教育背景

博士，都市社会工学（城市社会工程），日本京都大学，2017-2020

硕士，都市社会工学（城市社会工程），日本京都大学，2014-2016

学士，交通运输工程，同济大学，2009-2013

工作经历

特聘教授，77779193永利官网，2024至今

研究员，智能交通系统（ITS）实验室，日本京都大学，2020-2024

访问学者，交通地理信息（tGIS）实验室，西班牙马德里康普顿斯大学，2022

访问学者，交通与物流研究所（ITLS），澳大利亚悉尼大学永利，2018

研究助理，都市社会工学，日本京都大学，2016-2017

科研项目

2024.04-2026.12  核心参与  国家自然科学基金国际合作与交流项目（52411540030）

2021.04-2024.03  共同主持  日本科学技术振兴机构（JST）国际战略合作项目（JPMJSC20C4）

2021.04-2024.03  参与      日本国土交通部综合研究院“新道路”科研项目

2018.08-2019.02  主持      京都市未来交通系统创新工程项目

2017.08-2018.02  参与      京都市未来交通系统创新工程项目

国际期刊论文

[1]Zhou, Y., Sun, W.*, & Schmöcker, J. D. (2024). Transit fares integrating alternative modes as a delay insurance. Transportation Research Part C: Emerging Technologies, 104745. https://doi.org/10.1016/j.trc.2024.104745 (Accepted for presentation at the 25th International Symposium on Transportation and Traffic Theory, ISTTT25)

[2]Santiago-Iglesias, E., Romanillos, G., Carpio-Pinedo, J., Sun, W., & García-Palomares, J. C. (2024). Recovering urban nightlife: COVID-19 insights from Google Places activity trends in Madrid. Journal of Maps, 20(1), 2371927. https://doi.org/10.1080/17445647.2024.2371927

[3]Santiago-Iglesias, E., Romanillos, G., Sun, W., Schmöcker, J. D., Moya-Gómez, B., & García-Palomares, J. C. (2024). Light in the darkness: Urban nightlife, analyzing the impact and recovery of COVID-19 using mobile phone data. Cities, 153, 105276. https://doi.org/10.1016/j.cities.2024.105276

[4]Lu, Q. L., Sun, W.*, Dai, J., Schmöcker, J. D., & Antoniou, C. (2024). Traffic resilience quantification based on macroscopic fundamental diagrams and analysis using topological attributes. Reliability Engineering & System Safety, 247, 110095. https://doi.org/10.1016/j.ress.2024.110095

[5]Nozawa, K., Sun, W., Schmoecker, J. D., & Nakao, S. (2024). The Impact of COVID-19 Policies on Nightlife in Kyoto. Findings. https://doi.org/10.32866/001c.118552

[6]Ma, Y., Schmöcker, J. D., Sun, W.*, & Nakao, S. (2024). Unravelling route choices of large trucks using trajectory clustering and conditional Logit models. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2024.04.007

[7]Dai, J., Schmöcker, J. D., & Sun, W. (2024). Analyzing demand reduction and recovery of major rail stations in Japan during COVID-19 using mobile spatial statistics. Asian Transport Studies, 10, 100120. https://doi.org/10.1016/j.eastsj.2023.100120

[8]Sun, W.*, Kobayashi, H., Nakao, S., & Schmöcker, J. D. (2023). On the Relationship Between Crowdsourced Sentiments and Mobility Trends During COVID-19: A Case Study of Kyoto. Data Science for Transportation, 5(3), 17. https://doi.org/10.1007/s42421-023-00080-z

[9]Santiago-Iglesias, E., Schmöcker, J. D., Carpio-Pinedo, J., García-Palomares, J. C., & Sun, W. (2023). Activity Reduction as Resilience Indicator: Evidence with Filomena Data. Findings. https://doi.org/10.32866/001c.88980

[10]Jee, H., Sun, W.*, Schmöcker, J. D., & Nakamura, T. (2023). Demonstrating the feasibility of using Wi-Fi sensors for dynamic bus-stop queue length estimation. Public Transport, 1-18. https://doi.org/10.1007/s12469-023-00336-5

[11]Santiago-Iglesias, E., Carpio-Pinedo, J., Sun, W., & García-Palomares, J. C. (2023). Frozen city: Analysing the disruption and resilience of urban activities during a heavy snowfall event using Google Popular Times. Urban Climate, 51, 101644. https://doi.org/10.1016/j.uclim.2023.101644

[12]Vongvanich, T., Sun, W.*, & Schmöcker, J. D. (2023). Explaining and Predicting Station Demand Patterns Using Google Popular Times Data. Data Science for Transportation, 5(2), 10. https://doi.org/10.1007/s42421-023-00072-z

