Ride-hailing Services Research Articles

To Sustainably Ride or Not to Ride: Examining the Green Consumption Intention of Ride-Hailing Services in the Sharing Economy by University Students

An increase in ride-hailing services in the sharing economy can help to reduce the number of vehicles on the road, which will lead to a decrease in air pollution and noise pollution, an improvement in environmental conditions, a decrease in travel costs, and an increase in social benefits to travelers. Hence, there is a great need to examine the consumer’s intention toward usage of ride-hailing services in the sharing economy. The current study aims to examine the green consumption intention of eco-friendly services as an outcome of environmental responsibility and environmental knowledge. It also attempts to examine the serial mediation of green concern, value co-creation, and mediated moderation of social support as an explanatory mechanism of green consumption intention of eco-friendly services. The research design was cross-section and deductive. The respondents of the study were registered university students in Islamabad who were active consumers of major ride-hailing services in Pakistan, i.e., Uber, Careem, Uplift, InDriver, B4U Cabes, and SUVL. A total of 402 responses were gathered using purposive sampling. Partial Least Squares Structural Equation Modeling (PLS-SEM) in Smart PLS is used to evaluate the reliability of measurement instruments and the validity of the research model. The current study results showed that environmental responsibility and knowledge positively and significantly affect motivation to engage in green consumption. Furthermore, environmental concern and value co-creation partially mediate the proposed relationship. In addition, social support also moderates the association between green concern and value co-creation such that it strengthens the connection. The current research findings are an addition to the existing literature and have managerial applications with limitations preceded by future research directions.

Decoupling Online Ride-Hailing Services: A Privacy Protection Scheme Based on Decentralized Identity

Online ride-hailing services have become a vital component of urban transportation worldwide due to their convenience and flexibility. However, the expansion of their user base has dramatically heightened the risks of user privacy information leakage. Among these risks, the privacy leakage problem caused by the direct correlation between user (driver and passenger) identity information and location-based ride information is of particular concern. This paper proposes a novel privacy protection scheme for ride-hailing services. In this scheme, decentralized identities are employed for user authentication, separating the identity registration service from the ride-hailing platform, thereby preventing the platform from obtaining user privacy data. The scheme also employs a fuzzy matching strategy based on location Points of Interest (POI) and a ciphertext-policy attribute-based hybrid encryption algorithm to hide the user’s precise location and restrict access to location information. Crucially, the scheme achieves the complete decoupling of identity registration services and location-based ride services on the ride-hailing platform, ensuring that users’ real identities and ride data cannot be directly associated, effectively protecting user privacy. Within the decoupled architecture, regulatory authorities are established to handle emergencies within ride-hailing services. Through simulation experiments and security analysis, this scheme is demonstrated to be both feasible and practical, providing a new privacy protection solution for the ride-hailing industry.

Do Smart Loading Zones help reduce traffic congestion? A causal analysis in Pittsburgh

Rising demand for ride-hailing services and e-commerce delivery intensifies competition for urban curbside spaces, leading to uncoordinated travel behavior, increased traffic congestion and social costs. One possible solution to address those issues is Smart Loading Zones (SLZs), equipped with advanced technologies to optimize curbside use. Yet, the real-world impact of SLZs on traffic flow is unclear due to a lack of real-world data and rigorous studies investigating SLZ’s causal effect on traffic speed. With granular speed data and real-world implementations of SLZs from Pittsburgh, PA, this study applies the regression discontinuity design method to rigorously examine the causal impact of SLZs on traffic speed in the downtown network. The results showed that the introduction of SLZs could enhance the traffic speed of the nearby road segments by 4.5%, while controlling for the underlying trend of speed and multiple influential factors such as time, weather, and road characteristics. In addition, SLZs with a short length could statistically improve traffic speed but those with a long length exert no significant effect. These heterogenous effects might be attributed to the weak enforcement at the time of SLZ deployment in Pittsburgh. The results confirmed the overall positive impact of SLZs on improving congestion. However, policies such as effective dimension planning and robust enforcement policies are essential to maximize the benefits of SLZs.

