Intersection Signal Control Research Articles

Investigation of the effect of autonomous vehicles (AV) on the capacity of an urban transport network

In this paper, we assess the effects of different shares of autonomous vehicles (AVs) on the traffic flow and, in particular, on the maximum possible capacity at signal-controlled intersections. For this purpose, all signal-controlled nodes in the traffic network of the Düsseldorf metropolitan area were systematically simulated and evaluated using the microscopic traffic simulation tool SUMO.The analysis shows that defensively parameterized AVs – as envisaged in the umbrella project of this research – may decrease the maximum possible traffic at signal-controlled intersections. Moreover, the simulation runs indicate that capacity at these intersections decreases almost linearly with a growing share of AV. In a second part of this analysis, a freeway section was simulated with the same varying shares of CV and AV to investigate free-flow traffic. In this case, the simulation results of the maximum traffic flow can be approximated by a third-order polynomial fit. The minimum capacity is found for the uniform share of both vehicle types (i.e. 50 % AV and 50 % CV).The overall intent of this project is to provide an approach to determine system-wide and long-term effects of AVs from local microscopic observations. To this end, the SUMO microscopic traffic simulation will be utilized to derive realistic flow capacities for signal-controlled intersections. In a next step, these capacities will be transferred to a mesoscopic traffic simulation. Subsequently, flow capacities can be systematically adjusted in this network-wide mobility simulation to parameterize the influence of future infrastructure and vehicle technologies.

Vehicle Delay Analysis at Signalized Intersection

Traffic delay is an important measure to evaluate road operation efficiency and service level, which can reflect the rationality of road design and intersection signal control scheme design. Therefore, delay analysis is of great significance to the evaluation of road service level and intersection signal control scheme design. In this paper, the point sample method and VISSIM simulation software are used to study the traffic delay of Yingbin Road and Century Road intersection in Jiaozuo City, and the traffic condition of the intersection is evaluated according to the calculation results. In this paper, the point sample method and VISSIM simulation software are used to study the traffic delay of Yingbin Road and Century Road intersection in Jiaozuo City, and the traffic condition of the intersection is evaluated according to the calculation results.

Evaluating the Sustainable Traffic Flow Operational Features of U-turn Design with Advance Left Turn

Median U-turn intersection treatment (MUIT) has been considered as an alternative measure to reduce congestion and traffic conflict at intersection areas, but the required spacing between the U-turn opening and the intersection limits its applicability. In this paper, a U-turn design with Advance Left Turn (UALT) is proposed with the aim of addressing the disadvantages of insufficient intersection spacing and difficulty in the continuous vehicle lane change. UALT provides a dedicated lane to advance the turning vehicle out of the intersection and directly to the U-turn opening without interacting with through traffic. The effectiveness and traffic volume applicability of UALT was demonstrated through field data investigation, simulation and analysis with VISSIM software. The proposed design was evaluated in terms of three parameters: delay, queue length and the number of stops. The results show that when the traffic volume range of the main road is (1900, 2200) pcu/h and the traffic volume of the secondary road is more than 900 pcu/h, the optimization effect of UALT on both conventional intersections and MUIT is very significant. Taking a signal-controlled intersection in Zhengzhou City, China, as an example to build a simulation model, compared with the conventional intersection and MUIT, the delay drop is reduced by 73.48% and 41.48%, the queue length is reduced by 84.85% and 41.66%, and the operation efficiency is significantly improved.

A Simulation Approach for Evaluating Congestion and Its Mitigation Measures on Urban Arterials Operating with Mixed Traffic Conditions

Present study evaluates the various congestion indexes in terms of speed performance index, volume to capacity ratio and congestion index based on travel time on the urban roadway network of 5.7 km in length, operating with mixed traffic conditions (traffic with varying static and dynamic characteristics of vehicles). The mitigations measures have been proposed to cater to the present traffic demand by converting the uncontrolled intersection to a partially controlled or signal-controlled intersection. In order to check the effectiveness of such mitigation measures, various scenarios have been generated in the developed and a well calibrated simulation model. It has also been observed that the speed performance index has improved by 8.88 percent while providing channelized intersection at one of the point elements and 11.52 %when the two-point elements have converted to channelizing intersection.

