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For proper planning, design and operation of an electronic highway toll facility, the capacity of the toll lane is an important aspect that needs to be considered. Although the capacities of roads and intersections are generally referred to as stochastic in nature, toll lane capacity is still considered as a deterministic value. A study was undertaken into the variation in service headway and thus reliability related to capacity values for toll lanes. Videographic traffic data were collected at three electronic toll plazas in India. The service headway of cars was found to be lower compared to vehicles with trailers. A Monte Carlo simulation was used to generate the different variations that may occur in the service headway owing to supply-side and demand-side changes. Finally, the capacity for 80%, 90% and 95% reliability was found as 494, 554 and 608 passenger cars per hour, respectively. The developed capacity values can be used to check the volume-to-capacity ratio in a mixed traffic lane and can be used to develop the level-of-service thresholds for electronic toll collection lanes in future studies. [ABSTRACT FROM AUTHOR]

Current literature on joint optimization of intersection signal timing and connected automated vehicle (CAV) trajectory mostly focuses on vehicular movements paying no or little attention to pedestrians. This paper presents a methodology to safely incorporate pedestrians into signalized intersections with CAVs and connected human‐driven vehicles (CHVs). The movements of vehicles are controlled using both traffic lights and mobile CAV controllers during our newly introduced "white phase." CAVs navigate platoons of CHVs through the intersection when the white phases are active. In addition to optimizing CAV trajectories, the model optimally selects the status of the traffic light signal among white and green indications for vehicular and walk and do‐not‐walk intervals for pedestrian movements. A receding horizon‐based methodology is used to capture the stochastic nature of the problem and to reduce computational complexity. The case study results show the successful operation of fleets consisting of pedestrians, CAVs, and CHVs with various demand levels through isolated intersections. The results also show that increasing the CAV market penetration rate (MPR) can decrease average intersection delay by up to 27%. Moreover, the average pedestrian, CHV, and CAV delays decrease as the CAV MPR increases and reach their minimum values with a fully CAV fleet. In addition, the presence of the white phase can decrease the intersection average delay by up to 14.7%. [ABSTRACT FROM AUTHOR]

In this paper is present the procedure for introducing the coordination of light signals at five intersections on the Boulevard "St. Kliment Ohridski" in Skopje. The primary purpose of a traffic signal is to assign the right-of-way to intersecting traffic streams for the purpose of ensuring that all streams are served safely and without excessive delay. A properly designed and timed signalized intersection will minimize fuel consumption, delay, and stops without having an adverse effect on safety. Travelers will realize one or more of the following benefits at intersections where the traffic signal is needed, properly designed, and well timed: orderly movement of traffic, increase in the traffic-carrying capacity of the intersection, reduction in the frequency and severity of certain types of crashes, progressed traffic when traveling in a coordinated signal system, and interruption of heavy traffic flow to provide safe opportunities for minor movements. Coordinated signal timing synchronizes traffic movements and manages the progression speed of specific modes where uninterrupted flow is desired along a corridor. While traditionally applied to increase vehicular traffic flow and reduce peak-hour delay, coordinated signal timing can also be optimized for slower speeds, creating an uninterrupted flow for bicyclists or low vehicle progression speeds for a pedestrian-friendly downtown. Signals may also be timed to coordinate transit headways along routes where regular transit service is consistent and has low variability. Coordination of traffic signals can reduce the number of stops along a corridor and provide for a continuous flow of traffic at the target speed. Progression speeds should be set at or below the target speed, rather than existing 85th percentile speeds. [ABSTRACT FROM AUTHOR]

The paper presents a model-based demand-responsive traffic control system for mixed traffic conditions using sample travel time data. The model incorporates mixed traffic characteristics such as heterogeneity, limited lane discipline of varied vehicle types, and spatio-temporal traffic dynamics across the width of the road. The methodology includes optimization of intersection performance by accommodating the varying traffic demand through signal timing variables. On validation, the model yielded reliable queue estimates within a close proximity of the actual, ranging from 20 to 40 meters. Upon optimization, the proposed model reduced total intersection delay by 15.42% on an average across 14 cycles, for near-saturated traffic conditions. The optimal green splits are found to be responsive to the varying traffic demand. The proposed system is simple and can be easily implemented in the mixed traffic conditions. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to intersection improvement planning utilizing telematics data from connected vehicles to generate performance measures for mobility, safety, and emissions. Congestion, crashes, and emissions are three major issues in urban areas, particularly at intersections, and agencies often struggle to prioritize improvement plans because of a lack of objective data. Traditional infrastructure sensors provide limited information at selected locations, but it is not feasible to deploy them at all intersections. The use of telematics data from connected vehicles provides a high granularity of information on driving events and trajectories that can be used in conjunction with vehicle emission modeling to efficiently generate performance measures for all intersections. In a case study of over 300 intersections in Arlington, Texas, the Pareto front method was used to evaluate and rank intersections based on multiple criteria. Intersections falling on the Pareto front were identified as having at least one outstanding (poor) performance measure and were required to be given priority for improvement. The results were cross-validated with historical crash reports and the judgments of city traffic engineers, demonstrating the effectiveness of the proposed framework in generating objective and reliable intersection performance measures. This approach has the potential to significantly improve intersection safety, mobility, and environmental impact, and can serve as a valuable decision-support tool for transportation agencies. [ABSTRACT FROM AUTHOR]

Traffic signal priority issues have been a research subject for several decades in Poland and worldwide. Traffic control algorithms have evolved considerably during this period and have become increasingly advanced. Most of them operate within coordinated street sequences, which adds to their complexity. Tramway priority affects traffic conditions for other road users, so many aspects must be taken into account when choosing a priority solution. Typically, one of the main criteria for evaluating the effectiveness of priority is reducing travel time for the priority vehicle while ensuring that the travel times of other traffic participants through the intersection are maintained or slightly deteriorated. However, the energy aspects are often overlooked. This publication aims to investigate how local priority for tramways in traffic signals of coordinated streets affects energy consumption for tramway traction needs. The study was conducted using a microscopic modeling method with PTV Vissim software (ver. 2021). The models were built for coordinated sequences with different levels of priority. Real traffic control algorithms with priority were implemented into the model on the sequence of Marymoncka Street and Grochowska Street in Warsaw. Then, by introducing changes to the parameters of the algorithms, their effect on traffic characteristics, including estimated power consumption, was studied. The results obtained from the computer simulation were statistically processed using R software (ver. 4.3.2). The analysis results prove the effectiveness of tramway priority operation, show its impact on electricity consumption, and allow us to determine the limits of its effective application. Thus, they complement the knowledge of the impact of tramway priority on traffic. The research results also have practical value, as they help us to make rational decisions in the process of designing traffic control algorithms at intersections with a multi-criteria approach. [ABSTRACT FROM AUTHOR]

The article focuses on designing coordinated traffic light signal plans. Topics discussed include the complexity of traffic management; the concept of signal coordination; and the methodology for line coordination design. It emphasizes the importance of reducing time losses and congestion through effective traffic signal coordination; and highlighting the benefits in terms of mobility, road user experience and environmental impact.

