Integration of demand responsive system in bus based public transportation

Abstract

The world urban population is increasing at a high pace than ever. Increasing population leads to increasing demand for services. Many countries have started encouraging their citizens to utilize public transportation over private mode of transport, through various initiatives. Studies show that public transport usage helps to mitigate several socio-economic and environmental issues. Public transport enables accessibility to social activities, goods, and services and this can reduce social exclusion and poverty (Hine and Mitchell, 2001). Bus is one of the major public transits and is easily accessible with better connectivity. Increasing the fleet size to meet the growing demand will lead to congestion, increased emissions and it is an unsustainable approach. Existing bus-based public transportation has been a supply-based service which operates on fixed routes, fixed locations with pre planned schedule on a fixed timetable. Users must adapt to this fixed timetable to use this service. Passenger demand is unevenly distributed along different routes and it is changing constantly. Due to the fixed timetables, buses travel with low occupancy for a considerate part of the trips. Various cities have adopted Demand Responsive Transport (DRT) service under various approaches to overcome the above issue. DRT is a type of Mobility-onDemand service, where Public Transportation Service responds and operates on passengers’ demand. The fleets continuously reoptimizing their running routes using real-time data of passengers’ demand, without impacting other passengers. Integrating DRT in Public Transportation is a sustainable approach, which can optimize the route and maximize the utilization of existing resources(fleet). This thesis aims to develop a model that integrates DRT in existing bus-based public transportation. This research focuses on service area delineation and categorization in a city based on varying passenger demand, followed by developing a dynamic route optimization model for bus routes which will be continuously adjusted to serve the real-time passenger demand, and integrating it in existing transportation system.