[13]Fei, F., Sun, W.*, Iacobucci, R., & Schmöcker, J. D. (2023). Exploring the profitability of using electric bus fleets for transport and power grid services. Transportation Research Part C: Emerging Technologies, 149, 104060. https://doi.org/10.1016/j.trc.2023.104060

[14]Lai, Y., Sun, W., Schmöcker, J.D., Fukuda, K. & Axhausen, K.W. (2022). Explaining a century of Swiss regional development by deep learning and SHAP values. Environment and Planning B: Urban Analytics and City Science, 1-16. https://doi.org/10.1177/23998083221116895

[15]Shen, K., Schmöcker, J.D., Sun, W. & Qureshi, A.G. (2022). Calibration of sightseeing tour choices considering multiple decision criteria with diminishing reward. Transportation, 1-25. https://doi.org/10.1007/s11116-022-10296-7

[16]Sun, W.*, Schmöcker, J.D., & Nakao, S. (2022). Restrictive and stimulative impacts of COVID-19 policies on activity trends: A case study of Kyoto. Transportation Research Interdisciplinary Perspectives, 13, 100551. https://doi.org/10.1016/j.trip.2022.100551

[17]Sun, W.*, Schmöcker, J.D., & Fukuda, K. (2021). Estimating the route-level passenger demand profile from bus dwell times. Transportation Research Part C: Emerging Technologies, 130, 103273. https://doi.org/10.1016/j.trc.2021.103273

[18]Sun, W., Schmöcker, J.D., & Nakamura, T. (2021). On the tradeoff between sensitivity and specificity in bus bunching prediction. Journal of Intelligent Transportation Systems, 25(4), 384-400. https://doi.org/10.1080/15472450.2020.1725887

[19]Sun, W., & Schmöcker, J.D. (2018). Considering passenger choices and overtaking in the bus bunching problem. Transportmetrica B: Transport Dynamics, 6(2), 151-168. https://doi.org/10.1080/21680566.2017.1387876

[20]Schmöcker, J.D., Sun, W., Fonzone, A., & Liu, R. (2016). Bus bunching along a corridor served by two lines. Transportation Research Part B: Methodological, 93, 300-317. https://doi.org/10.1016/j.trb.2016.07.005

参编英文专著

[1]Sun, W.*, Schmöcker, J.D., Lai, Y., & Fukuda, K. (2023). The potential of explainable deep learning for public transport planning. In The Handbook on Artificial Intelligence and Transport (pp. 155-175). Edited by Hussein Dia. Edward Elgar. https://doi.org/10.4337/9781803929545.00013

[2]Sun, W.*, & Schmöcker, J.D. (2021). Demand estimation for public transport network planning. In The Routledge Handbook of Public Transport (pp. 289-305). Edited by Corinne Mulley, John D. Nelson, Stephen Ison. Routledge. https://doi.org/10.4324/9780367816698-24

期刊审稿人

[1]Case Studies on Transport Policy, Elsevier

[2]Computers & Industrial Engineering, Elsevier

[3]IEEE Open Journal of Intelligent Transportation Systems, IEEE

[4]IEEE Transactions on Intelligent Transportation Systems, IEEE

[5]IET Intelligent Transport Systems, Wiley

[6]Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Taylor & Francis

[7]Public Transport, Springer Nature

[8]Scientific Data, Nature

[9]Transportation, Springer Nature

[10]Transportation Research Interdisciplinary Perspectives

[11]Transportation Research Part A: Policy and Practice, Elsevier

[12]Transportation Research Part B: Methodological, Elsevier

[13]Transportation Research Part C: Emerging Technologies, Elsevier

[14]Transportation Research Part F: Traffic Psychology and Behaviour, Elsevier

[15]Transportmetrica B: Transport Dynamics, Taylor & Francis

东亚运输协会（Eastern Asia Society for Transportation Studies，EASTS）会员

日本土木学会会员

人才计划

2023.11，上海领军人才（海外）

科研获奖

2023.10，欧洲交通学会（Euro Working Group on Transportation，EWGT），EWGT 2023会议最佳论文奖，第二完成人、通讯作者（第一完成人为指导学生）

2023.09，东亚交通研究学会（Eastern Asia Society for Transportation Studies，EASTS），EASTS 2023会议最佳青年墙报奖，第二完成人、通讯作者（第一完成人为指导学生）

2016.02，京都大学都市社会工学最佳硕士论文

2016.02，京都大学都市社会工学荣誉工程师奖

其他

2020.09，京都大学工学研究科毕业生代表

2018.04-2020.03，乐天财团奖学金