By facilitating public busesʼ movement through traffic signal-controlled intersections, a transit signal priority (TSP) strategy can contribute to the reduction of queuing time at intersections. However, the traditional TSP also has a significant downside; it is detrimental to non-prioritized movements and other transport modes. This research proposes new TSP strategies that account for optimization for the person delay at an intersection along a corridor. This research focused both on major arterial roads and minor arterial roads, whereas the majority of the publications only addressed the major arterial approach. As part of this research, the bus schedule was also taken into consideration, whereas in other studies, the measure of giving priority was only the approach to the intersection. The proposed method is described in detail and also is implemented on arterial corridor in VISSIM. The study area simulation results indicated that the proposed TSP methods performed better than the conventional TSP. [ABSTRACT FROM AUTHOR]

Rapid increase in the private and public vehicles fleet causes urban centers heavily populated with limited transport road infrastructure. To overcome this, in real-time scenarios, queue length-based traffic light controllers are being designed utilizing light-weighted S-Edge devices. This system suffers from starvation problems if a road lane at the intersection continuously receives vehicles during peak hours. With this, higher green phase duration can be allocated to the same-lane multiple times despite vehicles on the other lanes' longer waiting time. To tackle this problem, an efficient and smart edge computing (S-Edge)-driven traffic light controller is proposed by accounting the real-time heterogeneous vehicular dynamics at the fog computing node. The fog node executes the proposed fuzzy inference system to generate phase-cycle duration. Further, to allocate the phase duration effectively, a method for estimating the lane pressure is proposed for the edge controller utilizing average queue length and waiting time. S-Edge is a light-weighted actuated traffic light controller that generates traffic light control cycle duration and phase (red/yellow/green) duration. To validate the S-Edge controller, a prototype is developed on an Indian city OpenStreetMap utilizing the low-computing power IoT devices, i.e., Raspberry Pi, and a well-known open-source simulator, i.e., Simulation of Urban MObility. [ABSTRACT FROM AUTHOR]

Intelligent traffic control at nonsignalized intersections under a connected and automated vehicle (CAV) environment can reduce traffic congestion, vehicle emissions [e.g., carbon dioxide ðCO2Þ, carbon monoxide (CO), nitrogen oxide (NO)], but it is still not safe enough for an unpredictable emergency. For example, an accidental stop or slow down of a vehicle in the conflict area will cause secondary accidents of vehicles from other directions because they do not have enough distance to brake. Such risks have not been given sufficient attention in the literature. This paper proposed a safety guarantee mechanism of intersection control to avoid secondary accidents by adding a safe buffer area outside the conflict area of the intersection. To improve the traffic efficiency of intersections and reduce vehicle emissions under the mechanism, we proposed a heuristic vehicle trajectory planning control strategy based on vehicle kinematics. Three planning principles are applied to generate the microscopic trajectory for each vehicle: (1) the vehicle first decelerates and then accelerates before passing the intersection; (2) try not to change the running state of the vehicle; and (3) the vehicles do not collide with each other. Finally, to further improve efficiency, we proposed the concept of a dynamic safety area, which reduces the delay by self-adjusting the safety area to each vehicle’s trajectory. The paper selected three simulation scenarios of road intersections to validate our methodology. The simulation results show that the proposed control method with the safe buffer significantly reduces average vehicle delay and emissions compared to traditional signal control systems. Compared with the fixed buffer, in the dynamic safe buffer scenario, the average vehicle delay can be reduced by 66.94%, the throughput is increased by 18.75%, and CO2 emissions are reduced by 9.9%. The dynamic safe buffer can not only relax the safety concern of a nonsignalized intersection, which is not well addressed in the literature, but also has much better performance than the traditional intersection. [ABSTRACT FROM AUTHOR]

Queue length is one of the important indexes to evaluate the operation efficiency of signalized intersection and also the key parameter of intersection signal control optimization. Traditional queue length estimation models are mostly based on fixed detection equipment, and the models assumptions are too harsh; there are certain limitations. Based on the probe vehicle data, this paper establishes a model of queue length estimation for signalized intersection based on shockwave theory. First, based on the speed and location data of the probe vehicle, the vehicle density is calculated to estimate the intersection stop line. A real-time calculation method of vehicle arrival rate is proposed to improve the applicability of the model. Then, based on the shockwave theory, the meeting time of the queue forming wave and the queue discharging wave are calculated after the green light is on. Finally, the queue length is summed in sections, including the distance between the last queued probe vehicle and the stop line during the red light period, the length of the subsequent vehicles arriving during the residual red light time, and the newly increased queue length within the queue discharging time. This paper uses the VISSIM software to simulate the actual intersection. The simulation results show that when the penetration of probe vehicle is 50%, 25%, and 10%, their corresponding mean absolute relative error are 11.27%, 27.77%, and 39.12%, respectively. It can be seen that with the increase of penetration, the error gradually decreases. The average absolute relative error is within the acceptable range. After analyzing the existing similar methods, although the accuracy of the method proposed in this paper does not reach the highest level, it has the advantages of simple operation, less computation, and good real-time computation. Relevant research results can provide support for traffic control at signalized intersections. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are in the main focus for automotive companies and urban traffic engineers as well. As their penetration rate in traffic becomes more and more pronounced due to improvement in sensor technologies and the corresponding infrastructure, new methods for autonomous vehicle controls become a necessity. For instance, autonomous vehicles can improve the performance of urban traffic and prevent the formation of congestions with the usage of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication based control methods. One of the key area for improvement is centralized intersection control for autonomous vehicles, by which traveling times can be reduced and efficiency of traffic flow can be improved, while safety of passengers can be guaranteed through constraints built in the centralized design. The paper presents the analysis of a Model Predictive Control (MPC) method for the coordination of autonomous vehicles at intersections by comparing it with an offline constraint optimization considering time and energy optimal intervention of vehicles. The analysis has been evaluated in high-fidelity simulation environment CarSim, where the speed trajectories, traveling times and energy consumptions have been compared for the different methods. The simulations show that the proposed time-optimal MPC intersection control method results in similar traveling times of that given by the time-optimal offline constraint optimization, while the energy optimal optimization re-quires significantly more time for the autonomous vehicle to achieve. Due to the possibility of a congestion forming in the latter case, the proposed centralized MPC method is more applicable in real traffic scenarios. [ABSTRACT FROM AUTHOR]

This study proposes an approach to determine transport delays at unsignalized intersections, according to the theory of stationary Poisson flow and considering the priority of vehicle traffic in competing directions. The accuracy of the proposed approach was estimated by comparing the average values of delays of vehicles performing a left turn from a minor road and their actual values obtained as a result of processing data from field studies. The results of the accuracy evaluation at unsignalized intersections of non-equivalent roads indicate the obtained results' reliability, because the calculation error does not exceed 10%, as well as the possibility of using the developed approach in the process of transport planning and unsignalized intersections modelling. [ABSTRACT FROM AUTHOR]

Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation's simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go vehicle dynamics. Low deviation from the road speed limit is hence continuously sustained for cohorts of at most 50 m long. This limitation can be mitigated with additional lanes that the cohorts can split into. The concept also allows the testing and implementation of fast-turning lanes by simply replicating and reconnecting the control architecture at each new road crossing, enabling high scalability for complex road network analysis. The controller is also successfully validated within a high-fidelity vehicle dynamic environment, showing its potential for driverless vehicle control in addition to offering a new traffic control simulation model for future autonomous operation studies. [ABSTRACT FROM AUTHOR]

