Ridesharing Services Research Articles

User preferences in ride-sharing mathematical models for enhanced matching

Ride-sharing services have attracted significant interest due to overcrowding, limited energy resources, and environmental concerns. This study proposes a mathematical programming model that integrates user preferences into the ride-sharing problem with transfer. Passenger transfer in ride-sharing problems addresses the limitations of the matching process, especially in less-populated areas. It allows passengers to be dropped off at meeting points and continue with another driver. Moreover, considering user preferences in ride-sharing systems is crucial for enhancing efficiency and user satisfaction. Accordingly, we propose a Preference-Driven Matching Algorithm for the matching process. Our proposed algorithm captures user preferences and provides potential matches. In addition, we introduce an Iterative Enhance-and-Optimize Algorithm capable of producing high-quality solutions within short computational times. We evaluate the efficiency of these approaches across various instances, focusing on real-scale scenarios. Based on the results, the model with preferences demonstrates effective performance compared to other methods. Our findings underscore the importance of user preferences in optimizing ride-sharing problems and highlight the trade-off between efficiency and user satisfaction. By incorporating user preferences, our approach results in higher user satisfaction, a more responsive and efficient system with reduced response times, and increased demand and revenue by servicing more users.

Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing

Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterise the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems.

Optimization of ride-sharing services: An investigation of the ride-hailing service providers

This study investigates the optimization of ride-sharing services (RSS) on the ride-hailing service (RHS) providers in Bangladesh. This study employed an explanatory sequential mixed method research design- a qualitative study followed by a quantitative one. Qualitative data were collected through focus group discussions and in-depth interviews with twenty (20) riders and drivers in Bangladesh, and quantitative data were collected from 300 respondents consisting of riders and drivers using a convenience sampling technique. Factor analysis and hierarchical cluster analysis were applied to the data analysis. The qualitative analysis reveals several significant factors associated with RSS and RHS, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, business growth, and security. The quantitative results indicate that using RSS is associated with more significant benefits than RHS in various aspects, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, and expansion of the automobile industry. The findings may assist policymakers in understanding how RSS can yield more incredible economic, environmental, and social benefits than RHS by analyzing fare sharing among passengers, carbon emissions, fuel consumption, and the expansion of the vehicle markets etc. Therefore, the government can formulate distinct policies for RSS holders due to their contributions to economic, social, and environmental concerns. While RHS services are available in many cities in Bangladesh, this study considered only Dhaka and Sylhet cities. Thus, future studies can consider more respondents from other cities for a holistic understanding.

Exploring the feasibility of high-capacity vehicle strategy on ridesharing service from the perspective of economic, social, and environmental sustainability

The emergence of ridesharing services is poised to significantly reshape the urban sustainability paradigm. With ever-growing demand, the adoption of high-capacity vehicles in shared mobility is becoming increasingly feasible. In this study, we present a high-capacity vehicle strategy to improve the economic, social, and environmental sustainability of the ridesharing system. To optimize the initial solution, deep reinforcement learning (DRL) is introduced into the framework of the NSGA-II algorithm. Based on the priority order, we apply lexicographic optimization to achieve the final solution. Ultimately, a case study is investigated to simulate the system performance on a realistic network of Chicago area. The results suggest that the high-capacity strategy with an incentive mechanism can substantially benefit all the stakeholders. Per-vehicle profit has drastically promoted from $82.50 to $95.00, meanwhile regret utility and carbon emissions decline to some extent. The best incentive discount for the high-capacity vehicle strategy is approximately 0.2. Overall, this study is significantly conducive to providing a new strategy to promote economic-social-environmental sustainability.

