A Dynamic Model for Bus Arrival Time Estimation based on Spatial Patterns using Machine Learning

A Dynamic Model for Bus Arrival Time Estimation based on Spatial Patterns using Machine Learning

© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : B. P. Ashwini, R. Sumathi, H. S. Sudhira
DOI : 10.14445/22315381/IJETT-V70I9P219

How to Cite?

B. P. Ashwini, R. Sumathi, H. S. Sudhira, "A Dynamic Model for Bus Arrival Time Estimation based on Spatial Patterns using Machine Learning" International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 185-193, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P219

The notion of smart cities is being adapted globally to provide a better quality of living. A smart city's smart mobility component focuses on providing smooth and safe commuting for its residents and promotes eco-friendly and sustainable alternatives such as public transit (bus). Among several smart applications, a system that provides up-to-the-minute information like bus arrival, travel duration, schedule, etc., improves the reliability of public transit services. Still, this application needs live information on traffic flow, accidents, events, and the location of the buses. Most cities lack the infrastructure to provide these data. In this context, a bus arrival prediction model is proposed for forecasting the arrival time using limited data sets. The location data of public transit buses and spatial characteristics are used for the study. One of the routes of Tumakuru city service, Tumakuru, India, is selected and divided into two spatial patterns: sections with intersections and sections without intersections. The machine learning model XGBoost is modeled for both spatial patterns individually. A model to dynamically predict bus arrival time is developed using the preceding trip information and the machine learning model to estimate the arrival time at a downstream bus stop. The performance of models is compared based on the R-squared values of the predictions made, and the proposed model established superior results. It is suggested to predict bus arrival in the study area. The proposed model can also be extended to other similar cities with limited traffic-related infrastructure.

Bus Arrival Time, Intelligent Transportation System, Machine Learning, Public Transit, Smart Cities.