Evaluation of driver Acceleration/Deceleration behavior at signalized intersections using vehicle trajectory data

ABSTRACT Vehicle Acceleration/Deceleration (A/D) characteristics are fundamental measures often used in designing intersection geometrics, A/D lanes, analyzing vehicle fuel consumption and emission, and traffic simulation modeling. The flow scenario at a signal-controlled intersection involves stop-and-go conditions with variability of A/D characteristics. This study attempts to analyze the vehicle’s A/D behavior at an urban signalized intersection under weak lane disciplined mixed traffic stream using vehicle trajectory data. The result shows that vehicles like car and motorized two-wheeler are associated with high acceleration behavior at lower speeds and need less time and distance to accelerate. Also, these vehicles can decelerate at a higher rate at a lower speed during the stop or clearing of the intersection. The statistical results show that a single and multi-regime polynomial model is more suitable to represent the behavior of A/D characteristics in terms of the approach speed of vehicles.

Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach

This manuscript presents an Adam optimization-based Deep Reinforcement Learning model for Mixed Traffic Flow control (ADRL-MTF), so as to guide Connected and Autonomous vehicle’s (CAV) longitudinal trajectory on a typical urban roadway with signal-controlled intersections. Two improvements are made when compared with prior literatures. First, the common assumptions to simplify the problem solving, such as dividing a vehicle trajectory into several segments with constant acceleration/deceleration, are avoided, to improve the modeling realism. Second, built on the efficient Adam Optimization and Deep Q-Learning, the proposed model avoids the enumeration of states and actions, and is computational efficient and suitable for real time applications. The mixed traffic flow dynamic is firstly formulated as a finite Markov decision process (MDP) model. Due to the discretization of time, space and speed, this MDP model becomes high-dimensional in state, and is very challenging to solve. We then propose a temporal difference-based deep reinforcement learning approach, with -greedy for exploration-exploitation balance. Two neural networks are developed to replace the traditional Q function and generate the targets in the Q-learning update. These two neural networks are trained by the Adam optimization algorithm, which extends stochastic gradient descent and considers the second moments of the gradients, and is thus highly computational efficient and has lower memory requirements. The proposed model is shown to reduce fuel consumption by 7.8%, which outperforms a prior benchmark model based on Monte Carlo Tree Search. The model’s runtime efficiency and stability are tested, and the sensitivity analysis is also performed.

Signalized Intersection Control in Mixed Autonomous and Regular Vehicles Traffic Environment—A Critical Review Focusing on Future Control

The recent advancement in industrial technology has offered new opportunities to overcome different problems of stochastic driving behavior of humans through effective implementation of autonomous vehicles (AVs). Optimum utilization of driving behavior and advanced capabilities of the AVs has enabled researchers to propose autonomous cooperative-based methods for signalized intersection control under an AV traffic environment. In the future, AVs will share road networks with regular vehicles (RVs), representing a dynamic mixed traffic environment of two groups of vehicles with different characteristics. Without compromising the safety and level of service, traffic operation and control of such a complex environment is a challenging task. The current study includes a comprehensive review focused on the signalized intersection control methods under a mixed traffic environment. The different proposed methods in the literature are based on certain assumptions, requirements, and constraints mainly associated with traffic composition, connectivity, road infrastructures, intersection, and functional network design. Therefore, these methods should be evaluated with appropriate consideration of the underlying assumptions and limitations. This study concludes that the application of adaptive traffic signal control can effectively optimize traffic signal plans for variations of AV traffic environments. However, artificial intelligence approaches primarily focusing on reinforcement learning should be considered to better utilization of the improved AV characteristics.

Development of Reliable Models of Signal-Controlled Intersections

Abstract The paper considers an approach to building various mathematical models for homogeneous groups of intersections manifested through the use of clustering methods. This is because of a significant spread in their traffic capacity, as well as the influence of several random factors. The initial data on the traffic flow of many intersections was obtained from real-time recorders of the convolutional neural network. As a result of the analysis, we revealed statistically significant differences between the groups of intersections and compiled their linear regression models as a basis for the subsequent formation of generic management decisions. To demonstrate visually the influence of random factors on the traffic capacity of intersections, we built distribution fields based on the fuzzy logic methods for one of the clusters consisting of 14 homogeneous intersections. Modeling was based on the Gaussian type of membership functions as it most fully reflects the random nature of the pedestrian flow and its discontinuity.

