The week's pick
Reseach Article
A Machine Learning Approach to Eliminate Bus Bunching of Public Transportation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 35 |
| Year of Publication: 2020 |
| Authors: Harshit Kaushik, Pallavi Tomar, Shrishti Katiyar, Prachit Luthra, Partha Sarathi Chakraborty |
10.5120/ijca2020920900
|
Harshit Kaushik, Pallavi Tomar, Shrishti Katiyar, Prachit Luthra, Partha Sarathi Chakraborty . A Machine Learning Approach to Eliminate Bus Bunching of Public Transportation. International Journal of Computer Applications. 175, 35 ( Dec 2020), 1-9. DOI=10.5120/ijca2020920900
Abstract
The framework presents the opportunity to provide a solution in dealing with the problem of Bus Bunching (BB) which adversely affects the traffic in India. Recent advancements in technology have allowed real time monitoring and interactive usage of the Public Transport (PT) System. Regression Analysis and Gradient Descent are the Machine Learing (ML) principles used to make a predictive methodology that helps to prevent BB. The methodology is then used to provide a Corrective Solution (CS) in order to eliminate the BB event. The proposed method is performed for Kaushambi - Meerut bus route using the schedule of the bus time table and various assumption based data for traffic and BB events. The system proposed could be used to provide a decision support system and improve the control room operations. This methodology considers the adverse effects of Bus Holding (BH) and provides discrete and streamlined solutions, given the complexity of BB problem.
References
- Matias, L. M., Cats, O., Gama, J., Moreira, J. M., and Sousa, J. F. (2016). An online learning approach to eliminate bus bunching in real-time.
- Ercan, T., Onat, N. C., Tatari, O., and Mathias, J.-D. (2017). Public transportation adoption requires a paradigm shift in urban development structure. Journal of Cleaner Production, 142, 17891799.
- Santos, D., Kokkinogenis, Z., de Sousa, J. F., Perrotta, D., and Rossetti, R. J. (2016). Towards the integration of electric buses in conventional bus fleets. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), IEEE. 8893.
- Wakker, K. F. (2015). Fundamentals of astrodynamics.
- Xuan, Y., Argote, J., and Daganzo, C. F. (2011). Dynamic bus holding strategies for schedule reliability: Optimal linear control and performance analysis. Transportation Research Part B: Methodological, 45(10), 18311845.
- Dessouky, M., Hall, R., Zhang, L., and Singh, A. (2003). Real-time control of buses for schedule coordination at a terminal. Transportation Research Part A: Policy and Practice, 37(2), 145164.
- Daganzo, C. F. and Pilachowski, J. (2011). Reducing bunching with bus-to-bus coop- eration. Transportation Research Part B: Methodological, 45(1), 267277.
- Daganzo, C. F. (2009). A headway-based approach to eliminate bus bunching: Systematic analysis and comparisons. Transportation Research Part B: Methodological, 43(10), 913921.
- Boyd, C. W. (1983). Notes on the theoretical dynamics of intermittent public passenger transportation systems. Transportation Research Part A: General, 17(5), 347354.
- Abkowitz, M., Knier, F., Yuh, H.,Weagley, R., and Stolka, M. (1987). Electronic transport in amorphous silicon backbone polymers. Solid state communications, 62(8), 547550.
- Hassan, S. M., Moreira-Matias, L., Khiari, J., and Cats, O. (2016). Feature selection issues in long-term travel time prediction. International Symposium on Intelligent Data Analysis, Springer. 98109.
- Khiari, J., Moreira-Matias, L., Cerqueira, V., and Cats, O. (2016). Automated setting of bus schedule coverage using unsupervised machine learning. Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer. 552564.
- Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain.. Psychological review, 65(6), 386.
- Moreira-Matias, L., Ferreira, C., Gama, J., Mendes-Moreira, J., and de Sousa, J. F. (2012). Bus bunching detection by mining sequences of headway deviations. Indus- trial Conference on Data Mining, Springer. 7791.
- Moreira-Matias, L., Gama, J., Mendes-Moreira, J., and de Sousa, J. F. (2014). An incremental probabilistic model to predict bus bunching in real-time. International Symposium on Intelligent Data Analysis, Springer. 227238.
- Neapolitan, R. E. (2012). Probabilistic reasoning in expert systems: theory and algorithms. CreateSpace Independent Publishing Platform.
Index Terms
Keywords