[1] S. Joshi, S. Saxena, T. Godbole, En Shreya, "Developing Smart Cities: An Integrated Framework", Procedia Comput. Sci., vol 93, no. September, pp. 902–909, 2016, Doi: 10.1016/J.Procs.2016.07.258.
[2] M. S.Kumbhar En P. S.Yalagi, "Urban Resources for Smart City Application", Int. J. Eng. Trends Technol., vol.40, no.6, pp. 366–370, 2016, Doi: 10.14445/22315381/Ijett-V40p259.
[3] Polytechnique Montreal, "Smart Cities and Integrated Mobility: A White Paper", Next Gener. Integr. Mobility Drive. Smart City, pp. 1– 78, 2018.
[4] R. M. Savithramma, B. P. Ashwini, En R. Sumathi, "Smart Mobility Implementation in Smart Cities: A Comprehensive Review on State-of-Art Technologies", 2022 4th Int. Conf. Smart Syst. Inven. Technol., pp. 10–17, 2022, Doi: 10.1109/Icssit53264.2022.9716288.
[5] M. Kumar, N. Kumari, S. tomar, En T. Kumar, "Smart Public Transportation for Smart Cities", SSRN Electron. J., pp. 1–6, 2019, Doi: 10.2139/Ssrn.3404487.
[6] S. Porru, F. E. Misso, F. E. Pani, En C. Repetto, "Smart Mobility and Public Transport: Opportunities and Challenges in Rural and Urban Areas", J. Traffic Transp. Eng. English Ed, vol 7, no 1, pp. 88–97, 2020, Doi: 10.1016/J.Jtte.2019.10.002.
[7] R. S. Chhillar, "A Review of Intelligent Transportation Systems in Existing Framework Using Iot", vol.70, no. 6, pp. 137–143, 2022.
[8] S. Muthuramalingam, A. Bharathi, S. Rakesh Kumar, N. Gayathri, R. Sathiyaraj, En B. Balamurugan, "Iot Based Intelligent Transportation System (Iot-Its) for Global Perspective: A Case Study", Intell. Syst. Ref. Libr., vol.154, pp. 279–300, 2019, Doi: 10.1007/978-3-030-04203-5_13.
[9] K. Iqbal, M. A. Khan, S. Abbas, Z. Hasan, En A. Fatima, "Intelligent Transportation System (Its) for Smart-Cities Using Mamdani Fuzzy Inference System", Int. J. Adv. Comput. Sci. Appl., vol.9, no.2, pp. 94–105, 2018, Doi: 10.14569/Ijacsa.2018.090215.
[10] A. Monzon, S. Hernandez, En R. Cascajo, "Quality of Bus Services Performance: Benefits of Real Time Passenger Information Systems", Transp. Telecommun., vol.14, no.2, pp. 155–166, 2013, Doi: 10.2478/Ttj-2013-0013.
[11] M. Bai, Y. Lin, M. Ma, En P. Wang, Travel-Time Prediction Methods: A Review, vol.11344, Lncs. Springer International Publishing, 2018.
[12] D. Panovski En T. Zaharia, "Long and Short-Term Bus Arrival Time Prediction With Traffic Density Matrix", Ieee Access, vol.8, pp. 226267–226284, 2020, Doi: 10.1109/Access.2020.3044173.
[13] P. B. Ashwini En R. Sumathi, "Data Sources for Urban Traffic Prediction: A Review on Classification, Comparison and Technologies", Proc. 3rd Int. Conf. Intell. Sustain. Syst. Iciss 2020, pp. 628–635, 2020, Doi: 10.1109/Iciss49785.2020.9316096.
[14] L. Wang, Z. Zuo, En J. Fu, "Bus Arrival Time Prediction Using Rbf Neural Networks Adjusted By online Data", Procedia - Soc. Behav. Sci., vol 138, no.0, pp. 67–75, 2014, Doi: 10.1016/J.Sbspro.2014.07.182.
[15] W. Fan En Z. Gurmu, "Dynamic Travel Time Prediction Models for Buses Using only Gps Data", Int. J. Transp. Sci. Technol., vol.4, no.4, pp. 353–366, 2015, Doi: 10.1016/S2046-0430(16)30168-X.
[16] C. Bai, Z. R. Peng, Q. C. Lu, En J. Sun, "Dynamic Bus Travel Time Prediction Models on Road With Multiple Bus Routes", Comput. Intell. Neurosci., vol.2015, 2015, Doi: 10.1155/2015/432389.
[17] G. De Blasio En C. R. Garc, "Bus Travel Time Prediction Model Based on Profile Similarity", Encycl. Soc. Netw. Anal. Min., pp. 1942– 1942, 2018, Doi: 10.1007/978-1-4939-7131-2_100921.
[18] R. Jairam, B. A. Kumar, S. S. Arkatkar, En L. Vanajakshi, "Performance Comparison of Bus Travel Time Prediction Models Across Indian Cities", Transp. Res. Rec., vol.2672, no.31, pp. 87–98, 2018, Doi: 10.1177/0361198118770175.
[19] A. A. Agafonov En A. S. Yumaganov, "Performance Comparison of Machine Learning Methods in the Bus Arrival Time Prediction Problem", Ceur Workshop Proc., vol.2416, pp. 57–62, 2019, Doi: 10.18287/1613-0073-2019-2416-57-62.
[20] A. Kviesis, A. Zacepins, V. Komasilovs, En M. Munizaga, "Bus Arrival Time Prediction With Limited Data Set Using Regression Models", no Vehits 2018, pp. 643–647, 2019, Doi: 10.5220/0006816306430647.
[21] A. K. Bachu, K. K. Reddy, En L. Vanajakshi, "Bus Travel Time Prediction Using Support Vector Machines for High Variance Conditions", vol.36, no.3, pp. 221–234, 2021.
[22] B. Qiu En W. D. Fan, "Machine Learning Based Short-Term Travel Time Prediction : Numerical Results and Comparative Analyses," 2021.
[23] Y. Yuan, C. Shao, Z. Cao, Z. He, En C. Zhu, "Bus Dynamic Travel Time Prediction : Using a Deep Feature Extraction Framework Based on Rnn and Dnn", Electron, Mdpi, 2020.
[24] B. P. Ashwini, R. Sumathi, En H. S. Sudhira, "Bus Travel Time Prediction: A Comparative Study of Linear and Non-Linear Machine Learning Models", J. Phys. Conf. Ser., vol.2161, no.1, pp.012053, 2022, Doi: 10.1088/1742-6596/2161/1/012053.
[25] R. Ashwini, B.P., Sumathi, "Variability Analysis of Public Transit Bus Travel Time Using Gps Logs: A Case Study", in Proceedings of the Third International Conference on Information Management and Machine Intelligence. Algorithms for Intelligent Systems. Springer, Singapore., pp. 567–577, 2022. Doi: 10.1007/978-981-19-2065-3_60.
[26] F. Zhang, X. Zhu, T. Hu, W. Guo, C. Chen, En L. Liu, "Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations", Isprs Int. J. Geo-Information, vol.5, no.11, 2016, Doi: 10.3390/Ijgi5110201.
[27] X. Li En R. Bai, "Freight Vehicle Travel Time Prediction Using Gradient Boosting Regression Tree", no.December, pp. 1010–1015, 2017, Doi: 10.1109/Icmla.2016.0182.
[28] Y. Zhang En A. Haghani, "A Gradient Boosting Method to Improve Travel Time Prediction", Transp. Res. Part C Emerg. Technol., vol,58, pp. 308–324, 2015, Doi: 10.1016/J.Trc.2015.02.019.
[29] A. G. F Pedregosa, G Varoquaux, “Scikit-Learn: Machine Learning in Python”, J. Mach. Learn. Res., vol.12, pp. 2825--2830, 2011, [online]. Available At: Https://Scikit-Learn.Org/Dev/Modules/Ensemble.Html#Gradient-Tree-Boosting.
[30] S. Senthilnathan, "Usefulness of Correlation Analysis", SSRN Electron. J, 2019. Doi: 10.2139/Ssrn.3416918.
[31] C. Managwu, D.Matthias, N. Nwaibu, "Random forest Regression Model for Estimation of Neonatal Levels in Nigeria," SSRG International Journal of Computer Science and Engineering, vol.7, no. 7, pp. 41-44, 2020. Crossref, Https://Doi.Org/10.14445/23488387/Ijcse-V7i7p107
[32] E. Summary, "Comprehensive Mobility Plan for Cma Final Report," no.January, 2014.
[33] U. L. Transport, "Tumkur City Bus Service Directorate of Urban Land Transport," 2013, [online]. Available At: Http://Www.Urbantransport.Kar.Gov.in/Tumkur City Bus Service.Pdf.
[34] R. Savithramma, R.M., Sumathi, "Comparative Analysis of Waiting Delay Estimation Models for Signalized Intersections: A Case Study Tumakuru City," in Proceedings of the Third International Conference on Information Management and Machine Intelligence, Algorithms for Intelligent Systems, pp. 579–589, 2022. Doi: 10.1007/978-981-19-2065-3_61.