scholarly journals Bus Priority Signal Control Considering Delays of Passengers and Pedestrians of Adjacent Intersections

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Jiali Li ◽  
Yugang Liu ◽  
Hongtai Yang ◽  
Bin Chen
Keyword(s):  
Traffic Flow ◽  
Coordinated Control ◽  
Signal Control ◽  
Signal Timing ◽  
Bus Priority ◽  
Total Delay ◽  
Phase Extension ◽  
Pedestrian Delays ◽  
Adjustment Scheme ◽  
Optimal Signal

In this paper, a bus priority signal control (BPSC) method based on delays of passengers and pedestrians at adjacent intersections, is proposed. The influences of BPSC on passenger and pedestrian delay at adjacent intersections under the condition of coordinated control of green waves are studied. The implementation of BPSC at intersections not only reduces the delay of bus passengers, social vehicle passengers and pedestrians, but also improves the traffic flow of priority buses and social vehicles at downstream intersections. This study takes the green phase extension as an example of the active BPSC strategy, and analyzes three cases of priority vehicles reaching downstream intersection. Firstly, passenger and pedestrian delays at adjacent intersections are calculated under different traffic situations. Secondly, models with the goal of maximizing the reduced total delays are established. Thirdly, three algorithms are used to solve the problem to obtain the optimal signal timing adjustment scheme at upstream intersections. Ultimately, the result shows that the BPSC can effectively reduce pedestrian delays at intersections, protect the rights and interests of pedestrians, reduce the delays of priority vehicles, and maximize the reduced total delay.

scholarly journals Bilevel Programming for Traffic Signal Coordinated Control considering Pedestrian Crossing

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Dawei Li ◽  
Yuchen Song ◽  
Qiong Chen
Keyword(s):  
Traffic Flow ◽  
Bilevel Programming ◽  
Motor Vehicle ◽  
Rapid Development ◽  
Coordinated Control ◽  
Traffic Signal ◽  
Motor Vehicles ◽  
Traffic Efficiency ◽  
Pedestrian Delays ◽  
Pedestrian Crossing

With the rapid development of the subway, more and more people choose it as the main method of transportation. However, practically, the large number of pedestrians near some large metro stations can also correspondingly affect the traffic of motor vehicles on the roads adjacent to the stations. In this study, coordinated control of the traffic signal which considers the pedestrian crossing delay is studied based on this background. Firstly, the model of progression band in adjacent intersections is analyzed comprehensively, and the calculation formulas of progression bandwidth and the delay of vehicles which are from the progression of traffic flow under different conditions are given. Secondly, five different models of pedestrian delay are analyzed. Under different conditions of motor vehicle and pedestrian traffic flow, the Vissim fitting and proofreading are carried out and the optimal models under different conditions are obtained. Finally, the bilevel programming problem which fuses the above two models is determined; by coding an algorithm, it can be resolved. Furthermore, taking eight signalized intersections from Jiming Temple to Daxinggong along Nanjing Metro Line 3 as the actual background, the calculation and optimization of coordinated control are carried out. It is found that at the expense of the traffic efficiency of large intersections to a certain extent, a wider progression band can be formulated on the roads between them, and pedestrian delays can be reduced in general.

Intelligent Traffic Signal Control Based on Reinforcement Learning with State Reduction for Smart Cities

2021 ◽  
Vol 21 (4) ◽  
pp. 1-24
Author(s):  
Li Kuang ◽  
Jianbo Zheng ◽  
Kemu Li ◽  
Honghao Gao
Keyword(s):  
Reinforcement Learning ◽  
Traffic Flow ◽  
Control Algorithm ◽  
The State ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
State Reduction ◽  
Traffic Demand ◽  
Matching Degree

Efficient signal control at isolated intersections is vital for relieving congestion, accidents, and environmental pollution caused by increasing numbers of vehicles. However, most of the existing studies not only ignore the constraint of the limited computing resources available at isolated intersections but also the matching degree between the signal timing and the traffic demand, leading to high complexity and reduced learning efficiency. In this article, we propose a traffic signal control method based on reinforcement learning with state reduction. First, a reinforcement learning model is established based on historical traffic flow data, and we propose a dual-objective reward function that can reduce vehicle delay and improve the matching degree between signal time allocation and traffic demand, allowing the agent to learn the optimal signal timing strategy quickly. Second, the state and action spaces of the model are preliminarily reduced by selecting a proper control phase combination; then, the state space is further reduced by eliminating rare or nonexistent states based on the historical traffic flow. Finally, a simplified Q-table is generated and used to optimize the complexity of the control algorithm. The results of simulation experiments show that our proposed control algorithm effectively improves the capacity of isolated intersections while reducing the time and space costs of the signal control algorithm.

scholarly journals A MULTI-INTERSECTION MODEL AND SIGNAL TIMING PLAN ALGORITHM FOR URBAN TRAFFIC SIGNAL CONTROL

Transport ◽  
2014 ◽  
Vol 32 (4) ◽  
pp. 368-378 ◽  
Author(s):  
Wenbin Hu ◽  
Huan Wang ◽  
Bo Du ◽  
Liping Yan
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Delay Time ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Traffic Network ◽  
Intersection Model ◽  
Volume Algorithm

The urban traffic signal control system is complex, non-linear and non-equilibrium in real conditions. The existing methods could not satisfy the requirement of real-time and dynamic control. In order to solve these difficulties and challenges, this paper proposes a novel Multi-Intersection Model (MIM) based on Cellular Automata (CA) and a Multi-Intersection Signal Timing Plan Algorithm (MISTPA), which can reduce the delay time at each intersection and effectively alleviate the traffic pressure on each intersection in the urban traffic network. Our work is divided into several parts: (1) a multi-intersection model based on CA is defined to build the dynamic urban traffic network; (2) MISTPA is proposed, which truly reflects the real-time demand degree to green time of the traffic flow at each intersection. The MISTPA is composed Single Intersection Volume Algorithm (SIVA), Single-Lane Volume Algorithm (SLVA) and single intersection signal timing plan algorithm (SISTPA). Extensive experiments show that when the saturation is greater than 0.3, the MIM and the MISTPA achieve good performance, and can significantly reduce the vehicle delay time at each intersection. The average delay time of the traffic flow at each intersection can obviously be reduced. Finally, a practical case study demonstrates that the proposed model and the corresponding algorithm are correct and effective.