An investigation of perceived risk dimensions in acceptability of shared autonomous vehicles, a mediation-moderation analysis

The integration of shared and autonomous mobility has led to the emergence of shared autonomous vehicles with ride-sharing services (SAVWRS), which have the potential to significantly reduce private car usage and promote sustainable transportation. Despite numerous studies on this topic, there is still no research examining the impact of all dimensions of perceived risk theory on usage intention. Therefore, we aim to investigate these relationships and gain deeper insights by examining the mediating effect of trust and the moderating effect of generation (Millennials vs. Baby Boomers) to address potential disparities across generations. To gather insights, we design an online survey that was completed by a random sample of 723 individuals in 2021. The estimation results of the structural equation model reveal that all perceived risk dimensions (social, performance, time, physical, security, and financial risks, in descending order) are negatively related to consumers’ intention. Additionally, trust fully mediates the relationships between performance, physical, financial, and security risks and usage intention, whereas it partially mediates the relationships between social and time risks and the intention to use. Furthermore, moderation analysis revealed that Millennials are less concerned about most dimensions of perceived risk theory, except for social and time risks. In conclusion, our study contributes to a deeper understanding of the complex relationships between perceived risk dimensions, trust, and usage intention in SAVWRS. Our findings suggest that policymakers and industry stakeholders should consider strategies to address these concerns to promote widespread acceptance of SAVWRS.

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.

Exploring attitudes and perceptions regarding a self-driving demonstration

Purpose Public opinion regarding autonomous vehicles (AV) heavily influences how quickly the technology will be implemented and adopted in the future. However, there is a dearth of empirical evaluations in the literature about riders' perceptions toward service characteristics of shared autonomous vehicles (SAVs) and their attitudes toward developing AVs. Therefore, the aim of this study is to identify attitudes, views and concerns regarding a self-driving demonstration called RAPID (Rideshare, Automation and Payment Integration Demonstration) incorporated with an already-existing on-demand ridesharing service in Arlington, Texas. Design/methodology/approach This study developed a ridership survey to collect data from those who had experience using the service at least once during the service deployment. As the RAPID service operations were restricted to the areas near the University of Texas at Arlington (UTA) campus, sample population of this study is highly skewed with all participants being affiliated with UTA. Findings Findings indicated that survey respondents positively perceived the service features, including comfort, boarding the vehicles, ride safety, booking and scheduling, vehicle speed, climate control and service cost. To complement the survey results, the authors conducted interviews and a focus group study and used conventional content analysis to gain more in-depth insights about RAPID service operations from the perspectives of users and non-users in the post-implementation period. The results indicated that geographic accessibility, service availability and trip cost were the primary concerns of the focus group participants. Originality/value This study offers critical insights into individual attitudes and perceptions toward shared AVs (SAVs) that will assist local, state and federal transit authorities and planners in formulating policies and transportation strategies to target SAV ridership when the service is more widespread.

Spatiotemporal Exploration of Ridesharing Services Ridership through Geovisualization: A Case Study of the New York City Region

App-based ridesharing services (RSSs), exemplified by platforms like Uber, play a pivotal role in modern transportation by offering convenient and on-demand services. The exploration of RSSs necessitates a comprehensive consideration of the inherent spatiotemporal variability within the data. Prior research, however, has tended to analyze the spatial and temporal dimensions separately, with many studies omitting the temporal aspect. This study addresses the gap by using geovisualization techniques to illustrate emerging hot spot analysis in New York City in 2022, derived from space–time data mining. Overall, despite temporal variations in overall RSSs ridership, certain taxi zones maintain distinct ridership patterns. Across the five New York City boroughs (Manhattan, Bronx, Queens, Brooklyn, and Staten Island), Midtown Manhattan and the Brooklyn areas adjacent to Queens exhibit saturated intensifying hot spots, signaling a notable increase in RSSs ridership throughout 2022, surrounded by sporadic hot spots. Conversely, peripheral areas of New York City reveal diminishing cold spots, indicating a decrease in their intensity as cold spots. Furthermore, the study conducts separate spatial and temporal profiling. By presenting the spatiotemporal trends of RSSs, this research complements existing literature and provides valuable insights for more informed interventions. The study also highlights certain limitations that could be addressed in future endeavors.

