Estimation of Bus Transport Ridership Accounting Accessibility

Document Type

Journal Article

Publication Date


Subject Area

ridership - modelling, ridership - attitudes, place - asia, mode - bus, infrastructure - stop


Accessibility Indices, Ridership Estimation Models, Urban Transit


The accessibility measures encompass a range of available opportunities with respect to the attractiveness and travel impedance. In this approach the accessibility measures were considered in geographically aggregated level mainly because of two reasons. One is that this framework is intended for planning decisions which by necessity deals with areas and not with individuals living in it. Another reason is to showcase the methodology of interrelating the accessibility measures and ridership of a transport system. Hence these measures are indispensable to be of plain and easy to understand and work with.

The bus route (21G) from Tambaram to Broadway in Chennai city was chosen for this study. It is of 35 km in length. Bus passenger opinion was collected at the bus stops of the study route. The questionnaire was prepared in such a way that it records the passengers’ accessible distance, time and cost of trips from their home to destination including transfers. Global Positioning System (GPS) and Electronic Ticketing Machines (ETM) are provided in all the buses operated in the route and these data were collected for a week. The OD pattern of travelers was taken from the Chennai City Traffic Study (CCTS) of Chennai Metropolitan Development Authority and updated with the ETM and passenger opinion survey data. The in-vehicle travel time of the trips and headway of buses at each bus stop were obtained from GPS data. The headway of the trips alludes the waiting time of the passenger at bus stops. The study route was split up into 16 zones. Accessibility Index (AI) of the each zone was determined using Composite Impedance Gravity measure of Urban Accessibility Index tool of TransCAD. Multinomial Logit Model was chosen for aggregation of the accessibility measures in 2 dimensions viz. Time and Purpose. The AIs of the zones were presented in graphical form to show in which areas the accessibility of the public transportation is to be improved. A model was developed to relate the boarding and alighting of passengers at each bus stop with that of the AIs using Multiple Linear Regression.

The various parameters of accessibility measures were determined and specific indices to represent the accessibility level of the transit corridor pertaining to key factors influencing the trip production and attraction are presented. It is demonstrated in the paper that how this approach could be developed into a decision supporting tool to assist in a range of transport and land-use planning activities. The impact on ridership for changes in the Accessibility Indices due to variation in bus transit fare and travel time is reported.


Permission to publish the abstract has been given by Elsevier, copyright remains with them.


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