Modeling and Simulation on Left-Turn Waiting Area at Isolated Signalized Intersection

Through movement capacity is an essential factor used to reflect intersection performance, especially for signalized intersections, where a large proportion of vehicle demand is making through movements. Generally, left-turn spillback is considered a key contributor to affect through movement capacity, and blockage to the left-turn bay is known to decrease left-turn capacity. Previous studies have focused primarily on estimating the through movement capacity under a lagging protected only left-turn (lagging POLT) signal setting, as a left-turn spillback is more likely to happen under such a condition. However, previous studies contained assumptions (e.g., omit spillback), or were dedicated to one specific signal setting. Therefore, in this study, through movement capacity models based on probabilistic modeling of spillback and blockage scenarios are established under four different signal settings (i.e., leading protected only left-turn [leading POLT], lagging left-turn, protected plus permitted left-turn, and permitted plus protected left-turn). Through microscopic simulations, the proposed models are validated, and compared with existing capacity models and the one in the Highway Capacity Manual (HCM). The results of the comparisons demonstrate that the proposed models achieved significant advantages over all the other models and obtained high accuracies in all signal settings. Each proposed model for a given signal setting maintains consistent accuracy across various left-turn bay lengths. The proposed models of this study have the potential to serve as useful tools, for practicing transportation engineers, when determining the appropriate length of a left-turn bay with the consideration of spillback and blockage, and the adequate cycle length with a given bay length.

Download Full-text

Deep Reinforcement Learning Based Left-Turn Connected and Automated Vehicle Control at Signalized Intersection in Vehicle-to-Infrastructure Environment

Information ◽

10.3390/info11020077 ◽

2020 ◽

Vol 11 (2) ◽

pp. 77 ◽

Cited By ~ 1

Author(s):

Juan Chen ◽

Zhengxuan Xue ◽

Daiqian Fan

Keyword(s):

Reinforcement Learning ◽

Control Method ◽

Signalized Intersection ◽

Signal Control ◽

Left Turn ◽

Automated Vehicle ◽

Whole Process ◽

Policy Gradient ◽

Experience Replay ◽

Automated Vehicle Control

In order to solve the problem of vehicle delay caused by stops at signalized intersections, a micro-control method of a left-turning connected and automated vehicle (CAV) based on an improved deep deterministic policy gradient (DDPG) is designed in this paper. In this paper, the micro-control of the whole process of a left-turn vehicle approaching, entering, and leaving a signalized intersection is considered. In addition, in order to solve the problems of low sampling efficiency and overestimation of the critic network of the DDPG algorithm, a positive and negative reward experience replay buffer sampling mechanism and multi-critic network structure are adopted in the DDPG algorithm in this paper. Finally, the effectiveness of the signal control method, six DDPG-based methods (DDPG, PNRERB-1C-DDPG, PNRERB-3C-DDPG, PNRERB-5C-DDPG, PNRERB-5CNG-DDPG, and PNRERB-7C-DDPG), and four DQN-based methods (DQN, Dueling DQN, Double DQN, and Prioritized Replay DQN) are verified under 0.2, 0.5, and 0.7 saturation degrees of left-turning vehicles at a signalized intersection within a VISSIM simulation environment. The results show that the proposed deep reinforcement learning method can get a number of stops benefits ranging from 5% to 94%, stop time benefits ranging from 1% to 99%, and delay benefits ranging from −17% to 93%, respectively compared with the traditional signal control method.

Download Full-text

The Release Characteristics and Selection Model for Vehicle Lane Changing of the Quadruple Left-Turn Traffic Flow at Signalized Intersection

2018 IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE) ◽

10.1109/ccsse.2018.8724805 ◽

2018 ◽

Author(s):

Yan Jiang

Keyword(s):

Traffic Flow ◽

Selection Model ◽

Signalized Intersection ◽

Lane Changing ◽

Left Turn

Download Full-text

Control Method for Signalized Intersection with Integrated Waiting Area

Applied Sciences ◽

10.3390/app9050968 ◽

2019 ◽

Vol 9 (5) ◽

pp. 968 ◽

Cited By ~ 4

Author(s):

Xiaomei Xia ◽

Xiaodan Ma ◽

Jin Wang

Keyword(s):