Urban Odyssey: “Pioneering multimodal routes for Tomorrow's smart cities”

Getting around in modern cities has become a daily puzzle for both residents and travelers. As cities increasingly evolve into complex hubs of innovation and development, the demand for efficient transportation solutions has never been higher. In the multifaceted field of urban transportation, cost-effective and efficient mobility remains a high priority. This paper looks at how cities are planning better ways for people to travel and move within the growing scope of multimodal transportation, a paradigm shift beyond traditional single-mode transit systems. Our approach to improving urban transport revolves around a few key principles: integration, innovation, and collaboration. Integration means bringing together different modes of transportation – like buses, trains, bikes, and even new technologies like ride-sharing services to make it easier for people to switch between them. This transformative approach not only aims to reduce congestion and reduce environmental footprints but also prioritizes user experience while ensuring a harmonious blend of convenience, sustainability, and accessibility. We use new technology and ideas to ensure that travel is easy, quick, and good for the environment. Looking at new trends and examples from around the world, this overview shows how cities are shaping a better future for everyone's daily commute. In this paper, we use Lingo software to solve a numerical example related to multi-modal transportation, demonstrating practical solutions for real-world implementation. Through innovation, we can find creative solutions to urban transportation challenges. Additionally, public transportation enhancements, such as the introduction of synchronized bus routes and electric vehicle charging stations, underline the commitment to sustainability and inclusivity. In the dynamic urban transportation landscape, cities will revolutionize our approach to providing cost-effective and efficient mobility solutions.

External and internal factors affecting collaborative consumption practices: insights from new-middle classes in an emerging market

Purpose This study aims to unravel the perceived barriers, and external and internal factors affecting the new middle-class Turkish consumers’ collaborative consumption practices (e.g. accommodation rental and ride-sharing services). Design/methodology/approach This study conducts semi-structured, in-depth interviews with 21 new middle-class consumers from Turkey. The qualitative data were analyzed with reflexive thematic analysis. Findings Findings show that environmental stimuli (economic crisis, transparent legal requirements, word-of-mouth, urban mobility, unique experiences, cosmopolitan thinking) affect organism factors (financial gains and psychological barriers such as difficulty in relying on service providers, guarded vulnerability, unfavorable brand image of local companies, conventional constraints). Organism factors affect the response of intention to engage in collaborative consumption. Practical implications Collaborative consumption services can integrate socially responsible projects into their lower-priced services giving signals of “unique,” “feeling at home” and “rely on”. This will help to overcome perceived barriers of conventional constraints, difficulty in relying on service providers and contribute to financial gains and unique experiences. Originality/value The extant literature focuses mainly on intrinsic and extrinsic motives without thoroughly investigating barriers and their connections to external and internal factors influencing collaborative consumption. This study answers the calls for further research on barriers, external and internal factors affecting collaborative consumption practices. Moreover, this study addresses the need for research on the collaborative consumption practices of middle-class consumers.

Tourist acceptance of using ride-sharing services in a tourism destination: hedonia vs eudaimonia and technophilia vs technophobia

Purpose This study aims to examine how affective and technological attitudes influence tourists’ intentions to use ride-sharing services. The authors also explore the moderating effects of perceived freedom and digital literacy on these relationships. Design/methodology/approach The sample consisted of local and international tourists using ride-sharing services in Bali, Indonesia. The authors used convenience sampling to distribute questionnaires to the respondents, collecting 258 responses. Out of these, 222 responses were fully completed and suitable for analysis. Findings The results revealed that hedonia, eudaimonia, technophilia and technophobia each differently influence tourists’ intentions to use ride-sharing services. Perceived freedom and digital literacy moderate the strength of these relationships. Research limitations/implications This research builds on previous studies by investigating how various aspects of happiness affect tourists' intentions to use ride-sharing services. It also emphasizes the role of perceived freedom and digital literacy in moderating the relationship between happiness, technology adoption attitudes and ride-sharing intentions. Originality/value This study contributes to the existing ride-sharing literature by integrating two dimensions of affective attitude (hedonia and eudaimonia) and technology adoption attitude (technophilia and technophobia) to examine their effects on tourists’ intentions to use ride-sharing services.

