Ride-hailing Services Research Articles

How do fares affect the utilization of ride-hailing services: Evidence from Uber Japan's experiments

Ride-hailing has been introduced and has become popular in many major cities worldwide. The service often relies on the use of dynamic pricing, in which fares are adjusted in real time. Therefore, understanding the impact of fare on demand is necessary for the operation of ride-hailing. The aim of this study is to empirically investigate the impact of fares on demand through price elasticities using the session data of Uber taxis from Uber Japan's experiments in two cities: Nagoya and Kyoto. A mixed logit model with a flexible mixing distribution was estimated to capture the taste heterogeneity among riders, which increased the reliability of the result. The estimation results indicated that most riders were price inelastic, with an average price elasticity of approximately −0.2 to −0.1. These findings are useful for ride-hailing companies and policymakers because they provide valuable information to enable the maximization of profit or benefit to customers.

Study of Adoption of Ride-hailing Services: Moderating Role of Consumer Frugality and Status Consumption in Collaborative Consumption

Study of Adoption of Ride-hailing Services: Moderating Role of Consumer Frugality and Status Consumption in Collaborative Consumption

Risk-Averse Ride-Hailing Platform Operations With Safety Risk-Averse Consumers Under Pandemics: Roles of Blockchain Technology and Government Sponsors

Today, ride-hailing platform operations are popular. Facing pandemics (e.g., COVID-19) some customers feel unsafe for the ride-hailing service and possess a “safety risk-averse” (SRA) attitude. The proportion of this type of SRA customers is unfortunately unknown, which makes it difficult for the ride-hailing platform to decide its optimal service price. In this article, understanding that blockchain technology (BT) based systems can help improve market estimation for the proportion of SRA customers, we conduct a theoretical study to explore the impacts that the BT-based system can bring to the platform, customers, and drivers. We consider the case in which the platform is risk-averse (in profit) and serves a market with both SRA and non-SRA customers. We analytically prove that using BT, the optimal service price will be increased and BT is especially helpful for the case with a more risk-averse ride-hailing platform. However, whether it is more or less significant for the more risk-averse SRA customers depends on their degree of risk aversion. We uncover that when the use of BT is beneficial to the customers, it will also be beneficial to the drivers, and vice versa. We derive in closed-form the analytical conditions under which the use of BT can be beneficial to the ride-hailing platform, customers, and drivers (i.e., achieving “all-win”). When all-win cannot be achieved automatically, we explore how governments can provide sponsors to help. We further extend the analysis to consider the general case in which BT incurs both a fixed cost as well as a cost increasing in demand. We prove that the main conclusion remains robust. In addition, we reveal that the required amount of government sponsor to achieve all-win is the same between the two different costing models explored in this article.

Evaluating the brand equity of ride-hailing services from the users' perspective: a cross-country study

Evaluating the brand equity of ride-hailing services from the users' perspective: a cross-country study

The Appropriate Scale of Competition Between Online Taxis and Taxis Based on the Lotka-Volterra Evolutionary Model

In order to determine the optimal scale for urban ride-hailing services and taxis while promoting their sustainable growth, we have developed a Lotka-Volterra evolutionary model that accounts for the competitive, cooperative, and mixed dynamics between these two entities. This model is rooted in the theory of synergistic evolution and is supported by data simulation and analysis. By employing this model, we can identify the appropriate size for urban ride-hailing services and taxis when they reach equilibrium under different environmental conditions. The study’s findings reveal that the evolutionary outcomes of online ride-hailing services and traditional taxis are closely linked to the competitive impact coefficient and the cooperative effect coefficient. In highly competitive environments, intense rivalry can lead to the elimination of the less competitive party, while the dominant player ultimately attains a specific size threshold. As competition moderates, both entities can achieve a balanced and stable coexistence in the market. In cooperative environments, both online ride-hailing services and traditional taxis have more room for development, which facilitates the integration of existing and innovative business models. In environments marked by competition, the development trends of both entities mirror those in competitive settings, but cooperation can slow down the decline of the less competitive party. In conclusion, we propose strategies to foster fair competition between online ride-hailing services and traditional taxis, consider the coexistence of old and new business models, and promote their integrated development.

