Rolling‐horizon–based strategy of fully cooperative traffic under signalized intersections

Abstract

Dedicated left-turn lanes are traditionally used at intersections. This practice may not be optimal where heavy traffic exists from multiple directions. As is well known, the capacity can be increased if vehicles are grouped in the same direction in advance, but the additional infrastructure is usually needed, such as presignal systems. Fortunately, under the development of connected and automated vehicle (CAV) technology, the presignal system can be achieved by coordinating vehicles directly. To this end, we establish a modeling framework and management strategies for pure CAV traffic to achieve direction-based grouping. A rolling time-horizon-based sorting strategy is developed to achieve real-time coordination under uncertain vehicle arrivals. A series of numerical experiments have shown that the improvement of the proposed sorting strategy is significant and robust compared with traditional dedicated left-turn lane intersections without coordination, especially when the traffic is busy.

Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method

“…based on the V2X information interaction technology. (2) Ten, in OPA, root parallelization is applied to implement the distributed cooperation (i.e., each CAV calculates a nearly global-optimal passing order Input: A passing order P Output: Te total acceleration adjustments A of the covered CAVs and their required acceleration adjustments a i , respectively (1) Initialize a i as 0 (2) for each i ∈ [1, length (P)] do (3) t i � actual_time (i) − min_time (i) [35] (4) adjustment_required � Requirement (i) (5) Te Requirement function determines whether CAV i needs to make the acceleration adjustment (6) while adjustment_required do (7) a i � acc_calculate (i) (8) for each j ∈ [i, length (P)] do (9) if lane i � � lane j then (10) a j + � a i (11) end if (12) end for (13) adjustment_required � Requirement (i) ( 14) end while (15) end for (16) based on the MCTS algorithm with heuristic rules, and then, all CAVs apply the majority voting rule to determine the fnal uniform passing order) to specify the following driving behaviors of all CAVs. (3) Next, in EXA, each CAV carries out the corresponding trajectory planning and adjustments in real-time to meet the desired driving trajectory determined in task 2 and keeps intervehicle safety gaps to arrive at IPA on time.…”

“…Over the past decade, extensive research has been conducted on trajectory control for CAVs. A variety of control strategies have been developed, including adaptive cruise control (ACC) [8], cooperative adaptive cruise control (CACC) [9,10], model predictive control (MPC) [11][12][13], and deep reinforcement learning (DRL) control [14][15][16][17], are developed to optimize the trajectories of CAVs.…”

A deep marked graph process model for citywide traffic congestion forecasting

“…As transportation management authorities seek to alleviate traffic congestion, there is an urgent need to predict the occurrence and propagation of traffic congestion using traffic surveillance data collected by various sensors. Reliable and robust congestion prediction models can provide practical benefits for traffic signal control, traffic flow guidance, as well as road infrastructure performance evaluation and improvement planning (Han et al., 2023; Yao et al., 2023). They are also beneficial for local residents to make informed travel decisions.…”

A 2D‐connected automated vehicle car‐following control algorithm

“…On the other hand, the aforementioned CAV car‐following and trajectory optimization approaches are largely constructed in the time domain (e.g., Guo et al., 2018; Mohebifard & Hajbabaie, 2022; Yao et al., 2023). However, the attributes and disturbances of the highway, such as the speed limit and work zones, are represented by coefficients that vary in space.…”