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Analyzing User Behavior in Selection of Ride-Hailing Services for Urban Travel in Developing Countries

Authors:
Priyanshu Raj
Priyanshu Raj
Eeshan Bhaduri at University of Leeds
Eeshan Bhaduri
Rolf Moeckel
Rolf Moeckel
Rolf Moeckel
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Arkopal Goswami at Indian Institute of Technology Kharagpur
Arkopal Goswami
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Abstract and Figures

Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. Two models were developed, a multinomial logit (MNL) model, to estimate the factors that lead to the adoption of RHS, and an ordered logit (OL) model, to estimate the frequency of usage of RHS. The results reveal a comprehensive set of socio-demographic and behavioral factors which leads to greater adoption of RHS. The variables such as household income, vehicle ownership, and use of smartphone are found to be important predictors (with a 95% significance level) of service adoption of RHS. The model results also suggest that RHS are likely to be used infrequently, and when it is being used, they are more likely to be used by the younger population and during the weekends. Overall, this research brings valuable and novel insights into the adoption and usage of RHS in India.
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Vol.:(0123456789)
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Transportation in Developing Economies (2023) 9:1
https://doi.org/10.1007/s40890-022-00172-5
ORIGINAL ARTICLE
Analyzing User Behavior inSelection ofRide‑Hailing Services
forUrban Travel inDeveloping Countries
PriyanshuRaj1· EeshanBhaduri2 · RolfMoeckel3 · ArkopalKishoreGoswami2
Received: 11 October 2021 / Accepted: 15 September 2022 / Published online: 3 October 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022
Abstract
Recent developments in urban transportation services are rapidly transforming the way people make their trips. Around the
world, the most controversial and rapidly growing mobility services in recent years are ride-hailing services (RHS) offered
by transportation network companies (TNCs) such as Uber and Ola. This research estimates the demand for RHS vis-à-vis
other modes and further expands to estimate usage propensity of RHS in the capital city of India, New Delhi. A discrete
choice modeling framework is developed based on a household travel surveys (N = 426) conducted in 2019. Two models
were developed, a multinomial logit (MNL) model, to estimate the factors that lead to the adoption of RHS, and an ordered
logit (OL) model, to estimate the frequency of usage of RHS. The results reveal a comprehensive set of socio-demographic
and behavioral factors which leads to greater adoption of RHS. The variables such as household income, vehicle ownership,
and use of smartphone are found to be important predictors (with a 95% significance level) of service adoption of RHS. The
model results also suggest that RHS are likely to be used infrequently, and when it is being used, they are more likely to be
used by the younger population and during the weekends. Overall, this research brings valuable and novel insights into the
adoption and usage of RHS in India.
Keywords Ride-hailing services· Mode choice· Multinomial logistic regression· Ordered logistic regression· Public
transport
Introduction
In the past decade, urban transportation systems around the
globe have witnessed disruptive transformations, largely
attributed to the continuous advancement in information
and communication technologies (ICT). One of the most
popular, widely adopted rapidly evolving, and controversial
products of such advancements are ride-hailing services
(RHS) being provided by the transportation network
companies (TNCs). RHS are quite different from traditional
modes of travel, where passengers and drivers traveling to
the same destination are paired using a mobile application.