Collaborative control of traffic signal and variable guiding lane for isolated intersection under connected and automated vehicle environment

AbstractExisting 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.

Crash injury severity analysis of E-Bike Riders: A random parameters generalized ordered probit model with heterogeneity in means

Electric bike (E-bike) fatal crashes have increased by 34% during the period between 2014 and 2016, raising a great challenge for traffic safety in China. This study examines the effects of road traffic characteristics, environmental factors, crash characteristics and rider demographic factors on e-bike riders' injury severity. A total of 2222 police-reported crash records of e-bike riders in a representative developing area in China-Hunan province from 2014 to 2016 is used for the current study. To account for the ordinal nature of crash severity and to incorporate unobserved heterogeneity at the observation level, a random parameters generalized ordered probit model with heterogeneity in means (RGOP-HM) is applied for the injury severity analysis. For examining the efficiency of the proposed model in modeling e-bike rider injury severity, ordered probit models, random parameters ordered probit models, random parameters generalized ordered probit models were also estimated. The superiority of RGOP-HM in terms of model fitness statistics indicates the importance of relaxing the limitations of traditional ordinal probability methods. The results of RGOP-HM revealed a wide range of factors associated with the e-bike injuries, including horizontal curves, roads with a high posted speed limit, traffic sign-controlled intersections, dim light, unlighted darkness, single-vehicle crashes, collisions with a heavy motorized vehicle, rider age over 44 (45–59, above 59), and rural areas. Based on the factors contributing to the increased injury severity, several safety implications are proposed from the perspective of engineering, education, and enforcement (3E). This study's findings could provide references for the development of targeted countermeasures to improve e-bike traffic safety in China.

Predicting the Traffic Capacity of an Intersection Using Fuzzy Logic and Computer Vision

This paper presents the application of simulation to assess and predict the influence of random factors of pedestrian flow and its continuity on the traffic capacity of a signal-controlled intersection during a right turn. The data were collected from the surveillance cameras of 25 signal-controlled intersections in the city of Chelyabinsk, Russia, and interpreted by a neural network. We considered the influence of both the intensity of the pedestrian flow and its continuity on the traffic capacity of a signal-controlled intersection in the stochastic approach, provided that the flow of vehicles is redundant. We used a reasonably minimized regression model as the basis for our intersection models. At the first stage, we obtained and tested a simulated continuous-stochastic intersection model that accounts for the dynamics of traffic flow. The second approach, due to the unpredictability of pedestrian flow, used a relevant method for analysing traffic flows based on the fuzzy logic theory. The second was also used as the foundation to build and graphically demonstrate a computer model in the fuzzy TECH suite for predictive visualization of the values of a traffic flow crossing a signal-controlled intersection. The results of this study can contribute to understanding the real conditions at a signal-controlled intersection and making grounded decisions on its focused control.

Behaviour Analysis of Left-Turning Mopeds at Signal Controlled Intersections – A Case Study in Yancheng City

Mopeds (electric bicycles and light motorcycles) are commonly used as a personal transportation mode in China. However, the understanding of characteristics of left-turning mopeds at signal-controlled intersections has been relatively limited. To bridge this gap, firstly, this paper proposed a video conversion method of moped movement data acquisition. Then, a method of data accuracy verification was introduced by comparing the results between the field experiment and the video conversion method. Secondly, the ideal traffic space for left-turn mopeds from different entrances was defined to analyse the characteristics of the left-turning mopeds at intersections. Further, three indicators, namely, transverse distance, the proportion of left-turning mopeds with crossing behaviour, and the average number of avoidance behaviour, were proposed and used to analyse the asymmetrical characteristics behaviour, crossing behaviour, and avoidance behaviour. Finally, based on empirical data collected from five signal-controlled intersections, the proposed methods and behaviours were analysed. This paper provides both a valid method of obtaining the position data of mopeds and a reliable basis for improving the safety of left-turning moped riders at urban signal-controlled intersections.