Perceptions of Women’s Safety in Transient Environments and the Potential Role of AI in Enhancing Safety: An Inclusive Mobility Study in India

Travel safety for women is a concern, particularly in India, where gender-based violence and harassment are significant issues. This study examines how the perception of safety influences women’s travel behaviour and assesses the potential of technology solutions to ensure their safety. Additionally, it explores how AI and machine learning techniques may be leveraged to enhance women’s travel safety. A comprehensive mobility survey was designed to uncover the complex relationship between travel behaviour, reasons for mode choice, built environment, feelings, future mobility, and technological solutions. The responses revealed that security and safety are the most critical factors affecting women’s travel mode choices, with 54% and 41%, respectively. Moreover, over 80% of women indicated a willingness to change their travel behaviour after experiencing fear, anxiety, or danger during their everyday journeys. Participants were 24% less willing to use ride-sharing services than ride-hailing services, which could affect the transition towards more sustainable transportation options. Furthermore, AI-based sentiment analysis revealed that 46% of the respondents exhibited signs of ‘anger’ regarding what could help women feel safer in transient environments. The practical implications of this study’s findings are discussed, highlighting the potential of AI to enhance travel safety and optimise future sustainable transport planning.

Investigating Factors Impacting the Use of Ride-Hailing Service Applications

Environmental impact of modern-day transport has made citizens concerned about the footprint left by these innovations. Mobile application-based ride-hailing services, recently emerged in developing countries are regarded as technology-based innovative products to meet the growing travel demand among commuters. Technology upgrades have allowed smartphone users to access the ride-hailing services apps with ease. Ride-hailing service entered the Pakistani market in the year 2016. Since then, the demand for these services has been on the rise. Therefore, this study aims to investigate which factors have a causal relationship with travelers’ intention to use ride-hailing services in the backdrop of unified theory of acceptance and use of technology (UTAUT). Data were collected through questionnaire from 1,694 respondents at two time points. This study analyzes the hypothesized relationships with structural equation modeling in partial least square with SmartPLS 4. Results shows that environmental citizenship and perceived convenience along with UTAUT constructs have a significant effect on intention to use ride-hailing applications in the long-run, indicating causal relationships. The study extends UTAUT model with two antecedents of perceived convenience and environmental citizenship behavior.

Enhancing Urban Resilience through Strategic Parking Pricing in Jakarta’s Bus Rapid Transit Corridors

Although Jakarta has invested in various mass transport systems, these efforts have not successfully reduced private vehicle use. Due to this, this study aims to analyze the impact of implementing TransJakarta bus rapid transit corridor-based high parking tariffs on travel mode choice, including road- and rail-based public transport, ride-hailing, taxi, car, and motorcycle. Involving 478 private vehicle users and implementing a nested logit model, some variables, including respondents’ income, travel time, egress time, parking costs, parking distance, travel cost, and parking surcharge, are considered to understand to what extent these variables influence the use of proposed travel mode in the future. The nested logit model shows that not all variables significantly influence travel mode use, specifically related to rail-based public transport choice among motorcyclists. Meanwhile,parking distance insignificantly influences the choice of all travel modes except cars among car users. The results also indicate that increasing parking tariffs insignificantly influences the likelihood of both motorcyclists and car users shifting to public transport. Motorcyclists and car users tend to continue using motorcycles but change parking locations with higher tariffs. Additionally, some shifts towards ride-hailing services and TransJakarta Bus Rapid Transit are found, meaning that there is potential for these alternatives to play a significant role in reducing private vehicle use. Based on the model results, additional push-based policies, such as the odd-even license plate rule, are necessary to effectively support the transition from private vehicle use to public transport. Implementing these policies is expected to significantly contribute to reducing traffic congestion and promoting a sustainable and resilient urban environment.

