PRECOM: A Parallel Recommendation Engine for Control, Operations, and Management on Congested Urban Traffic Networks
| Autor: | Fenghua Zhu, Haifeng Guo, Junchen Jin, Rong Dingding, Fei-Yue Wang, Xiaoliang Ma, Pang Yuqi |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Scheme (programming language)
Loop (graph theory) Computer science Mechanical Engineering Distributed computing Control (management) Recommender system Optimal control Metropolitan area Computer Science Applications System model Automotive Engineering computer Generator (mathematics) computer.programming_language |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 23:7332-7342 |
| ISSN: | 1558-0016 1524-9050 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|