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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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About This Project
About This Project
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Posts
Lu, Q.L., Qurashi, M., & Antoniou, C. (2022). A Two-Stage Stochastic Programming approach for Dynamic OD Estimation. In 102th TRB Annual Meeting 2023.
Published:
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Lu, Q.L., Qurashi, M., & Antoniou, C. (2022). A Stochastic Programming Method for OD Estimation Using LBSN Check-In Data. In 4th Symposium on Management of Future Motorway and Urban Traffic Systems.
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N. Matsuoka, COVID-19が京都に与えた影響に関する時系列分析, Annual Meeting of the JSCE Kansai Branch, online.
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portfolio
The First On-Site Workshop in Budapest
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The Second On-Site Workshop in Kyoto
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The agenda of the workshop in Munich
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The Third On-Site Workshop in Munich
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publications
Sun, W., & Schmöcker, J. D. (2021). Demand estimation for public transport network planning. In The Routledge Handbook of Public Transport (pp. 289-305). Routledge.
Published in The Routledge Handbook of Public Transport, 2021
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Lu, Q., & Tettamanti, T. (2021). Traffic control scheme for social optimum traffic assignment with dynamic route pricing for automated vehicles. Periodica Polytechnica Transportation Engineering, 49(3), 301-307.
Published in Periodica Polytechnica Transportation Engineering, 2021
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Lu, Q., & Tettamanti, T. (2021). Impacts of connected and automated vehicles on freeway with increased speed limit. International Journal of Simulation Modelling, 20(3), 453-464.
Published in International Journal of Simulation Modelling, 2021
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Gressai, M., Varga, B., Tettamanti, T., & Varga, I. (2021). Investigating the impacts of urban speed limit reduction through microscopic traffic simulation. Communications in transportation research, 1, 100018.
Published in Communications in Transportation Research, 2021
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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.
Published in Transportation Research Interdisciplinary Perspectives, 2022
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Condeço-Melhorado, A. M., Palomares, J. C. G., Arroyo, G. R., & Puebla, J. G. (2022). Destinos ganadores y destinos perdedores en los veranos de la pandemia: Un análisis basado en datos de telefonía móvil. In Leyendo el territorio: Homenaje a Miguel Ángel Troitiño (pp. 556-568). Universidad de Guadalajara.
Published in Leyendo el territorio: Homenaje a Miguel Ángel Troitiño, 2022
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Schmöcker, J. D. Urban public transport fare design dimensions Post Covid and in a Sharing Society with a focus on Japan.
Published in New Developments in Regional Public Transport Policy, 2022
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Palomares, J. C. G., & Molinares, D. A. (2022). Nuevas formas de movilidad en la ciudad. In El mundo visto de las ciudades (pp. 583-590). Tirant Humanidades.
Published in El mundo visto de las ciudades, 2022
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Arias-Molinares, D., García-Palomares, J. C., & Gutiérrez, J. (2022). Micromobility services before and after a global pandemic: impact on spatio-temporal travel patterns. International Journal of Sustainable Transportation, 1-16.
Published in International Journal of Sustainable Transportation, 2022
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Nishigaki, T., Schmöcker, J. D., Yamada, T., & Nakao, S. (2023, February). Estimating the Number of Tourists in Kyoto Based on GPS Traces and Aggregate Mobile Statistics. In Proceedings of the 12th International Scientific Conference on Mobility and Transport: Mobility Innovations for Growing Megacities (pp. 221-243). Singapore: Springer Nature Singapore.
Published in Mobil.TUM 2022, 2023
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Fernández, O. A. P., & Gómez, B. M. (2023). El sistema médico de emergencias de Madrid a prueba: análisis del rendimiento espaciotemporal del SAMUR-PC en los primeros meses de la nueva normalidad postCOVID-19. Boletín de la Asociación de Geógrafos Españoles, (96), 3.
Published in Boletín de la Asociación de Geógrafos Españoles, 2023
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Vongvanich, T. Sun, W. and Schmöcker, J.-D.(2023). Explaining and predicting station demand patterns using Google Popular Times data. Data Science for Transportation, 5(10).
Published in Data Science for Transportation, 2023
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Lu, Q. L., Qurashi, M., & Antoniou, C. (2023). Simulation-based policy analysis: The case of urban speed limits. Transportation Research Part A: Policy and Practice, 175, 103754.
Published in Transportation Research Part A: Policy and Practice, 2023
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Sun, W.Z., Kobayashi, H., Nakao, S. and 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, 5:17.
Published in Data Science for Transportation, 2023
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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.
