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1
Pradhan, R. K., S. Shrestha, and D. B. Gurung. "Mathematical modeling of mixed-traffic in urban areas." Mathematical Modeling and Computing 9, no. 2 (2022): 226–40. http://dx.doi.org/10.23939/mmc2022.02.226.
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Transportation is the means of mobility. Due to the growth in the population, rising traffic on road, delay in the movement of vehicles and traffic chaos could be observed in urban areas. Traffic congestion causes many social and economic problems. Because of the convenience and the quickness, motor-bikes gradually become the main travel mode of urban cities. In this paper, we extend the Lighthill–Whitham–Richards (LWR) traffic flow model equation into the mixed-traffic flow of two entities: car and motor-bike in a unidirectional single-lane road segment. The flow of cars is modeled by the advection equation and the flow of motor-bikes is modeled by the advection-diffusion equation. The model equations for cars and motor-bikes are coupled based on total traffic density on the road section, and they are non-dimensionalized to introduce a non-dimensional number widely known as Péclet number. Explicit finite difference schemes satisfying the CFL conditions are employed to solve the model equations numerically to compute the densities of cars and motor-bikes. The simulation of densities over various time instants is studied and presented graphically. Finally, the average densities of cars and motor-bikes on the road section are calculated for various values of Péclet numbers and mixed-traffic behavior are discussed. It is observed that the mixed-traffic behavior of cars and motor-bikes depends upon the Péclet number. The densities of motor-bikes and cars in the mixed-traffic flow approach the equilibrium state earlier in time for smaller values of Péclet number whereas densities take longer time to approach the equilibrium for the greater values of Péclet number.
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Liao, Zhuhua, Haokai Huang, Yijiang Zhao, Yizhi Liu, and Guoqiang Zhang. "Analysis and Forecast of Traffic Flow between Urban Functional Areas Based on Ride-Hailing Trajectories." ISPRS International Journal of Geo-Information 12, no. 4 (March 28, 2023): 144. http://dx.doi.org/10.3390/ijgi12040144.
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Urban planning and function layout have important implications for the journeys of a large percentage of commuters, which often make up the majority of daily traffic in many cities. Therefore, the analysis and forecast of traffic flow among urban functional areas are of great significance for detecting urban traffic flow directions and traffic congestion causes, as well as helping commuters plan routes in advance. Existing methods based on ride-hailing trajectories are relatively effective solution schemes, but they often lack in-depth analyses on time and space. In the paper, to explore the rules and trends of traffic flow among functional areas, a new spatiotemporal characteristics analysis and forecast method of traffic flow among functional areas based on urban ride-hailing trajectories is proposed. Firstly, a city is divided into areas based on the actual urban road topology, and all functional areas are generated by using areas of interest (AOI); then, according to the proximity and periodicity of inter-area traffic flow data, the periodic sequence and the adjacent sequence are established, and the topological structure is learned through graph convolutional neural (GCN) networks to extract the spatial correlation of traffic flow among functional areas. Furthermore, we propose an attention-based gated graph convolutional network (AG-GCN) forecast method, which is used to extract the temporal features of traffic flow among functional areas and make predictions. In the experiment, the proposed method is verified by using real urban traffic flow data. The results show that the method can not only mine the traffic flow characteristics among functional areas under different time periods, directions, and distances, but also forecast the spatiotemporal change trend of traffic flow among functional areas in a multi-step manner, and the accuracy of the forecasting results is higher than that of common benchmark methods, reaching 96.82%.
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Dong, Yu Bo. "Discussion on Urban Road Traffic Congestion Algorithm for Automatically Determining." Advanced Materials Research 926-930 (May 2014): 3790–93. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3790.
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Compared with the expressway, most of the traffic flow in urban road network can be denoted as interrupted traffic flow. Based on the current employed equipment for traffic flow collection and traffic signal control in urban roads, different types of traffic flow in urban roads are analyzed with the traffic flow arrival/departure model in transportation engineering. Mathematical models complying with traffic flow changes are utilized to match the traffic flow in both entry and exit road blocks, thus, enabled the automatic detection of traffic incident. This algorithm provides a measurement for the automatic judgment of urban road congestion and the expansion utility of intelligent transportation facilities in urban areas.
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Nemtanu, Florin, Ilona Madalina Costea, and Catalin Dumitrescu. "Spectral Analysis of Traffic Functions in Urban Areas." PROMET - Traffic&Transportation 27, no. 6 (December 17, 2015): 477–84. http://dx.doi.org/10.7307/ptt.v27i6.1686.
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The paper is focused on the Fourier transform application in urban traffic analysis and the use of said transform in traffic decomposition. The traffic function is defined as traffic flow generated by different categories of traffic participants. A Fourier analysis was elaborated in terms of identifying the main traffic function components, called traffic sub-functions. This paper presents the results of the method being applied in a real case situation, that is, an intersection in the city of Bucharest where the effect of a bus line was analysed. The analysis was done using different time scales, while three different traffic functions were defined to demonstrate the theoretical effect of the proposed method of analysis. An extension of the method is proposed to be applied in urban areas, especially in the areas covered by predictive traffic control.
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Qin, Jiayu, Gang Mei, and Lei Xiao. "Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning." Sustainability 13, no. 1 (December 30, 2020): 266. http://dx.doi.org/10.3390/su13010266.
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Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems.
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Striewski, Sören, Ingo Thomsen, and Sven Tomforde. "Adaptive Approaches for Tidal-Flow Lanes in Urban-Road Networks." Future Transportation 2, no. 3 (June 27, 2022): 567–88. http://dx.doi.org/10.3390/futuretransp2030031.
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Every year, traffic congestion costs the global economy billions of dollars in lost productivity, particularly in urban areas. Traffic congestion is a complex problem, as traffic conditions may change at any time. Tidal-flow lanes can be utilised as a feasible traffic-congestion-mitigation strategy to balance the fluctuating traffic demands throughout the day. This paper proposes an adaptive-lane-reversal approach for tidal-flow lanes, to decrease the impact of traffic congestion in urban areas. In order to evaluate the adaptive approach under various traffic conditions, several algorithms and parameter sets are examined, using various network models and traffic demands. As a result, the total travel time of the vehicles in the various networks was decreased by up to 81%.
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Zambrano-Martinez, Jorge, Carlos Calafate, David Soler, Lenin-Guillermo Lemus-Zúñiga, Juan-Carlos Cano, Pietro Manzoni, and Thierry Gayraud. "A Centralized Route-Management Solution for Autonomous Vehicles in Urban Areas." Electronics 8, no. 7 (June 26, 2019): 722. http://dx.doi.org/10.3390/electronics8070722.
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Currently, one of the main challenges that large metropolitan areas must face is traffic congestion. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, environmental pollution. By properly analyzing traffic demand, it is possible to predict future traffic conditions, using this information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution, thereby improving the traffic flow in a city in a fully centralized manner. This paper represents a step forward towards this novel traffic management paradigm by proposing a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. We perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed traffic prediction equation, combined with frequent updating of traffic conditions on the route server, can achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity.