Traffic at urban intersections constitute a series of complex conflicting vehicular movements and contribute greatly to the problems of traffic congestion and air pollution. The emerging of connected and automated vehicle (CAV) technologies inspires innovative ideas in traffic management at intersections; it not only enables new control paradigms, but also allows for possibilities to revolutionize the geometric layouts of intersections. This study proposes a novel intersection design called knotted intersection (KI), to resolve the complexity of conflicting relations at intersections in a full CAV environment. This new design is associated with a set of neat control rules for vehicles to realize the smooth traffic operation at these intersections. The design problem of intersection geometry and control strategy is optimized jointly and formulated as a mixed-integer linear program (MILP), and some basic theoretical properties of KI design are derived, including nearly throughput-maximizing property and bounded within-intersection delay of vehicles. Furthermore, the KI design is then extended to a road network composed of multiple intersections to improve the overall efficiency. The optimal design of multiple knotted intersections (MKI) is developed to coordinate the traffic organization methods among intersections and a two-level solution method is proposed to solve the optimization efficiently. Numerical and simulation experiments are conducted at both the intersection level and network level under various cases, and the results validate the superior performance of the proposed methods compared to classical traffic signal control (TSC) strategies. • Knotted intersection design for resolving the complexity of intersection conflicts • Joint design of intersection geometric layout and traffic control strategy • Theoretical properties for nearly-maximum throughput and bounded delay • Optimal design of multiple knotted intersections to enhance network efficiency [ABSTRACT FROM AUTHOR]

To better prepare and guide both intersection planning and operations under different market penetration rates (MPRs) of connected and autonomous vehicles (CAVs) and traffic demands, this research estimates both the lane-level and intersection-level capacity. On the lane-level, adjustment factors for saturation headway and saturation traffic flow rate for each lane under different MPRs of CAVs are investigated. On the intersection-level, the maximum throughput function for different MPRs of CAVs is calibrated. With 100% CAVs, the saturation headways for the exclusive through lane, exclusive left-turn lane, and shared-right-and-through lane decrease by 55.8%, 48.9%, and 42.4%, respectively. The maximum throughput of the intersection with 100% CAVs increases by 70% compared to the scenario with only human driving vehicles (HDVs). Moreover, the maximum throughput increases rapidly after 60% MPRs of CAVs. The research could provide a solid reference for traffic engineers and planners in calculating the intersection capacity under different MPRs of CAVs. [ABSTRACT FROM AUTHOR]

To enhance the traffic operation efficiency at signalized intersections, a model for coupled control of traffic signals and connected autonomous vehicles at isolated signalized intersections is proposed. This model estimates the time that CAVs reach stop lines with real-time information about the speed and position of CAVs. The arrival time is leveraged to optimize traffic signal timing by rolling horizon, with the maximization of phase saturation as the optimization objective. Based on the optimized traffic signal timing, the speed profile of CAVs is optimized by a linear integer programming, with the maximization of speed at the moment of reaching the stop line as the optimization objective. Through the coupled control of travel speed and the traffic signal, CAVs can pass through the intersection safely, efficiently, and smoothly. NetLogo, a multiagent microscopic simulator, is developed to test this strategy, and an intersection in Weihai is taken for verification and analysis lastly. The simulation results demonstrate that, compared with the fixed traffic signal timing control and the model optimizing only speed profile of CAVs, the proposed model can reduce the average number of stops by 47.0% and the queuing time by 41.3%. In addition, the optimization is better during off-peak hours, about 10% higher than the peak hours. [ABSTRACT FROM AUTHOR]

Waiting in a long queue at traffic lights not only wastes valuable time but also pollutes the environment. With the advances in autonomous vehicles and 5G networks, the previous jamming scenarios at intersections may be turned into non-stop weaving traffic flows. Toward this vision, we propose a highly efficient traffic planning system, namely DASHX, which enables connected autonomous vehicles to cross multi-way intersections without a stop. Specifically, DASHX has a comprehensive model to represent intersections and vehicle status. It can constantly process large volumes of vehicle information, resolve scheduling conflicts, and generate optimal travel plans for all vehicles coming toward the intersection in real time. Unlike existing works that are limited to certain types of intersections and lack considerations of practicability, DASHX is universal for any type of 3D intersection and yields the near-maximum throughput while still ensuring riding comfort. To better evaluate the effectiveness of traffic scheduling systems in real-world scenarios, we developed a sophisticated open source 3D traffic simulation platform (DASHX-SIM) that can handle complicated 3D road layouts and simulate vehicles' networking and decision-making processes. We have conducted extensive experiments, and the experimental results demonstrate the practicality, effectiveness, and efficiency of the DASHX system and the simulator. [ABSTRACT FROM AUTHOR]

Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle's bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections. [ABSTRACT FROM AUTHOR]

Delay imposed on buses is witnessed at isolated signalized intersections, as part of the delay is created due to bus and car interactions at the signalized approaches. Inexpensive solutions that do not involve new infrastructure are the most desired. Toward reducing person delay, presignal lights are installed at the upstream of the intersection. Presignal is a control tool that can manage the interaction and space conflict between cars and buses at the mixed lanes, located after the presignals, approaching the intersection. This paper presents an optimal presignal control policy that determines signal settings based on minimization of total person-time spent. The developed model is comprised of a simplified signalized intersection with one approach, where two presignal lights are located: one for bus lane and one for car lane. A continuous-time model is first developed to describe the queue dynamics for the mixed lanes after the presignals and the bus and car lanes before the presignals. The optimal solutions are derived analytically via the Pontryagin maximum principle, and the optimal presignal control policy is determined and synthesized. The derived analytical solutions are verified through the use of the optimal control software, DIDO, which uses an advanced optimization method and solves complex dynamic optimization problems without prior knowledge of the analytical computations or the solution. Several examples are used to test and analyze the derived solutions. Finally, the microscopic simulation software AIMSUN is used to test, assess, and evaluate the performance of the proposed optimal control policy in a more realistic traffic condition, where stochastic arrivals are incorporated. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1172. [ABSTRACT FROM AUTHOR]

When multiple bus vehicles send priority requests at a single intersection, the existing fixed-phase sequence control methods cannot provide priority traffic request services for multiphase bus vehicles. In view of the conflict of multiphase bus priority requests at intersections, the priority vehicle traffic sequence is determined, which is the focus of this study. In this paper, a connected vehicle-enabled transit signal priority system (CV-TSPS) has been proposed, which uses vehicle-infrastructure communication function (V2I) technology to obtain real-time vehicle movement, road traffic states, and traffic signal light phase information. By developing a deep Q-learning neural network (DQNN), especially for optimizing traffic signal control strategy, the public transit vehicles will be prioritized to improve their travel efficiency, while the overall delay of road traffic flow will be balanced to ensure the safe and orderly passage of intersections. In order to verify the validity of the model, the SUMO traffic analysis software has been applied to simulate real-time traffic control, and the experimental results show that compared with the traditional timing signal control, the loss time of vehicles is reduced by nearly 40%, and the cumulative loss time per capita is reduced by nearly 43.5%, and a good control effect is achieved. In the case of medium and low densities, it is better than the solid scheduled traffic control scheme. [ABSTRACT FROM AUTHOR]