Published in Urban Climate, 2023
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Santiago-Iglesias, E., Schmöcker, J. D., Carpio-Pinedo, J., García-Palomares, J.C. and Sun, W.Z. (2023). Activity Reduction as Resilience Indicator: Evidence with Filomena Data. Findings. Section: Resilience Findings,
Published in Resilience Findings, 2023
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Dai, J., Schmöcker, J. D. and Sun, W.Z. (2024). Analyzing demand reduction and recovery of major rail stations in Japan during COVID-19 using mobile spatial statistics. Asian Transport Studies, 10, 100120.
Published in Asian Transport Studies, 2023
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Lu, Q. L., Mahajan, V., Lyu, C., & Antoniou, C. (2024). Analyzing the impact of fare-free public transport policies on crowding patterns at stations using crowdsensing data. Transportation Research Part A: Policy and Practice, 179, 103944.
Published in Transportation Research Part A: Policy and Practice, 2023
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Lu, Q. L., Qurashi, M., & Antoniou, C. (2024). A two-stage stochastic programming approach for dynamic OD estimation using LBSN data. Transportation Research Part C: Emerging Technologies, 158, 104460.
Published in Transportation Research Part C: Emerging Technologies, 2023
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Lyu, C., Lu, Q. L., Wu, X., & Antoniou, C. (2024). Tucker factorization-based tensor completion for robust traffic data imputation. Transportation Research Part C: Emerging Technologies, 160, 104502.
Published in Transportation Research Part C: Emerging Technologies, 2024
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Nishigaki, T., Schmöcker, J. D., Yamada, T., & Nakao, S. (2024). Using entropy maximisation for establishing city-wide touristic tour patterns. Applied Soft Computing, 154, 111316.
Published in Applied Soft Computing, 2024
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Yang, N., Lu, Q. L., Lyu, C., & Antoniou, C. (2024). Transfer Learning for Transportation Demand Resilience Pattern Prediction Using Floating Car Data. Transportation Research Record, 03611981241245681.
Published in Transportation research record, 2024
Understanding the response of a transportation system to disruptive events is significant for evaluating the resilience of the system. However, data collection during such events is always challenging, and the data volume is insufficient for building a robust model. Transfer learning provides an effective solution to this problem. In this study, we propose a floating car data (FCD) driven transfer learning framework for predicting the resilience of target transportation systems to similar disruptive events to those that have ever occurred in the source systems. The core of the framework is an unsupervised pattern extractor that combines the k-Shape clustering and Bayes inference methods for extracting resilience patterns from the FCD collected in the source systems during the disruption period. The extracted patterns can then be used to assist in the prediction of the resilience of the target systems. We examine the effectiveness of the proposed framework by conducting a case study under the context of the COVID-19 pandemic, in which the source domain cities include Antwerp and Bangkok, and the target domain city is Barcelona. Results show that the extracted resilience patterns can improve the prediction performance of transfer learning neural networks with less pre-event information and limited data volume.
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Nozawa, K., Sun, W., Schmoecker, J. D., & Nakao, S. (2024). The Impact of COVID-19 Policies on Nightlife in Kyoto. Findings.
Published in Findings, 2024
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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.
Published in Reliability Engineering & System Safety, 2024
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Lu, Q.L., Dai, J., Sun, W.Z., Schmöcker, J. D. and Antoniou, C. (2024). Framework for Evaluating Urban Resilience to Pandemics: Analysis of Transport-Related Containment Policies. Research Handbook on Transport and COVID-19.
Published in Research Handbook on Transport and COVID-19, 2024
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Santiago-Iglesias, E., Romanillos, G., Sun, W., Schmöcker, J. D., Moya-Gómez, B. and García-Palomares, J.C. (2024). Light in the Darkness: Urban Nightlife, Analyzing the Impact and Recovery of COVID-19 using Mobile Phone Data. Cities.
Published in Cities, 2024
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Santiago-Iglesias, E., Romanillos, G., Carpio-Pinedo, J., Sun, W. and García-Palomares, J.C. (2024). Recovering Urban Nightlife: COVID-19 Insights from Google Places Activity Trends in Madrid. Journal of Maps.
Published in Journal of Maps, 2024
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talks
WP1: Conceptual Framework (Kyoto U), Months 1-6
Published:
To specify details of the proposed framework resilience frameworks such as the 4R model (robustness,redundancy, resourcefulness and rapidity) will be reviewed. Further, other studies regarding behavioural changes, anxiety during Covid 19 (meta study) will be reviewed as well as economic indicators. This will feed into a critical discussion as to what learning methods are useful, required data formats for the case studies and e.g. the aspects should be emphasized in the simulation.