proposed method is simple but effective for creating an accurate inventory of traffic signs.
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González, Cesar Leonardo, Santiago L. Delgado, Juan M. Alberola, Luis Fernando Niño, and Vicente Julián. "Toward Autonomous and Distributed Intersection Management with Emergency Vehicles." Electronics 11, no. 7 (March 30, 2022): 1089. http://dx.doi.org/10.3390/electronics11071089.
Full textAbstract:
Numerous approaches have attempted to develop systems that more appropriately manage street crossings in cities in recent years. Solutions range from intelligent traffic lights to complex, centralized protocols that evaluate the policies that vehicles must comply with at intersections. Such works attempt to provide traffic-control strategies at intersections where the complexity of a dynamic environment, with vehicles crossing in different directions and multiple conflict points, pose a significant challenge for city traffic optimization. Traditionally, a traffic-control system at an intersection gives the green light to one lane while keeping the other lanes on red. But there may be situations in which there are different levels of vehicle priority; for example, emergency vehicles may have priority at intersections. Thus, this work proposes a distributed junction-management protocol that pays special attention to emergency vehicles. The proposed algorithm implements rules based on the distributed intersection management (DIM) protocol; such rules are used by vehicles while negotiating their crossing through the intersection. The proposal also seeks to affect the traffic flow of non-priority vehicles minimally. An evaluation and comparison of the proposed algorithm are presented in the paper.
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Liu, Cao, Hong Zheng, Dian Yu, and Xiaohang Xu. "A Novel Method of Adaptive Traffic Image Enhancement for Complex Environments." Journal of Sensors 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/516326.
Full textAbstract:
There exist two main drawbacks for traffic images in classic image enhancement methods. First is the performance degradation that occurs under frontlight, backlight, and extremely dark conditions. The second drawback is complicated manual settings, such as transform functions and multiple parameter selection mechanisms. Thus, this paper proposes an effective and adaptive parameter optimization enhancement algorithm based on adaptive brightness baseline drift (ABBD) for color traffic images under different luminance conditions. This method consists of two parts: brightness baseline model acquisition and adaptive color image compensation. The brightness baseline model can be attained by analyzing changes in light along a timeline. The adaptive color image compensation involves color space remapping and adaptive compensation specific color components. Our experiments were tested on various traffic images under frontlight, backlight, and during nighttime. The experimental results show that the proposed method achieved better effects compared with other available methods under different luminance conditions, which also effectively reduced the influence of the weather.
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Yue, Rui, Hao Xu, Jianqing Wu, Renjuan Sun, and Changwei Yuan. "Data Registration with Ground Points for Roadside LiDAR Sensors." Remote Sensing 11, no. 11 (June 5, 2019): 1354. http://dx.doi.org/10.3390/rs11111354.
Full textAbstract:
The Light Detection and Ranging (LiDAR) sensors are being considered as new traffic infrastructure sensors to detect road users’ trajectories for connected/autonomous vehicles and other traffic engineering applications. A LiDAR-enhanced traffic infrastructure system requires multiple LiDAR sensors around intersections, along with road segments, which can provide a seamless detection range at intersections or along arterials. Each LiDAR sensor generates cloud points of surrounding objects in a local coordinate system with the sensor at the origin, so it is necessary to integrate multiple roadside LiDAR sensors’ data into the same coordinate system. None of existing methods can integrate the data from roadside LiDAR sensors, because the extensive detection range of roadside sensors generates low-density cloud points and the alignment of roadside sensors is different from mapping scans or autonomous sensing systems. This paper presents a method to register datasets from multiple roadside LiDAR sensors. This approach innovatively integrates LiDAR datasets with 3D cloud points of road surface and 2D reference point features, so the method is abbreviated as RGP (Registration with Ground and Points). The RGP method applies optimization algorithms to identify the optimized linear coordinate transformation. This research considered the genetic algorithm (global optimization) and the hill climbing algorithm (local optimization). The performance of the RGP method and the different optimization algorithms was evaluated with field LiDAR sensors data. When the developed process can integrate data from roadside sensors, it can also register LiDAR sensors’ data on an autonomous vehicle or a robot.