Mobile App for Passenger Boarding and Alighting Survey

By Sakitha Kumarage

Public transport supply and demand of Sri Lanka has not been close to equilibrium for a longer time. It could be observed a 45% of ridership in daily commuting in the western region of Sri Lanka being facilitated by public transport which includes buses and trains. The National Transport Commission is the governing body which regulates the operation and schedule of the public transport system. The routes, schedules, and fare attributes are formulated and maintained by The National Transport Commission to provide better service quality and reliability.

It is required to collect data on passenger demand for public transport to identify optimum operating schedules and attributes. Data collection by means of manual counting and estimations based on the experience of officers are the current existing main modes of collecting data required for passenger demand calculation. The accuracy of the collected data is at a lower level due to the high possibility of measuring errors which could occur in current methods of data acquisition.

Transite24 has developed a novel method to increase the accuracy of data collection required to identify passenger demand and adequacy of transport supply. A novel approach was taken by developing a mobile application to collect passenger counts in public buses. The mobile application is used by data collecting agents to conduct the boarding and alighting survey. The application act as a data counting device which has the capacity of collecting boarding and alighting counts of passengers at each bus stop. Further, it is possible to collect demographic data of passengers such as average age and sex using the application, which is useful in identifying the adequacy of safety and facilities available in public transport fleets. The data collected using a mobile application is transferred to a cloud web server at the end of the survey.

The mobile application enhances the performance of data collecting agent and reduces the human error and the loss of data. The collected data could be used to identify spatial and temporal variation of passenger demand, future requirements of public transport fleet facilities and schedules and estimate the operating costs and fare attributes to optimize the public transport revenue generation.

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