Real-time Multi-platform Route Planning in ridesharing

The increasing availability of mobile Internet and portable devices has led to the popularity of spatial crowdsourcing recently. The significant burgeoning of ridesharing services in spatial crowdsourcing has transformed urban mobility. However, due to the imbalance between supply and demand, some requests may be rejected or workers may keep idle for a long time. Fortunately, sharing tasks and workers across multiple platforms in a collaborative manner can diminish the negative effects of non-uniform distribution. In this paper, we propose a Multi-platform Route Planning in ridesharing problem (MPRP), which integrates route planning and task assignment in spatial crowdsourcing. We study how to improve the total revenue of platforms through coordination with multiple ridesharing platforms. To solve the problem, we propose the Greedy Multi-platform Route Planning algorithm (G-MPRP), which extends the dynamic programming insertion operation and assigns workers in a greedy manner. To overcome the shortcoming of G-MPRP, we further propose the Pack-based Multi-platform Route Planning algorithm (P-MPRP), which packs requests through a ranking-based function. Extensive experimental results shows the proposed algorithms can improve the revenues significantly.

A Branch-and-Price algorithm for the electric autonomous Dial-A-Ride Problem

The Electric Autonomous Dial-A-Ride Problem (E-ADARP) consists in scheduling a fleet of electric autonomous vehicles to provide ride-sharing services for customers that specify their origins and destinations. The E-ADARP considers the following perspectives: (i) a weighted-sum objective that minimizes both total travel time and total excess user ride time; (ii) the employment of electric autonomous vehicles and a partial recharging policy. This paper presents the first labeling algorithm for a path-based formulation of the DARP/E-ADARP, where the main ingredient includes: (1) fragment-based representation of paths, (2) a novel approach that abstracts fragments to arcs while ensuring excess-user-ride-time optimality, (3) construction of a sparser new graph with the abstracted arcs, which is proven to preserve all feasible routes of the original graph, and (4) strong dominance rules and constant-time feasibility checks to compute the shortest paths efficiently. This labeling algorithm is then integrated into Branch-and-Price (B&P) algorithms to solve the E-ADARP. In the computational experiments, the B&P algorithm achieves optimality in 71 out of 84 instances. Remarkably, among these instances, 50 were solved optimally at the root node without branching. We identify 26 new best solutions, improve 30 previously reported lower bounds, and provide 17 new lower bounds for large-scale instances with up to 8 vehicles and 96 requests. In total 42 new best solutions are generated on previously solved and unsolved instances. In addition, we analyze the impact of incorporating the total excess user ride time within the objectives and allowing unlimited visits to recharging stations. The following managerial insights are provided: (1) solving a weighted-sum objective function can significantly enhance the service quality, while still maintaining operational costs at nearly optimal levels, (2) the relaxation on charging visits allows us to solve all instances feasibly and further reduces the average solution cost.

Perspectives and possibilities for developing ride-sharing services to promote sustainable mode of transport: Bangladesh perspective

The ride-sharing trend is booming because of modern technologies and plays a significant role in transitioning to sustainable transportation methods. This study utilizes a comprehensive dataset collected from an online survey administered to over 1300 individuals in Bangladesh in 2023. Its specific focus is to examine the usage patterns of ride-sharing applications, such as Pathao and Uber. It builds upon previous research to analyze the factors influencing the use of these services and current trends in shared transportation. Additionally, it investigates the challenges associated with utilizing shared transportation amenities and proposes potential improvements to make ride-sharing or pooling more attractive. The study diligently worked to establish an inclusive framework and terminology to enhance policies and gain a more detailed understanding of the transportation choices and requirements of communities. To bridge the gap between theory and practice, we adopted an implementation-oriented research approach and conducted a comprehensive quantitative and inferential analysis of the survey data. The inclination of an individual to opt for or perceive collective or merged services depends on several demographic, geographical, and built-environment factors. The findings highlight the necessity of enhanced security measures, increased public awareness of ride-sharing benefits, and the formulation of legislative measures to promote sustainable mobility. Two examples of potential measures we have found to improve and encourage the use of ride-sharing services include reducing uncertainty about shared rides and minimizing passenger discomfort. These measures can be implemented through operational adjustments, government regulations, and employer programs. An essential conclusion was reached regarding the importance of prioritizing improvements in ride-sharing operations, rather than relying solely on cash incentives to promote behavioral change. Furthermore, it was determined that regardless of the changes, policies, or programs implemented, it is imperative to enhance public awareness and education.

