Abstract

The demand for transit is linked to a country's economic and population growth. As per the Ministry of Road Transport and Highways (MORTH), from 2009 to 2019, India witnessed an average growth rate of 9.9% per annum in registered vehicles. Even though the vehicle ownership is often viewed as a symbol of economic prosperity, the associated negative externalities (congestion, air pollution, road crashes, noise pollution, etc.) are substantial. To counter this, carpooling has been proposed as a solution globally. However, there is a paucity of literature for analysing the difference in travel characteristics of car-poolers among other private vehicle users. The present study aims to bridge this gap by comparing the demographic and travel characteristics of existing car-poolers with two-wheelers and single-occupant car users in Gurugram in the national capital region of India. Using an unsupervised machine learning clustering method (two-step cluster), the findings of the present study indicate that the profile of single-occupant car (SOC) users and car-poolers are highly distinguished from two-wheeler users. However, car-poolers have similar characteristics to single-occupant car users, except for travel cost, income level, and gender, suggesting that SOC users are potential car-poolers. A comparative study of this kind would help city planners and policymakers identify prospective car-poolers, and formulate policies to encourage SOC users to adopt carpooling and promote urban transportation sustainability.

Full Text
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