Ride-Hailing Assignment Problem under Waiting Time Uncertainty using Interval-Valued Fuzzy Quadratic

Ride-hailing is a creative idea created by transportation supported by science and technology. Ride-hailing services can help daily community activities. The issue with ride-hailing is that traffic conditions are unpredictable, implying that waiting times are uncertain. The time passengers spend waiting from when they book a ride service until the driver arrives at the pick-up location is called waiting time. This study suggests a quadratic programming technique for minimizing waiting time while accounting for the unpredictability of pick-up travel time. The interval-valued fuzzy quadratic programming method handles the uncertainty and imprecision of the anticipated journey time. When allocating drivers to pick up passengers, interval-valued fuzzy numbers can provide a more realistic representation of waiting time uncertainty. As a result, the interval-valued fuzzy quadratic programming model can handle the uncertainty in waiting time for ride-hailing assignment problems. The model's performance is evaluated using waiting time and the number of people served. The model's performance is demonstrated numerically using the simulation-based case study. This study shows how to utilize a mathematical method to solve real-world problems with uncertainty and improve user welfare.

The role of mindfulness in promoting purchase intention

Purpose This study aims to examine the role of mindfulness in promoting customers’ purchase intentions, especially with the mediation effects of perceived ease of use and perceived usefulness toward online ride-hailing services. As such, several recommendations for ride-hailing service providers and researchers are suggested for better implementation in a practical and theoretical context. Design/methodology/approach The data collection process is carried out online during the period of February 2022 to March 2022, with a sample of 237 respondents being analyzed by the covariance-based structural equation modeling approach. Findings First of all, mindfulness had a direct influence on perceived ease of use and perceived usefulness, as well as an indirect influence on purchase intention through the mediation role of perceived usefulness. Besides, perceived usefulness is found to have a positive influence on the customer’s purchase intention, while perceived ease of use positively affects both perceived usefulness and intention. Research limitations/implications This study just ends with customers’ intentions to adopt ride-hailing services; nevertheless, the actual behavior has not been examined. Further research might consider actual usage as a dependent factor when investigating the topic of ride-hailing services. Originality/value This study is notably different from the existing literature by filling the literature gap on the role of mindfulness in promoting customers’ purchase intentions toward ride-hailing services.

Improving First-stage Retrieval of Point-of-interest Search by Pre-training Models

Point-of-interest (POI) search is important for location-based services, such as navigation and online ride-hailing service. The goal of POI search is to find the most relevant destinations from a large-scale POI database given a text query. To improve the effectiveness and efficiency of POI search, most existing approaches are based on a multi-stage pipeline that consists of an efficiency-oriented retrieval stage and one or more effectiveness-oriented re-rank stages. In this article, we focus on the first efficiency-oriented retrieval stage of the POI search. We first identify the limitations of existing first-stage POI retrieval models in capturing the semantic-geography relationship and modeling the fine-grained geographical context information. Then, we propose a Geo-Enhanced Dense Retrieval framework for POI search to alleviate the above problems. Specifically, the proposed framework leverages the capacity of pre-trained language models (e.g., BERT) and designs a pre-training approach to better model the semantic match between the query prefix and POIs. With the POI collection, we first perform a token-level pre-training task based on a geographical-sensitive masked language prediction and design two retrieval-oriented pre-training tasks that link the address of each POI to its name and geo-location. With the user behavior logs collected from an online POI search system, we design two additional pre-training tasks based on users’ query reformulation behavior and the transitions between POIs. We also utilize a late-interaction network structure to model the fine-grained interactions between the text and geographical context information within an acceptable query latency. Extensive experiments on the real-world datasets collected from the Didichuxing application demonstrate that the proposed framework can achieve superior retrieval performance over existing first-stage POI retrieval methods.