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Huang, Zhufei, Zihan Zhang, Haijian Li, Lingqiao Qin, and Jian Rong. "Determining Appropriate Lane-Changing Spacing for Off-Ramp Areas of Urban Expressways." Sustainability 11, no. 7 (April 8, 2019): 2087. http://dx.doi.org/10.3390/su11072087.
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Congestion has become a significant issue in recent years and has greatly affected the efficiency of urban traffic operation. Random and disorderly lane-changing behavior greatly reduces traffic capacity and safety. This paper is mainly concerned with the relationship of lane-changing spacing intervals provided by off-ramp facilities and traffic flow conditions. Through field investigations in Beijing, several typical lane-changing behaviors at off-ramp areas are analyzed. By using field traffic data and actual road geometry parameters, VISSIM-based micro-behavior simulations at off-ramp areas are implemented to obtain traffic flow conditions with different lane-changing spacing intervals and other model parameters, such as traffic volume and ratio of off-ramp vehicles. Then, the numerical relationships between traffic flow state and model parameters can be shown. The results show that with increasing traffic volume and the ratio of off-ramp vehicles, the lane-changing spacing interval required by vehicles should be increased. For the same ratio of off-ramp vehicles, if the traffic volume increases by 100 pcu/h/lane (pcu is a unit to stand for a standard passenger car), the corresponding lane-changing spacing interval should be increased by a spacing of 50–100 m to avoid increasing congestion. Based on the results of this paper, smart lane management can be implemented by optimizing lane-changing spacing intervals and lane-changing behaviors to improve traffic capacity.
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Kuang, Weiming, Shi An, and Huifu Jiang. "Detecting Traffic Anomalies in Urban Areas Using Taxi GPS Data." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/809582.
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Large-scale GPS data contain hidden information and provide us with the opportunity to discover knowledge that may be useful for transportation systems using advanced data mining techniques. In major metropolitan cities, many taxicabs are equipped with GPS devices. Because taxies operate continuously for nearly 24 hours per day, they can be used as reliable sensors for the perceived traffic state. In this paper, the entire city was divided into subregions by roads, and taxi GPS data were transformed into traffic flow data to build a traffic flow matrix. In addition, a highly efficient anomaly detection method was proposed based on wavelet transform and PCA (principal component analysis) for detecting anomalous traffic events in urban regions. The traffic anomaly is considered to occur in a subregion when the values of the corresponding indicators deviate significantly from the expected values. This method was evaluated using a GPS dataset that was generated by more than 15,000 taxies over a period of half a year in Harbin, China. The results show that this detection method is effective and efficient.
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J, Cynthia, G. Sakthi Priya, C. Kevin Samuel, Suguna M, Senthil J, and S. Abraham Jebaraj. "Traffic Flow Forecasting Using Machine Learning Techniques." Webology 18, no. 04 (September 28, 2021): 1512–26. http://dx.doi.org/10.14704/web/v18si04/web18295.
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Congestion due to traffic, results in wasted fuel, increase in pollution level, increase in travel time and vehicular queuing. Smart city initiatives are aimed to improve the quality of urban life. Intelligent Transportation System (ITS) provides solution for many smart city projects, as they capture real time data without any fixed infrastructure. The real-time prediction of traffic flow aids in alleviating congestion. Accurate and timely prediction on the future traffic flow helps individual travellers, public transport, and transport planning. Existing systems are designed to predict specific traffic parameters like weekday, weekend, and holidays. This research presents a machine learning based traffic flow forecasting for the city of Bloomington, US not with any precise parameter. The day-wise dataset for the 5 areas is taken from Jan 1, 2017 to Dec 31, 2019. The algorithm used for implementation is Support Vector Regression (SVR) and Long Short-Term Memory (LSTM). LSTM algorithm provides better traffic prediction with least root means square error value.
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Padrón, José D., David Soler, Carlos T. Calafate, Juan-Carlos Cano, and Pietro Manzoni. "Improving Air Quality in Urban Recreational Areas through Smart Traffic Management." Sustainability 14, no. 6 (March 15, 2022): 3445. http://dx.doi.org/10.3390/su14063445.
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Green parks are the only natural places for recreation in many metropolitan areas, and the European Commission is seeking to improve their air quality and, consequently, citizens’ physical and mental health. One of the recently adopted approaches is to achieve pollution abatement in these green areas by reducing nearby traffic. In this paper, we analyze the impact of reducing the traffic in nearby streets to avoid pollution by proposing two different approaches. Our goal is to improve the pollution levels in Valencia’s most significant green areas by limiting vehicular traffic flow in nearby streets. To this end, we consider two alternative solutions—a more restrictive one and a less restrictive approach—in an attempt to achieve a tradeoff between emission control and congestion avoidance. Moreover, we show how our proposal can reroute traffic throughout the city without having traffic jam problems associated with the proposed approaches. In addition, we determine how the traffic flow data and the emissions in the city vary due to the traffic restrictions that we enforce. The experimental results show that it is possible to achieve improvements in terms of pollution with both of our restriction approaches; in particular, with the partial traffic isolation model, the pollution rates in the target area decreased by 17%, which we consider an excellent initial result for the applicability and effectiveness of these methods when an adequate traffic routing system is adopted.
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Ballari, Syed Omar. "Area Occupancy Characteristics in Traffic Flow on Urban Highway: A Case Study." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10 (October 31, 2019): 18–26. http://dx.doi.org/10.5373/jardcs/v11i10/20193001.
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Tu, Ping, Wei Yao, Zhiyuan Zhao, Pengzhou Wang, Sheng Wu, and Zhixiang Fang. "Interday Stability of Taxi Travel Flow in Urban Areas." ISPRS International Journal of Geo-Information 11, no. 12 (November 24, 2022): 590. http://dx.doi.org/10.3390/ijgi11120590.
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Taxi travel flow patterns and their interday stability play an important role in the planning of urban transportation and public service facilities. Existing studies pay little attention to the stability of the travel flow patterns between days, and it is difficult to consider the impact of dynamic changes in daily travel demand analysis when supporting related decision making. Taxi trajectory data have been widely used in urban taxi travel-pattern analysis. This paper uses the taxi datasets of Shenzhen and New York to analyze and compare the interday stability of the taxi travel spatial structure and the flow volume based on the improved Levenshtein algorithm and geographic flow theory. The results show that (1) interday differences in taxi travel flow are obvious in both spatial structure and flow volume, high-frequency origin–destination (OD) trips are relatively stable; (2) the ODs between the central urban area and surrounding areas exhibit high traffic volume and high interday stability, and the ODs starting or ending at an airport exhibit high traffic stability; (3) one week’s data can describe 86% of the overall travel structure and 84% of travel flow in Shenzhen, and one week’s New York data can describe 73% of travel structure and 76% of travel flow. There are differences in the travel patterns of people in different cities, and the representativeness of datasets in different cities will be different. These findings can help to better understand the outcomes of taxi travel patterns derived from a relatively short period of data to avoid potential misuse in related decision making.