Traffic Congestion ◽

Control Method ◽

Heavy Traffic ◽

Control Process ◽

Simulation Software ◽

Signalized Intersection ◽

Traffic Demand ◽

Key Issues ◽

Waiting Area ◽

Implementation Effect

To alleviate traffic congestion in the city, an integrated waiting area is introduced to the signalized intersection in this paper. After the design idea and the typical form of the integrated waiting area is proposed, the control method at the signalized intersection is discussed. The coordination control process of the main and pre-signal at the signalized intersection with the integrated waiting area is analyzed and modeled. To assess the operational performance of the integrated waiting area at intersections, a microscopic traffic simulation software (VISSIM) is utilized to simulate intersections with and without integrated waiting areas. Key issues concerning signal timing plans are then discussed. With comparisons between the operation of intersections with and without integrated waiting areas, the implementation effect is quantified based on the statistical data of the simulation result. The results confirm the potential benefits of the integrated waiting areas at the signalized intersections and show that integrated waiting areas work best in heavy traffic demand.

Download Full-text

Car-following Behavior Analysis of Left-turn Vehicles at Signalized Intersections

Civil Engineering Journal ◽

10.28991/cej-2020-03091463 ◽

2020 ◽

Vol 6 (1) ◽

pp. 186-193 ◽

Cited By ~ 1

Author(s):

Fulu Wei ◽

Long Chen ◽

Yongqing Guo ◽

Mingtao Chen ◽

Jiaxiang Ma

Keyword(s):

Field Data ◽

Signalized Intersections ◽

Signalized Intersection ◽

Velocity Difference ◽

Left Turn ◽

Average Speed ◽

Car Following ◽

Turning Radius ◽

The Stability ◽

Stability And Accuracy

In order to enrich the car-following theory of urban signalized intersections, and reveal the car-following characteristics of left turn at signalized intersections, the car-following behavior of left turn at signalized intersections is studied. The car-following data acquisition test which was based on high precision GPS was designed. And the car-following characteristics of left-turning vehicles at signalized intersections with different turning radii were analyzed. Based on which, the influence of radius on the car-following behavior was explained, and the New Full Velocity Difference (NFVD) model was developed. The genetic algorithm was used to calibrate the parameters of the NFVD model. The stability and accuracy of the calibrated model was further analyzed by using field data. The results showed that the average speed of the following car increases with the turning radius of the signalized intersection; the car-following speed which the highest frequency occurs under different turning radii tends to increase with the enlargement of turning radius; the larger the average headway distance between the car-following vehicles, the more intense of the driver’s response to the deceleration of the front vehicle. These findings could be used in traffic simulation and to make engineering decisions.

Download Full-text

Before–After Evaluation of Left-Turn Lane Extension Considering Injury Severity and Collision Type

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120920270 ◽

2020 ◽

Vol 2674 (8) ◽

pp. 67-77

Author(s):

Yanyong Guo ◽

Tarek Sayed

Keyword(s):

Injury Severity ◽

Treatment Effectiveness ◽

Signalized Intersection ◽

Left Turn ◽

Remarkable Improvement ◽

Intervention Models ◽

Collision Type ◽

Effectiveness Index ◽

The City ◽

Vehicle Deceleration

Left-turn lanes are commonly used to provide space to accommodate vehicle deceleration and provide adequate storage of turning vehicles. The objective of this study is to evaluate the safety effectiveness of extending the length of left-turn lanes at signalized intersection approaches. Five years of collision data including injury severity and collision type from three treatment sites and 31 comparison sites in the City of Surrey, Canada were used in the study. The analysis focused on target crashes including left-turn-related rear-end and left-turn-related sideswipe collisions. A full Bayesian (FB) before–after analysis was conducted for all collisions, severity levels, and collision types. Multivariate Poisson–lognormal linear intervention models were used for the analysis. The treatment effectiveness index was calculated to quantitatively measure the effectiveness of the safety treatment. The FB before–after results showed that the treatment-related collisions were reduced by 57.4% following the implementation of extended left-turn lane. The reduction in injuries and fatalities collisions (63.8%) was greater than that in property damage only collisions (55.7%). The decrease in rear-end collisions (62.8%) was greater than that in sideswipe collisions (58.1%). The findings indicate a remarkable improvement in safety after the length extension of the left-turn lane.

Download Full-text