Exploring the differences between express and ride-pooling: A dual perspective on user perception and functional positioning in urban traffic system

Ride-hailing services, alongside public transportation (subway and bus), active modes (bike-sharing and walking), and private cars, are collectively reshaping urban transportation systems worldwide. However, the differences in user risk perception and functional positioning among different ride-hailing services, especially express and ride-pooling, are not yet clear. The study, conducted in Nanjing, China, designed two stated-preference questionnaires to assess the substitutability and complementarity of express and ride-pooling services. Four latent variables were introduced to quantify user risk perception: time risk, economic risk, service risk, and safety risk. The Integrated Choice and Latent Variable model used in this study is based on the Mixed Nested Logit model, which focuses more on the interdependence among different travel modes, as well as individual heterogeneity, compared with traditional logit models. The research findings indicate that in terms of risk perception, ride-hailing users have stronger service risk perception, weaker time risk perception, and weaker safety risk perception compared with public transportation users. Furthermore, compared with ride-pooling users, express users have stronger time risk perception and weaker economic risk perception. From a functional positioning perspective, ride-pooling services complement public transportation systems, while express services are preferred for long-distance door-to-door travel. Based on the research findings, this study offers a set of recommendations to support the sustainable growth of Transportation Network Companies (TNCs), guide government regulatory efforts, and explore opportunities for collaboration between the TNCs and government agencies, aiming to foster the robust development of urban transportation systems.

“We Are Here to Stay Longer” A Relational Benefits Perspective in RideHailing Services

The purpose of this paper is to investigate the influence of relational benefits on customer satisfaction in the context of ride-hailing service. In addition, this study explores the mediating effects of customer satisfaction between relational benefits with customer commitment. Based on a theoretical framework between relational benefits, customer satisfaction and customer commitment, an empirical study using a valid sample of 259 ride-hailing drivers were tested. The conceptual model and proposed relationship were tested using structural equations modelling method. The findings disclose that confidence benefits, special treatment benefits and honor benefits were positively to influence customer satisfaction in ride-hailing service. Confidence benefits, however, did not show any significant effect on customer commitment in a ride hailing service. Notably, customer satisfaction plays a vital mediating role between confidence benefit, special treatment benefit and honor benefit with customer commitment. Findings highlighted the significance of designing a driver-partners program strategically, as they can effectively satisfy driver and foster longer-term commitment with ride-hailing service provider. Given the growing research avenue of relational benefits and customer satisfaction, the present study provides useful insight on the relationship between specific relational benefits and customer satisfaction and the subsequent effects on customer commitment in ride-hailing service industry.

EXPLORING THE ROLE OF SOCIAL FACTORS AND CHANGES IN RIDE-HAIL (UBER/LYFT) USE AMONG OLDER ADULTS

Abstract Previous research has observed low ride-hail (e.g., Uber) use among older adults and identified some factors related to use. However, uptake of technology changes quickly and pandemic-related factors (e.g., social distancing) may have affected ride-hail use. The Pew Research Center’s American Trends Panel (ATP) datasets include variables related to political and social views, behavior, religion, geography, internet use, and other factors that have not been previously assessed in relation to ride-hailing among older adults. ATP waves from 2015, 2018, and 2021 included questions about ride-hail use, and were assessed in this study. In 2015, 15.4% of all respondents and 4.5% of older adults reported having ever used ride-hailing. In 2018, the numbers had increased to 36.1% overall and 18.1% for older adults. In 2021, participants were only asked about ride-hail use during the past year, with 22.4% overall and 8.7% of older adults reporting ride-hail use. Separate logistic regression models were used to determine factors associated with reporting ever using ride-hail services (2018) and having used ride-hailing during the past year (2021), among older adults. Higher odds of reporting any use (2018) were related to living in a metropolitan area, living in the US’s West, attending religious service monthly vs. never, high income, more liberal ideology, female gender, and volunteering. Higher odds of use during the past year (2021) were related to metropolitan area, male gender, more education, Hispanic ethnicity, divorced and single vs. married, unaffiliated vs Catholic religion, less frequent religious attendance, high income, and frequent internet use.

