Linsen Chong

462 citations
15 papers · 359 · h-index 7

Impact in

    • Transportation Planning and Optimization
    • Autonomous Vehicle Technology and Safety
    • Vehicle emissions and performance
    • Transportation and Mobility Innovations

Papers in

Linsen Chong

15 papers receiving 346 citations

Peers

Linsen Chong
Comparison fields: 5 of 46
  • Transportation 189
  • Automotive Engineering 160
  • Building and Construction 161
  • Control and Systems Engineering 250
  • Safety, Risk, Reliability and Quality 67
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Citations per year

Countries citing papers authored by Linsen Chong

Since Specialization
Citations

This map shows the geographic impact of Linsen Chong's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Linsen Chong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Linsen Chong more than expected).

Fields of papers citing papers by Linsen Chong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Linsen Chong. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Linsen Chong. The network helps show where Linsen Chong may publish in the future.

Co-authors

The 5 scholars most cited alongside Linsen Chong, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Linsen Chong Line = papers co-authored together Linsen Chong links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 2012107
2 201588
3 201775
4 201147
5 20127
6 20126
7 20116
8 20125
9
Agent-Based Reinforcement Learning Model for Simulating Driver Heterogeneous Behavior During Safety-Critical Events in Traffic
20124
10 20114
11
Driver Car-following Behavior Simulation using Fuzzy Rule-based Neural Network
20113
12 20113
13
Naturalistic Longitudinal and Lateral Risk-Taking Driving Behavior Modeling during Safety-Critical Events
20112
14
Car-Following Trajectory Modeling with Machine Learning: Showcase for Merits of Artificial Intelligence
20131
15 20101

About Linsen Chong

Linsen Chong is a scholar working on Control and Systems Engineering, Automotive Engineering, Building and Construction, Transportation and Safety, Risk, Reliability and Quality, having authored 15 papers that have together received 359 indexed citations. Recurring topics across this work include Traffic control and management (15 papers), Autonomous Vehicle Technology and Safety (9 papers), Traffic Prediction and Management Techniques (7 papers), Transportation Planning and Optimization (6 papers), Traffic and Road Safety (3 papers), Vehicle emissions and performance (2 papers), Simulation Techniques and Applications (2 papers) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Transportation (189 citations), Automotive Engineering (160 citations), Building and Construction (161 citations), Control and Systems Engineering (250 citations) and Safety, Risk, Reliability and Quality (67 citations). Linsen Chong has collaborated with scholars based in United States. Frequent co-authors include Carolina Osorio, Montasir Abbas, Bryan Higgs, Alejandra Medina and Canqian Yang. Their work appears in journals such as Transportation Science, Transportation Research Part C Emerging Technologies, Transportation Research Record Journal of the Transportation Research Board, Winter Simulation Conference and Transportation Research Board 92nd Annual MeetingTransportation Research Board.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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