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Jereb, Borut, Tadej Bratina, and Samo Kumperščak. "Green wave in urban traffic." Ekonomika i Organizacja Logistyki 1, no. 4 (January 17, 2017): 51–59. http://dx.doi.org/10.22630/eiol.2016.1.4.35.
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Over 70% of the European population lives in urban areas. This puts pressure on urban transport systems, leading to increased environmental impacts. Such impacts are particularly large in cases where the traffic caused by vehicles with internal combustion engines is repeatedly braking and accelerating due to poorly planned traffic flow. We do not know exactly how much of the pollution is increased by braking and accelerating vehicles at traffic lights; on the other hand, we can evaluate the contribution of higher fuel consumption. We have evaluated such an increase in fuel consumption in the real case of a road section in Celje. Based on the obtained results, we are able to estimate the factor of pollution increasing caused by accelerating. By adding the estimates of pollution caused by braking, we get an overall picture of the importance of ensuring a steady flow of traffic to reduce pollution in urban environments.
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Li, Dazhou, Chuan Lin, Wei Gao, Zeying Chen, Zeshen Wang, and Guangqi Liu. "Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction." Wireless Communications and Mobile Computing 2020 (June 4, 2020): 1–15. http://dx.doi.org/10.1155/2020/6896579.
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Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification.
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Leclercq, Ludovic. "Calibration of Flow–Density Relationships on Urban Streets." Transportation Research Record: Journal of the Transportation Research Board 1934, no. 1 (January 2005): 226–34. http://dx.doi.org/10.1177/0361198105193400124.
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The aim of this paper is to gather some observations resulting from the study of traffic data in an urban environment (Toulouse, France) rather than on freeways, as is often the case in papers that deal with flow–density relationship calibration. Methods developed for freeway data are studied and new ones are presented to address the specificity of traffic behavior in urban areas. A two-step approach is proposed. First, the data are processed to obtain consistent observations for simulation use. This processing includes the calculation of density from occupancy and space mean speed and the aggregation and the selection of data to make them suitable for equilibrium traffic state representation. Then, a best-fit curve is applied to the selected data to obtain flow–density relationships. Finally, the resulting relationships are studied to highlight the peculiarities of traffic behavior in urban streets.
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Stan, Ioan, Daniel Alexandru Ghere, Paula Iarina Dan, and Rodica Potolea. "Urban Congestion Avoidance Methodology Based on Vehicular Traffic Thresholding." Applied Sciences 13, no. 4 (February 7, 2023): 2143. http://dx.doi.org/10.3390/app13042143.
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Vehicular traffic in urban areas faces congestion challenges that negatively impact our lives. The infrastructure associated with intelligent transportation systems provides means for addressing the associated challenges in urban areas. This study proposes an effective and scalable vehicular traffic congestion avoidance methodology. It introduces a traffic thresholding mechanism to predict and avoid vehicular traffic congestion during route computation. Our methodology was evaluated and validated by employing four road network topologies, three vehicular traffic density levels and various traffic light configurations, resulting in 26 urban traffic scenarios. Using our approach, the number of vehicles that can run in free flow can be increased by up to 200%, whereas for traffic congestion scenarios, the time spent in traffic may be reduced by up to 69% and CO2 emissions by up to 61%. To the best of our knowledge, in the vehicular traffic flow prediction domain, this is the first approach that covers a set of road network topologies and a large and representative set of scenarios for simulated urban traffic congestion testing. Moreover, the comparative analysis with different other solutions in the domain, showed that we obtained the best driving time and CO2 emission reduction.
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Li, Y., Q. Zhao, and M. Wang. "ANALYSIS THE INFLUENCING FACTORS OF URBAN TRAFFIC FLOWS BY USING NEW AND EMERGING URBAN BIG DATA AND DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 537–43. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-537-2022.
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Abstract. Urban traffic analysis has acted an important role in the process of urban development, which can provide insights for urban planning, traffic management and resource allocation. Meanwhile, the advancement of Intelligent Transportation Systems has produced a variety of traffic-related data from sensors and cameras to monitor urban traffic conditions in high spatio-temporal resolution. This research applies spatial regression models combined with computer vision and deep learning to analyse traffic flow distributions via various factors in the urban areas and traffic flow data. We include road characteristics and surrounding environments such as land use/cover, nearby points of interest (POI) and Google Street View images. The results show that the daily average traffic flow on main roads is much higher than smaller roads, and nearby POIs numbers have positive effect on traffic flows. The impact of land cover type is insignificant in the linear regression model, while demonstrates significant contribution to traffic flows in spatial regression models. Although the spatial autocorrelation still exists after the spatial regression, the spatial error model generates a better fit on the dataset. Further analysis will focus on extend the current model with the time parameters and understand what influence the changes of traffic flow in the different spatio-temporal scales.
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Maghrour Zefreh, Mohammad, and Adam Torok. "Theoretical Comparison of the Effects of Different Traffic Conditions on Urban Road Traffic Noise." Journal of Advanced Transportation 2018 (October 15, 2018): 1–11. http://dx.doi.org/10.1155/2018/7949574.
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Road traffic noise is one of the most relevant sources in the environmental noise pollution of the urban areas where dynamics of the traffic flow are much more complicated than uninterrupted traffic flows. It is evident that different traffic conditions would play the role in the urban traffic flow considering the dynamic nature of the traffic flow on one hand and presence of traffic lights, roundabouts, etc. on the other hand. The main aim of the current paper is to investigate the effect of different traffic conditions on urban road traffic noise. To do so, different traffic conditions have been theoretically generated by the Monte Carlo Simulation technique following the distribution of traffic speed in the urban roads. The “ASJ RTN-Model” has been considered as a base road traffic noise prediction model which would deal with different traffic conditions including steady and nonsteady traffic flow that would cover the urban traffic flow conditions properly. Having generated the vehicles speeds in different traffic conditions, the emitted noise (LWA) and subsequently the noise level at receiver (LA) were estimated by “ASJ RTN-Model.” Having estimated LWA and LA for each and every vehicle in each traffic condition and taking the concept of transient noise into account, the single event sound exposure levels (SEL) in different traffic conditions are calculated and compared to each other. The results showed that decelerated traffic flow had the lowest contribution, compared to congestion, accelerated flow, free flow, oversaturated congestion, and undersaturated flow by 16%, 14%, 12%, 12%, and 10%, respectively. Moreover, the distribution of emitted noise and noise level at receiver were compared in different traffic conditions. The results showed that traffic congestion had considerably the maximum peak compared to other traffic conditions which would highlight the importance of the range of generated noise in different traffic conditions.
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Jiang, H. "Traffic optimization based on a macroscopic fundamental diagram in urban bizonal system." Russian Automobile and Highway Industry Journal 19, no. 2 (May 24, 2022): 246–57. http://dx.doi.org/10.26518/2071-7296-2022-19-2-246-257.