Towards a Greener and Fairer Transportation System: A Survey of Route Recommendation Techniques

In recent years, ride-hailing services have emerged as a popular means of transportation for the residents of urban areas. There is an inequality in the spatio-temporal distribution of demand and supply, which requires the proper recommendation of routes to drivers in order to guide them towards riders optimally. This paper provides a review of different route recommendation strategies that have been applied in ride-hailing platforms with the main focus on fairness, and environmental issues. It is important to consider the environmental aspects of route recommendation systems as the transportation sector is one of the major sources of air pollution and has reduced the life expectancy of people around the globe. Moreover, there is an unfair distribution of resources and opportunities among the drivers and riders of the platform which has affected their long-term sustainability in the market. In this paper, we highlight the critical challenges and opportunities inherent in the design of green and fair route recommendation systems and indicate some possible directions for future research.

Strategic driver repositioning in ride-hailing networks with dual sourcing

In ride-hailing markets, the spatial mismatch between supply and demand characterizes one of the most fundamental operational bottlenecks. The voluntary customer search of drivers is oftentimes inadequate for narrowing the demand-supply gaps and falls short in responding timely to the dynamic market conditions. To facilitate better supply management, some ride-hailing platforms now adopt a dual-sourcing strategy by converting some freelance drivers as contractors, who would then follow platforms’ guidance on repositioning movements when idle. This paper thus aims to analyze the implications of such a dual-sourcing strategy on the overall operations of ride-hailing services. We consider a monopolistic platform that owns a blended workforce, with freelance drivers searching for customers following their own preferences whilst contracted drivers repositioning in a centralized manner as system actuators. A mixed network equilibrium model is devised to capture the interplay between the idle repositioning movements of both parties. The theoretical existence of the mixed equilibrium is proved, and real-world data is invited to quantify the effects of dual sourcing on various market sectors. Our numerical results confirm the effectiveness of dual sourcing in smoothing operations, increasing customer demand, and driving up the revenue of ride-hailing services. However, the benefit of dual sourcing to the platform remains sensitive to the proportion of contractors in the entire workforce as well as the stimulative commission beyond their regular service income.

Real-time optimization for relocation and dispatching of Emergency Medical Services with balanced workload and outsourced ride-hailing services

Real-time optimization of Emergency medical services (EMS) leads to excessive and unbalanced workload unless balance is directly modeled. Online redeployment policies aim to reduce response times, compensating for the coverage gap left by busy ambulances, however, it is important to account for the additional effort required to relocate to a new position. Additionally, irregular demand patterns result in unequal distributions of workload among the personnel. In this regard, we propose a new survival-based real-time optimization framework that maximizes the survival probability of patients suffering from life-threatening emergencies while maintaining a balanced workload among the crew. Our approach implements an optimization model inside a discrete event simulation model that feeds the former with real-time vehicle utilization metrics to maintain an optimal overall relocation policy. Moreover, we evaluate the impact on the system’s congestion of a mixed dispatched policy that includes external ride-hailing services for minor emergencies. Our case study in New York City indicates that the proposed model increases the average percentage of life-threatening emergencies responded in less than four minutes in more than 10% through the use of survival functions compared to a coverage model from the literature. Furthermore, even though the fleet’s aggregated driving distances increased compared to the coverage approach, our online model effectively balanced the additional workload among the crew. Additionally, ride-hailing services’ introduction presents an important opportunity for reducing personnel workload by 13.5%, on average, and operational costs in $141,220 per day of operation for the entire system. Finally, when ambulance availability is severely reduced, outsourced services significantly help maintaining the response level for low severity emergencies.

