Characterisation and Prediction of Motorised Three Wheelers Travel Time in Urban Roadways

Abstract

AbstractMotorised three wheeler is one of the most popular paratransit vehicles in India, due to its small size, cost-effectiveness, manoeuvrability, and availability. It provides the most flexible, rider-friendly, quick travel even in traffic-choked streets and narrow roads, exhibiting entirely different characteristics in terms of travel speed, and trip lengths compared to other paratransit modes like taxicabs. Reported studies on the behaviour of paratransit mainly concentrated on taxicabs and hence there is a need to analyse the travel behaviours of motorised three wheelers. In this regard, the present study aims to understand and characterise the travel patterns of motorised three wheelers. In addition, travel time prediction is an inevitable aspect for demand-responsive paratransit services like motorised three wheelers, taxicabs etc. It helps both the drivers and passengers to make smart choices about the routes by avoiding congested streets and to have information about the pickup and arrival time. The present study proposes a methodology using Support Vector Regression (SVR) to predict the travel time of motorised three wheelers by incorporating the trip characteristics under heterogenous lane less traffic conditions. The performance of the proposed method showed a clear improvement when compared with a Median based prediction methodology that was reported to be working well for travel time prediction problems.KeywordsTravel time predictionParatransitMachine learningTravel pattern