scholarly journals Joint Optimization of Intersection Control and Trajectory Planning Accounting for Pedestrians in a Connected and Automated Vehicle Environment

2021 ◽  
Vol 13 (3) ◽  
pp. 1135
Author(s):  
Biao Yin ◽  
Monica Menendez ◽  
Kaidi Yang
Keyword(s):  
Control Method ◽  
Optimal Solution ◽  
Joint Optimization ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Ant Colony System ◽  
Automated Vehicle ◽  
Vehicle Delay ◽  
Vehicle Demand ◽  
Pedestrian Crossing

Connected and automated vehicle (CAV) technology makes it possible to track and control the movement of vehicles, thus providing enormous potential to improve intersection operations. In this paper, we study the traffic signal control problem at an isolated intersection in a CAV environment, considering mixed traffic including various types of vehicles and pedestrians. Both the vehicle delay and the pedestrian delay are incorporated into the model formulation. This introduces some additional complexity, as any benefits to pedestrians will come at the expense of higher delays for the vehicles. Thus, some valid questions we answer in this paper are as follows: Under which circumstances could we provide priority to pedestrians without over penalizing the vehicles at the intersection? How important are the connectivity and autonomy associated with CAV technology in this context? What type of signal control algorithm could be used to minimize person delay accounting for both vehicles and pedestrians? How could it be solved efficiently? To address these questions, we present a model that optimizes signal control (i.e., vehicle departure sequence), automated vehicle trajectories, and the treatment of pedestrian crossing. In each decision step, the weighted sum of the vehicle delay and the pedestrian delay (e.g., the total person delay) is minimized by the joint optimization on the basis of the predicted departure sequences of vehicles and pedestrians. Moreover, a near-optimal solution of the integrated problem is obtained with an ant colony system algorithm, which is computationally very efficient. Simulations are conducted for different demand scenarios and different CAV penetration rates. The performance of the proposed algorithm in terms of the average person delay is investigated. The simulation results show that the proposed algorithm has potential to reduce the delay compared to an actuated signal control method. Moreover, in comparison to a CAV-based signal control that does not account for the pedestrian delay, the joint optimization proposed here can achieve improvement in the low- and moderate-vehicle-demand scenarios.

scholarly journals Intersection signal timing optimization considering the travel safety of the elderly

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989721 ◽  
Author(s):  
Changxi Ma ◽  
Pengfei Liu
Keyword(s):  
Genetic Algorithm ◽  
Control Method ◽  
The Elderly ◽  
Calibration Method ◽  
Optimization Method ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Improved Genetic Algorithm ◽  
Signal Ratio ◽  
Vehicle Delay

With the rapid growth of the elderly population in China, the proportion of middle-aged and elderly pedestrians crossing streets at signalized intersections has been increasing gradually, mandating the consideration of the crossing characteristics and travel safety of the elderly in signal matching. This article proposes a new signal control parameter optimization method for intersections based on an improved genetic algorithm. According to the crossing characteristics and travel safety of the elderly, the average vehicle delay is used as the control objective, and the green signal ratio and cycle time are used as control variables. The improved genetic algorithm with an improved fitness calibration method and an adaptive cross-mutation function is used to solve the signal control model. Based on the optimization analysis of traffic signal control parameters at a traffic intersection, the study shows that the improved signal control method can effectively reduce the average vehicle delay compared to the Webster algorithm and the traditional genetic algorithm.

scholarly journals Investigating the Applicability of Upstream Detection Strategy at Pedestrian Signalised Crossings

2017 ◽  
Vol 29 (5) ◽  
pp. 503-510 ◽  
Author(s):  
Sitti A Hassan ◽  
Nick B Hounsell ◽  
Birendra P Shrestha
Keyword(s):  
Traffic Control ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Flows ◽  
Detection Strategy ◽  
Vehicle Delay ◽  
Simulation Results ◽  
The Uk ◽  
User Friendly

In the UK, the Puffin crossing has provision to extend pedestrian green time for those who take longer to cross. However, even at such a pedestrian friendly facility, the traffic signal control is usually designed to minimise vehicle delay while providing the crossing facility. This situation is rather contrary to the current policies to encourage walking. It is this inequity that has prompted the need to re-examine the traffic control of signalised crossings to provide more benefit to both pedestrians and vehicles. In this context, this paper explores the possibility of implementing an Upstream Detection strategy at a Puffin crossing to provide a user friendly crossing. The study has been carried out by simulating a mid-block Puffin crossing for various detector distances and a number of combinations of pedestrian and traffic flows. This paper presents the simulation results and recommends the situations at which Upstream Detection would be suitable.

scholarly journals Urban Traffic Signal Control Based on Multiobjective Joint Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yongrong Wu ◽  
Yijie Zhou ◽  
Yanming Feng ◽  
Yutian Xiao ◽  
Shaojie He ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Urban Traffic ◽  
Joint Optimization ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Timing Optimization ◽  
Average Delay ◽  
Annealing Algorithm

This paper proposes two algorithms for signal timing optimization of single intersections, namely, microbial genetic algorithm and simulated annealing algorithm. The basis of the optimization of these two algorithms is the original timing scheme of the SCATS, and the optimized parameters are the average delay of vehicles and the capacity. Experiments verify that these two algorithms are, respectively, improved by 67.47% and 46.88%, based on the original timing scheme.

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

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 77 ◽  
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.

scholarly journals An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4291 ◽  
Author(s):  
Qiang Wu ◽  
Jianqing Wu ◽  
Jun Shen ◽  
Binbin Yong ◽  
Qingguo Zhou
Keyword(s):  
Reinforcement Learning ◽  
Control Method ◽  
Communication Protocol ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Light Control ◽  
Traffic Light ◽  
Traffic Light Control ◽  
Multi Agent

With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment.

scholarly journals Self-organisation in traffic signal control algorithms under light traffic conditions

ORiON ◽  
2019 ◽  
Vol 35 (1) ◽  
pp. 57-87
Author(s):  
SJ Movius ◽  
JH Van Vuuren
Keyword(s):  
Simulation Model ◽  
Traffic Simulation ◽  
Control Method ◽  
Fixed Time ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Light Traffic ◽  
Traffic Conditions ◽  
Self Organisation

Fixed-time control and vehicle-actuated control are two distinct types of traffic signal control. The latter control method involves switching traffic signals based on detected traffic flows and thus offers more flexibility (appropriate for lighter traffic conditions) than the former, which relies solely on cyclic, predetermined signal phases that are better suited for heavier traffic conditions. The notion of self-organisation has relatively recently been proposed as an alternative approach towards improving traffic signal control, particularly under light traffic conditions, due to its flexible nature and its potential to result in emergent behaviour. The effectiveness of five existing self-organising traffic signal control strategies from the literature and a fixed-control strategy are compared in this paper within a newly designed agent-based, microscopic traffic simulation model. Various shortcomings of three of these algorithms are identified and algorithmic improvements are suggested to remedy these deficiencies. The relative performance improvements resulting from these algorithmic modifications are then quantified by their implementation in the aforementioned traffic simulation model. Finally, a new self-organising algorithm is proposed that is particularly effective under lighter traffic conditions.

Sign in / Sign up

Export Citation Format

Share Document