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Introduction. The article considers the possibility of forming a traffic optimization strategy based on a macroscopic fundamental diagram between urban areas. The zones of influence are constructed with the maximum and minimum values of border control, respectively, according to the category of different types of the zone of influence. The stability zones were formed by connecting the zones of influence and their shape changes with different values of exogenous flow and endogenous flow were analysed.Materials and methods. The data on the traffic flow of the central part of the Jinan (PRC) road network are presented and an urban bizonal system is constructed using the obtained macroscopic fundamental diagrams. The modelling of the state of traffic flows was carried out and the regularity of their changes at different parameters of border control was obtained using the mathematical modelling MATLAB complex.Results. As a result of the study, the traffic management solutions were obtained for the relatively parameter of completed and incomplete trips. The generalized options for optimizing traffic in the considered urban areas are proposed – four combinations of strategies for traffic management are performed serving the design optimization option with different traffic flow conditions.Discussion and conclusions. For each multi-zone system of the road network, it is necessary to form its own characteristic variant of optimizing the traffic flow.
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Majstorović, Željko, Leo Tišljarić, Edouard Ivanjko, and Tonči Carić. "Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review." Applied Sciences 13, no. 7 (April 1, 2023): 4484. http://dx.doi.org/10.3390/app13074484.
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Mixed traffic flows are opening up new areas for research and are seen as key drivers in the field of data and services that will make roads safer and more environmentally friendly. Understanding the effects of Connected Vehicles (CVs) and Connected Autonomous Vehicles (CAVs), as one of the vehicle components of mixed traffic flows, will make it easier to avoid traffic congestion and contribute to the creation of innovative applications and solutions. It is notable that the literature related to the analysis of the impact of mixed traffic flows on traffic signal control in urban areas rarely considers mixed traffic flow containing CVs, CAVs, and Human Driven Vehicles (HDVs). Therefore, this paper provides an overview of the relevant research papers covering the topic of urban Traffic Signal Control (TSC) and mixed traffic flows. Best practices for intersection state estimation and TSC in the case of mixed traffic flows in an urban environment are summarized and possible approaches for utilizing CVs and CAVs as mobile sensors and actuators are discussed.
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Su, Xiaokun, Chenrouyu Zheng, Yefei Yang, Yafei Yang, Wen Zhao, and Yue Yu. "Spatial Structure and Development Patterns of Urban Traffic Flow Network in Less Developed Areas: A Sustainable Development Perspective." Sustainability 14, no. 13 (July 2, 2022): 8095. http://dx.doi.org/10.3390/su14138095.
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Sustainable development is a scientific development requirement for economic, social, and ecological development and is particularly important for less developed areas to achieve high quality development. Among them, the traffic flow network is a key contributor to economic activity and an inclusive society, as well as influencing the regional ecology, and is an important way to reflect the connection and structure of cities and towns. Based on the literature related to sustainable development, the article takes the passenger traffic data of highways, railways, and aviation of Inner Mongolia in 2021 as the sample and applies the complex network analysis method to analyze the traffic flow network structure and refine the spatial development patterns. The results show that: (1) The highway network is manifested as the connection between the central urban areas and surrounding banner counties and the connection between the adjacent banner counties. The railroad flow is extended and expanded by the railway line with core cities as the development axis. The internal and external connections of Hohhot are the general form of aviation network. The less developed areas under traffic flow network show obvious pointing of core cities and important node towns. (2) Each traffic flow network has the tendency of scale-free and small-world properties. The influence of key town nodes in the traffic flow network is relatively limited. (3) The town connection patterns under the highway, railway, and air flow networks are “single-core and multi-point”, “axis-spoke”, and “hub-spoke”, respectively. The multiple traffic flows support the development framework of towns in less developed areas. This paper also proposes strategies for the regional transport and urban pattern with complementary advantages and high quality and sustainable development in less developed areas.
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Younes, H., and M. Owais. "Modelling Pedestrians & Cyclists Interaction with Traffic Flow in Urban Areas.(Dept.C)." MEJ. Mansoura Engineering Journal 31, no. 1 (December 12, 2020): 127–36. http://dx.doi.org/10.21608/bfemu.2020.129217.
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Mo, Tong, Keyi Li, Junjie Zhang, Lingqiao Qin, Zhufei Huang, and Haijian Li. "Collaborative Strategies and Simulation of Vehicle Group Behaviors for Off-Ramp Areas." Journal of Advanced Transportation 2020 (December 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/8817364.
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With the increase of vehicle ownership and the rapid growth of urban traffic, the problem of congestion in the off-ramp area of the main expressway has become the main factor restricting overall section efficiency and inducing traffic accidents. This paper focuses on the problem of group collaborative lane-changing behaviors of off-ramp vehicles and through vehicles in off-ramp areas and proposes four kinds of vehicle group collaborative strategies based on different road space balance conditions. According to a three-lane expressway scene, a VISSIM-based simulation model is built and the optimization scheme is simulated and evaluated. The simulation results show that with the increase of traffic flow in off-ramp areas, a flow-balance strategy for downstream lanes where off-ramp vehicles merge with the outside lane in advance is more advantageous. When vehicles are leaving the main road, if traffic flow is heavy, the flow-balance strategy for lanes where off-ramp vehicles merge with the outside lane in advance (for example, the proportion of off-ramp vehicles in three lanes is 0 : 0 : 1) is better; otherwise, when the traffic flow on the main road is relatively small, the flow-balance strategy for lanes where off-ramp vehicles are distributed in lanes with different ratios (e.g., 1 : 3 : 6) is better. What is more, for future traffic management in connected vehicle environments, it can be concluded that collaborative vehicle lane-changing strategies with different traffic flow states can help to enhance traffic efficiency.
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Ullman, Gerald L., and Conrad L. Dudek. "Theoretical Approach to Predicting Traffic Queues at Short-Term Work Zones on High-Volume Roadways in Urban Areas." Transportation Research Record: Journal of the Transportation Research Board 1824, no. 1 (January 2003): 29–36. http://dx.doi.org/10.3141/1824-04.
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A new theoretical approach is described that more accurately predicts the length of queues that will develop when one or more lanes are closed for short-term freeway road work in an urban area. A model was developed that represents the interactions between diversion and traffic queuing on the freeway. The model is based on macroscopic fluid-flow analogies of traffic and a new perspective of the freeway corridor as a section of permeable pipe. Historical traffic volumes on the roadway serve as the primary data input into the model. Unlike other work zone analysis methods, the model presents a rational representation of how traffic queues stabilize upstream of temporary work zone bottlenecks in urban areas. The model can be calibrated to reasonably represent the magnitude of traffic queues developing upstream of actual work zone lane closures on urban freeways in Texas.
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Tafidis, Pavlos, Ali Pirdavani, Tom Brijs, and Haneen Farah. "Can Automated Vehicles Improve Cyclist Safety in Urban Areas?" Safety 5, no. 3 (August 23, 2019): 57. http://dx.doi.org/10.3390/safety5030057.