Optimizing routing and scheduling of shared autonomous electric taxis considering capacity constrained parking facilities

This paper focuses on routing and scheduling of autonomous electric vehicles to provide reservation-based shared ride services, while a set of parking facilities with limited capacity are used for vehicle intermittent charging. A mixed-integer linear program model is formulated in the form of a vehicle routing problem with satellite facilities (VRPSF), subject to a series of additional time and capacity-related constraints. The objective of the model is to minimize the total operating costs of the system, including those related to vehicle miles traveled and the deployed vehicle fleet size. The number of vehicles inside each parking facility is tracked so as to ensure that the capacity is never exceeded throughout the service horizon. A customized solution method based on an adaptive large neighborhood search algorithm with an explicit treatment of parking facility choices is developed. A series of numerical experiments, consisting of both hypothetical examples and a real-world case study in Hangzhou, China, have been conducted to evaluate the effectiveness and applicability of the proposed model and algorithm. The results demonstrate that ride-sharing services and parking facilities have the potential to significantly reduce the total vehicle energy consumption and operating costs for a shared autonomous electric taxi (SAET) operator in practical scenarios.

A movement-aware measure for trajectory similarity and its application for ride-sharing path extraction in a road network

Recognizing common travel paths of crowds in a road network is valuable for understanding human mobility patterns and developing intelligent ride-sharing services. To achieve this, it is critical to measure the similarity of their trajectories. Although many measures have been proposed in the past decades, they often ignore movement consistency, exhibit one or more deficiencies in the face of noise and misaligned trajectories, or require extra parameters to tune predictions. In this paper, we propose an improved similarity measure called the directed segment path distance (DSPD), which considers the spatial proximity and movement consistency of trajectories. By integrating the spatial proximity distance and moving direction similarity between trajectories, the DSPD is a competitive parameter-free similarity measure that can effectively distinguish trajectories with different movement characteristics. To verify the effectiveness of the DSPD, we conducted a quantitative comparative study between the DSPD measure and 11 state-of-the-art trajectory similarity measures on six simulated trajectory datasets and applied the DSPD to two typical application scenarios: trajectory clustering for road network generation and retrieving common trajectories for ride-sharing path planning. The results demonstrate the effectiveness, robustness, and superiority of the DSPD and its great potential in trajectory search, clustering, and classification.

The labor market impacts of ridesharing on American Cities

This paper examines the impact of the emergence of the “gig economy” on the broader labor market by exploiting the staggered introduction of the ridesharing service Uber to American Cities between 2013 and 2018. Using difference-in-differences methods, Callaway and Sant’Anna’s doubly robust difference-in-differences estimator, Chaisemartin and D’Haultoeuille’s time-corrected Wald estimator, and Abadie et al’s synthetic control method, I estimate that Uber’s arrival to a city resulted in decline in the unemployment rate by between a fifth and a half of a percentage point. This suggests that Uber allowed many workers to supplement their earnings during periods of unemployment, framing the ridesharing service as a complement to, rather than a substitute for, traditional employment. I also find some evidence that Uber had a very small positive effect on wages at the lower end of the wage distribution, suggesting that Uber may have altered worker search behavior or affected bargaining power.

