Education:

2017.04 - 2020.05, PhD, “Bus bunching prediction and transit route demand estimation using automatic vehicle location data” supervised by Prof. Jan-Dirk Schmöcker, Urban Management, Kyoto University, Japan

2014.04 - 2016.03, MSc, Urban Management, Kyoto University, Japan

2009.09 - 2013.07, BSc, Transportation Management, Tongji University, China

Working and Visiting Experience:

2024.08 - Present, Professor, Business School, University of Shanghai for Science and Technology

2020.06 - 2024.03, Researcher, Lab of Intelligent Transportation Systems, Department of Urban Management, Kyoto University, Japan

2022.09 - 2022.10, Visiting Scholar, Lab of Transport, Infrastructure and Territory - tGIS, Complutense University of Madrid, Spain, hosted by Prof. Gustavo Arroyo Romanillos

2018.01 - 2018.02, Visiting Scholar, Institute of Transport and Logistics Studies (ITLS), The University of Sydney Business School, Australia, supervised by Prof. Michael GH Bell

2016.04 - 2017.03, Research Assistant, Department of Urban Management, Kyoto University, Japan

Scientific Research Projects:

2024.04 - 2027.12, CI, Operation and long-term bus fleet investment optimization considering bus-to-grid technology, National Natural Science Foundation of China, Grant No. 52411540030

2021.04 - 2024.03, Co-PI, Deep learning Anticipated Urban Mobility peaks (DARUMA), Strategic International Collaborative Research Program of Japan Science and Technology Agency, Grant No. JPMJSC20C4

2018.08 - 2019.02, PI, Estimation of passenger on-board numbers and OD flows based on bus GPS data, Kyoto Innovation Project for Future Transportation Systems

2017.08 - 2018.02, CI, Analysis on bus delay propagation using GPS data and improvement measures, Kyoto Innovation Project for Future Transportation Systems

Peer-reviewed Journal Papers:

[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

Book Chapters:

[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

Selected Conference Papers:

[1]Zhou, Y., Sun, W., & Schmöcker, J.D. (2023). Optimal public transport fare with delay insurance to improve travel time reliability. 9th International Symposium on Transport Network Reliability (INSTR), Hong Kong, China.

[2]Lu, Q.-L., Sun, W., Dai, J., Schmöcker, J.D., & Antoniou, C. (2023). Surrogate modeling for recovery measures optimization to improve traffic resilience. 9th International Symposium on Transport Network Reliability (INSTR), Hong Kong, China.

[3]Dai, J., Sun, W., Lu, Q.-L., Schmöcker, J.D., & Antoniou, C. (2023). On the resilience of railway station demand in response to unexpected events: A case study of Japan in COVID-19. EWGT 2023, Bilbao, Spain.

[4]Bi, M., Sun, W., Schmöcker, J.D., Moya-Gómez, B, & Ma Y. (2023) Using geo-tagged tweets to infer the temporal and spatial distribution of activity participation. EASTS 2023, Shah Alam, Malaysia.

[5]Vongvanich, T., Sun, W., & Schmöcker, J.D. (2022). Explaining station demand patterns using Google Popular Times data. CASPT2022 and TransitData 2022, Tel Aviv, Israel.

[6]Fei, F., Iacobucci, R., Sun, W., Schmöcker, J.D. (2022). Exploring the feasibility and profitability of using electric bus fleets for power grid services. 6th Conference on Sustainable Urban Mobility (CSUM2022), Skiathos Island, Greece.

[7]Sun, W., Schmöcker, J.D., Nakao, S., & Matsuoka, N. (2022). Restrictive and stimulative impacts of COVID-19 policies on human mobility trends: A case study of Kyoto. Transportation Research Board (TRB) 101st Annual Meeting, Washington D.C., USA.

[8]Jee, H., Sun, W., & Schmöcker, J.D. (2021). Estimation of bus line specific waiting times using Wi-Fi signal data. TransitData 2021, Tel Aviv, Israel.

[9]Sun, W., Schmöcker, J.D., Fukuda, K., & Nakamura, T. (2020). Estimating Route-Level Passenger Demand Profile from Bus AVL Data. TransitData 2020, Toronto, Canada.

[10]Sun, W., Schmöcker, J.D., & Fukuda, K. (2019). Real-time estimation of bus passenger OD patterns based on AVL data. TransitData 2019, Paris, France.

[11]Sun, W., & Schmöcker, J.D. (2019). A method to classify bus bunching events using AVL data. Transportation Research Board (TRB) 98th Annual Meeting, Washington D.C., USA.

[12]Sun, W., Schmöcker, J.D., Nakamura, T., & Shimamoto, H. (2018). Bus bunching prediction based on logistic regression considering rare event bias. CASPT 2018 and TransitData 2018, Brisbane, Australia.

[13]Sun, W., Schmöcker, J.D., & Nakamura, T. (2018). Analysis of delay propagation within a bus network based on GPS data. 7th International Symposium on Transport Network Reliability (INSTR), Sydney, Australia.

[14]Nakamura, T., Schmoecker, J.D., Fujii, A., Sun, W., & Uno, N. (2017). Location optimization of charging stations for electric fleet trucks based on given tour patterns. Transportation Research Board (TRB) 96th Annual Meeting, Washington D.C., USA.

[15]Sun, W., & Schmöcker, J.D. (2016). Considering passenger choices and overtaking in the bus bunching problem. Transportation Research Board (TRB) 95th Annual Meeting, Washington D.C., USA.

[16]Schmöcker, J.D., Sun, W., Fonzone, A., & Liu, R. (2015). Bus bunching along a corridor served by two lines. 6th International Symposium on Transportation Network Reliability (INSTR), Nara, Japan. 

Journal reviewer:

[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

Conference reviewer:

[1]IEEE Intelligent Transportation Systems Society Conference (ITSC)

[2]International Scientific Conference on Mobility and Transport

[3]Transportation Research Board (TRB) Annual Meeting

2023.10, EWGT 2023 (Euro Working Group on Transportation) Best Paper Award (First author: Jiannan Dai, PhD student)

2023.09, EASTS 2023 (Eastern Asia Society for Transportation Studies) Outstanding Young Researcher Poster Award (First author: Mingyu Bi, Master student)

2018/04 - 2020/03, Lotte Foundation Scholarship

2016.02, Best Master’s Thesis Prize, Department of Urban Management, Kyoto University

2016.02, Honorable Urban Management Engineer, Department of Urban Management, Kyoto University