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Automated vehicles (AVs) are expected to assist in decreasing road traffic fatalities, particularly among passenger cars. However, until now limited research has been conducted on how they will impact the safety of vulnerable road users (VRUs) (i.e., cyclists and pedestrians). Therefore, there is a clear need to start taking into account the interactions between AVs and VRUs as an integrated element of the transport network, especially in urban areas where they are dominant. The objective of this study is to verify whether the anticipated implementation of AVs can actually improve cyclists’ safety. For this purpose, the microscopic traffic flow simulation software PTV Vissim combined with the surrogate safety assessment model (SSAM) were utilized. The road network used for this analysis was generated based on a real study case in a medium-sized city in Belgium, where narrow streets in the city center are shared on many occasions between vehicles and cyclists. The findings of the analysis show a notable reduction in the total number of conflicts between cars, but also between cars and cyclists, compared to the current situation, assuming a 100% market penetration scenario for AVs. Moreover, the severity level of conflicts also decreased as a result of the lack of human-driven vehicles in the traffic streams.
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Monazzam, Mohammad R., Vahideh Abolhasannejad, Bibi Narjes Moasheri, Vahid Abolhasannejad, and Hamid Kardanmoghaddam. "Noise pollution in old and new urban fabric with focus on traffic flow." Journal of Low Frequency Noise, Vibration and Active Control 35, no. 4 (August 1, 2016): 257–63. http://dx.doi.org/10.1177/0263092316661056.
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Noise pollution is considered as one of the effective factors affecting people’s quality of life in urban areas throughout the world. Traffic is a major source of noise in urban areas with wide ranging effects on human. The present study aims to determine the mean of noise levels in old and new urban fabric and their relationship with the traffic flow. Acoustic survey was performed during one week in each month of the summer in assigned stations. In this investigation, using a sound level meter and BS 7445-1-2003, BS 7445-3-1991, Leq10 min was measured and the percentage of highly annoyed people was estimated. The data were analyzed by using statistical tests and SPSS software. The results show that the mean of noise levels measured in all stations during the three months of the summer was higher than the standard limit. The relationship between the mean of noise level and the traffic flow in areas in new fabric was significant but not in those in old fabric. Higher rates of noise pollution in old fabrics and lack of significant relationship with the traffic flow in those points show that noise pollution can be reduced in the city to a great extent by considering effective traffic rules, widening crowded streets, and improving those conditions that govern urbanization. Of course, the amount of the porosity of asphalt, bumpy streets, and even technical defect of motor vehicles should not be ignored.
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Atouf, Issan, Wahban Yahya Al Okaishi, Abdelmoghit Zaaran, Ibtissam Slimani, and Mohamed Benrabh. "A real-time system for vehicle detection with shadow removal and vehicle classification based on vehicle features at urban roads." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 4 (December 1, 2020): 2091. http://dx.doi.org/10.11591/ijpeds.v11.i4.pp2091-2098.
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Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.
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Zyryanov, Vladimir. "Simulation network-level relationships of traffic flow." MATEC Web of Conferences 334 (2021): 01005. http://dx.doi.org/10.1051/matecconf/202133401005.
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This paper describes possibilities of the macroscopic fundamental diagram (MFD) of traffic flow to predict the conditions of operation of the road network in urban areas. This study examines relationships between traffic flow parameters on the network level. Microscopic traffic simulation has provided important data on the estimation of road capacity, velocity, trip time, and detection of congestions reasons. Data of spatial distribution density in network useful for implementing approach based on gating policy on subnetwork using MFD. It presents the results of a simulation using the example of central area of Rostov-on-Don
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Roca-Riu, Mireia, Jin Cao, Igor Dakic, and Monica Menendez. "Designing Dynamic Delivery Parking Spots in Urban Areas to Reduce Traffic Disruptions." Journal of Advanced Transportation 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/6296720.
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Pick-up and delivery services are essential for businesses in urban areas. However, due to the limited space in city centers, it might be unfeasible to provide sufficient loading/unloading spots. As a result, this type of operations often interferes with traffic by occupying road space (e.g., illegal parking). In this study, a potential solution is investigated: Dynamic Delivery Parking Spots (DDPS). With this concept, based on the time-varying traffic demand, the area allowed for delivery parking changes over time in order to maximize delivery opportunities while reducing traffic disruptions. Using the hydrodynamic theory of traffic flow, we analyze the traffic discharging rate on an urban link with DDPS. In comparison to the situation without delivery parking, the results show that although DDPS occupy some space on a driving lane, it is possible to keep the delay at a local level, that is, without spreading to the network. In this paper, we provide a methodology for the DDPS design, so that the delivery requests can be satisfied while their negative impacts on traffic are reduced. A simulation study is used to validate the model and to estimate delay compared to real situations with illegal parking, showing that DDPS can reduce system’s delay.
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Teterina, I. A., and E. V. Parsaev. "The method of calculating emissions of pollutants from road transport in an urban environment." MATEC Web of Conferences 298 (2019): 00084. http://dx.doi.org/10.1051/matecconf/201929800084.
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To assess the quality of road traffic organization on environmental indicators, two methods of five regulations operating in the Russian Federation, which take into account the parameters of traffic flow, affecting the volume of emissions Pollutants. The parameter that affects the results of calculations in both methods is the average speed of movement. In real-world conditions on urban highways there are areas where the traffic flow is uneven (braking, acceleration, stopping), which entails an increase in the amount of pollutants from vehicles. At the same time, this uneven traffic is largely due to the existing the road design and traffic engineering on certain elements of the road network. This is the basis for the development of a methodology for calculating pollutant emissions to assess the quality of the traffic engineering, taking into account the uneven flow of traffic.
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Zhou, Weiqiang, Haoxu Guo, and Lihao Yao. "Statistical Modeling of Traffic Flow in Commercial Clusters Based on a Street Network." Sustainability 15, no. 3 (January 18, 2023): 1832. http://dx.doi.org/10.3390/su15031832.
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Traffic flow characterizes vitality in commercial clusters, and the accurate prediction of traffic flow based on the street network has significant implications for street planning and vitality regulation in commercial clusters. However, existing studies are limited by certain problems, such as difficulty in obtaining traffic flow data and carrying out technical methods. The purpose of this study is to use urban physical data to study traffic flow so as to quickly and effectively estimate the traffic flow in commercial clusters. This study takes the street networks of 100 commercial clusters in China as the research objects and classifies them into three forms according to the theory of “A city is not a tree”. Taking typical commercial clusters in these three forms as the research unit, space syntax was used to study five metrics of street network connectivity, and integration (Dn) was selected as a proxy variable for street network connectivity. The results show that the traffic flow in the three forms of commercial clusters can be predicted using the multiple regression models established based on the three metrics of integration, the traffic level, and the operation cycle. This study establishes the connection between the street network form and the traffic flow, which enables the possibility of obtaining the traffic flow of commercial clusters quickly and effectively. For areas with poorly structured urban data, the results can help urban planning administrators to predict the potential economic attributes using easily accessible street network data in commercial clusters.
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Srinath, M. "Vehicular Traffic Flow Prediction Model Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 109–12. http://dx.doi.org/10.22214/ijraset.2023.54576.