Ride-hailing technology mitigates effects of driver racial discrimination, but effects of residential segregation persist

We assess racial disparities in the service quality of app-based ride-hailing services, like Uber and Lyft, by simulating their operations in the city of Chicago using empirical data. To generate driver cancellation rate disparities consistent with controlled experiments (up to twice as large for Black riders as for White riders), we estimate that more than 3% of drivers discriminate by race. We find that the capabilities of ride-hailing technology to rapidly rematch after a cancellation and prioritize long-waiting customers heavily mitigates the effects of driver discrimination on rider wait times, reducing average discrimination-induced disparities to less than 1 min-an order of magnitude less than traditional taxis. However, our results suggest that even in the absence of direct driver discrimination, Black riders in Chicago wait about 50% longer, on average, than White riders because of historically informed geographic residential patterns. We estimate that if Black riders in the city had the same wait times as White riders, the collective travel time saved would be worth $4.2 million to $7.0 million per year.

Pricing model of ride-hailing platform considering rationally inattentive passengers

ABSTRACT The ride-hailing services are booming in our daily lives, but it is unclear that how the platforms should set prices to maximize their profits when facing one kind of rationally inattentive passengers in a two-sided market. To fill this gap, we establish a profit maximization model for the ride-hailing platform based on queuing theory and rational inattention theory and analyze the properties of the model. Numerical examples are presented to demonstrate the impacts of perceived high and low service levels, information cost and prior belief on the optimal price and commission rate of the ride-hailing platform. The results show that (1) for different cities, there is always an optimal pricing strategy to maximize the profit of the platform. (2) To ensure maximum profit, the platform should disclose the service information of ride-hailing as much as possible, but also maintain the unknownness of ride-hailing services appropriately.

Ride-Hailing vs. Fixed-Route: Preferences of the Transit-Dependent Populations in U.S. Urbanized Areas

Transportation inequality poses significant challenges in car-dependent cities in the US, especially for Transit-Dependent Populations (TDPs) who rely on public transportation to meet their travel requirements. Research indicates that despite the rapid growth of ride-hailing services like Uber, TDPs primarily depend on conventional transit options such as buses and metros. This study differentiates between two classifications of ride-hailing services: Private Ride-hailing (PRH), represented by platforms like Uber and Lyft, where individuals hire and pay for their rides, occasionally sharing the journey with guests; and ride-sharing (RS), like UberPool, which allows users to divide travel expenses. However, a more profound comprehension is necessary to clarify why TDPs show a preference for one mode of transportation over another and the factors that influence their decision-making. This study utilises Structural Equation Modelling (SEM) to examine the direct and indirect relationships between explanatory and outcome variables, while investigating modal preferences and usage frequency in 48 densely populated urban areas in the US (N = 384). TDPs choose to use transportation services like Uber because of their effectiveness in reducing both travel and waiting time, in contrast to traditional Fixed-Route Transit Services (FRTS), which are relatively less efficient. TDPs’ inclination towards PRH leads to a greater dependence on ride-hailing services, whereas a preference for FRTS reduces this dependence. Significantly, the study demonstrates that TDPs’ modal preferences are not only influenced by their socioeconomic status or rational decision-making, but also by the complex interaction between these factors. These findings provide valuable information for policymakers who want to evaluate the sustainability of ride-hailing services for TDPs and effectively tackle transportation inequalities.

Simulation analysis of dual-end queuing ride-hailing system considering driver-side queue management

Ride-hailing services have transformed urban transportation through convenience yet introduced new complexities around efficiency and traffic management. This study investigates the dual-queuing problem in ride-hailing from the driver perspective using a multi-agent simulation approach. The focus is dissecting the dynamics between driver search times and passenger wait times, which critically influence operational efficiency especially during peak demand. Exploring these interactions aims to uncover insights that could improve service efficiency and customer satisfaction. Addressing such ride-hailing challenges is vital not just for individual providers but also for advancing sustainable mobility across rapidly growing metropolitan regions. Enhanced efficiency connects to broader urban development narratives around livability, accessibility, and responsible mobility ecosystems.

