Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations
Abstract This paper introduces an innovative approach to predicting bus ridership andanalysing transportation scenarios through a fusion of machine learning (ML) techniques and multi-agent simulations. Utilising a comprehensive dataset from an urban bus system, we employ ML models to accurately forecast passenger flows, factoring in diverse variables such as weather conditions. The novelty of our method lies in the application of these predictions to generate detailed simulation scenarios, which are meticulously executed to evaluate the efficacy of public transportation services. Our research uniquely demonstrates the synergy between ML predictions and agent-based simulations, offering a robust tool for optimising urban mobility. The results reveal critical insights into resource allocation, service efficiency, and potential improvements in public transport systems. This study significantly advances the field by providing a practical framework for transportation providers to optimise services and address long-term challenges in urban mobility
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AlKhereibi, A. H., Wakjira, T. G., Kucukvar, M., and Onat, N. C., 2023. Predictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of Transit-Oriented Development. Sustainability, 15(2). ISSN 2071-1050. 10.3390/su15021718.
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Drucker, H., Burges, C. J., Kaufman, L., Smola, A., and Vapnik, V., 1996. Support vector regression machines. Advances in neural information processing systems, 9.
Elman, J. L., 1990. Finding structure in time. Cognitive science, 14(2):179–211.
Erdös, P. and Rényi, A., 1970. On a new law of large numbers. Journal d’Analyse Mathématique, 22:103–111.
Gers, F. A., Schmidhuber, J., and Cummins, F., 2000. Learning to forget: Continual prediction with LSTM. Neural computation, 12(10):2451–2471.
Goodfellow, I., Bengio, Y., and Courville, A., 2016. Deep learning. MIT press.
Gunawan, F., Suharjito, S., and Gunawan, A., 2014. Simulation Model of Bus Rapid Transit. EPJ Web of Conferences, 68.
Hajinasab, B., Davidsson, P., Persson, J., and Holmgren, J., 2016. Towards an Agent-Based Model of Passenger Transportation. pages 132–145.
Haykin, S., 2009. Neural networks and learning machines, 3/E. Pearson Education India.
He, M., Muaz, U., Jiang, H., Lei, Z., Chen, X., Ukkusuri, S. V., and Sobolevsky, S., 2022. Ridership prediction and anomaly detection in transportation hubs: an application to New York City. The European Physical Journal Special Topics, 231(9):1655–1671.
Ibáñez, A., Jordán, J., and Julian, V., 2023. Improving Public Transportation Efficiency Through Accurate Bus Passenger Demand. In Durães, D., González-Briones, A., Lujak, M., El Bolock, A., and Carneiro, J., editors, Highlights in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection, pages 18–29. Springer Nature Switzerland, Cham. ISBN 978-3-031-37593-4.
Julong, D. et al., 1989. Introduction to grey system theory. The Journal of grey system, 1(1):1–24.
Liaw, A., Wiener, M. et al., 2002. Classification and regression by randomForest. R news, 2(3):18–22.
Liu, S. and Forrest, J. Y. L., 2010. Grey systems: theory and applications. Springer Science & Business Media.
Liu, Y., Liu, Z., and Jia, R., 2019. DeepPF: A deep learning based architecture for metro passenger flow prediction. Transportation Research Part C: Emerging Technologies, 101:18–34.
Liyanage, S., Abduljabbar, R., Dia, H., and Tsai, P., 2022. AI-based neural network models for bus passenger demand forecasting using smart card data. Journal of Urban Management, 11:365–380.
Lv, L., Hu, D., and Liu, X., 2024. An EEMD-EWT-LSTM-based short-term prediction approach for inbound metro ridership. Journal of Industrial and Management Optimization. ISSN 1547-5816. 10.3934/jimo.2024035.
Ming, W., Bao, Y., Hu, Z., and Xiong, T., 2014. Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models. The Scientific World Journal, 2014.
Nagaraj, N., Gururaj, H., Swathi, B., and Hu, Y., 2022. Passenger flow prediction in bus transportation system using deep learning. Multimed Tools Appl, 81:12519–12542.
Nair, G. S., Mirzaei, A., and Ruiz-Juri, N., 2023. Investigating the Use of Machine Learning Methods in Direct Ridership Models for Bus Transit. Transportation Research Record, 2677(3):768–781. 10.1177/03611981221117540.
Palanca, J., Terrasa, A., Carrascosa, C., and Julián, V., 2019. SimFleet: A New Transport Fleet Simulator Based on MAS. In Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems. PAAMS Collection, pages 257–264. Springer.
Santanam, T., Trasatti, A., Hentenryck, P. V., and Zhang, H., 2024. Public Transit for Special Events: Ridership Prediction and Train Scheduling. IEEE Transactions on Intelligent Transportation Systems, pages 1–17. 10.1109/TITS.2024.3373634.
Schmidhuber, J. and Hochreiter, S., 1997. Long Short-Term Memory. Neural Computation, 9:1735–1780.
Vapnik, V., 2013. The nature of statistical learning theory. Springer science & business media.
Wang, X., Guo, Y., Bai, C., Liu, S., Liu, S., and Han, J., 2020. The Effects of Weather on Passenger Flow of Urban Rail Transit. Civil Engineering Journal, Vol 6, No 1:11–20.
