Development of Coordinated Schedules using Genetic Algorithms

Abstract

Suburban trains and public buses can play a better role in public transportation if they are coordinated. Coordination of these two services will reduce the journeys made by intermediate public transport services and private vehicles from railway stations, which have become major traffic generators. Thus, congestion, delays, and environmental pollution due to these services can be reduced to a great extent. In this study, the Andheri and Vileparle suburban railway stations in Mumbai, India are taken as study locations, and schedule coordination between suburban trains and public buses [Bombay Electric and Suburban Transport (BEST) buses] at these suburban railway stations is attempted. The coordinated schedules of BEST buses have been determined on already developed feeder routes for these two stations using the schedule optimization model (SOM). The objective function of the SOM is the minimization of transfer time between two services and vehicle operating costs of BEST buses. The objective function and constraints make the problem nonlinear and nonconvex with a large number of variables, making it difficult to solve by classical approaches. Therefore, the genetic algorithm, a robust optimization technique, is used for optimization. So far there have been few studies pertaining to the integration of public transport modes, and these studies were limited to analytical modeling. Analytical models do not meet real-life objectives under realistic constraints. In the absence of studies related to realistic modeling, it can be claimed that this study is a specific contribution toward operational integration of public transport modes.