Complements or competitors? Equity implications of taxis and ride-hail use in Chicago

Taxis have provided on-demand mobility for decades, including to people earning the lowest incomes. However, studies reveal gaps in taxi service across many US cities due to factors such as fleet size limitations, information asymmetry, and discriminatory practices. Since 2010, the emergence of ride-hail services such as Uber and Lyft has revolutionized the transportation landscape, offering faster and more affordable alternatives to traditional taxis. Yet it remains unclear the extent to which ride-hail services may fill spatial-temporal gaps in taxi services or how car access shifted after the arrival of ride-hail services. Additionally, taxis may still play an important role in car access for people who rely on cash payments. This study uses longitudinal trip-level ride-hail and taxi data in Chicago, IL, to examine the shifting trip patterns across the two modes. We compared the spatial-temporal pattern of taxi and ride-hail trips, as well as the share of taxi trips paid for in cash across three periods; we then developed spatial regression models to investigate what factors relate to taxi cash payments. Results show that ride-hail services may, directly and indirectly, fill taxi service gaps. Additionally, population density, land use mix, and the proportion of people of color and zero-car households are positively related to the share of taxi trips paid in cash, while the number of ride-hail trips and banking density are negatively associated with the percentage of cash payments. Policymakers should consider the evolving interplay between taxi and ride-hail services and the significance of cash payments to ensure equitable access to transportation options for individuals without cars.

Environmental Impacts of Transportation Network Company (TNC)/Ride-Hailing Services: Evaluating Net Vehicle Miles Traveled and Greenhouse Gas Emission Impacts within San Francisco, Los Angeles, and Washington, D.C. Using Survey and Activity Data

Transportation Network Companies (TNCs) play a prominent role in mobility within cities across the globe. However, their activity has impacts on vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. This study quantifies the change in personal vehicle ownership and total miles driven by TNC drivers in three metropolitan areas: San Francisco, CA; Los Angeles, CA; and Washington, D.C. The data sources for this analysis comprise two surveys, one for TNC passengers (N = 8630) and one for TNC drivers (N = 5034), in addition to data provided by the TNC operators Uber and Lyft. The passenger survey was deployed within the three metropolitan areas in July and August 2016, while the driver survey was deployed from October to November 2016. The TNC operator data corresponded with these time frames and informed the distance driven by vehicles, passenger frequency of use, and fleet level fuel economies. The data from these sources were analyzed to estimate the impact of TNCs on travel behavior, personal vehicle ownership and associated VMT changes, as well as the VMT of TNCs, including app-off driving. These impacts were scaled to the population level and collectively evaluated to determine the net impacts of TNCs on VMT and GHG emissions using fuel economy factors. The results showed that the presence of TNCs led to a net increase of 234 and 242 miles per passenger per year, respectively, in Los Angeles and San Francisco, while yielding a net decrease of 83 miles per passenger per year in Washington, D.C. A sensitivity analysis evaluating net VMT change resulting from vehicle activity and key behavioral impacts revealed the conditions under which TNCs can contribute to transportation sustainability goals.

Exploring determinants of travelers’ discontinuance behavioral intention on integrated ride-hailing services: A perspective on perceived risk

The emergence of integrated ride-hailing services offers significant conveniences. However, the involvement of multiple providers and the lack of regulation within integrated ride-hailing platforms yield potential adverse impacts. Considering the perceptive of risk perception, this study aims to examine the factors influencing travelers' discontinuance behavioral intention on integrated ride-hailing services. By employing a reflective-formative second-order structure, perceived risk is classified into perceived driver-related risk and perceived platform-related risk. Additionally, the influence of satisfaction and switching cost is included. To validate the theoretical framework, an empirical study was conducted with method of the Partial Least Squares Structural Equation Modeling. It shows that attitude, subjective norm, and perceived behavioral control jointly influence travelers' discontinuance behavioral intention of integrated ride-hailing services, thereby confirming effectiveness of the theoretical framework. Noteworthily, perceived behavioral control is the strongest factor influencing discontinuance behavioral intention. Perceived risk, encompassing both driver-related and platform-related factors, negatively impacts satisfaction and attitude, subsequently influencing discontinuance behavioral intention. Further, perceived platform-related risk shows a higher impact than perceived driver-related risk. Moreover, switching cost exhibits negative correlations with both satisfaction and attitude, while satisfaction positively correlates with attitude. However, both factors negatively influence travelers' discontinuance behavioral intention regarding the service. Overall, these findings deepen the understanding of travelers’ discontinuance behavioral intention concerning integrated ride-hailing services and offer valuable insights to manage risks effectively for service providers.