Smart Cities and Data Analytics for Intelligent Transportation Systems: An Analytical Model for Scheduling Phases and Traffic Lights at Signalized Intersections

This paper is intended to improve the performance of signalized intersections, one of the most important systems of traffic control explained within the scope of smart transportation systems. These structures, which have the main role in ensuring the order and flow of traffic, are alternative systems depending on the different methods and techniques used. Determining the operation principles of these systems requires an extremely careful and planned study, considering their important role. Performance outputs obtained from the queue analyses made in previous studies formed the input of this study. The most important techniques are used in the effective control of intersections, such as signal timing: in particular, the use of effective green time and order of the transitions between phases. In this research, a traffic-sensitive signalized intersection control system was designed with the suggested methods against these two problems. The sample intersections were selected from three cities with the highest population density as the case study area. In the analysis of the performance of the connection arms of the selected intersections, flow intensity data were taken into consideration, as well as the arrival and service rates. Based on this, the outputs of the two proposed models regarding the use of phase transitions and effective green durations were compared with two other adaptive control systems and their positive results were shared. The results showed that signalized intersections, operating with a well-planned and correctly chosen technique, better regulate density and queuing.

Design of On-line Simulation System for Signal Control of Urban Intersections Based on Visual Sensing Technology

Intersection area is the area with the most serious conflicts and accidents in the whole urban traffic system. Therefore, it is of great significance to combine modern computer technology, sensing technology and communication technology to intelligently control intersections, improve the efficiency of traffic signal control and reduce the delay time in conflict areas. Starting with video detection and using deep learning in the detection module, a video detection mechanism based on restricted Boltzmann machine network is proposed. Based on C # programming language, the intersection traffic signal simulation module is designed through COM interface provided by traffic simulation software VISSIM, which realizes road network import, parameter setting, signal timing change, simulation operation and simulation result output. Numerical experiments show that fuzzy control method with self-correcting factor is superior to timing control method and classical fuzzy control method in average delay time of vehicles.

Exploring the effects of connected and automated vehicles at fixed and actuated signalized intersections with different market penetration rates

ABSTRACT To investigate the effects of different market penetration rates (MPRs) of intelligent vehicles – Intelligent Driving Model (IDM) for autonomous vehicles (AVs), Adaptive Cruise Control (ACC) for AVs, and Cooperative Adaptive Cruise Control (CACC) for connected and automated vehicles (CAVs) – in mixed traffic flows with human driving vehicles (HDVs) at intersections, three signalized intersections (fixed signal, gap-based actuated signal, and delay-based actuated signal-controlled intersections) with low, medium, and high traffic demands are investigated. The simulation results indicate that CAVs with the CACC system outperform AVs with ACC or IDM systems and could reduce the average delay under low and high demand scenarios by 49% to 96%. CAVs with the CACC system could also significantly reduce average delay with a 20% MPR, while significant drops could only be observed after 60% and 80% MPRs for AVs with the ACC/IDM system. Gap-based and delay-based actuated signal control schemes are preferred under medium traffic flow demand, and CACC/ACC systems could significantly improve the performance of actuated signal-controlled intersections under high traffic flow demand.

Enhancing Mixed Traffic Flow Safety via Connected and Autonomous Vehicle Trajectory Planning with a Reinforcement Learning Approach

The longitudinal trajectory planning of connected and autonomous vehicle (CAV) has been widely studied in the literature to reduce travel time or fuel consumptions. The safety impact of CAV trajectory planning to the mixed traffic flow with both CAV and human-driven vehicle (HDV), however, is not well understood yet. This study presents a reinforcement learning modeling approach, named Monte Carlo tree search-based autonomous vehicle safety algorithm, or MCTS-AVS, to optimize the safety of mixed traffic flow, on a one-lane roadway with signalized intersection control. Crash potential index (CPI) is defined to quantitively measure the safety performance of the mixed traffic flow. The CAV trajectory planning problem is firstly formulated as an optimization model; then, the solution procedure based on reinforcement learning is proposed. The tree-expansion determination module and rollout termination module are developed to identify and reduce the unnecessary tree expansion, so as to train the model more efficiently towards the desired direction. The case study results showed that the proposed algorithm was able to reduce the CPI by 76.56%, when compared with a benchmark model without any intelligence, and 12.08%, when compared with another benchmark model that the team developed earlier. These results demonstrated the satisfactory performance of the proposed algorithm in enhancing the safety of the mixed traffic flow.

