Abstract:
The field of transportation planning has undergone a significant transformation in the past decade with the advent of Ride-Hailing Services (RHS). As Transportation Network Companies (TNCs) continue to grow in popularity, there is a need to understand the implications of these services on urban mobility. One way to gain insights into the movement of Ride-Hailing services is by utilizing Big Data. Uber Technologies Inc.'s “Uber Movement Data”, an anonymized dataset that includes trip times from one zone to another, has the potential to provide new insights that can improve decision-making during the planning process. This study focuses on the demand and spatiotemporal usage patterns for RideHailing Services in Delhi, broken down quarterly between 2016 to 2019. The demand and supply of Ride-Hailing Services are studied spatially as well as through in-degree and weighted in-degree analysis and then linked with land use and public transport availability. An interesting relationship between Public Transport and Ride Hailing Services is understood by superimposing the Transit Supply Index (TSI). It has been observed that Ride-Hailing Service production and Production are linked to government and transportation land use, whereas residential and recreational land use is linked to ride-hailing service attraction. Additionally, areas with better public transportation availability tend to attract more Ride-Hailing Services. The primary survey indicated that there is a lack of last-mile connectivity which causes people to shift towards Ride-Hailing Services instead of Public Transportation. The findings of this study can also help in identifying zones where dynamic on-demand services can be implemented. In addition, the findings from this data are investigated to be applied to various policy implications of decision-making for the planning process. These findings can be useful for policymakers and government authorities to lead to a better future. There are currently very few global studies and no significant national studies; therefore, it is necessary to establish the utilization of such data-driven studies.