To get a sense of how popular their usage has rapidly
increased the RHS are, Uber, the largest RHS provider in the
world, completed 2 billion rides up until the first 6 months
of 2016, but and then subsequently doubled the ridership
within just the next 6 months [2]. In addition to Uber, which
is already operating in more than 500 cities globally, there
are other RHS providers that operate in different parts of
the world, such as Lyft, Didi, and Ola. RHS are one of the
fastest-growing sectors, which is evident from the Fortune’s
list of unicorns. Out of the top 25 companies in that list,
4 are engaged in providing such shared mobility services,
with Uber topping the list, with a total valuation of US $
69 billion [3]. This certainly indicates an increasing public
interest in such services. Needless to say, these services
provide a higher level of comfort and convenience in terms
* Eeshan Bhaduri
eeshanbhaduri@iitkgp.ac.in
Priyanshu Raj
priyanshu.maniraj@gmail.com
Rolf Moeckel
rolf.moeckel@tum.de
Arkopal Kishore Goswami
akgoswami@infra.iitkgp.ac.in
1 CRISIL House, Mumbai400076, India
2 Ranbir andChitra Gupta School ofInfrastructure Design
andManagement, Indian Institute ofTechnology Kharagpur,
Kharagpur721302, India
3 Department ofCivil, Geo andEnvironmental Engineering,
Technical University ofMunich, 80333Munich, Germany
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... They, however, play a very different function in the developing world, where personal vehicle ownership is far lower than in developed nations. RHS has experienced enormous success in developing countries, which can be attributed to several factors, such as inefficient alternative transportation services coupled with the absence of strong regulatory frameworks, ill-developed public transportation, an increase in vehicle ownership, and other sociodemographic factors [5,25,33]. ...
... Ride-hailing services' adoption is influenced by various factors, as reported by studies over the past few decades, including socio-economic and demographic characteristics, trip-related characteristics, and attitudes [13,33]. Studies on the relationship between the built environment and ridehailing services, though are scarce. ...
Joint Estimation of Ride-Hailing Services (RHS) Adoption and Frequency: Assessing Impacts of Built Environment on Work and Discretionary Trips
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In recent years, ride-hailing services (RHS) (also known as on-demand ride services), such as Uber, Ola, Lyft, Didi, etc., have transformed the urban transportation environment. RHS promises to promote sustainable urban mobility as it combines the flexibility of personal vehicles and the shared nature of public transport. In developing countries like India, research on these emerging RHS is still in its infancy, and the role of the built environment (BE) in influencing RHS choice and usage has not been explored. The current study aims to do so, for the city of Kolkata, India which has the highest modal share of ride-hailing amongst million-plus cities in India. Revealed preference (RP) household surveys were conducted, and information on 841 ride-hailing user trips was collected and a semi-ordered bivariate probit model was developed to estimate RHS adoption and usage frequency simultaneously. Model results show that BE variables like destination accessibility, bus stop density, road density, and population density significantly influence the adoption of RHS and use frequency of RHS with varied intensity for work and discretionary trips. Users residing in neighbourhoods with higher accessibility and better public transit connectivity are the least likely to adopt RHS and are also likely to be infrequent users. On the other hand, individuals living in high-density neighbourhoods are more likely to adopt RHS. Also, with increasing distances between origin and destination, commuters tend to adopt and use RHS frequently.
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... Studies have also explored user behavior and preferences related to ride-hailing services in developing countries 2019. Factors influencing the demand for ride-hailing services were assessed, including socio-demographic variables, trip features, cost, and service attributes like comfort and reliability (Raj et al., 2023). The impact of ride-sharing on public transport usage has been examined in studies conducted in the US, the Netherlands, and Canada, highlighting how ride-hailing services can both compete and complement public transport (Cats et al., 2021;Tarnovetckaia & Mostofi, 2022;Zhang & Zhang, 2018). ...
