Traffic Light Prediction and Trajectory Planning for Congestion Reduction [supporting dataset]

Jiang, Zhong-Ping; Ozbay, Kaan; Li, Xianning

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Traffic Light Prediction and Trajectory Planning for Congestion Reduction [supporting dataset]

Dataset Supporting Files

2024-09-01
By Jiang, Zhong-Ping ; Ozbay, Kaan ; Li, Xianning

File Language:

English

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Creators:

Jiang, Zhong-Ping ; Ozbay, Kaan ; Li, Xianning
Corporate Creators:

New York University. Tandon School of Engineering
Corporate Contributors:

Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:

Autonomous Vehicles Congestion Management Systems Connected Vehicles Machine Learning Trajectory Control Vehicle Mix
Resource Type:

Dataset
Right Statement:

Open Access
Geographical Coverage:

United States
Corporate Publisher:

Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC)
Abstract:

Based on the traffic light prediction and trajectory optimization techniques for a stream of CAVs, the proposed congestion-reducing scheme can increase the throughput of the transportation network by attenuating the deceleration and acceleration of vehicles before the signalized intersections, accompanied by decreased fuel consumption and emissions. By integrating the state-of-art reinforcement learning and control-theoretic methodologies, the motion of the autonomous vehicles can be well guided such that fewer numbers of lane changes are possible and the reference trajectory from the trajectory planning module can be accurately tracked with stability guarantee and collision avoidance with surrounding vehicles and pedestrians.
Content Notes:

This dataset additional data and software in a GitHub repository. This repository is accessible online: https://github.com/XianningLi/Traffic-Light-Prediction-and-Trajectory-Planning-for-Congestion-Reduction
Format:

ZIP
Funding:

69A3552348326
Collection(s):

University Transportation Centers Autonomous Vehicles
Main Document Checksum:

urn:sha-512:f3da0185b15e495f04be8e6249489c5d5cf2f6a5d0482f7de2b3fc9efa72a7e2581b485cfdbe694d9c0049f4ed6d58a0e2929754d1f6ae96034c94a1f8763634
Download URL:

https://rosap.ntl.bts.gov/view/dot/86351/dot_86351_DS1.md
File Type:

[md - 150.00 B ]

Control of Connected and Autonomous Vehicles for Congestion Reduction in Mixed Traffic: A Learning-Based Approach

Based on the traffic light prediction and trajectory optimization techniques for a stream of CAVs, the proposed congestion-reducing scheme can increas ...

Jiang, Zhong-Ping

;

Ozbay, Kaan

File Language:

English

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Details Supporting Files Publications

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