Comparative Analysis of Usage Patterns and Underlying Determinants for Ride-hailing and Traditional Taxi Services: A Chicago Case Study

As app-based ride-hailing (or e-hailing) services have achieved great success worldwide, the traditional taxi industry faces an unprecedented crisis. To gain a deeper understanding of these two distinct modes that both offer door-to-door mobility services, in this study, we use Chicago as a case to comparatively analyze the travel patterns of the app-based ride-hailing and traditional taxi services and understand how socio-demographic and urban land use attributes affect the usage of two services in temporal and spatial dimensions. We employ the local regression models named geographically and temporally weighted regression (GTWR) models that can capture the spatiotemporal nonstationarity between variables as the primary analysis tool. Four GTWR models that predict the average hourly taxi or ride-hailing trip volume on weekdays or weekends for each community area are carefully constructed with R^2 all above 0.98. The nonstationarity test shows all variables exhibit extra local variations, indicating the necessity of using the GTWR models to explore the attributes’ spatiotemporal impacts. Spatial error models (SEM) and ordinary least square (OLS) models, global regression models, are also built as comparisons and benchmark references. The differences between global and local regression models are illustrated through clustering analysis. To compare and contrast the usage patterns of ride-hailing and taxi services, we analyze the temporal-spatially varying coefficients of GTWR models. Interpretations are provided for our interesting findings, including the strong trip generation power of the population in the South Side area, the attitude of white people towards taxi services is positive but ambiguous towards ride-hailing services, young people's opposing behaviors towards taxi services on weekdays and weekends, and the opposite relationship between transit and taxi or ride-hailing in certain areas. Based on these key findings, we offer some planning and operational suggestions, such as developing mobile apps, implementing price-related strategies, strengthening partnerships with local businesses, building multi-modal transportation, clarifying customer segmentation, providing auxiliary services, etc.

SAFETY AND SECURITY OF WOMEN ON TRANSPORT: A CASE OF RIDE-HAILING SERVICES IN GAUTENG, SOUTH AFRICA

Women, particularly in developing countries, rely heavily on public transport for their daily commute. This study explores the safety and security of women using ride-hailing services in Gauteng. Specifically, the study seeks to understand women’s experiences using ride-hailing services, which might influence their mobility. This study employed a constructivist research philosophy, utilizing qualitative, semi-structured interviews to explore the experiences of women who use ride-hailing services. The study findings reveal several disturbing incidents reported by female passengers using ride-hailing services; however, it is important to note that not all drivers exhibit inappropriate behaviour. Some drivers make inappropriate remarks, while others exhibit rude behaviour. The study found that some drivers use longer routes, leading to higher fares, which can be seen as exploitation. The study also reports some incidents where ride-hailing drivers almost kidnapped some female passengers, indicating a severe safety concern. The study contributes to the ongoing discourse on gender and transport studies, providing empirical evidence on the experiences of women using ride-hailing services in Gauteng, South Africa.

Seamless Multimodal Transportation Scheduling

Ride-hailing services have expanded the role of shared mobility in passenger transportation systems, creating new markets and creative planning solutions for major urban centers. In this paper, we consider their use for the first-mile or last-mile passenger transportation in coordination with a mass transit service to provide a seamless multimodal transportation experience for the user. A system that provides passengers with predictable information on travel and waiting times in their commutes is immensely valuable. We envision that the passengers will inform the system of their desired travel and arrival windows so that the system can jointly optimize the schedules of passengers. The problem we study balances minimizing travel time and the number of trips taken by the last-mile vehicles, so that long-term planning, maintenance, and environmental impact are all taken into account. We focus on the case where the last-mile service aggregates passengers by destination. We show that this problem is NP-hard, and we propose a decision diagram–based branch-and-price decomposition model that can solve instances of real-world size (10,000 passengers spread over an hour, 50 last-mile destinations, 600 last-mile vehicles) in computational time (∼1 minute) that is orders of magnitude faster than the solution times of other methods appearing in the literature. Our experiments also indicate that aggregating passengers by destination on the last-mile service provides high-quality solutions to more general settings. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods and Analysis. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2019.0163 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2019.0163 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Undercutting Transit? Exploring Potential Competition Between Automated Vehicles and Public Transportation in the United States

