Exploratory Data Analysis of Passenger Movement on Delhi Urban Bus Route

Authors: Sourabh Jain, Sukhvir Singh Jain, Gaurav V. Jain

Abstract:

Intelligent Transportation System is an integrated application of communication, control and monitoring and display process technologies for developing a user–friendly transportation system for urban areas in developing countries. In fact, the development of a country and the progress of its transportation system are complementary to each other. Urban traffic has been growing vigorously due to population growth as well as escalation of vehicle ownership causing congestion, delays, pollution, accidents, high-energy consumption and low productivity of resources. The development and management of urban transport in developing countries like India however, is at tryout stage with very few accumulations. Under the umbrella of ITS, urban corridor management strategy have proven to be one of the most successful system in accomplishing these objectives. The present study interprets and figures out the performance of the 27.4 km long Urban Bus route having six intersections, five flyovers and 29 bus stops that covers significant area of the city by causality analysis. Performance interpretations incorporate Passenger Boarding and Alighting, Dwell time, Distance between Bus Stops and Total trip time taken by bus on selected urban route.

Keywords: Congestion, Dwell time, delay, passengers boarding alighting, travel time.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132353

References:

[1] Abdelfattah, A. M., and Khan, A. M. (1998), “Models for Predicting Bus Delays”, Transportation Research Record, No. 1623, pp. 8-15.
[2] Bertini, R. L., and El-Geneidy, A. M. (2004), “Modeling Transit Trip Time Using Archived Bus Dispatch System Data,” Journal of Transportation Engineering, Vol. 130, Issue 1, pp. 56-67.
[3] Chalumuri R. S., and Yasuo A. (2014), “Modelling Travel Time Distribution under Various Uncertainties on Hanshin Expressway of Japan”, European Transport Research Review, 6(1), DOI 10.1007/s12544-013-0111-3, pp.85- 92.
[4] Chen, S., Ray, C., Tan, J., and Claramunt, C. (2006), “Integrated Transportation GIS for the City of Guangzhou, China", Proceedings of 6th International Conference on ITS Telecommunications, Chengdu, DOI: 10.1109/ITST.2006.288698, pp. 894- 897.
[5] Chien, S., Ding, Y., and Wei, C. (2002), “Dynamic Bus Arrival Time Prediction with Artificial Neural Networks”, Journal of Transportation Engineering, Vol. 128, Issue 5, pp. 429-438.
[6] Dueker, K. J, Kimpel, T. J., and Strathman, J. G. (2004), “Determinants of Bus Dwell Time”, Journal of Public Transportation, Vol. 7, Issue 1, pp. 21-40.
[7] El-Geneidy, A. M., and Vijayakumar, N. (2011), “The Effects of Articulated Buses on Dwell and Running Times”, Journal of Public Transportation, Vol. 14, Issue 3, pp. 63-86.
[8] Jaiswal, S., Bunker, J. M., and Ferreira, L. (2009), “Modelling the Relationships Between Passenger Demand and Bus Delays at Busway Stations”, 88th Transportation Research Board Annual Meeting, Washington D. C., USA.
[9] Jeong, R., and Rilett, L. R. (2004), “Bus Arrival Time Prediction Using Artificial Neural Network Model”, In Proceedings of the IEEE Intelligent Transportation Systems Conference, Washington, D.C., USA, pp. 988-993.
[10] Patnaik, J., Chien, S., and Bladikas, A. (2004), “Estimation of Bus Arrival Times Using APC Data”, Journal of Public Transportation, Vol. 7, Issue 1, pp. 1-20.
[11] Smith, B. L., and Demetsky, M. J. (1995), “Short-Term Traffic Flow Prediction: Neural Network Approach”, Transportation Research Record, No. 1453, pp. 98-104.
[12] Zhang, M., Xiao, F., and Chen, D. (2013),” Bus Travel Time Prediction based on GPS Data”, In Proceedings of the fourth International Conference on Transportation Engineering (ICTE 2013), Chengdu, China, pp. 1470- 1475.