Open AccessMultilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles
Author(s) -
Pangwei Wang,
Yunfeng Wang,
Hui Deng,
Mingfang Zhang,
Juan Zhang
Publication year - 2020
Publication title -
journal of advanced transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 46
eISSN - 2042-3195
pISSN - 0197-6729
DOI - 10.1155/2020/8819911
Subject(s) - trajectory , intersection (aeronautics) , computer science , traffic flow (computer networking) , trajectory optimization , automotive engineering , mathematical optimization , simulation , engineering , transport engineering , mathematics , computer network , physics , astronomy
It is agreed that connected vehicle technologies have broad implications to traffic management systems. In order to alleviate urban congestion and improve road capacity, this paper proposes a multilane spatiotemporal trajectory optimization method (MSTTOM) to reach full potential of connected vehicles by considering vehicular safety, traffic capacity, fuel efficiency, and driver comfort. In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin’s maximum principle and reinforcement learning (RL). A typical scenario of intersection with a one-way 4-lane section is measured, and the data within 24 hours are collected for tests. The results demonstrate that the proposed method can optimize the traffic flow by enhancing vehicle fuel efficiency by 32% and reducing pollutants emissions by 17% compared with the advanced glidepath prototype application (GPPA) scheme.