Electronic ticket machine data analytics for public bus transport planning

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.