Abstract

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.

Highlights

  • We found that the existing control strategies of connected and automated vehicle (CAV) triannot safety they fail to pay enough attention to the efficiency aspect,only i.e., focus on pedestrian safety aspects, while they fail to pay enough attention to the efficiency pedestrian delay

  • We assume that the zone of interest is defined by a 100 m radius around the intersection, and that there is no delay for connected vehicles (CVs) and automated vehicles (AVs) to send and receive information

  • We propose a bilevel optimization model that integrates the optimization of departure sequences and the trajectory design for AVs

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Summary

Introduction

Some approaches use the joint optimization of signal timings (or vehicle departure sequence) and CAV trajectory planning to improve the control efficiency [18,19]. Even when providing priority to pedestrians, it is still possible to optimize intersection control systems by adjusting traffic signal timings or operation modes. The optimization problems of traffic signal timings considering pedestrian delay have already been studied in the context of human-driven (i.e., conventional) vehicles [31,32,33]. To the best of our knowledge, there is no pedestrian delay model considered for intersection control under a CAV environment. Sustainability 2021, 13, x FOR PEER REVIEW the best of our knowledge, there is no pedestrian delay model considered for intersection control under a CAV environment.

Integrated
Model Framework
Model Formulation
Vehicle Delay
Pedestrian Delay
Trajectory Design for AVs
Solution Algorithm Based on the Ant Colony System
ACS State Transition Rule
Local Pheromone Updating Rule
Global Pheromone Updating Rule
Simulation
Algorithm and Model Analysis
Method
Performance of the Control Algorithm
Performance of of average person delay with different weights
Comparison of the average person delay between the balanced demand and the
7.7.Conclusions
Evaluation of a Cooperative
A Multiagent
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