Net Change in Energy Use from Ridehail Services in Five California Regions

Net Change in Energy Use from Ridehail Services in Five California Regions

Operator Responsibilities in Safeguarding Consumer Rights Against GPS Spoofing in Ride-Hailing Services

Operator Responsibilities in Safeguarding Consumer Rights Against GPS Spoofing in Ride-Hailing Services

Enhancing bivariate spatial association analysis of network-constrained geographical flows: An incremental scale-based method

Measuring bivariate spatial association plays a key role in understanding the spatial relationships between two types of geographical flow (hereafter referred to as “flow”). However, existing studies usually use multiple scales to analyze bivariate associations of flows, leading to the results at larger scales can be strongly affected by the results at smaller scales. Moreover, the planar space assumption of most existing studies is unsuitable for network-constrained flows. To solve these problems, a network incremental flow cross K-function (NIFK) is developed in this study by extending the cross K-function for points into a flow context. Specifically, two versions of NIFK were developed in this study: the global version to check whether bivariate associations exist in the whole study area and the local version to identify specific locations where associations occur. Experiments on three simulated datasets demonstrate the advantages of the proposed method over an available alternative method. A case study conducted using Xiamen taxi and ride-hailing service datasets demonstrates the usefulness of the proposed method. The detected bivariate spatial association provides deep insights for understanding the competition between taxi services and ride-hailing services.

Control of dynamic ride-hailing networks with a mixed fleet of autonomous vehicles and for-hire human drivers

This study examines a ride-hailing platform that employs a mixed fleet of autonomous vehicles (AVs) and human drivers to offer on-demand ride-hailing services across a network over a finite planning horizon. In the mixed fleet, AVs have a fixed fleet size and are centrally managed by the platform, while human drivers can dynamically enter and exit from the ride-hailing market based on the prospective earning opportunities at various time periods. We introduce a game-theoretic model that encapsulates the strategic behavior of human drivers, passengers, and the ride-hailing platform. In this model, passengers choose between ride-hailing and alternative transportation options by considering fares and waiting times. Human drivers schedule their working hours in response to potential earnings and strategically relocate to maximize passenger pickups, while the platform adjusts pricing and AV repositioning strategies to maximize its profit. The solution is characterized as a Nash equilibrium within a two-player game, with one player representing the ride-hailing platform, and the other as a virtual representative of the collective human drivers. To address the non-convex nature of the game, we employ a convex relaxation technique to ascertain a near-optimal solution framed as an ϵ-Nash equilibrium, with ϵ accurately characterized. The proposed model and solution algorithm are validated in a case study for Manhattan, New York City. The simulation results underscore that the distribution of human drivers is closely aligned with the immediate, minute-to-minute fluctuations in passenger demand, enabling them to effectively meet the surge during peak demand windows. Conversely, the deployment patterns of AVs are more attuned to the extended, daily cycles of passenger movements throughout different city zones, from daytime to nighttime. We further compare the mixed fleet model with scenarios where the platform exclusively utilizes human drivers or AVs, respectively. The comparison show that the mixed fleet model excels in balancing the supply and demand over space and time, thereby leading to shorter wait times and reduced travel cost for ride-hailing passengers, as well as improved profit for the ride-hailing platform.

