USING CO-SIMULATION AND TIME SIGNAL AT RED (TSAR) TO DETERMINE IMPACT OF DRIVER BEHAVIOR ON RAIL NETWORK PERFORMANCE

被引:2
作者
Pierce, Ken [1 ]
Bhattacharyya, Anirban [1 ]
Golightly, David [2 ]
da Silva, Pedro Pinto [1 ,3 ]
Merricks, Seb [1 ,2 ]
Palacin, Roberto [2 ]
Guo, Ziqi [1 ]
机构
[1] Newcastle Univ, Sch Comp, Newcastle Upon Tyne, Tyne & Wear, England
[2] Newcastle Univ, Sch Engn, Newcastle Upon Tyne, Tyne & Wear, England
[3] Westminster, Dept Business & Trade, London, England
来源
2024 ANNUAL MODELING AND SIMULATION CONFERENCE, ANNSIM 2024 | 2024年
关键词
co-simulation; rail transport performance; driver behavior; time signal at red (TSAR);
D O I
10.23919/ANNSIM61499.2024.10732075
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Railway performance is hindered by reactionary delays due to interference between trains. On congested networks, driver performance is a key element affecting delays. Time Signal at Red (TSAR) is a performance metric with a fine level of granularity that provides insight into performance. We present an initial co-simulation of a section of railway in Great Britain, the Surbiton to Weybridge line near London, that includes high-fidelity physics models of trains, models of drivers with individual behavior characteristics, and a signalling model. The co-simulation generates TSAR data, which demonstrates the impact of driver behavior and interference between trains on performance. The simulated TSAR data is compared with real data, which shows qualitatively the potential of the approach. Future work will include improvements to the driver model, a quantitative validation of the simulation results with the real data, and extension of the co-simulation to include other models with complementary characteristics.
引用
收藏
页数:13
相关论文
共 23 条
[1]   Modelling the Relationship between the Nature of Work Factors and Driving Performance Mediating by Role of Fatigue [J].
Al-Mekhlafi, Al-Baraa Abdulrahman ;
Isha, Ahmad Shahrul Nizam ;
Chileshe, Nicholas ;
Abdulrab, Mohammed ;
Saeed, Anwar Ameen Hezam ;
Kineber, Ahmed Farouk .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 18 (13)
[2]  
[Anonymous], 2012, PROC 9 INT MODELICA, DOI DOI 10.3384/ECP12076173
[3]   Resilience in railway transport systems: a literature review and research agenda [J].
Besinovic, Nikola .
TRANSPORT REVIEWS, 2020, 40 (04) :457-478
[4]  
Bratton O., 2019, RAILNORRKOPING 2019, P169
[5]  
Broenink J. F., 1997, Journal A, V38, P22
[6]  
Dong Hongzhi, 2022, 2022 3rd International Conference on Advanced Electrical and Energy Systems (AEES), P475, DOI 10.1109/AEES56284.2022.10079355
[7]   Multi-modelling for Decarbonisation in Urban Rail Systems [J].
Golightly, David ;
Gamble, Carl ;
Palacin, Roberto ;
Pierce, Ken .
URBAN RAIL TRANSIT, 2019, 5 (04) :254-266
[8]  
Hamilton W.I., 2017, Rail Human Factors, P25
[9]   A closed form railway line delay propagation model [J].
Harrod, Steven ;
Cerreto, Fabrizio ;
Nielsen, Otto Anker .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 102 :189-209
[10]  
Hwang C.-C., 2009, 8 INT C E AS SOC TRA, V7, P213