Autonomous vehicles are being viewed with scepticism in their ability to improve safety and the driving experience. A critical issue with automated driving at this stage of its development is that it ...is not yet reliable and safe. When automated driving fails, or is limited, the autonomous mode disengages and the drivers are expected to resume manual driving. For this transition to occur safely, it is imperative that drivers react in an appropriate and timely manner. Recent data released from the California trials provide compelling insights into the current factors influencing disengagements of autonomous mode. Here we show that the number of accidents observed has a significantly high correlation with the autonomous miles travelled. The reaction times to take control of the vehicle in the event of a disengagement was found to have a stable distribution across different companies at 0.83 seconds on average. However, there were differences observed in reaction times based on the type of disengagements, type of roadway and autonomous miles travelled. Lack of trust caused by the exposure to automated disengagements was found to increase the likelihood to take control of the vehicle manually. Further, with increased vehicle miles travelled the reaction times were found to increase, which suggests an increased level of trust with more vehicle miles travelled. We believe that this research would provide insurers, planners, traffic management officials and engineers fundamental insights into trust and reaction times that would help them design and engineer their systems.
•Fundamental diagram over a wide range of possible traffic states.•S-shaped three-parameter (S3) traffic flow model.•Microscopic car-following model consistent with S3 model.•Calibration with ...real-world traffic data.
In this study, a new s-shaped three-parameter (S3) traffic flow model is proposed to represent the relationships between three fundamental variables (i.e., flow, speed, and density) in highway traffic. An s-shaped speed-density function is proposed to capture the speed-density relationship under a wide range of possible densities. A consistent car-following model was derived in terms of the proposed s-shaped speed-density function. Both the S3 macroscopic model and the derived microscopic car-following model were calibrated using real-world traffic data.
Dieses Open-Access-Handbuch versteht sich als praktisches Hilfsmittel für den gezielten Einsatz eines automatisierten ÖPNV auf dem Weg zur Mobilitätswende. Es richtet sich in erster Linie an ...Entscheidungsträger, Verwaltungen kleinerer und größerer Kommunen sowie an Verkehrsbetriebe. Das Handbuch vermittelt der Leserin und dem Leser einen umfassenden Überblick über verschiedene Entwicklungslinien des automatisierten Fahrens sowie über die damit verbundenen Chancen und Risiken für Mobilität und Gesellschaft. Die Autorinnen und Autoren verdeutlichen sehr anschaulich, dass die Chancen des automatisierten Fahrens vor allem in der Ergänzung oder Erweiterung des ÖPNV liegen. Davon ausgehend adressiert das Buch sehr praxisnah und umsetzungsorientiert alle wesentlichen Fragestellungen, die vor dem Start eines Pilotprojekts zur Einführung eines automatisierten ÖPNV bedacht werden sollten. Der Fokus liegt auf den oft unberücksichtigten ländlichen Räumen bzw. jenen Räumen größerer Städte und Regionen, die heute von den Verkehrsbetrieben und -verbünden nicht oder nur sehr unzureichend versorgt werden. Den Herausgebern und der Herausgeberin ist das Anliegen, die Mobilitätswende so gut und rasch als möglich zu schaffen, sowie das Wissen um die herausragende Rolle des ÖPNV als kollektives Verkehrsmittel gemein. Die Autorinnen und Autoren stammen aus der Wissenschaft und aus der täglichen Planungs- und Beratungspraxis. Es ist ihnen in hervorragender Weise gelungen, eine sowohl systemische wie auch sehr lösungsorientierte Sicht auf die oft komplexe Aufgabe der Mobilitätswende zu eröffnen und die Rolle zu beschreiben, die ein automatisierter ÖPNV bei der Mobilitätswende spielen könnte.
Traffic lights have been installed throughout road networks to control competing traffic flows at road intersections. These traffic lights are primarily intended to enhance vehicle safety while ...crossing road intersections, by scheduling conflicting traffic flows. However, traffic lights decrease vehicles’ efficiency over road networks. This reduction occurs because vehicles must wait for the green phase of the traffic light to pass through the intersection. The reduction in traffic efficiency becomes more severe in the presence of emergency vehicles. Emergency vehicles always take priority over all other vehicles when proceeding through any signalized road intersection, even during the red phase of the traffic light. Inexperienced or careless drivers may cause an accident if they take inappropriate action during these scenarios. In this paper, we aim to design a dynamic and efficient traffic light scheduling algorithm that adjusts the best green phase time of each traffic flow, based on the real-time traffic distribution around the signalized road intersection. This proposed algorithm has also considered the presence of emergency vehicles, allowing them to pass through the signalized intersection as soon as possible. The phases of each traffic light are set to allow any emergency vehicle approaching the signalized intersection to pass smoothly. Furthermore, scenarios in which multiple emergency vehicles approach the signalized intersection have been investigated to select the most efficient and suitable schedule. Finally, an extensive set of experiments have been utilized to evaluate the performance of the proposed algorithm.
