Paper Info
Title
PRECOM: A Parallel Recommendation Engine for Control, Operations, and Management on Congested Urban Traffic Networks
Abstract
This paper proposes a parallel recommendation engine, PRECOM, for traffic control operations to mitigate congestion of road traffic in the metropolitan area. The recommendation engine can provide, in real-time, effective and optimal control plans to traffic engineers, who are responsible for manually calibrating traffic signal plans especially when a road network suffers from heavy congestion due to disruptive events. With the idea of incorporating expert knowledge in the operation loop, the PRECOM system is designed to include three conceptual components: an artificial system model, a computational experiment module, and a parallel execution module. Meanwhile, three essential algorithmic steps are implemented in the recommendation engine: a candidate generator based on a graph model, a spatiotemporal ranker, and a context-aware re-ranker. The PRECOM system has been deployed in the city of Hangzhou, China, through both offline and online evaluation. The experimental results are promising, and prove that the recommendation system can provide effective support to the current human-in-the-loop control scheme in the practice of traffic control, operations, and management.
Year
DOI
Venue
2022
10.1109/TITS.2021.3068874
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Keywords
DocType
Volume
Spatial-temporal recommender system, urban traffic control, parallel traffic management, human-in-the-loop system
Journal
23
Issue
ISSN
Citations 
7
1524-9050
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Junchen Jin100.34
Dingding Rong200.68
Yuqi Pang300.34
Fenghua Zhu419333.75
Haifeng Guo500.68
Xiaoliang Ma600.34
Fei-Yue Wang722.05