Exploring the influence of driver’s presence on passengers’ willingness level to take autonomous taxis – Evidence from Seattle metropolitan area

This study aims to identify the potential determinants of people's willingness to adopt autonomous vehicle (AV) taxis, with or without a backup driver, using Binary Logistic Regression in combination with the enhanced Random Forest attribute selection method. The results indicate that young men with frequent use of ride-sharing services, walk frequently, and have household incomes of approximately $150,000 − $200,000 are more likely to be interested in using AV taxis regardless of the presence of a backup driver. The study finds that previous travel habits (use of different modes) can greatly influence individuals' interest levels in using AV taxis. The study points AV taxi service providers should address female passengers' concerns to pursue a larger market. This study innovatively uses individuals’ preferences for their residential locations, such as proximity to workplace, walkability, and affordability. Through the inclusion of these variables in the analysis, this study offers a more comprehensive insight into the potential users of AV taxis, thus enabling policymakers and planners to pursue a transportation system that is sustainable, equitable, and responsive to the diverse needs and preferences of different populations. The study has limitations in that it lacks users' perceived benefits and their specific needs pertaining to AVs. Finally, the study emphasizes the importance of examining the causes of inequality and promoting equitable access to AV technology, particularly for vulnerable groups.

Examining Travel Mode Preference for the COVID-19 Post-Vaccination Period: Insights from Dhaka, Bangladesh

This research investigates preferences of general public towards travel modes in the COVID-19 post-vaccination times. First, a questionnaire survey is conducted in Dhaka, Bangladesh, to gather information on people's socio-demographic characteristics, travel choices before, during, and after the pandemic, and attitudes towards public transit, active transport, and personal car. Next, three ordered logit models are developed to explore how mode preferences vary across socio-demographics, travel characteristics, and risk perceptions. This research identifies factors that contribute to commuters' travel mode preference as well as factors that act as deterrents. Analysis reveals that there will be a noticeable shift from public transport to private car and active transport. To be able to walk or bike to work, people may consider relocating closer to their workplaces. Individuals who used a private car before vaccination may continue to do so after being vaccinated. Additionally, women are more likely to prefer own vehicles while people aged more than 49 years are less likely to prefer public transport. Results also suggest that use of food delivery services, ridesharing, and working from home significantly affects travel mode preference. Individuals who never used ridesharing services are more likely to prefer walking and bicycling after vaccination. The outcomes of this study are expected to assist transport policymakers in the Global South to better understand COVID-19′s long-term influence on mode choice behavior and help in developing pragmatic policies to promote sustainable travel habits.

Drivers of loyalty and consumer satisfaction with shearing urban mobility services: Carsharing, ridesharing, bikesharing, and kicksharing

Aim. To identify drivers of loyalty and consumer satisfaction with urban mobility shearing services.Objectives. To determine the characteristics of the use of shearing services; to formulate issues for shearing services that require priority attention; to identify drivers of loyalty and barriers to the use of shearing services, as well as drivers of user satisfaction with shearing services; to formulate recommendations for the development of the quality of shearing services.Methods. The authors applied the method of sociological survey (with participation of 615 respondents who use urban mobility sharing services at least once a year). The NPS index was used to identify drivers of loyalty, CSI index was used to detect drivers of customer satisfaction. The study was conducted in the context of such sharing services as carsharing, ridesharing, bikesharing and kicksharing, taking into account the relevant specific determinants of the quality of sharing services. The results were processed using general scientific methods of analysis and synthesis of information. Results. Users are loyal to carsharing, bikesharing and kicksharing, but not loyal to ridesharing. However, they are satisfied with ridesharing services in all four segments. The main drivers of loyalty were the convenience of using the services as an alternative to public transportation and the relative cheapness of the service. Respondents consider safety issues to be the main barrier. To a greater extent, consumers are satisfied with the technical side of the use of sharing services (we are talking about the convenience of the mobile application, confidentiality of personal data, technical condition of the car). Consumers are less satisfied with the speed of registration, loyalty programs, and the cost of insurance.Conclusions. Due to the fact that users are not loyal to ridesharing, operators of these services should primarily pay attention to safety during the trip, social capital of drivers (rating system), as well as the variety and number of available routes. In general, the area of growth of competitiveness of the shearing services on transport can be traced in the improvement of product metrics of web and mobile versions of applications, development of loyalty programs and pricing of insurance products