Ride hailing services: quantifying the impact of deadheading in urban mobility

Abstract

As the popularity of ride-hailing services continues to increase across the globe over the past few decades, understanding their effects on urban transport is becoming more crucial for transport planning. Ride hailing services provided by transit network companies like Ola, Uber have significantly enhanced urban mobility in India too, reducing the gap between demand and supply of public transport in Indian Cities. However, the rapid growth of these services has also introduced the challenge of deadheading, empty trips between passenger pick-ups and drop-offs along with waiting times for new rides. Deadheading contributes to increased vehicle kilometers travelled (VKT) and vehicle travel time (VTT) in comparison to passenger kilometers travelled (PKT) and passenger travel time (PTT), exacerbating traffic congestion, vehicular emissions, and inefficiencies. The study aims to quantify the extent and impact of deadheading in Delhi, utilizing a combination of driver trip data, passenger preferences and traffic volume data. Empirical analysis and predictive modelling of the collected dataset helps quantify the severe impacts of deadheading on urban mobility. The research employs a framework to model deadheading trips at an individual trip level, accounting for socio-demographic, economic, and built-environment characteristics. Additionally, a demand prediction and optimization model are developed to minimize deadheading by simulating various policy scenarios, including fleet restrictions and geofencing. The economic implications of deadheading are substantial. From a monetary perspective, deadheading leads to increased fuel consumption, contributing to higher operating costs for drivers and increased fares for passengers. Additionally, lost time due to congestion reduces the number of trips a driver can complete, resulting in income loss. For passengers, delays caused by traffic congestion translate to lost productivity, which has an associated opportunity cost. From an environmental standpoint, increased emissions from deadheading worsen air quality and contribute to carbon footprints. To analyses this, the study aims to perform an analysis of average generalised cost for a ride hailing driver accounting for total losses due to deadheading. A crucial aspect of optimizing ride hailing services involves increasing cab utilization through shared rides. Willingness to share a cab varies across user demographics and trip characteristics. Passenger behaviour behind ride-sharing preferences involves parameters like cost, waiting times, and environmental concerns that influence PKT per trip. By promoting shared rides through fare discounts, pooling only zones, and integrating ride hailing services with existing public transport systems, deadheading can be reduced significantly. Higher PKT will result into reduced fleet size without compromising service availability. The management of urban infrastructure in rapidly expanding cities like Delhi requires innovative approaches to mitigate transport inefficiencies while ensuring sustainable mobility solutions. Infrastructure and policy interventions can play a crucial role in optimizing ride-hailing services through smart curb management, dedicated ride-hailing zones, and dynamic pricing mechanisms. Current regulatory frameworks often lag behind the rapid expansion of TNCs, failing to impose effective measures for fleet management and congestion control. The outcomes of this research provide a scalable framework for policymakers and urban planners to integrate ride-hailing services with large scale infrastructure projects, fostering an efficient urban transport ecosystem with minimum externalities. Keywords: Ride-hailing Services, Deadheading, Urban Infrastructure, Traffic congestion, Average Generalised Cost, Optimisation Model, Policy.