Short-Term Origin-Destination Demand Prediction Based on Spatiotemporal Encoder-Decoder Network with a Residual Feature Extractor

Online ride-hailing services play a crucial role in daily transportation, However, challenges persist in certain regions with limited access, and drivers encounter difficulties in receiving orders. Accurate prediction of short-term origin-destination (OD) demand is crucial for addressing these issues. This study leverages recent advancements in artificial intelligence and big data to introduce a spatiotemporal encoder-decoder network with a residual feature extractor (RF-STED) for short-term OD demand prediction in online ride-hailing services. The RF-STED model, built on deep learning models such as graph convolutional networks and convolutional long short-term memory (Conv-LSTM), includes spatiotemporal networks, encoding layers, and a residual feature extractor. The spatiotemporal network has two branches: branch one processes multi-pattern OD data using a multi-pattern temporal feature extraction module, utilizing a multi-channel Conv-LSTM to capture temporal correlations. Branch two utilizes a multi-spatial feature extraction module to convert OD pair associations into a spatial topology, extracting multi-spatial correlations. The encoding layer captures spatiotemporal dependencies, while the residual feature extractor decodes compressed vectors back into an OD graph for forecasting future demand. Experiments with a Manhattan taxi dataset in the U.S. show the RF-STED model outperforms 10 baseline models and four ablation models. The results emphasize the model’s strength and robustness in short-term OD flow prediction.

Research on Dynamic Subsidy Based on Deep Reinforcement Learning for Non-Stationary Stochastic Demand in Ride-Hailing

The ride-hailing market often experiences significant fluctuations in traffic demand, resulting in supply-demand imbalances. In this regard, the dynamic subsidy strategy is frequently employed by ride-hailing platforms to incentivize drivers to relocate to zones with high demand. However, determining the appropriate amount of subsidy at the appropriate time remains challenging. First, traffic demand exhibits high non-stationarity, characterized by multi-context patterns with time-varying statistical features. Second, high-dimensional state/action spaces contain multiple spatiotemporal dimensions and context patterns. Third, decision-making should satisfy real-time requirements. To address the above challenges, we first construct a Non-Stationary Markov Decision Process (NSMDP) based on the assumption of ride-hailing service systems dynamics. Then, we develop a solution framework for the NSMDP. A change point detection method based on feature-enhanced LSTM within the framework can identify the changepoints and time-varying context patterns of stochastic demand. Moreover, the framework also includes a deterministic policy deep reinforcement learning algorithm to optimize. Finally, through simulated experiments with real-world historical data, we demonstrate the effectiveness of the proposed approach. It performs well in improving the platform’s profits and alleviating supply-demand imbalances under the dynamic subsidy strategy. The results also prove that a scientific dynamic subsidy strategy is particularly effective in the high-demand context pattern with more drastic fluctuations. Additionally, the profitability of dynamic subsidy strategy will increase with the increase of the non-stationary level.

The Application of the Piecewise Linear Method for Non-Linear Programming Problems in Ride-Hailing Assignment Based on Service Level, Driver Workload, and Fuel Consumption

Ride-hailing services have grown rapidly, presenting challenges such as increased traffic congestion, inefficient driver workload distribution, and environmental concerns like higher fuel consumption and emissions. This study develops a non-linear ride-hailing assignment model addressing these issues by considering service level, driver workload, and fuel consumption. A piecewise linear method was employed to handle a non-linear programming model, and the method was modified to function autonomously without operator intervention. The model’s performance was evaluated using a publicly accessible dataset of taxi trips in Manhattan, focusing on indicators such as passenger waiting time, driver workload distribution, and fuel consumption. Numerical simulations demonstrated significant improvements: a 15% reduction in average passenger waiting time, a 20% improvement in balancing driver workloads, and a 10% decrease in overall fuel consumption, contributing to reduced emissions and environmental impact. The modified piecewise linear method proved effective in optimizing ride-hailing assignments, providing a more efficient and sustainable solution. The model also showed robustness in handling large datasets, ensuring scalability and applicability to various urban settings. These findings highlight the model’s potential to enhance operational efficiency and promote sustainability in ride-hailing services. By integrating considerations for service level, driver workload, and fuel consumption, the model offers a holistic approach to addressing the key challenges faced by the ride-hailing industry. This study provides valuable insights for future ride-hailing development and implementations of ride-hailing systems, promoting practices that are both efficient and environmentally friendly.