With the rapid development of urbanization in the world, it has brought enormous pressure on urban traffic management and control such as traffic congestion. An excellent urban traffic management and ...control system consists of three critical aspects: obtaining traffic parameters, developing traffic control scheme, and evaluating traffic control scheme. Intersection signal timing is one of the most important parts in urban traffic control. This paper proposed an intersection signal timing system based on traffic video which consists of three parts: acquisition of video-based traffic parameters, calculation of traffic flow-based signal timing scheme, and evaluation of intersection signal timing scheme. In the first part, we used advanced techniques such as deep learning and image processing to obtain traffic parameters such as traffic flow, vehicle type, composition of different vehicle types, and speed of vehicles passing through a scene in a traffic video. In the second part, we calculated the signal timing scheme of the video at the traffic scene through the obtained traffic flow information with Webster method. In the third part, the detailed traffic parameters and signal timing scheme were input into the VISSIM software for traffic microscopic simulation, which was used to evaluate the signal timing scheme. The experimental results show that the accuracy of the detailed traffic flow information obtained by the proposed system can reach more than 90%, the accuracy of composition of different vehicle types can be achieved more than 98%, and the vehicle speed accuracy can reach more than 95%. Therefore, the system improves the reliability and adaptability of the whole signal timing network. At the same time, the simulation results show that the proposed system integrates the acquisition of traffic parameters and the calculation and evaluation of signal timing schemes, and provides a good solution for solving research problems and actual needs such as signal timing optimization.
The self-adaptive traffic signal control system serves as an effective measure for relieving urban traffic congestion. The system is capable of adjusting the signal timing parameters in real time ...according to the seasonal changes and short-term fluctuation of traffic demand, resulting in improvement of the efficiency of traffic operation on urban road networks. The development of information technologies on computing science, autonomous driving, vehicle-to-vehicle, and mobile Internet has created a sufficient abundance of acquisition means for traffic data. Great improvements for data acquisition include the increase of available amount of holographic data, available data types, and accuracy. The article investigates the development of commonly used self-adaptive signal control systems in the world, their technical characteristics, the current research status of self-adaptive control methods, and the signal control methods for heterogeneous traffic flow composed of connected vehicles and autonomous vehicles. Finally, the article concluded that signal control based on multiagent reinforcement learning is a kind of closed-loop feedback adaptive control method, which outperforms many counterparts in terms of real-time characteristic, accuracy, and self-learning and therefore will be an important research focus of control method in future due to the property of “model-free” and “self-learning” that well accommodates the abundance of traffic information data. Besides, it will also provide an entry point and technical support for the development of Vehicle-to-X systems, Internet of vehicles, and autonomous driving industries. Therefore, the related achievements of the adaptive control system for the future traffic environment have extremely broad application prospects.
Intelligent traffic control at urban intersections is vital to ensure efficient and sustainable traffic operations. Urban road intersections are hotspots of congestion and traffic accidents. Poor ...traffic management at these locations could cause numerous issues, such as longer travel time, low travel speed, long vehicle queues, delays, increased fuel consumption, and environmental emissions, and so forth. Previous studies have shown that the mentioned traffic performance measures or measures of effectiveness (MOEs) could be significantly improved by adopting intelligent traffic control protocols. The majority of studies in this regard have focused on mono or bi-objective optimization with homogenous and lane-based traffic conditions. However, decision-makers often have to deal with multiple conflicting objectives to find an optimal solution under heterogeneous stochastic traffic conditions. Therefore, it is essential to determine the optimum decision plan that offers the least conflict among several objectives. Hence, the current study aimed to develop a multi-objective intelligent traffic control protocol based on the non-dominated sorting genetic algorithm II (NSGA-II) at isolated signalized intersections in the city of Dhahran, Kingdom of Saudi Arabia. The MOEs (optimization objectives) that were considered included average vehicle delay, the total number of vehicle stops, average fuel consumption, and vehicular emissions. NSGA-II simulations were run with different initial populations. The study results showed that the proposed method was effective in optimizing considered performance measures along the optimal Pareto front. MOEs were improved in the range of 16% to 23% compared to existing conditions. To assess the efficacy of the proposed approach, an optimization analysis was performed using a Synchro traffic light simulation and optimization tool. Although the Synchro optimization resulted in a relatively lower signal timing plan than NSGA-II, the proposed algorithm outperformed the Synchro optimization results in terms of percentage reduction in MOE values.
Recent advances in combining deep neural network architectures with reinforcement learning (RL) techniques have shown promising potential results in solving complex control problems with ...high-dimensional state and action spaces. Inspired by these successes, in this study, the authors built two kinds of RL algorithms: deep policy-gradient (PG) and value-function-based agents which can predict the best possible traffic signal for a traffic intersection. At each time step, these adaptive traffic light control agents receive a snapshot of the current state of a graphical traffic simulator and produce control signals. The PG-based agent maps its observation directly to the control signal; however, the value-function-based agent first estimates values for all legal control signals. The agent then selects the optimal control action with the highest value. Their methods show promising results in a traffic network simulated in the simulation of urban mobility traffic simulator, without suffering from instability issues during the training process.
•This review summarizes and examines the recent methodological advances of dynamic traffic assignment (DTA) models in environmentally sustainable road transportation applications.•This review ...presents the systematic classifications of emission estimation models and DTA models.•This review identifies some research gaps in current studies and highlights several highly inspiring research directions.
The fact that road transportation negatively affects the quality of the environment and deteriorates its bearing capacity has drawn a wide range of concerns among researchers. In order to provide more realistic traffic data for estimations of environmental impacts, dynamic traffic assignment (DTA) models have been adopted in transportation planning and traffic management models concerning environmental sustainability. This review summarizes and examines the recent methodological advances of DTA models in environmentally sustainable road transportation applications including traffic signal control concerning vehicular emissions and emission pricing. A classification of emission estimation models and their integration with DTA models are accordingly reviewed as supplementary to the existing reviews. Finally, a variety of future research prospects of DTA for environmentally sustainable road transportation research are discussed. In particular, this review also points out that at present the research about DTA models in conjunction with noise predictive models is relatively deficient.
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