Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
2 results
Search Results
Item Electronic ticket machine data analytics for public bus transport planning(Institute of Electrical and Electronics Engineers Inc., 2018) Cyril, A.; George, V.; Mulangi, R.H.This paper investigates various aspects related to demand modeling and line-planning for bus transport systems based on data elicited from Electronic Ticket Machine (ETM). The ETM data has not been explored thoroughly for transportation planning although it is nowadays collected and compiled by public transport undertakings on a regular basis. The data used in the study is part of transactions on ticket sales by Kerala State Road Transport Corporation (KSRTC) maintained at 6 bus depots in Trivandrum city for the period between 2010 and 2013. The data collected through ETM is immensely huge with average monthly passenger transactions of approximately one million. The database can be audited and compiled to determine the passenger demand, operator's performance, and effectiveness of the service provided. It is possible to determine the origin-destination (OD) matrix of the bus commuters by querying the ETM database using a specially developed program in MATLAB®. The OD data will assist in travel demand modelling, decision-making, and formulation of strategies for future preplanning of the transit system. The work presented in this paper provides details on the block-diagram developed for the formulation of the programme, and a demonstration of its capabilities. In a similar manner, it is also possible to determine the link-volume in terms of passenger flow on the transit network using a specially developed program. Additionally, it is also possible to elicit details on the load-rate with information on boarding and alighting of passengers at bus stops in addition to performance-related statistical details can be elicited from the ETM database. It is proposed to develop MATLAB® based programs for the same. The work described herein also includes description on the use of the time-series approach in short-term demand forecasting. The present work proposes a number of analytical methods that can be employed to derive information from ETM data for travel demand modeling, and strategic and operational planning of public transport. © 2017 IEEE.Item Bus passenger demand modelling using time-series techniques-big data analytics(Bentham Science Publishers B.V. P.O. Box 294 Bussum 1400 AG, 2019) Cyril, A.; Mulangi, R.H.; George, V.Background: Public transport demand forecasting is the fundamental process of transport planning activity. It plays a pivotal role in the decision making, policy formulations and urban transport planning procedures. In this paper, public bus passenger demand forecasting model is developed using a novel approach. The empirical passenger demand for a bus depot is modelled and forecasted using a data-driven method. The big data generated by Electronic Ticketing Machines (ETM) used for issuing tickets and collecting fares is sourced as the data for demand modelling. This big data is time indexed and hence has the potential for use in time-series applications which were not previously explored. Objectives: This paper studies the application of time-series method for forecasting public bus passenger demand using ETM based time-series data. The time-series approach used is the four Holt-Winters’ modeling methods. Holt-Winters’ additive and multiplicative models with and without damping have been empirically compared in this study using the data from the inter-zonal buses. The data used in the study is a part of the transaction on ticket sales by Kerala State Road Transport Corporation (KSRTC) maintained at the Trivandrum City depot of an Indian state Kerala, for the period between 2010 and 2013. The forecasting performance of four time-series models is compared using Mean Absolute Percentage Error (MAPE) and the model goodness of fit is determined using information criteria. Conclusion: The forecasts indicate that multiplicative models with and without damping, which better account for seasonal variations, outperform the additive models. © 2019 Cyril et al.