Urban Traffic Signal Coordinated Control Optimization with Bus Priority Based on Quantum Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 829-834
Author(s):  
Huan Che ◽  
Hai Zhang ◽  
Zheng Lin ◽  
Da Ming Luo ◽  
Jia Qing Wu
Keyword(s):  
Fitness Function ◽  
Coordinated Control ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Control ◽  
Bus Priority ◽  
Quantum Genetic Algorithm ◽  
Traffic Efficiency ◽  
Passing Rate ◽  
No Wait

An optimization algorithm of urban traffic signal coordinated control with bus priority, which is aiming at achieving optimal comprehensive traffic efficiency, is proposed in this paper. Six kinds of key parameters that dominate signal control effects are extracted from abundant factors. Based on these parameters, an advance and flexible coding scheme which can generate detail signal control information in QGA is put forward. Considering travel time, no wait passing rate, crossing delay, green wave effect and other factors, a new fitness function is constructed. Verified by simulations in PARAMICS, the optimized signal control scheme can obviously improve comprehensive regional traffic efficiency.

scholarly journals Investigation of Self-Organizing Traffic Signal Control with Graphical Signal Performance Measures

2017 ◽  
Vol 2620 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Christopher M. Day ◽  
Darcy M. Bullock
Keyword(s):  
Performance Measures ◽  
Local Control ◽  
Coordinated Control ◽  
Signal Control ◽  
Traffic Signal Control ◽  
System Control ◽  
Control Logic ◽  
Total Delay ◽  
The Right ◽  
Self Organizing

Adaptive signal control is the subject of an increasing amount of research, development, and implementation. Most existing adaptive control systems achieve coordination by applying system control as a constraining layer on top of local control. Some researchers have suggested that, with the right local control logic, coordination might be achieved as a dynamically emergent phenomenon without the need for a management layer. This paper describes how the potential of a self-organizing signal control algorithm was explored with various performance measures. First, the initially reported algorithm performance was reproduced in an idealized environment; next, the algorithm was applied in a realistic road network to compare its performance with that of actuated coordinated control, with and without pedestrian phases. Comparisons were made under ( a) the same base volumes used to design the actuated coordinated timing plan and ( b) variant volumes. Self-organizing control was found to be more flexible than coordinated control and induced a performance trade-off between movement types. Delay reductions of 38% to 56% were observed in an environment with no pedestrian phase. However, with pedestrian phases in recall, self-organizing control performed worse (39% increase in delay) under base volumes and achieved a weak benefit (6% reduction in delay) under variant volumes. Because of the large total delay reductions in some scenarios, the results show promise for future development.

Signal-Planning Optimization Model for Bus Priority Signal Intersections Based on Loss Equilibrium

2013 ◽  
Vol 336-338 ◽  
pp. 619-623
Author(s):  
Bing Long ◽  
Xiao Ning Zhu
Keyword(s):  
Optimization Model ◽  
Arrival Process ◽  
Signal Control ◽  
Signal Timing ◽  
Optimal Model ◽  
Bus Priority ◽  
Process Procedures ◽  
Induction Signal ◽  
Planning Optimization ◽  
Loss Offset

In order to equilibrate the influence of bus priority induction signal control in non-priority phases, signal timing should be optimized in the intersection. Equilibrium methods are proposed for three bus priority strategies: green extension, red truncation and phase insertion. The unsaturated intersection delay is analyzed for a single bus priority request based on linear vehicle flow arrival process, procedures of green loss and loss offset are considered to infer intersection delay formulation. The signal-planning optimal model with total passengers delay minimum and other phase vehicles passing intersection normally for restraint condition is suggested.

scholarly journals Estimating Signal Timing of Actuated Signal Control Using Pattern Recognition under Connected Vehicle Environment

2021 ◽  
Vol 33 (1) ◽  
pp. 153-163
Author(s):  
Ruochen Hao ◽  
Ling Wang ◽  
Wanjing Ma ◽  
Chunhui Yu
Keyword(s):  
Pattern Recognition ◽  
Traffic Flow ◽  
Quantitative Description ◽  
Estimation Method ◽  
Average Error ◽  
Signal Control ◽  
Signal Timing ◽  
Control Logic ◽  
Traffic Demand ◽  
Actuated Signal

The Signal Phase and Timing (SPaT) message is an important input for research and applications of Connected Vehicles (CVs). However, the actuated signal controllers are not able to directly give the SPaT information since the SPaT is influenced by both signal control logic and real-time traffic demand. This study elaborates an estimation method which is proposed according to the idea that an actuated signal controller would provide similar signal timing for similar traffic states. Thus, the quantitative description of traffic states is important. The traffic flow at each approaching lane has been compared to fluids. The state of fluids can be indicated by state parameters, e.g. speed or height, and its energy, which includes kinetic energy and potential energy. Similar to the fluids, this paper has proposed an energy model for traffic flow, and it has also added the queue length as an additional state parameter. Based on that, the traffic state of intersections can be descripted. Then, a pattern recognition algorithm was developed to identify the most similar historical states and also their corresponding SPaTs, whose average is the estimated SPaT of this second. The result shows that the average error is 3.1 seconds.

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