Intersections are at the core of congestion in urban areas. After the end of the Second World War, the problem of intersection management has benefited from a growing body of advances to address the optimization of the traffic lights' phase splits, timing, and offset. These contributions have significantly improved traffic safety and efficiency in urban areas. However, with the growth of transportation demand and motorization, traffic lights show their limits. At the end of the 1990s, the perspective of autonomous and connected driving systems motivated researchers to introduce a paradigm shift for controlling intersections. This new paradigm is well known today as autonomous intersection management (AIM). It harnesses the self-organization ability of future vehicles to provide more accurate control approaches that use the smallest available time window to reach unprecedented traffic performances. This is achieved by optimizing two main points of the interaction of connected and autonomous vehicles at intersections: the motion control of vehicles and the schedule of their accesses. Considering the great potential of AIM and the complexity of the problem, the proposed approaches are very different, starting from various assumptions. With the increasing popularity of AIM, this paper provides readers with a comprehensive vision of noticeable advances toward enhancing traffic efficiency. It shows that it is possible to tailor vehicles' speed and schedule according to the traffic demand by using distributed particle swarm optimization. Moreover, it brings the most relevant contributions in the light of traffic engineering, where flow–speed diagrams are used to measure the impact of the proposed optimizations. Finally, this paper presents the current challenging issues to be addressed. [ABSTRACT FROM AUTHOR]

The positive effect of traffic-responsive signal control can be assured when real-time traffic data is reliable, but data reliability may be an issue that depends on the number of probe vehicles equipped with navigation devices or smartphones. However, there is a high chance of improving reliability with the recent deployment of connected vehicles (CVs) that use the vehicle-to-everything (V2X) communication data. Therefore, this paper proposes a traffic signal control strategy that utilizes V2X communication data obtained from CV operations, which is called the capacity waste reduction (CWR) strategy. In this strategy, vehicle queues on each road lane as an intersection approaches are initially estimated using V2X data. Then, the signal control algorithm determines the duration of the green signal for the currently applied phase based on the estimated vehicle queues. Furthermore, the strategy includes an algorithm for active priority signal control for the vehicles of bus rapid transit systems. The efficiency of the provided control strategy is tested with the VISSIM microsimulation program at different levels of the market penetration rate (MPR) of CVs. Based on the results of the experiment, the proposed strategy shows positive effects in both decreasing travel delay and increasing traffic flow even at the low levels of MPR of CVs. The results of the proposed strategy can be used as the base data for the development of smart intersection operations. [ABSTRACT FROM AUTHOR]

Microscopic traffic flow models enable predictions of traffic operations, which allows traffic engineers to assess the efficiency and safety effects of roadway designs. Modeling vehicle trajectories inside intersections is challenging because there is an infinite number of possible paths in a two-dimensional space, and drivers can simultaneously adapt their speeds as well. To date, human driver models for simultaneous longitudinal and lateral vehicle control based on the infrastructure characteristics and interactions with other drivers inside an intersection are still lacking. The contribution of this paper is threefold. First, it proposes an integrated microscopic traffic flow model to describe human-driven vehicle maneuvers under interactions. Drivers plan their heading and acceleration in the predicted future to minimize costs representing undesirable situations. The model works with a joint optimization for an interaction cost term. The weights associated with the interaction cost reflect how selfish or altruistic drivers are. Second, the proposed model endogenously gives the order of vehicles in case of crossing paths. Third, the paper develops a clustered validation method for microscopic traffic flow models with interacting vehicles, which account for interdriver variations. Results show that the model can accurately describe vehicle passing orders of interacting maneuvers, paths, and speeds against empirical data. The model can be applied to assess various intersection designs. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71971140 and 52122215] and the Natural Science Foundation of Shanghai [Grant 20ZR1439300]. [ABSTRACT FROM AUTHOR]

COVID-19 has become a major public health emergency in the world, which seriously affects the normal operation of cities. Epidemic prevention and control is not only needed in big cities, but also in small and medium-sized cities. In view of this, the paper takes Beian city, China as the research area. This study establishes a street network model through spatial syntax, and predicts the crossing potential and arrival potential of its street network. This will play a reference role for traffic flow control in Beian city. The article uses emerging data. Through GIS spatial analysis method, we identify the hidden danger space of city. Therefore, this summarizes the places where people are easy to gather and some problems of the current situation of the city. The results show that: (1) Beian bridge and Wuyuer street have a good traffic potential. The intersection of Longjiang Road and Beidahuang street and the intersection of Tianyuan North Road and Baocheng road have good accessibility. (2) The intersection of Ping'an Street and Shanghai road is a potential hidden danger space of the city, and the focus of epidemic prevention and control. (3) The coverage rate of urban community medical services to residential land is 58.61%, and the existing medical infrastructure is insufficient. Under public health emergencies, the paper will argue a new development ideas for health and safety small town planning by visualizing the hidden danger space of the city. [ABSTRACT FROM AUTHOR]

Existing signalized intersection control methods allocate intersection resources to each phase from the time dimension based on traffic conflict. Solving the traffic control issues caused by the unbalanced traffic demands at intersections is always a challenge. Recently developed connected and automated vehicle (CAV) technologies render it possible to collect detailed real‐time traffic information from vehicles for the optimal control and management of time‐space resources at signalized intersections. In this paper, we propose a mixed integer quadratic programming to jointly optimize signal timings and variable guide lane settings for a typical four‐arm intersection in the CAV environment. From the time dimension, restrictions of the conventional concept of the signal cycle are overcome. Phase sequences, green start, and duration are freely assigned for each CAV platoon based on the movement‐based signal timing. From the space dimension, lane resources can be reasonably allocated in consideration of the dynamic traffic demand distribution. In this study, a demand‐response control is realized for the signalized intersection through time‐space resources cooperated optimization. Furthermore, vehicle trajectory control is integrated into the collaborative control framework to reduce or eliminate wasted green time. At last, we propose the dynamic control process using the collaborative control framework. Numerical simulation results show that the collaborative control method outperforms over fixed‐time and signal optimization control modes in terms of travel time with both under‐saturated and over‐saturated conditions. [ABSTRACT FROM AUTHOR]

In industrial applications, many systems present serious productivity problems due to limited resources. Generally, the dynamics of resource allocation are inherently discrete-event driven, such as the buffer allocation in production line systems. In this paper, we develop a discrete-event mathematical model for resource allocation optimization. In this work, we consider two crucial optimization objectives, e.g., deadlock-free and efficiency, that originate from the customer's actual requirements. The main aim is to develop a resource allocation scheme for fulfilling the production process (without deadlock) while ensuring that the cost of the process is minimized. As a case study, we consider the vehicle scheduling problem in a signal-free intersection. The intersection is divided into several disjoint spatial traffic resources, and vehicles need to occupy different traffic resources for passing through the intersection. Thus, the traffic control problem at the signal-free intersection is transformed into a scheduling problem with limited resource constraints. An online control approach is developed to schedule vehicles to go through the intersection safely and efficiently by optimizing the resource allocation order. Simulation results demonstrate the efficiency and robustness of the proposed model and optimization approach. [ABSTRACT FROM AUTHOR]