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Abstract: Efficient traffic flow prediction is crucial for effective traffic management and congestion reduction in urban areas. However, traditional statistical models often struggle to accurately capture the intricate dynamics of vehicular traffic flow, particularly under dynamic conditions. In this research project, we propose a novel approach that leverages deep learning techniques, specifically Long Short-Term Memory (LSTM) neural networks, AdaBoost, and gradient descent, to enhance the accuracy of traffic flow predictions .By harnessing historical traffic data, our model generates precise predictions for the next time step, empowering traffic managers to optimize signal timings and proactively reroute traffic. To boost the model's performance, we incorporate AdaBoost, which integrates LSTM predictions as additional input features. We evaluate the accuracy of our model using mean absolute error (MAE) and R2 score techniques, comparing the predicted traffic flow against the actual traffic flow .Experimental results demonstrate that our proposed model outperforms traditional statistical models, exhibiting lower MAE and higher R2 scores. This indicates its efficacy in accurately predicting traffic flow and presents a promising solution for traffic management and congestion reduction. Our research contributes to the advancement of traffic flow prediction models by offering a more reliable and accurate approach. Future work may explore the integration of real- time data streams and external factors, such as weather conditions and events, to further enhance prediction accuracy and effectively address dynamic traffic situations. By optimizing traffic management strategies, reducing congestion, and improving overall traffic flow efficiency, our proposed model holds significant potential for improving urban traffic conditions.
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Dzoljic, Jovana, Vladimir Popovic, Srdjan Jovkovic, and Jovan Misic. "Urban traffic air pollution - case study City of Vranje, Serbia." IOP Conference Series: Earth and Environmental Science 1123, no. 1 (December 1, 2022): 012084. http://dx.doi.org/10.1088/1755-1315/1123/1/012084.
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Abstract The expansion of urban areas with unequal, rapidly growing population results in higher vehicle numbers in the cities. Traffic in the urban environment is one of the main concerns, since it is one of the most prominent sources of air pollution, connected to the mortality rate. Regarding the Sustainable Development Goals (SDG) and SDG 11, urban areas should meet environmental needs following the process of environmental transition. The Vranje city, Serbia follows the global trend of vehicle number increment. Regarding the specific geomorphological and climate conditions, it is of importance to emphasize the traffic air pollution control in the urban environment. Numerous studies found that vehicular exhaust emissions near traffic intersections are largely dependent on fleet speed, queuing time in idle mode with a red signal time, traffic-flow rate, ambient conditions, etc. Traffic management has the key role, especially regarding the intersection of traffic flows, since there are time losses on the intersections’ driveways that increase the energy consumption to overcome certain conflict flows. The scope of this study should point out the importance of traffic planning and management, especially at the traffic flows for reducing air pollution in the urban environment.
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Lin, Ciyun, Yongli Yu, Dayong Wu, and Bowen Gong. "Traffic Flow Catastrophe Border Identification for Urban High-Density Area Based on Cusp Catastrophe Theory: A Case Study under Sudden Fire Disaster." Applied Sciences 10, no. 9 (May 4, 2020): 3197. http://dx.doi.org/10.3390/app10093197.
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For traffic management under sudden disasters in high-density areas, the first and foremost step is to prevent traffic congestion in the disaster-affected area by traffic flow management and control, so as to provide enough and flexible traffic capacity for emergency evacuation and emergency rescue. Catastrophe border identification is the foundation and the key to traffic congestion prediction under sudden disaster. This paper uses a mathematical model to study the regional traffic flow in the high-density area under sudden fire disaster based on the Cusp Catastrophe Theory (CCT). The catastrophe border is identified by fitting the CCT-based regional traffic flow model to explore the stable traffic flow changing to the instable state, as to provide a theoretical basis for traffic flow management and control in disaster-affected areas, and to prevent the traffic flow being caught into disorder and congestion. Based on VISSIM simulator data by building simulation scenarios with and without sudden fire disaster in a Sudoku traffic network, the catastrophe border is identified as 439 pcu/lane/h, 529 pcu/lane/h, 377 pcu/lane/h at 5 s, 10 s, 15 s data collection interval in a Sudoku traffic network respectively. The corresponding relative precision, which compares to the method of Capacity Assessment Approach (CAA), is 89.1%, 92.7% and 76.5% respectively. It means that 10 s data collection interval would be the suitable data collection interval in catastrophe border identification and regional traffic flow control in high-density area under sudden fire disaster.
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Namoun, Abdallah, Ali Tufail, Nikolay Mehandjiev, Ahmed Alrehaili, Javad Akhlaghinia, and Evtim Peytchev. "An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology." Applied Sciences 11, no. 5 (February 25, 2021): 2057. http://dx.doi.org/10.3390/app11052057.
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The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments.
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Shi, Junqing, Lin Cheng, Jiancheng Long, and Yuanlin Liu. "A New Cellular Automaton Model for Urban Two-Way Road Networks." Computational Intelligence and Neuroscience 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/685047.
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A new cellular automaton (CA) model is proposed to simulate traffic dynamics in urban two-way road network systems. The NaSch rule is adopted to represent vehicle movements on road sections. Two novel rules are proposed to move the vehicles in intersection areas, and an additional rule is developed to avoid the “gridlock” phenomenon. Simulation results show that the network fundamental diagram is very similar to that of road traffic flow. We found that the randomization probability and the maximum vehicle speed have significant impact on network traffic mobility for free-flow state. Their effect may be weak when the network is congested.
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Daraghmi, Yousef-Awwad, Chih-Wei Yi, and Tsun-Chieh Chiang. "Negative Binomial Additive Models for Short-Term Traffic Flow Forecasting in Urban Areas." IEEE Transactions on Intelligent Transportation Systems 15, no. 2 (April 2014): 784–93. http://dx.doi.org/10.1109/tits.2013.2287512.
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Lykov, Stanislav, and Yasuo Asakura. "Anomalous Traffic Pattern Detection in Large Urban Areas: Tensor-Based Approach with Continuum Modeling of Traffic Flow." International Journal of Intelligent Transportation Systems Research 18, no. 1 (November 3, 2018): 13–21. http://dx.doi.org/10.1007/s13177-018-0167-5.
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Mironyuk, Oleg. "Assessment of the noise level on arterial streets depending on traffic flow indicators." Transport technologies 2021, no. 2 (December 10, 2021): 52–63. http://dx.doi.org/10.23939/tt2021.02.052.