Wilbur, K., 2022. CyRide Automatic Passenger Counter Data, 10/2021-06/2022.
Zhang, Z., Xu, X., and Wang, Z., 2017. Application of grey prediction model to short-time passenger flow forecast. AIP Conference Proceedings, 1839.
Baghbani, A., Bouguila, N., and Patterson, Z., 2023. Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model. Transportation Research Record, pages 1331–1340.
Bishop, C., 1995. Neural networks for pattern recognition. In Oxford university press.
Breiman, L., 2001. Random Forests. Machine Learning, Vol 45:5–32.
Cortes, C. and Vapnik, V., 1995. Support-vector networks. Machine Learning, 20:273–297.
Drucker, H., Burges, C. J., Kaufman, L., Smola, A., and Vapnik, V., 1996. Support vector regression machines. Advances in neural information processing systems, 9.
Elman, J. L., 1990. Finding structure in time. Cognitive science, 14(2):179–211.
Erdös, P. and Rényi, A., 1970. On a new law of large numbers. Journal d’Analyse Mathématique, 22:103–111.
Gers, F. A., Schmidhuber, J., and Cummins, F., 2000. Learning to forget: Continual prediction with LSTM. Neural computation, 12(10):2451–2471.
Goodfellow, I., Bengio, Y., and Courville, A., 2016. Deep learning. MIT press.
Gunawan, F., Suharjito, S., and Gunawan, A., 2014. Simulation Model of Bus Rapid Transit. EPJ Web of Conferences, 68.
Hajinasab, B., Davidsson, P., Persson, J., and Holmgren, J., 2016. Towards an Agent-Based Model of Passenger Transportation. pages 132–145.
Haykin, S., 2009. Neural networks and learning machines, 3/E. Pearson Education India.
He, M., Muaz, U., Jiang, H., Lei, Z., Chen, X., Ukkusuri, S. V., and Sobolevsky, S., 2022. Ridership prediction and anomaly detection in transportation hubs: an application to New York City. The European Physical Journal Special Topics, 231(9):1655–1671.
Ibáñez, A., Jordán, J., and Julian, V., 2023. Improving Public Transportation Efficiency Through Accurate Bus Passenger Demand. In Durães, D., González-Briones, A., Lujak, M., El Bolock, A., and Carneiro, J., editors, Highlights in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection, pages 18–29. Springer Nature Switzerland, Cham. ISBN 978-3-031-37593-4.
Julong, D. et al., 1989. Introduction to grey system theory. The Journal of grey system, 1(1):1–24.
Liaw, A., Wiener, M. et al., 2002. Classification and regression by randomForest. R news, 2(3):18–22.
Liu, S. and Forrest, J. Y. L., 2010. Grey systems: theory and applications. Springer Science & Business Media.
Liu, Y., Liu, Z., and Jia, R., 2019. DeepPF: A deep learning based architecture for metro passenger flow prediction. Transportation Research Part C: Emerging Technologies, 101:18–34.
Liyanage, S., Abduljabbar, R., Dia, H., and Tsai, P., 2022. AI-based neural network models for bus passenger demand forecasting using smart card data. Journal of Urban Management, 11:365–380.
Lv, L., Hu, D., and Liu, X., 2024. An EEMD-EWT-LSTM-based short-term prediction approach for inbound metro ridership. Journal of Industrial and Management Optimization. ISSN 1547-5816. 10.3934/jimo.2024035.
Ming, W., Bao, Y., Hu, Z., and Xiong, T., 2014. Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models. The Scientific World Journal, 2014.
Nagaraj, N., Gururaj, H., Swathi, B., and Hu, Y., 2022. Passenger flow prediction in bus transportation system using deep learning. Multimed Tools Appl, 81:12519–12542.
Nair, G. S., Mirzaei, A., and Ruiz-Juri, N., 2023. Investigating the Use of Machine Learning Methods in Direct Ridership Models for Bus Transit. Transportation Research Record, 2677(3):768–781. 10.1177/03611981221117540.
Palanca, J., Terrasa, A., Carrascosa, C., and Julián, V., 2019. SimFleet: A New Transport Fleet Simulator Based on MAS. In Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems. PAAMS Collection, pages 257–264. Springer.
Santanam, T., Trasatti, A., Hentenryck, P. V., and Zhang, H., 2024. Public Transit for Special Events: Ridership Prediction and Train Scheduling. IEEE Transactions on Intelligent Transportation Systems, pages 1–17. 10.1109/TITS.2024.3373634.
Schmidhuber, J. and Hochreiter, S., 1997. Long Short-Term Memory. Neural Computation, 9:1735–1780.
Vapnik, V., 2013. The nature of statistical learning theory. Springer science & business media.
Wang, X., Guo, Y., Bai, C., Liu, S., Liu, S., and Han, J., 2020. The Effects of Weather on Passenger Flow of Urban Rail Transit. Civil Engineering Journal, Vol 6, No 1:11–20.
Wilbur, K., 2022. CyRide Automatic Passenger Counter Data, 10/2021-06/2022.
Zhang, Z., Xu, X., and Wang, Z., 2017. Application of grey prediction model to short-time passenger flow forecast. AIP Conference Proceedings, 1839.
Martí, P., Ibáñez, A., Julian, V., Novais, P., & Jordán, J. (2024). Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31866. https://doi.org/10.14201/adcaij.31866
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