In this paper, we consider a simple road intersection with traffic light control and suggest a queueing model for the traffic flow in the intersection. The suggested model implements the well-known queue with state-dependent departure rates. Using this model, we define optimal state-dependent scheduling of the traffic lights in the intersection and consider its properties. Activity of the model is illustrated by numerical simulations. It is demonstrated that in practical conditions the suggested scheduling of the traffic lights allows the prevention of traffic jams in the intersection and resolves vehicles queues with reasonable waiting times in the crossing lanes. [ABSTRACT FROM AUTHOR]

GPS trajectories collected from automotive telematics for insurance purposes go beyond being a collection of points on the map. They are in fact a powerful data source that we can use to extract map and road network properties. While the location of road junctions is readily available, the information about the traffic control element regulating the intersection is typically unknown. However, this information would be helpful, e.g., for contextualizing a driver's behavior. Our focus is to use a map-matched GPS OBD-dongle dataset provided by a Canadian insurance company to classify intersections into three classes according to the type of traffic control element present: traffic light, stop sign, or no sign. We design a convolutional neural network (CNN) for classifying intersections. The network takes as entries, for a defined number of trips, the speed and the acceleration profiles over each segment of one meter on a window around the intersection. Our method outperforms two other competing approaches, achieving 99% overall accuracy. Furthermore, our CNN model can infer the three classes even with as few as 25 trips. [ABSTRACT FROM AUTHOR]

Traditional traffic signal control is one of the most widely used urban traffic control methods, due to its high efficiency, robustness, and simplicity. However, in the era of connected and automated vehicles (CAVs), it is prospective to enhance traffic control precision and achieve vehicle coordination in a more efficient manner. Focusing on an isolated intersection, this study proposes an innovative concept of Fine-grained Phase (FP) for CAV-enabled traffic control, which realizes more refined right-of-way allocation than traditional signal phases while preserving the simplicity and scalability. The development of FP jointly optimizes traffic control strategy and intersection layout design to fully utilize time-space resources, and multiple forms of FP could be generated in response to various traffic demands; meanwhile, vehicle coordination is achieved in an offline–online manner to balance solution quality and efficiency. Under the FP-based control, each lane is assigned with periodical entry time points using FP design results, then vehicles are supposed to catch up with the appropriate time points to fulfill their trips. Theoretical analyses are conducted under stationary vehicle arrival patterns, where the average delay and the intersection capacity associated with different forms of FP can be analytically acquired. For the implementation of FP-based control, we construct a general offline–online framework, consisting of an offline model that optimizes the geometry of the conflict zone at intersections and the control strategy of vehicles simultaneously, which is formulated as a mixed-integer linear programming problem, and an online FP adoption mechanism that can adapt to varying demand patterns. Under the framework, limited online computation is required, facilitating its practical real-time implementation. Simulation studies under a variety of demand settings show that FP-based control is advantageous over benchmark methods in reducing vehicle delay and also improving intersection capacity in most cases; even for cases with extremely imbalanced demands, FP-based control can admit comparable throughput, while the average delay is much smaller. In addition, results of computation time reveal that FP-based control could realize real-time implementation with negligible computation burden. • An innovative concept called FP for intersection traffic control with CAVs. • Joint optimization of traffic control and intersection layout design. • Offline–online implementation framework to balance solution quality and efficiency. • Theoretical analysis for average vehicle delay and admissible traffic capacity. • Improvement over signalized control and the state-of-art reservation strategy. [ABSTRACT FROM AUTHOR]

Traffic congestion is a significant issue in many countries today. The suggested method is a novel control method based on multiple intersections considering the kind of traffic light and the duration of the green phase to determine the optimal balance at intersections by using fuzzy logic control, for which the balance should be adaptable to the unchanging behavior of time. It should reduce traffic volume in transport, average waits for each vehicle, and collisions between cars by controlling this balance in response to the typical behavior of time and randomness in traffic conditions. The proposed method is investigated at intersections using a sampling multi-agent system to set traffic light timings appropriately. The program is provided with many intersections, each of which is an independent entity exchanging information with the others. The stability per entity is proven separately. Simulation results show that Takagi–Sugeno (TS) fuzzy modeling performs better than Takagi–Sugeno (TS) fixed-time scheduling in decreasing the length of queueing times for vehicles. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) are emerging with enormous potentials to solve many challenging road traffic problems. The AV emergence leads to a paradigm shift in the road traffic system, making the penetration of autonomous vehicles fast and its coexistence with human-driven cars inevitable. The migration from the traditional driving to the intelligent driving system with AV's gradual deployment needs supporting technology to address mixed traffic systems problems, mixed driving behaviour in a car-following model, variation in-vehicle type control means, the impact of a proportion of AV in traffic mixed traffic, and many more. The migration to fully AV will solve many traffic problems: desire to reclaim travel and commuting time, driving comfort, and accident reduction. Motivated by the above facts, this paper presents an extensive review of road intersection mixed traffic management techniques with a classification matrix of different traffic management strategies and technologies that could effectively describe a mix of human and autonomous vehicles. It explores the existing traffic control strategies and analyses their compatibility in a mixed traffic environment. Then review their drawback and build on it for the proposed robust mix of traffic management schemes. Though many traffic control strategies have been in existence, the analysis presented in this paper gives new insights to the readers on the applications of the cell reservation strategy in a mixed traffic environment. Though many traffic control strategies have been in existence, the Gipp's car-following model has shown to be very effective for optimal traffic flow performance. [ABSTRACT FROM AUTHOR]

The emerging technologies in both wireless communication and automation driving that have enabled vehicles to communicate with infrastructure, and with each other, are collectively known as connected vehicle and automated vehicle (CAV) technology. In existing connected automated vehicle-based and traditional traffic control systems, urban arterials with signalized intersections still face issues, including local demand uncertainty and automation level compatibility. Here, a distributed signal control system that integrates automated vehicle path planning information is adapted to address these issues. The proposed method includes planning-based local demand estimation and demand-aware max pressure control in an edge computing environment. A bivariable automation model is developed to represent the various, i.e., L1-L5, automatic driving levels. A real district with typical scenarios is implemented to verify the proposed method. The simulation results show that the proposed demand-aware edge control provides better control results than benchmarks for almost all different performance indices and network demands. The proposed control reduces the waiting time by up to 30%, and the time loss decreased by up to 20%. Moreover, better performances are always obtained at different automation levels, so there is a good compatibility of the automation driving for the adaptation of the CAV flows, especially at the L3-L5 levels. [ABSTRACT FROM AUTHOR]

