Study on dynamic influence of passenger flow on intelligent bus travel service model
Abstract
To improve the service quality and convenience of bus travel services, this paper proposes the Intelligent Bus Travel Service Model (IBTSM). The IBTSM makes it possible to provide a travel strategy considering every aspect of bus travel, specifically, delay in the peak period arising from limited carrying-capacities of buses. A three-step approach was executed toward implementing the IBTSM. First, the bus travel-time was predicted using Long Short-Term Memory (LSTM). Next, the crowding level in the bus was evaluated using a fuzzy expert system, based on which a reasonable start-off time was planned, and the delay caused by large passenger flow was circumvented. The k-Nearest Neighbours (k-NN) algorithm was used to provide input data of passenger flow. In this study, the correlation between passenger flow variation and bus services was investigated to extend the provisions of the travel strategy to include start-off time scheduling and target bus selection, rather than only bus running-time estimation. The proposed model was evaluated using a bus in China as a case study, and its reliability and positive impact on promoting both the quality of bus services and development of intelligent travel were demonstrated.
Keyword : intelligent bus travel service, urban travel, advanced public transportation system, passenger flow, travel-time prediction, fuzzy expert system inference
How to Cite
Liu, S., Li, X., & He, C. (2021). Study on dynamic influence of passenger flow on intelligent bus travel service model. Transport, 36(1), 25-37. https://doi.org/10.3846/transport.2021.14343
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Nagatani, T. 2001. Interaction between buses and passengers on a bus route, Physica A: Statistical Mechanics and Its Applications 296(1–2): 320–330. https://doi.org/10.1016/S0378-4371(01)00151-0
Patnaik, J.; Chien, S.; Bladikas, A. 2004. Estimation of bus arrival times using APC data, Journal of Public Transportation 7(1): 1–20. https://doi.org/10.5038/2375-0901.7.1.1
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Shalaby, A.; Farhan, A. 2004. Prediction model of bus arrival and departure times using AVL and APC data, Journal of Public Transportation 7(1): 41–61. https://doi.org/10.5038/2375-0901.7.1.3
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Van Hinsbergen, C. P. I. J.; Van Lint, J. W. C.; Van Zuylen, H. J. 2009. Bayesian committee of neural networks to predict travel times with confidence intervals, Transportation Research Part C: Emerging Technologies 17(5): 498–509. https://doi.org/10.1016/j.trc.2009.04.007
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Yu, B.; Yang, Z. Z.; Wang, J. 2010. Bus travel-time prediction based on bus speed, Proceedings of the Institution of Civil Engineers – Transport 163(1): 3–7. https://doi.org/10.1680/tran.2010.163.1.3
Yu, B.; Yang, Z.; Yao, B. 2006. Bus arrival time prediction using support vector machines, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 10(4): 151–158. https://doi.org/10.1080/15472450600981009
Zhang, J.; Shen, D.; Tu, L.; Zhang, F.; Xu, C.; Wang, Y.; Tian, C.; Li, X.; Huang, B.; Li, Z. 2017. A real-time passenger flow estimation and prediction method for urban bus transit systems, IEEE Transactions on Intelligent Transportation Systems 18(11): 3168–3178. https://doi.org/10.1109/TITS.2017.2686877
Zhao, S.-Z.; Ni, T.-H.; Wang, Y.; Gao, X.-T. 2011. A new approach to the prediction of passenger flow in a transit system, Computers & Mathematics with Applications 61(8): 1968–1974. https://doi.org/10.1016/j.camwa.2010.08.023
Zhao, Z.; Chen, W.; Wu, X.; Chen, P. C. Y.; Liu, J. 2017. LSTM network: a deep learning approach for short-term traffic forecast, IET Intelligent Transport Systems 11(2): 68–75. https://doi.org/10.1049/iet-its.2016.0208