Global sensitivity analysis of VISSIM parameters for project-level traffic emissions: a case study at a signalized intersection

ABSTRACT Combining traffic micro-simulation models and emission models is a primary method to estimate traffic emissions. However, there has been limited research on the impact of traffic micro-simulation model parameters on simulator outputs, which are critical to emissions calculations. Based on combining VISSIM and MOVES, this study uses the Morris and Sobol methods to explore the impact of parameters in VISSIM on operating mode distribution and travel time distribution. Taking an urban signal control intersection with three traffic scenarios in Chengdu, China as an example, this study verifies the methods’ feasibility. Apart from these parameters, which have been proved to be necessary calibration parameters, including the desired speed distribution, the desired acceleration function, and the desired deceleration function, an additional 24 parameters related to simulation setting and driving behaviour models are selected as the initial parameters. The number of interaction objects, maximum look-ahead distance, average standstill distance, additive part of safety distance, and safety distance reduction factor close to a stop line, are considered to be the important parameters for this case study. The impact of these five parameters on the bins of operating mode distribution and travel time distribution are further analyzed with One-at-a-time, and these parameters are compared with those reported in previous studies. It is concluded that the important parameters selected in this study are reasonable and can support the calibration of VISSIM parameters for this case’s traffic emissions.

A Study on Possibility of Predictive Deep Reinforcement Learners for Isolated Intersection Signal Control

A Study on Possibility of Predictive Deep Reinforcement Learners for Isolated Intersection Signal Control

A Back-of-Queue Model of a Signal-Controlled Intersection Approach Developed Based on Analysis of Vehicle Driver Behavior

In smart cities, it is expected that transport, communication as well as the movement of people and goods will take place in the shortest possible time while maintaining a high level of safety. In recent years, due to the significant increase in the number of passengers and vehicles on the road and the capacity limitations of transport networks, it has become necessary to use new technologies for intelligent control and traffic management. Intelligent transport systems use advanced technologies in the field of data gathering, information processing, and traffic control to meet current transport needs. To be able to effectively control and manage road traffic, it is necessary to have reliable mathematical models that allow for a faithful representation of the real traffic conditions. Models of this type are usually the basis of complex algorithms used in practice in road traffic control. The application of appropriate models reflecting the behavior of road users contributes to the reduction of congestion, the vehicles travel time on the transport network, fuel consumption and the emissions, which in turn support broadly understood energy savings. The article proposes a model that allows for the estimation of the maximum queue size at the signal-controlled intersection approach (so-called: maximum back-of-queue). This model takes into account the most important traffic characteristics of the vehicles forming this queue. The verification allowed for the conclusion that the proposed model is characterized by high compliance with the actual traffic and road conditions at the intersections with signal controllers located in built-up areas in Poland. The obtained compliance confirms the possibility of using the model for practical applications in calculating the maximum back-of-queue at signal-controlled intersections located in built-up areas in Poland.

A cooperative driving framework for urban arterials in mixed traffic conditions

Enabling technologies of connected and automated vehicles (CAVs) bring new opportunities to signalized intersection control. CAVs not only provide a new source of data for traffic management but also can be controlled as actuators to improve traffic flow. This study proposes a hierarchical and implementation-ready cooperative driving framework with a mixed traffic composition of CAVs, connected vehicles (CVs), and regular vehicles (RVs) for urban arterials. The proposed framework combines centralized and distributed control concepts, where the infrastructure generates optimal signal timing plans and provides high-level trajectory guidance to the CAVs while detailed trajectories are generated by each vehicle. The system consists of three levels of models. At the vehicle level, a state transition diagram is designed for different modes of operations of CAVs including eco-trajectory planning, cooperative adaptive cruise control (CACC) and collision avoidance. At the intersection level, a mixed-integer linear programming (MILP) problem is formulated to optimize the signal timing plan and arrival time of CAVs, with consideration of CACC platooning behaviors. At the corridor level, link performance functions are applied to calculate the total delay of the coordinated phases of each intersection, and a linear programming (LP) problem is formulated to optimize the offsets for every cycle, which are then passed to the intersection level. Simulation results from a calibrated real-world arterial corridor show that both mobility and fuel economy benefits from the cooperative driving framework. The total delay is reduced by 2.2%−33.0% and fuel consumption by 3.9%−7.4%, with different mixture of vehicle compositions and CAV penetration rates (e.g., 0%−100%). Sensitivity analysis on volume fluctuation is performed, which confirms the benefits of the dynamic offset optimization.