Auditing and mapping traffic infrastructure is a crucial task in urban management. For example, signalized intersections play an essential role in transportation management; however, effectively identifying these intersections remains unsolved. Traditionally, signalized intersection data are manually collected through field audits or checking street view images (SVIs), which is time-consuming and labor-intensive. This study proposes an effective protocol to identify signalized intersections using road networks and SVIs. First, we propose a six-step geoprocessing model to generate an intersection feature layer from road networks. Second, we utilize up to three nearest SVIs to capture streetscapes at each intersection. Then, a deep learning-based image segmentation model is adopted to recognize traffic light-related pixels from each SVI. Last, we design a post-processing step to generate new features characterizing SVIs' segmentation results at each intersection and build a decision tree model to determine the traffic control type. Results demonstrate that the proposed protocol can effectively identify signalized intersections with an overall accuracy of 97.05%. It also proves the effectiveness of SVIs for auditing urban infrastructures. This study can directly benefit transportation agencies by providing a ready-to-use smart audit and mapping solution for large-scale identification and mapping of signalized intersections. [ABSTRACT FROM AUTHOR]

In this paper, we present a decentralized control framework for the coordination of connected vehicles and traffic lights in urban intersections. The framework uses gradient-based methods to dynamically coordinate, at every time-step, the planned intersection arrival times of vehicles and the planned switching times of traffic lights. Assuming no constraints, vehicles then use analytical optimal control methods to determine a nominal acceleration profile intended to place them at the intersection at the desired arrival time. Finally, using control barrier functions, the agents' accelerations and arrival times are modified to ensure safety and feasibility. The work in this paper builds on existing research on safe-set nonlinear control methods, multi-agent gradient-based methods, and optimal control methods used to tackle the intelligent intersection management problem. Here, we integrate the use of these different approaches to present a comprehensive control architecture that can flexibly coordinate the timing of both vehicles and traffic lights, while maintaining safety and feasibility. Simulations show that the method can save between 4% and 15% in fuel consumption and between 55% and 70% in delay for different traffic conditions. [ABSTRACT FROM AUTHOR]

Dynamic left turning using lanes exiting an intersection in the opposite direction is a new type of left-turn traffic organization. When the left-turn traffic volume at an intersection is large, several exiting lanes adjacent to the original left-turn lanes can be converted into left-turn lanes during a certain period of free time under the control of a presignal. Based on kinematic wave theory, this paper analyzes the spatiotemporal process of the left-turn traffic flow. Three cases are analyzed, and the corresponding flow feasibility constraints are obtained in the form of requirements on the length of the contraflow left-turn lane and the coordination between the main signal and the presignal. Optimization of the lane length and signal coordination to maximize the number of vehicles entering the contraflow left-turn lane while guaranteeing traffic flow safety is discussed, and a practical example is analyzed. [ABSTRACT FROM AUTHOR]

Aiming at the low control accuracy of traditional traffic flow control methods of road intersections, a traffic flow control method based on V2X and Vehicle-Road-Cloud technology was proposed. Firstly, according to the traffic running state, the corresponding traffic flow data attributes of intersections are selected as the original data of traffic flow control. Then the traffic flow dynamic attribute data is collected and repaired and identified according to the selected attributes. Finally, based on V2X and Vehicle-Road-Cloud technology, the Vehicle flow control model of road intersections is established to realize the control of Vehicle flow at intersections. The experimental results show that the control accuracy of the method is high on flow data, speed data and road occupancy data, and its control accuracy can reach 98.48%, indicating that the method can effectively realize the control of traffic flow at intersections. [ABSTRACT FROM AUTHOR]

The key role of this research is a comparison between traditional strategic transport modelling of intersections of a road network with the real-time observed and calculated data. The significance of this research is to evaluate the traditional strategic transport modelling on mesoscopic level, which able to study the network level impacts of smaller traffic engineering and traffic management interventions in a precise way. This research attempts to quantify the correlation between highway intersection capacity and traffic parameters, for this there is a need to determine the capacity values of different node types. This situation gives rise to a question that is dealt with in this research. "How different traffic parameters can be interrelated with the capacity of intersections by using different simulations?". This paper used assignment procedure, and usage of real-time data set with the calibration of Budapest Transport Model. Five contiguous intersections are selected on a small section of road network and traffic data was collected on them. The intersections are simulated by the assignment of intersection capacity analysis (ICA) method. During analysis of traffic parameters, the actual values of saturation flow rate are quite different from the base model value, which results in different capacities of all nodes. Comparisons have been made between the travel times at zones, links, and nodes' levels while each comparison demonstrates a huge difference in actual traffic survey data model and base model. It has been concluded from the analysis that Budapest base transport model is very far from reality. [ABSTRACT FROM AUTHOR]

This study focuses on fully investigating the effects of bicycle facilities and other contributing features on bicycle crashes at intersections. Although multiple studies have been conducted to analyze bicycle crashes and volumes, no study to date has attempted to integrate bicycle crashes and volumes at the modeling stage. Thus, a novel methodological framework for simultaneously analyzing bicycle crashes and volumes is proposed to better identify the effects of bicycle facilities and other contributing features. Kruskal–Wallis test is adopted to classify different bicycle lanes and shared paths for the modeling analysis. The modeling results reveal the significant effects of bicycle facility-related factors such as different types of bicycle lanes, the width of shared path, and the distance between the shared path and the edge of roadway on bicycle crashes. Other factors such as vehicle volume, bicycle volume, number of lanes, percentage of government and institutional land use, and population density are also found to significantly affect bicycle crashes at intersections. Based on the estimated model, the safety-in-numbers effect was identified when bicycle crash rates decrease as bicycle volumes increase. Findings in this study could assist traffic engineers to select appropriate designs to improve bicycle safety at intersections. [ABSTRACT FROM AUTHOR]

• Thirty autonomous vehicle (AV) intersection pre-crash scenarios were identified. • The proportion of AVs being rear-ended by conventional vehicles was 58.38%. • Contributing factors of AV scenarios were identified using association rules. • Key roadway factors were intersection-related area, signal control, and on-street parking. • Insufficient stop, deceleration, and collision avoidance decisions were important causes for AV crashes. Intersections are high-risk locations for autonomous vehicles (AVs). Crash causation analysis based on pre-crash scenarios can provide new insight into these crashes that can lead to effective countermeasures, but there are significant differences in pre-crash scenarios between autonomous and conventional vehicles, and inadequate AV data has put limits on research. The association rule method, however, can yield useful results despite these limits. This study therefore aims to use the method with pre-crash scenarios to understand the characteristics and contributing factors of AV crashes at intersections from the latest 5-year AV crash data. Analysis of 197 AV crashes at intersections revealed 30 types of pre-crash scenarios. The rear-end crash (58.88%) and lane change crash (16.24%) were the most frequently occurring scenarios for AVs. The proportion of AVs being rear-ended by conventional vehicles was 58.38%. The main contributing factors of these two most common AV scenarios were identified by association rules and crash causes were analyzed from the perspective of AV decision-making. The main factors contributing to the AV rear-end scenario were location outside the intersection in the intersection-related area, traffic signal control, autonomous engaged mode, mixed-use or public land, and weekdays, while those for lane change scenarios were on-street parking and the time of 8:00 a.m. Important causes of rear-end crashes attributable to the AV were inadequate stop and deceleration decisions by the AV's automated driving system (ADS) and insufficient collision avoidance decisions in lane change crashes. Identification of the pre-crash characteristics and contributing factors provide new insight into AV crash causation and can be used in the determination of the AV's operational design domain and the development and optimization of the AV's ADS at intersections. These findings can also play a role in guiding traffic safety agencies to discover AV hotspots and propose AV management regulations. [ABSTRACT FROM AUTHOR]