Zhou, M.; Qu, X.; Li, X. 2017. A recurrent neural network based microscopic car following model to predict traffic oscillation, Transportation Research Part C: Emerging Technologies 84: 245–264. https://doi.org/10.1016/j.trc.2017.08.027
Amita, J.; Jain, S. S.; Garg, P. K. 2016. Prediction of bus travel time using ANN: a case study in Delhi, Transportation Research Procedia 17: 263–272. https://doi.org/10.1016/j.trpro.2016.11.091
Azadeh, A.; Fam, I. M.; Khoshnoud, M.; Nikafrouz, M. 2008. Design and implementation of a fuzzy expert system for performance assessment of an integrated health, safety, environment (HSE) and ergonomics system: the case of a gas refinery, Information Sciences 178(22): 4280–4300. https://doi.org/10.1016/j.ins.2008.06.026
Bai, Y.; Sun, Z.; Zeng, B.; Deng, J.; Li, C. 2017. A multi-pattern deep fusion model for short-term bus passenger flow forecasting, Applied Soft Computing 58: 669–680. https://doi.org/10.1016/j.asoc.2017.05.011
Cathey, F. W.; Dailey, D. J. 2003. A prescription for transit arrival/departure prediction using automatic vehicle location data, Transportation Research Part C: Emerging Technologies 11(3–4): 241–264. https://doi.org/10.1016/S0968-090X(03)00023-8
Chan, K. Y.; Dillon, T. S.; Singh, J.; Chang, E. 2012. Neuralnetwork-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm, IEEE Transactions on Intelligent Transportation Systems 13(2): 644–654. https://doi.org/10.1109/TITS.2011.2174051
Chang, H.; Park, D.; Lee, S.; Lee, H.; Baek, S. 2010. Dynamic multi-interval bus travel time prediction using bus transit data, Transportmetrica 6(1): 19–38. https://doi.org/10.1080/18128600902929591
Chen, D. 2017. Research on traffic flow prediction in the big data environment based on the improved RBF neural network, IEEE Transactions on Industrial Informatics 13(4): 2000–2008. https://doi.org/10.1109/TII.2017.2682855
Chen, M.; Liu, X.; Xia, J.; Chien, S. I. 2004. A dynamic bus‐arrival time prediction model based on APC data, Computer‐Aided Civil and Infrastructure Engineering 19(5): 364–376. https://doi.org/10.1111/j.1467-8667.2004.00363.x
Dimitrov, S.; Ceder, A.; Chowdhury, S.; Monot, M. 2017. Modeling the interaction between buses, passengers and cars on a bus route using a multi-agent system, Transportation Planning and Technology 40(5): 592–610. https://doi.org/10.1080/03081060.2017.1314504
Ding, X.-B.; Xu, X.-F. 2016. Algorithms of station passenger flow forecast of suburban rail transit based on distribution time, Journal of Transportation Systems Engineering and Information Technology 16(1): 116–122. (in Chinese).
Gerland, H. E. 1993. ITS intelligent transportation system: fleet management with GPS dead reckoning, advanced displays, smartcards, etc, in Proceedings of VNIS’93 – Vehicle Navigation and Information Systems Conference, 12–15 October 1993, Ottawa, Ontario, Canada, 606–611. https://doi.org/10.1109/VNIS.1993.585706
Gers, F. A.; Schmidhuber, J.; Cummins, F. 2000. Learning to forget: continual prediction with LSTM, Neural Computation 12(10): 2451–2471. https://doi.org/10.1162/089976600300015015
Hornik, S.; Ruf, B. M. 1997. Expert systems usage and knowledge acquisition: an empirical assessment of analogical reasoning in the evaluation of internal controls, Journal of Information Systems 11(2): 57–74.
Hu, Q.; Yu, D.; Xie, Z. 2008. Neighborhood classifiers, Expert Systems with Applications 34(2): 866–876. https://doi.org/10.1016/j.eswa.2006.10.043
Hua, X.; Wang, W.; Wang, Y.; Ren, M. 2018. Bus arrival time prediction using mixed multi-route arrival time data at previous stop, Transport 33(2): 543–554. https://doi.org/10.3846/16484142.2017.1298055
Huang, S. H.; Ran, B. 2003. An application of neural network on traffic speed prediction under adverse weather condition, in Transportation Research Board 82nd Annual Meeting, 12–16 January 2003, Washington, DC, US, 1–21.
Ji, Y.-J.; Lu, J.-W.; Chen, X.-S.; Hu, B. 2016. Prediction model of bus arrival time based on particle swarm optimization and wavelet neural network, Journal of Transportation Systems Engineering and Information Technology (3): 60–66. (in Chinese).