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The intensity of traffic flows increases every year and intensifies its impact significantly on urban territory's ecologic situation and natural environment. In Ukrainian cities, the growth of noise pollution is quite a topical problem as it has a significant impact on the environment because of the increase in car numbers. Traffic flow intensity and densification of the built-up area cause the increase of the noise level. As analytical methods of calculation of determination of traffic noise level have several disadvantages, then for assessing the level of noise on arterial streets of Lviv city, research is carried out with consideration of traffic flow indicators. One of the critical criteria that have an impact on noise pollution in the urban environment is traffic flow composition. Conducted research indicates that the speed of traffic flow creates significant noise pollution, especially on those streets where cobblestone is a road pavement. Freight and passenger transport that moves along arterial streets causes the increase of noise level and increases its impact on the city territory. On sections of arterial streets in peak periods of the day with high values of volume-capacity ratio and general share of freight and public transport of more than 30% in a flow, the noise exceeds the acceptable norms and has a harmful impact on the environment. That is why it is necessary to assess noise pollution and the development of protection measures from noise during the planning and construction of some urban regions or residential areas. It is established that to lower the noise level on streets, it is necessary to consider existing intersections of arterial streets and the regime of movement on them. To lower transport noise, it is necessary to distribute freight and transit transport by city streets more rationally, use qualitative public transport, and limit the maximal speed regime on city streets. Further research on the road network will provide an opportunity to assess the noise level distribution of traffic flows in Lviv city. It will help to determine the problem places and propose methods of combating traffic noise.
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Zhao, P. X., and S. M. Zhao. "UNDERSTANDING URBAN TRAFFIC FLOW CHARACTERISTICS FROM THE NETWORK CENTRALITY PERSPECTIVE AT DIFFERENT GRANULARITIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 263–68. http://dx.doi.org/10.5194/isprs-archives-xli-b2-263-2016.
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In this study, we analyze urban traffic flow using taxi trajectory data to understand the characteristics of traffic flow from the network centrality perspective at point (intersection), line (road), and area (community) granularities. The entire analysis process comprises three steps. The first step utilizes the taxi trajectory data to evaluate traffic flow at different granularities. Second, the centrality indices are calculated based on research units at different granularities. Third, correlation analysis between the centrality indices and corresponding urban traffic flow is performed. Experimental results indicate that urbaxperimental results indicate that urbaxperimental results indicate that urban traffic flow is relatively influenced by the road network structure. However, urban traffic flow also depends on the research unit size. Traditional centralities and traffic flow exhibit a low correlation at point granularity but exhibit a high correlation at line and area granularities. Furthermore, the conclusions of this study reflect the universality of the modifiable areal unit problem.
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Zhao, P. X., and S. M. Zhao. "UNDERSTANDING URBAN TRAFFIC FLOW CHARACTERISTICS FROM THE NETWORK CENTRALITY PERSPECTIVE AT DIFFERENT GRANULARITIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 263–68. http://dx.doi.org/10.5194/isprsarchives-xli-b2-263-2016.
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In this study, we analyze urban traffic flow using taxi trajectory data to understand the characteristics of traffic flow from the network centrality perspective at point (intersection), line (road), and area (community) granularities. The entire analysis process comprises three steps. The first step utilizes the taxi trajectory data to evaluate traffic flow at different granularities. Second, the centrality indices are calculated based on research units at different granularities. Third, correlation analysis between the centrality indices and corresponding urban traffic flow is performed. Experimental results indicate that urbaxperimental results indicate that urbaxperimental results indicate that urban traffic flow is relatively influenced by the road network structure. However, urban traffic flow also depends on the research unit size. Traditional centralities and traffic flow exhibit a low correlation at point granularity but exhibit a high correlation at line and area granularities. Furthermore, the conclusions of this study reflect the universality of the modifiable areal unit problem.
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Wang, Shaodong, Yanbin Liu, Wei Zhi, Xihua Wen, and Weihua Zhou. "Discovering Urban Functional Polycentricity: A Traffic Flow-Embedded and Topic Modeling-Based Methodology Framework." Sustainability 12, no. 5 (March 2, 2020): 1897. http://dx.doi.org/10.3390/su12051897.
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With the rapid development of communication and transportation technologies, the urban area is increasingly becoming an ever more dynamic, comprehensive, and complex system. Meanwhile, functional polycentricity as a distinctive feature has been characterizing urban areas around the world. However, the spatial structure of the urban area has yet to be fully comprehended from a dynamic perspective, and understanding the spatial organization of polycentric urban regions (PUR) is crucial for issues related to urban planning, traffic control, and urban risk management. The analysis of polycentricity strongly depends on the spatial scale. In order to identify functional polycentricity at the intra-unban scale, this paper presents a traffic flow-embedded and topic modeling-based methodology framework. This framework was evaluated on real-world datasets from the Wujiang district, Suzhou, China, which contains 151,419 records of taxi trajectory data and 86,036 records of points of interest (POI) data. This paper provides a novel approach to examining urban functional polycentricity via combining urban function distribution and spatial interactions. This proposed methodology can help urban authorities better understand urban dynamics in terms of function distribution and internal connectedness and facilitate urban development in terms of urban planning and traffic control.
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Perco, Paolo. "Comparison between Powered Two-Wheeler and Passenger Car Free-Flow Speeds in Urban Areas." Transportation Research Record: Journal of the Transportation Research Board 2074, no. 1 (January 2008): 77–84. http://dx.doi.org/10.3141/2074-10.
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Accident data indicate that motorcyclists are a particularly vulnerable group of road users because they run a higher risk of being injured or killed in an accident than passenger car drivers. One of the countermeasures that can improve traffic safety for motorcyclists is an increase in awareness and knowledge about powered two-wheelers (PTWs) in the planning, construction, operation, and maintenance of new and existing roads. Road design standards and policies that regulate design process and safety evaluations are usually based on passenger car characteristics only. In particular, the passenger car operating speed is used as the critical input for design choices of geometric elements connected with traffic safety and as a performance measure to evaluate road design and consistency. Because the passenger car operating speed might not be representative of the PTW operating speed, this study compared the PTW and passenger car speed distributions in urban areas to establish definitively whether they are similar. The results showed that PTW speeds are always considerably higher than passenger car speeds and that the corresponding operating speeds are correlated. Therefore, a prediction equation to estimate the PTW operating speed starting from passenger car operating speed was developed for urban streets. This equation can help highway engineers to estimate expected PTW speeds that can be effectively used in the safety evaluations of new and existing roads where the significant presence of PTWs in the traffic flow suggests that this vehicle type should be considered.
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González-Varona, José M., Félix Villafáñez, Fernando Acebes, Alfonso Redondo, and David Poza. "Reusing Newspaper Kiosks for Last-Mile Delivery in Urban Areas." Sustainability 12, no. 22 (November 23, 2020): 9770. http://dx.doi.org/10.3390/su12229770.
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The current increase in e-commerce is generating growing problems in urban areas in terms of both traffic flow (increasing traffic, no parking spaces) and environmental issues (noise, atmospheric pollution, etc.). In parallel, an iconic element of historic districts is disappearing: more and more newspaper kiosks are closing their business as their work dwindles. In this scenario, the objective of this paper is to propose a model for last-mile parcel delivery that exploits the current available newspaper kiosk network by using them as parcel lockers. To demonstrate the benefits of this proposal, we map the kiosk network of the city of Valladolid (Spain), and compare the environmental impact of a traditional (door-to-door) delivery and the proposed model which reuses old kiosks as parcel lockers. The necessary steps to carry out simulations are described in detail so that experiments can be replicated in other cities that face the same issues.