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Analyzing The Changes In Travel Mode Behavior In Uttara Model Town, Dhaka: A Comparative Study Before And After The Emergence Of Ride-Hailing Services
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The urban transportation landscape has witnessed a significant transformation with the advent of ride-hailing services. This study focuses on analyzing the shifts in travel mode behavior in Uttara Model Town, Dhaka, before and after the emergence of ride-hailing services such as Uber, Pathao, Obhai etc. The problem statement centers around understanding the dynamic changes in commuting patterns and the factors that influence the adoption of these ride-hailing platforms as preferred travel modes. Despite the growing popularity of ride-hailing services in Dhaka, there is a research gap in comprehensively assessing the impact of these services on traditional travel modes and uncovering the underlying determinants of users' preference shifts. This study aims to fill this gap by achieving two primary objectives: firstly, examining the alterations in travel mode behavior post the introduction of ride-hailing services; secondly, identifying the key factors driving the adoption of platforms like Uber and Pathao as preferred travel modes within Uttara Model Town. An online questionnaire survey has been done to compare travel patterns to the launch of ride-hailing services to achieve the research goals. This survey aims to gather information on participants' socioeconomic characteristics, changes in preferred modes of transportation, and travel habits. The findings will help make more informed opinions about improving urban transport networks and accommodating people's evolving travel needs in Uttara Model Town and other locations.
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Understanding Indian ride-sharing consumers: the role of psychographics and perceived value
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Purpose This study aims to explain the influence of four socio-psychological variables: social comparison orientation, face saving (FS), status consumption (STC) and frugality (FGL) on consumers’ value perception toward ride-sharing services – one of the most widely used collaborative consumption models. Furthermore, it assesses how perceived value affects consumers’ intention to use (IU) the ride-sharing services and intentions to substitute ride-sharing services for using a personally owned car. It also assesses the moderating effect of psychographics on the relationship between consumers’ perception and behavioral intention. Design/methodology/approach A structured questionnaire was developed using existing scales adapted from the literature to test the hypothesized relationships. The data for the study were collected from 489 users of ride-sharing services in India. Structural equation modelling was performed to test the proposed model using AMOS 18 and moderation analysis was performed using PROCESS MACRO. Findings The findings of the study suggest that social comparison, FS, STC and FGL have a significant influence on consumers’ value (utilitarian and hedonic) perception. Furthermore, the results supported the effect of consumers’ value perception on their IU the ride-sharing services as well as their intention to substitute ride-sharing services for using a personally owned car. Lastly, the results also evidenced the moderating role of psychographic variables. Originality/value Very few studies have examined the role of psychographics in the adoption of collaborative consumption services. The paper attempts to fill this gap. It assesses the effect of four relevant consumer traits on perceived value in the ride-sharing services context. Furthermore, it expands the understanding of the role of psychographics by measuring their moderating effects apart from direct effects. The results of the study bear important implications for academicians, policymakers and marketers.
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Mode Choice Modelling for Hailable Trips: An Investigation of the Competition of Uber with Other Modes by Using an Integrated Non-Compensatory Model with Probabilistic Choice Set Formation
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This paper presents an empirical investigation on demand for TNC services (e.g., Uber) in the Greater Toronto and Hamilton Areas (GTHA) through the application of an innovative discrete choice model. The proposed model combines Independent Availability Logit (IAL) and Constrained Multinomial Logit (CMNL) model formulation to reap the unique features of both. The proposed model is thus a Semi Compensatory Independent Availability Logit (SCIAL) model. For the empirical investigation, it uses a dataset of trip mode choices that suitable to represent ride-hailing service (e.g., Uber). Such trips are named as hailable trips in the dataset, which is drawn from a large scale household travel survey conducted in the region in 2016. To have a clear understanding of behavioural processes involved in the choice of travel mode of hailable trips, the proposed SCIAL model jointly models probabilistic choice set formation and conditional semi-compensatory choice. The empirical model does not reveal any evident competition between Uber and the private car, public transit, or non-motorized modes. It indicates that urban taxi is its main competitor, but there are notable differences in socio-demographic profiles of taxi and Uber users. For example, a taxi is preferred by older people, but younger people prefer uber, and there is no gender difference in such a pattern. In terms of the relationship between considering Uber as a feasible mode and choosing it for a trip, Uber has similarities to the car passenger mode. Merely accepting it as a feasible option has a significant influence on the final choice to use it. This indicates a potential new segment of the travel market, generated primarily for the advent of TNC service, e.g., Uber in Toronto.
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