Kumar, B. A.; Vanajakshi, L.; Subramanian, S. C. 2018. A hybrid model based method for bus travel time estimation, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 22(5): 390–406. https://doi.org/10.1080/15472450.2017.1378102
Lin, Y.; Yang, X.; Zou, N.; Jia, L. 2013. Real-time bus arrival time prediction: case study for Jinan, China, Journal of Transportation Engineering 133(11): 1133–1140. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000589
Liu, G.; Yin, Z.; Jia, Y.; Xie, Y. 2017. Passenger flow estimation based on convolutional neural network in public transportation system, Knowledge-Based Systems 123: 102–115. https://doi.org/10.1016/j.knosys.2017.02.016
Marfia, G.; Roccetti, M. 2011. Vehicular congestion detection and short-term forecasting: a new model with results, IEEE Transactions on Vehicular Technology 60(7): 2936–2948. https://doi.org/10.1109/TVT.2011.2158866
Mazloumi, E.; Currie, G.; Rose, G. 2010. Using GPS data to gain insight into public transport travel time variability, Journal of Transportation Engineering 136(7): 623–631. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000126
Nagatani, T. 2001. Interaction between buses and passengers on a bus route, Physica A: Statistical Mechanics and Its Applications 296(1–2): 320–330. https://doi.org/10.1016/S0378-4371(01)00151-0
Patnaik, J.; Chien, S.; Bladikas, A. 2004. Estimation of bus arrival times using APC data, Journal of Public Transportation 7(1): 1–20. https://doi.org/10.5038/2375-0901.7.1.1
Petersen, N. C.; Rodrigues, F.; Pereira, F. C. 2019. Multi-output bus travel time prediction with convolutional LSTM neural network, Expert Systems with Applications 120: 426–435. https://doi.org/10.1016/j.eswa.2018.11.028
Shalaby, A.; Farhan, A. 2004. Prediction model of bus arrival and departure times using AVL and APC data, Journal of Public Transportation 7(1): 41–61. https://doi.org/10.5038/2375-0901.7.1.3
Sun, D.; Luo, H.; Fu, L.; Liu, W.; Liao, X.; Zhao, M. 2007. Predicting bus arrival time on the basis of global positioning system data, Transportation Research Record: Journal of the Transportation Research Board 2034: 62–72. https://doi.org/10.3141/2034-08
Van Hinsbergen, C. P. I. J.; Van Lint, J. W. C.; Van Zuylen, H. J. 2009. Bayesian committee of neural networks to predict travel times with confidence intervals, Transportation Research Part C: Emerging Technologies 17(5): 498–509. https://doi.org/10.1016/j.trc.2009.04.007
Wang, L.; Zuo, Z.; Fu, J. 2014. Bus arrival time prediction using RBF neural networks adjusted by online data, Procedia – Social and Behavioral Sciences 138: 67–75. https://doi.org/10.1016/j.sbspro.2014.07.182
Williams, B. M.; Hoel, L. A. 2003. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results, Journal of Transportation Engineering 129(6): 664–672. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664)
Xu, M.; Guo, K.; Fang, J.; Chen, Z. 2019. Utilizing artificial neural network in GPS-equipped probe vehicles data-based travel time estimation, IEEE Access 7: 89412–89426. https://doi.org/10.1109/ACCESS.2019.2926851
Yang, M.; Chen, C.; Wang, L.; Yan, X.; Zhou, L. 2016. Bus arrival time prediction using support vector machine with genetic algorithm, Neural Network World (3): 205–217. https://doi.org/10.14311/NNW.2016.26.011
Yu, B.; Lam, W. H. K.; Tam, M. L. 2011. Bus arrival time prediction at bus stop with multiple routes, Transportation Research Part C: Emerging Technologies 19(6): 1157–1170. https://doi.org/10.1016/j.trc.2011.01.003
Yu, B.; Yang, Z. Z.; Wang, J. 2010. Bus travel-time prediction based on bus speed, Proceedings of the Institution of Civil Engineers – Transport 163(1): 3–7. https://doi.org/10.1680/tran.2010.163.1.3
Yu, B.; Yang, Z.; Yao, B. 2006. Bus arrival time prediction using support vector machines, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 10(4): 151–158. https://doi.org/10.1080/15472450600981009
Zhang, J.; Shen, D.; Tu, L.; Zhang, F.; Xu, C.; Wang, Y.; Tian, C.; Li, X.; Huang, B.; Li, Z. 2017. A real-time passenger flow estimation and prediction method for urban bus transit systems, IEEE Transactions on Intelligent Transportation Systems 18(11): 3168–3178. https://doi.org/10.1109/TITS.2017.2686877
Zhao, S.-Z.; Ni, T.-H.; Wang, Y.; Gao, X.-T. 2011. A new approach to the prediction of passenger flow in a transit system, Computers & Mathematics with Applications 61(8): 1968–1974. https://doi.org/10.1016/j.camwa.2010.08.023
Zhao, Z.; Chen, W.; Wu, X.; Chen, P. C. Y.; Liu, J. 2017. LSTM network: a deep learning approach for short-term traffic forecast, IET Intelligent Transport Systems 11(2): 68–75. https://doi.org/10.1049/iet-its.2016.0208
Zhou, M.; Qu, X.; Li, X. 2017. A recurrent neural network based microscopic car following model to predict traffic oscillation, Transportation Research Part C: Emerging Technologies 84: 245–264. https://doi.org/10.1016/j.trc.2017.08.027