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

Biao Yin, Monica Menendez and Kaidi Yang
Additional contact information
Biao Yin: Laboratoire Ville Mobilité Transport, École des Ponts ParisTech, 77455 Marne-la-Vallée CEDEX 2, France
Monica Menendez: Division of Engineering, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates
Kaidi Yang: Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA

Sustainability, 2021, vol. 13, issue 3, 1-25

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.

Keywords: CAV; intersection; signal control; pedestrians; trajectory planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/3/1135/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/3/1135/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:3:p:1135-:d:485296

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().