Automated vehicles (AVs) have the potential to dramatically disrupt current transportation patterns and practices. One particular area of concern is AVs’ impacts on public transit systems. If vehicle automation enables significant price decreases or performance improvements for ride-hailing services, some fear that it could undercut public transit, which could have significant implications for the environment and transportation equity. The extent to which individuals adopt automated transportation modes will drive many system-level outcomes, and research on public preferences for AVs is immature and inconclusive. In this study, we used responses from an online choice-based conjoint survey fielded in the Washington, D.C. metropolitan region (N = 1,694) in October 2021 to estimate discrete choice models of public preferences for different automated (ride-hailing, shared ride-hailing, bus) and nonautomated (ride-hailing, shared ride-hailing, bus, rail) modes. We used the estimated models to simulate future marketplace competition across a range of trip scenarios. Respondents on average were only willing to pay a premium for automated modes when a vehicle attendant was also present, limiting the potential cost-savings that AV operators might achieve by removing the driver. Scenario analysis additionally revealed that for trips where good transit options were available, transit remained competitive with automated ride-hailing modes. These results suggest that fears of a mass transition away from transit to AVs may be limited by people’s willingness to use AVs, at least in the short term. Future AV operators should also recognize the presence of an AV attendant as a critical feature for early AV adoption.

The role of travel satisfaction and attitudes toward travel modes in the prospect of adoption of urban air taxis: Evidence from a stated preference survey in Tehran

Urban air mobility (UAM) is a new mode of transportation which has the potential to curb congestion in urban areas by providing a fast alternative that bypasses surface-level traffic by employing low altitude urban airspace. However, the successful adoption of this innovation among other modes of transportation remains a critical challenge, as public acceptance plays a pivotal role in the uptake of any emerging technology or service. To transcend traditional ground transportation, it is imperative to investigate the factors that drive a transition from current modes, particularly private cars, to the utilization of urban air taxis (UATs). While prior research has explored different aspects of UAM adoption, there is a notable dearth of studies examining the influence of travel satisfaction and attitudes towards travel modes on the preference for UAM. Notably, existing transportation literature has established that both satisfaction and attitudes significantly shape travel behavior. To achieve this, a stated preference survey was conducted in the Tehran metropolitan area, and a multinomial logit model incorporating latent variables was developed to assess the influence of socio-demographic and psychological factors on UAM adoption in hypothetical scenarios with predetermined travel times and costs. The findings highlight the pivotal role of service cost and affordability, with individuals of higher incomes exhibiting a significant inclination toward UAT adoption. The adoption of UAT is significantly higher when the cost is comparable to the existing modes. Moreover, satisfaction components, specifically enjoyment and time-saving benefits, exert a significant influence on UAT preference. Unfavorable perceptions towards private cars and ride-hailing services positively contribute to the likelihood of preferring UAT, with a greater impact observed for negative attitudes towards ride-hailing. The study explicitly underscores the influence of travel satisfaction and attitudes towards conventional modes of transportation on the adoption of innovative alternatives such as air taxis within the context of mode choice scenarios.

Ride-hailing and transit accessibility considering the trade-off between time and money

Ride-hailing services can expand access to opportunities in urban areas, but out-of-pocket costs may limit its benefits for low-income individuals. This paper examines how ride-hailing shapes spatial and socioeconomic differences in access to opportunities while accounting for the trade-off between travel time and monetary costs. Using one year of aggregate Uber trip data for Rio de Janeiro in 2019 and a new multi-objective routing algorithm, we analyze the potential for ride-hailing services to improve employment accessibility when used as a standalone transportation mode and in conjunction with transit as a first-mile connection. We find that, compared to transit, standalone ride-hailing can significantly expand accessibility as a standalone mode for short trips, and as a first-mile feeder to transit in trips longer than 30 min. However, the accessibility benefits of ride-hailing accrue mostly to high-income groups due to affordability barriers. These findings suggest that policy efforts to integrate rideshare with transit are likely not going to benefit low-income communities without some form of subsidized fare discounts to alleviate affordability barriers. The paper also highlights how accounting for trade-offs between travel-time and monetary costs can importantly influence the results of transportation accessibility and equity studies.