• Traffic signal optimization is integrated with a comprehensive and vehicle-level trajectory planning model for mixed traffic conditions. • The low-level vehicle trajectory planning model integrates both car following and lane changing maneuvers. • The high-level model optimizes traffic signal parameters, and arrival time and lane assignment of CAVs. • A customized solution algorithm is designed to solve the complex problem. • The proposed model shows great mobility and energy benefits over fixed-time and adaptive signal control strategies. Infrastructure-assisted cooperative driving, which consists of connected and automated vehicle (CAV) trajectory planning and traffic signal control, can greatly improve the efficiency of signalized intersection operations. In existing studies, the CAV trajectory planning problem is simplified to only modeling longitudinal vehicle behaviors or assuming the lane changing can be executed instantaneously. The generated CAV trajectories are not realistic and can't be implemented at the vehicle level. Instantaneous lane changing process may also lead to safety issues or uncomfortable driving behavior of following vehicles in a mixed traffic environment. To fill this research gap, we propose an optimization framework that integrates a full CAV trajectory planning model with traffic signal control in a cooperative driving environment with both CAVs and human-driven vehicles (HDVs). A two-level optimization model is formulated based on discrete time. The high-level model optimizes traffic signal parameters, CAV arrival time, and lane assignment at the stop bar, to minimize total vehicle delay. Given arrival time and lane assignment, the low-level model generates trajectories with integrated car-following and lane-changing maneuvers to mimic a human driving policy based on imitation learning. Numerical experiments from a real-world intersection show that the proposed model outperforms adaptive and fixed time signal control by as much as 63.06% in terms of reduction of average vehicle delay, due to better utilization of lane capacity and reduced lost time. In addition to mobility benefits, fuel consumption is also significantly reduced under the proposed infrastructure-assisted cooperative driving framework. [ABSTRACT FROM AUTHOR]

The advent of connected vehicles (CVs) has enabled the availability of richer and more accurate data for more flexible and sophisticated traffic signal control. However, the complexity of optimization models, especially when individual vehicle trajectories are considered, makes solving existing CV-based signal control methods difficult. This study proposes a computationally efficient and refined signal control method for isolated intersections in a CV environment. A mixed-integer nonlinear program model, which minimizes the total vehicle travel time, is developed by employing a simplified car-following model to predict individual vehicle trajectories. To address the computational concerns, the signal optimization model is reformulated by clustering incoming vehicles into platoons and analyzing the platoon features based on the interactions between vehicles as well as between vehicles and traffic signals. Simulation studies are conducted to compare the proposed method with the adaptive signal and vehicle-actuated control methods. The results show significant reductions in vehicle travel time and fuel consumption compared to benchmark methods under different demand levels. Furthermore, the proposed control method has high computational efficiency with a solving time of less than 0.05 s, validating its potential for practical applications. [ABSTRACT FROM AUTHOR]

The traffic capacity of signalized intersections can be increased using vehicle to infrastructure (V2I) communication by broadcasting traffic control signals to connected autonomous vehicles (CAVs). However, the potential improvement in traffic capacity with only CAVs can be substantially reduced under mixed traffic environments that include autonomous vehicles (AVs) without V2I communication. The main contribution of this work is to increase the traffic capacity (close to that achieved by CAVs with V2I communication) when there is a substantial number of AVs without V2I communication — by using a delayed-self-reinforcement (DSR) approach for vehicle spacing control with already available history of the AVs own control inputs and sensed information. Moreover, analytical estimates are established to predict the improvements in traffic capacity achieved for AVs with the DSR approach compared to the case without the DSR approach. Simulation results match the analytical predictions and show that AVs using the DSR approach (without V2I communication) are able to recover 88% of intersection capacity achieved with CAVs (with V2I communication) and result in 2.7 times the intersection capacity achieved by AVs without DSR on one way traffic. Moreover, similar improvements with DSR are shown for a real-world intersection using the Simulation of Urban MObility (SUMO) platform. AVs with DSR approach are able to recover 96% of intersection capacity achieved with CAVs (with V2I communication) and result in 1.4 times the intersection capacity achieved by AVs without DSR. Improvements using DSR are also seen with mixed traffic that includes human-driven vehicles (HDVs). [ABSTRACT FROM AUTHOR]

• Systematic review on urban traffic control under mixed traffic with CAVs. • Cover signal timing, CAV trajectory/route planning, and their cooperative control. • Summarize mixed traffic control methods at intersections with CAV-dedicated facilities. • Outline promising research directions and challenges. Efficient traffic control can alleviate traffic congestion, reduce fuel consumption, and improve traffic safety. With the development of communication and automation technologies, regular vehicles (RVs), connected vehicles (CVs), and connected and automated vehicles (CAVs) will coexist on urban roads in the near future. In this paper, we systematically review the studies on urban traffic control under the mixed traffic environment, which utilize CVs and CAVs to improve the performance of urban traffic control. We first give a glance at the evolution of urban traffic control with the deployment of CVs and CAVs to identify the important and promising research fields in the mixed traffic control. We then review the recent advances in traffic signal timing with CAV/CV trajectory data, CAV trajectory/route planning, and the cooperative control of CAV trajectories and traffic signals from the intersection level to the network level under the mixed traffic environment. Besides, we summarize two types of studies on mixed traffic control at intersections with CAV-dedicated facilities: 1) traffic signal timing and vehicle trajectory planning at intersections with CAV-dedicated lanes, and 2) deployment of CAV-dedicated facilities including CAV-dedicated lanes, links, and zones. Finally, the future research directions and challenges are discussed. We hope that this review could provide researchers with a helpful roadmap for future research on urban traffic control under the mixed traffic environment. [ABSTRACT FROM AUTHOR]

Urban signal intersection as the key node in the urban road network, its operational efficiency directly affects the overall operational efficiency of the road network traffic. The existing urban road signal intersections cannot be broadened due to the limitation of road size and boundary lines of road. Some signalized intersections adopt straight-right lane design to improve the traffic efficiency of vehicles at intersections. This design can improve the traffic efficiency of straight-through vehicles during the green light phase. However, straight-through vehicles stopping in the straight-right lane during the red light will prevent right-turning vehicles from passing the intersection. This phenomenon causes delays for right-turning vehicles. In order to deal with the above issues, this paper takes the concept of separating straight and right-turning vehicles in spatial and temporal on the dynamic straight-right lane (DSRL). The organization form of vehicles entering DSRL is designed based on signal phase time. The control strategy for DSRL with the interaction of traffic lights and pre-signal is formulated integrated with the intersection design. The delay indexes of straight and right-turning vehicles under DSRL design are quantified using cellular automata model to calculate vehicle delays under the design form of DSRL. Finally, the test platform based on the actual intersection is established with MATLAB to simulate the operation of the vehicles under the DSRL design. The experiment shows that the delay reduction degree of straight-through vehicles is between 1.2% and 4.5%, and the reduction degree of right-turning vehicles delay is more than 66%, that the operation efficiency of intersection is improved. • In terms of design concept, this paper proposes DSRL design scheme, which separates straight-through vehicles and right-turning vehicles from temporal and spatial. Right-turning vehicles are not delayed during the red phase while both straight and right-turning vehicles can successfully pass the intersection during the green phase under DSRL. The traffic efficiency of vehicles at the intersection can be greatly improved. • In terms of control method, the control strategy proposed in this paper is more flexible than the original variable lanes. This paper proposes pre-signal and traffic signal control scheme under DSRL. The calculation method of signal timing parameters and suggesting parameters of signal timing are given according to the ideal operating conditions of the traffic flow. The signal control strategy and DSRL design cooperating together can make traffic flow operate better and fully exploit the lane traffic capacity. • In terms of test results, this paper builds a microscopic model to evaluate the performance of the DSRL. By systematically analyzing key parameters, DSRL design can effectively reduce vehicle delays in different traffic environments. This paper also lays the foundation for optimizing the indicators under DSRL design and provides guidance for the implementation of DSRL design. [ABSTRACT FROM AUTHOR]

