J
Junchen Jin
Researcher at Royal Institute of Technology
Publications - 36
Citations - 586
Junchen Jin is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Traffic simulation & Computer science. The author has an hindex of 8, co-authored 29 publications receiving 310 citations. Previous affiliations of Junchen Jin include Chinese Academy of Sciences.
Papers
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Journal ArticleDOI
A Multi-Objective Agent-Based Control Approach With Application in Intelligent Traffic Signal System
Junchen Jin,Xiaoliang Ma +1 more
TL;DR: A multi-agent framework that models traffic control instruments and their interactions with road traffic and a two-stage hybrid framework is established to improve the learning efficiency of the model.
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An intelligent control system for traffic lights with simulation-based evaluation
TL;DR: This paper introduces an intelligent control system for traffic signal applications, called Fuzzy Intelligent Traffic Signal (FITS) control, which provides a convenient and economic approach to improv ...
Journal ArticleDOI
Hierarchical multi-agent control of traffic lights based on collective learning
Junchen Jin,Xiaoliang Ma +1 more
TL;DR: The results show that the proposed traffic light system, after a collective machine learning process, not only improves the local signal operations at individual intersections but also enhances the traffic performance at the regional level through coordination of specific turning movements.
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Multi-criteria analysis of optimal signal plans using microscopic traffic models
Xiaoliang Ma,Junchen Jin,Wei Lei +2 more
TL;DR: An application of a model-based framework to quantify environmental impacts and fuel efficiency of road traffic, and to evaluate optimal signal plans with respect not only to traffic mobility performance but also other important measures for sustainability are presented.
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An End-to-End Recommendation System for Urban Traffic Controls and Management Under a Parallel Learning Framework
TL;DR: This work presents a human-in-the-loop parallel learning framework and its utilization in an end-to-end recommendation system that mimics and enhances professional signal control engineers’ behaviors and demonstrates significant improvements in traffic efficiency.