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Skřivánek Kubíková, Simona, Alica Kalašová, Kristián Čulík, and Ján Palúch. "Comparison of Traffic Flow Characteristics of Signal Controlled Intersection and Turbo Roundabout." Archives of Automotive Engineering – Archiwum Motoryzacji 88, no. 2 (June 30, 2020): 19–36. http://dx.doi.org/10.14669/am.vol88.art2.
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In the Slovak republic, there is an increase of building roundabouts in both urban and non-build-up areas. The construction of roundabouts in urban areas brings mainly calming of traffic intensity. A roundabout in non-build-up areas could be built only within certain conditions. In this paper, we have been studying a small roundabout location and its traffic characteristics in the city of Hlohovec. In some cases, a small roundabout could be very good solution for exceeded traffic flow capacity of signal controlled intersections as well as for intersections with a high number of traffic incidents. But in our case, a small roundabout is not suitable for such intensity of vehicles as we measured in transport survey. So we focused on other possibilities how to improve this current situation. We have decided to make a proposal of signal controlled intersection as well as a turbo roundabout and compare the results of traffic characteristics of each proposal. We have made several simulations of each variant of traffic situation, using transport-planning software Aimsun, and calculate average values of all recorded traffic characteristics. As inputs, we have used intensities and other basic data obtained from transport survey. Using simulations outputs of transport planning software, we have been able to compare a current state with signal controlled intersection and turbo roundabout. Traffic characteristics of turbo roundabout show significant improvements compare to signal controlled intersection, f.e. in delay time (more than 68 %), travel time (more than 22 %), number of stops (more than 73 %). Turbo roundabout seems to be the best solution for traffic organising at this chosen intersection in the city of Hlohovec, regarding travel time, delays, number of stops and safety at all.
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Tian, Zhao, Li-Min Jia, Hong-Hui Dong, Zun-Dong Zhang, and Yang-Dong Ye. "Fuzzy peak hour for urban road traffic network." Modern Physics Letters B 29, no. 15 (June 10, 2015): 1550074. http://dx.doi.org/10.1142/s0217984915500748.
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Traffic congestion is now nearly ubiquitous in many urban areas and frequently occurs during rush hour periods. Rush hour avoidance is an effective way to ease traffic congestion. It is significant to calculate the rush hour for alleviating traffic congestion. This paper provides a method to calculate the fuzzy peak hour of the urban traffic network considering the flow, speed and occupancy. The process of calculation is based on betweenness centrality of network theory, optimal separation method, time period weighting, probability–possibility transformations and trapezoidal approximations of fuzzy numbers. The fuzzy peak hour of the urban road traffic network (URTN) is a trapezoidal fuzzy number [m1, m2, m3, m4]. It helps us (i) to confirm a more detailed traffic condition at each moment, (ii) to distinguish the five traffic states of the traffic network in one day, (iii) to analyze the characteristic of appearance and disappearance processes of the each traffic state and (iv) to find out the time pattern of residents travel in one city.
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Gupta, Rajeev. "Development of a Smart Traffic Light Control System Using IoT Technology." Mathematical Statistician and Engineering Applications 70, no. 2 (February 26, 2021): 1112–20. http://dx.doi.org/10.17762/msea.v70i2.2181.
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The development of a smart traffic light control system using IoT technology has become an essential topic in urban traffic management. This research paper focuses on the development of an IoT-based real-time traffic monitoring system for city governance. The system integrates traffic data from various sources, including cameras, sensors, and other data sources, to provide real-time traffic updates to city officials and drivers. The proposed system utilizes IoT technology to collect data from various traffic sources and processes it to determine the optimal time for traffic lights to switch between green, yellow, and red. The system uses a combination of machine learning algorithms and traffic models to predict traffic flow patterns and optimize traffic light timings. Additionally, the system can identify potential traffic accidents and send alerts to nearby emergency services. The proposed system is evaluated using simulation techniques and has shown a significant improvement in reducing traffic congestion and improving traffic flow in a simulated urban environment. Furthermore, the system provides real-time data analytics to city officials, allowing them to make informed decisions on traffic management and city planning. Overall, the development of a smart traffic light control system using IoT technology has significant potential in improving traffic management in urban areas. The proposed system can reduce traffic congestion, improve traffic flow, and enhance overall traffic safety. Therefore, it is essential to continue research and development in this area to implement the proposed system on a larger scale.
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Iftikhar, Amir, Zawar H. Khan, T. Aaron Gulliver, Khurram S. Khattak, Mushtaq A. Khan, Murtaza Ali, and Nasru Minallahe. "Macroscopic Traffic Flow Characterization at Bottlenecks." Civil Engineering Journal 6, no. 7 (July 1, 2020): 1227–42. http://dx.doi.org/10.28991/cej-2020-03091543.
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Traffic congestion is a significant issue in urban areas. Realistic traffic flow models are crucial for understanding and mitigating congestion. Congestion occurs at bottlenecks where large changes in density occur. In this paper, a traffic flow model is proposed which characterizes traffic at the egress and ingress to bottlenecks. This model is based on driver response which includes driver reaction and traffic stimuli. Driver reaction is based on time headway and driver behavior which can be classified as sluggish, typical or aggressive. Traffic stimuli are affected by the transition width and changes in the equilibrium velocity distribution. The explicit upwind difference scheme is used to evaluate the Lighthill, Whitham, and Richards (LWR) and proposed models with a continuous injection of traffic into the system. A stability analysis of these models is given and both are evaluated over a road of length 10 km which has a bottleneck. The results obtained show that the behavior with the proposed model is more realistic than with the LWR model. This is because the LWR model cannot adequately characterize driver behavior during changes in traffic flow.
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Danilevičius, Algimantas, Mykola Karpenko, and Vítězslav Křivánek. "RESEARCH ON THE NOISE POLLUTION FROM DIFFERENT VEHICLE CATEGORIES IN THE URBAN AREA." Transport 38, no. 1 (February 28, 2023): 1–11. http://dx.doi.org/10.3846/transport.2023.18666.
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The noise pollution inside urban areas is one of the common problems for the inhabitants. The different levels of a noise are generated from the large amount of sources, including traffic flow on a road of urban areas. Therefore, it is essential to measure and evaluate the road traffic noise in urban areas and its population exposure in order to obtain models of a traffic noise as well as the noise mapping. The current research includes establishing a traffic noise from the different types of vehicles caused by the speed in urban areas and different road pavements (dry, wet and covered with a snow) with generalising the obtained data for more accurate using in future traffic noise models and the noise mapping. The study region is Vilnius (Lithuania), the speed range for different categories of the vehicles in the collected data is 40…130 km/h, with wide ranges of a noise level 20…180 dB. The approach presented in this research of experimental measurements is based on Statistical Pass-By (SPB) method with data proceeding upon implementation of Pearson correlation coefficient. In course of the analysis of the obtained results, it was found that the level of the curves of noise depends on the vehicles’ speed what corresponds to the best-measured values and can be used in the traffic noise models and the noise mapping.