• A deep reinforcement learning based traffic signal model is proposed to control vehicles and pedestrians. • An extended reward function is designed to capture the delays due to the road users' interactions. • The baselines are adopted based on real-world traffic signal control logic, parameters, and data. • The effect of pedestrian jaywalking and traffic flow interruptions are evaluated in different scenarios. • The results have shown the superiority of the proposed model to improve total user delays. Deep reinforcement learning (RL) has been widely studied in traffic signal control. Despite the promising results that indicate the superiority of deep RL in terms of the quality of solution and optimality over fixed time signal control, the real-world multi-modal traffic flows, especially pedestrians, are not properly considered nor sufficiently investigated. This study presents a novel deep RL-based adaptive traffic signal model to control the vehicles and pedestrian flows by allocating an equitable green time to each, aiming at minimizing "total user delays" as opposed to "total vehicle delays" dominantly being used in the literature. Our proposed intelligent vehicle pedestrian light (IVPL) method can perform in the absence or presence of pedestrians, especially when there is jaywalking at the intersection, interrupting vehicle flows. To this end, an extended reward function is designed to capture delays due to vehicle-to-vehicle, vehicle-to-pedestrian, and pedestrian-to-pedestrian interactions, as well as red-light delays for vehicles and pedestrians. To evaluate the performance of IVPL, a microsimulation model of an intersection in city of Melbourne is used as a case-study. The real traffic signal parameters of an existing operation system (SCATS) are employed, and the simulation is calibrated using video-based camera data and loop detectors data collected at intersection. The experimental results demonstrate the superiority of the proposed model over fully actuated traffic signal, not only in terms of the quality of optimal solution, but also considering the fact that the proposed model can minimize the "total user delays". [ABSTRACT FROM AUTHOR]

• A risk field model independent on yellow duration is proposed to quantify the risk constraints of traffic lights on vehicle movement. • A driving behavior model framework is established to explain the approaching behaviors to signalized intersections from the perspective of human behavioral mechanism. • By considering drivers' desired risks, the probability of passing the stop line during yellow period is obtained by simulation and compared with that calculated by existing models. • Considerations regarding the superiority of modeling based on human behavioral mechanisms compared to data-driven modeling are presented. The stop/go decisions made by drivers who are approaching signalized intersections during yellow period will affect the safety and efficiency of intersections. Existing research mostly modeled drivers' decision-making behaviors using real-world driving data, while these datasets were collected in different traffic flows and road environments, and it is difficult to develop models suitable for different intersections. Aiming at explaining the approaching behaviors to signalized intersections from the perspective of human behavioral mechanism, this study establishes a driving behavior model framework, including a risk field model of dynamic traffic control elements independent on yellow duration, and a trajectory planning model constructed according to the risk homeostasis theory and preview-follower theory. Probabilities of passing the stop line during yellow period and the distribution of acceleration and deceleration rates when passing are obtained in the simulation by the Monte Carlo method. Results show the validity of the proposed model and its applicability to drivers with different desired risks. Compared to the proposed model, drivers are more inclined to use smaller acceleration rates or greater deceleration rates when entering intersections in observed cases. The intervention of reaction time may decrease the probabilities of passing. This study is an indispensable supplement to our previous study, contributing a unified model based on risk quantification to comprehensively describe the risk of the traffic environment, and is an attempt to promote the development of driving behavior models. [ABSTRACT FROM AUTHOR]

• Various traffic noise prediction models for roundabouts and signalized intersections were developed. • The models were based on the calculation of road traffic noise (CORTN) method. • The general and type-specific noise models for intersections of different sizes were compared. • The intersection type-specific models fit the measured data better than the general models. Accurate transportation noise prediction models are needed by governments and researchers to predict traffic noise levels originating from roadways. This study aims to develop traffic noise prediction models appropriate for roundabouts and signalized intersections of different sizes. Noise measurements , traffic volumes, and different site characteristics were collected at eight two-lane and three-lane roundabouts and signalized intersections. A set of customized general and type-specific noise prediction models were developed and compared. The models were based on the calculation of road traffic noise (CORTN) method. The results indicated that intersection type-specific models fit the measured data for the different intersection types better than general models for the studied conditions. Using these models is expected to be advantageous in terms of predicting traffic noise at roundabouts and signalized intersections. The proposed models could help governments, policymakers, traffic engineers, and urban planners assess and implement necessary noise management and mitigation strategies. [ABSTRACT FROM AUTHOR]

• Develop a three-level model to integrate signal timing and CAV trajectory planning. • Left-turn and through CAVs share CAV-dedicated lanes with trajectory planning. • CAVs cross the intersection during shared phases with HVs. • Propose a repeated-barrier-group cycle structure in signal timing. • Design a rolling-horizon scheme to dynamically apply the model with varying traffic. Connected and automated vehicles (CAVs) and human-driven vehicles (HVs) are expected to coexist in the near future. CAV-dedicated lanes and phases have been explored to handle the uncertainty in the driving behavior of HVs in the mixed traffic environment. However, CAV-dedicated phases could significantly sacrifice HV benefits. This study proposes a shared-phase-dedicated-lane (SPDL)-based traffic control model at an isolated intersection with CAV dedicated lanes in the mixed traffic environment. In the proposed model, left-turn and through CAVs share CAV-dedicated lanes and cross the intersection during the shared phases with HVs. A three-level optimization model is developed. At the upper level, a standard NEMA (National Electrical Manufacturers Association) ring barrier structure is used for the signal optimization and barrier durations are optimized by dynamic programming to minimize the total vehicle delay. At the middle level, phase sequences and phase durations are optimized by enumeration for the given barrier from the upper level and the minimum vehicle delay is fed to the upper level. At the lower level, CAV platooning in the buffer zone and trajectory planning in the passing zone are conducted based on the signal timings of the barrier from the middle level and the travel time of CAVs is fed to the middle level. A rolling-horizon scheme is designed for the dynamical implementation of the proposed model with time-varying traffic conditions. The proposed model is applicable to intersections with more than two lanes in each approach and without shared lanes by left-turn and through HVs, which are commonly seen critical intersections in urban traffic control. Numerical studies validate the advantages of the SPDL-based control over the blue-phase-based control in previous studies in terms of average vehicle delay and intersection capacity. Further, the SPDL-based model is extended to serve as an alternative approach without the buffer zone. [ABSTRACT FROM AUTHOR]