Ride-hailing Services Research Articles

High-risk event prone driver identification considering driving behavior temporal covariate shift

Drivers who perform frequent high-risk events (e.g., hard braking maneuvers) pose a significant threat to traffic safety. Existing studies commonly estimated high-risk event occurrence probabilities based upon the assumption that data collected from different time periods are independent and identically distributed (referred to as i.i.d. assumption). Such approach ignored the issue of driving behavior temporal covariate shift, where the distributions of driving behavior factors vary over time. To fill the gap, this study targets at obtaining time-invariant driving behavior features and establishing their relationships with high-risk event occurrence probability. Specifically, a generalized modeling framework consisting of distribution characterization (DC) and distribution matching (DM) modules was proposed. The DC module split the whole dataset into several segments with the largest distribution gaps, while the DM module identified time-invariant driving behavior features through learning common knowledge among different segments. Then, gated recurrent unit (GRU) was employed to conduct time-invariant driving behavior feature mining for high-risk event occurrence probability estimation. Moreover, modified loss functions were introduced for imbalanced data learning caused by the rarity of high-risk events. The empirical analyses were conducted utilizing online ride-hailing services data. Experiment results showed that the proposed generalized modeling framework provided a 7.2% higher average precision compared to the traditional i.i.d. assumption based approach. The modified loss functions further improved the model performance by 3.8%. Finally, benefits for the driver management program improvement have been explored by a case study, demonstrating a 33.34% enhancement in the identification precision of high-risk event prone drivers.

Ride-hailing service availability and private transportation mode usage in a motorcycle-based city: Evidence from Hanoi

Ride-hailing services (RHS) are widely expected to serve as an alternative to conventional private transportation modes. However, numerous studies on RHS’s impact on private modes’ usage provide inconclusive evidence. Notably, few studies have analyzed RHS’s impacts on modal changes in the context of motorcycle-based societies. This study attempts to identify the critical factors affecting RHS users’ private mode usage (private cars and motorcycles) in response to RHS appearance in Hanoi, Vietnam, a motorcycle-based society. Two empirical analyses were performed with sample data of 642 individuals collected from face-to-face interviews using paper-based questionnaire sheets in Hanoi from December 19 to 21, 2020. The first analysis employed a linear regression model to explain the changes in individuals’ private mode usage before and after RHS adoption. The second analysis estimated a logistic regression model to examine an individual’s private mode choice when RHS are unavailable. Robustness tests were also performed. The results revealed that RHS users’ future vehicle ownership intentions, reasons for using RHS instead of private modes, motorcycle ownership, and public transport usage significantly reduce the impacts of RHS on private mode usage. The reasons for using RHS rather than private modes were found not strong enough to make an essential change to reduce the frequency of private modes usage. The results also showed there is a difference between the RHS’s impacts on motorcycle use and car use. They suggested that policy makers should be aware of the unique factors in motorcycle-based societies such as Hanoi when formulating urban transportation strategies. Finally, the findings further supported an idea that multivariate analyses are more suitable than the simple descriptive statistics method to understand how the motivation for using RHS influences the impacts of RHS on private mode usage.

Economic and environmental benefits of automated electric vehicle ride-hailing services in New York City

A precise, scalable, and computationally efficient mathematical framework is proposed for region-wide autonomous electric vehicle (AEV) fleet management, sizing and infrastructure planning for urban ride-hailing services. A comprehensive techno-economic analysis in New York City is conducted not only to calculate the societal costs but also to quantify the environmental and health benefits resulting from reduced emissions. The results reveal that strategic fleet management can reduce fleet size and unnecessary cruising mileage by up to 40% and 70%, respectively. This alleviates traffic congestion, saves travel time, and further reduces fleet sizes. Besides, neither large-battery-size AEVs nor high-power charging infrastructure is necessary to achieve efficient service. This effectively alleviates financial and operational burdens on fleet operators and power systems. Moreover, the reduced travel time and emissions resulting from efficient fleet autonomy create an economic value that exceeds the total capital investment and operational costs of fleet services.

Ride-hailing services and vehicle ownership: evidence from Indian metropolitan cities

PurposeThis study aims to examine the differential impact of ride-hailing services (RHS) on private and commercial vehicle ownership from five metropolitan cities in India.Design/methodology/approachUsing vehicle ownership data from five metropolitan cities over period 1991 to 2020, a panel corrected standard errors model was estimated to model the association between RHS and vehicle ownership.FindingsThe results indicate that advent of RHS has led to a significant reduction in private vehicle ownership rates and a corresponding increase in addition of intermediate public transport. The net effects of RHS on road congestion and pollution levels need to be studied in detail.Practical implicationsThe findings of this study can potentially assist policymakers and mobility planners in efforts to decarbonise and decongest urban transport.Originality/valueThis study sets precedence in analysing the impact of RHS on private and commercial vehicle independently. Further, to the best of the author’s knowledge, this is the first study to examine this association for the city of Delhi and Kolkata.

Safety of female ride-hailing passengers: Perception and prevention

Ride-hailing, as part of wider transitions to smart city, has emerged as a beneficial complement to urban public transport. However, the frequent occurrence of ride-hailing safety accidents in multiple countries has raised concerns about passenger safety on an international scale. Within this context, this study aims to investigate female passengers’ safety perception and prevention action when using ride-hailing services, helping establish better management and protection mechanisms for passenger safety. Using objective data from 7898 ride-hailing criminal adjudications (674 passenger victimizations) and subjective data from 596 Chinese female respondents, the safety perception scale and prevention tendency scale, with six potential variables, are developed to further examine the heterogeneity of perception and prevention. Results reveal that driver’s words and actions directly influence female passengers’ safety judgments regarding ride-hailing services and that traveling with companions significantly enhances their perceived safety. The main preventative actions identified include relying on mobile phones for information and communication, as well as avoiding risky ride-hailing situations. Furthermore, the analysis of individual heterogeneity has uncovered perception and prevention differences based on factors such as age, education level, residence conditions, ride-hailing frequency, and victimization experiences. These differences are evident in perception biases towards driver attitudes and accompanying travel, as well as variations in risk avoidance behaviors. The insights inductively derived hold fundamental conceptual and empirical value for scholarship on safety evaluation and crime prevention in the context of shared mobility. Additionally, the countermeasures proposed in this study can serve as a theoretical reference for enhancing the safety supervision of ride-hailing services, contributing to the establishment of safer, more reliable and more sustainable shared travel environment.

Strategy Formulation to Increase User Intention to Use Electric Ride Hailing Service in Indonesia

The emergence of electric ride-hailing services as a form of contribution from applicators in response to the government's transformation movement towards massive and equitable use of electric vehicles (EVs) by 2030 has captured the attention of the public in Indonesia. However, this new business model still needs to be reviewed to gain more awareness so that more users can use the service. Research on user perceptions of the technology is needed to develop electric ride-hailing services. Analysis of factors related to the user adoption process of electric online transportation services will be carried out using an integrated model of the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). Data processing and analysis will be carried out using the Structural Equation Modeling (SEM) method. The results showed that usefulness has a significant effect on Attitude Towards Use and Attitude Towards Use has a significant effect on Intention to Use so that applicators are expected to be able to implement strategies in increasing user perceptions of the value of usefulness in increasing the intention to use this service.

Causal Probabilistic Spatio-temporal Fusion Transformers in Two-sided Ride-Hailing Markets

In this work, we address the complex problem of multi-objective time series forecasting with a focus on predicting interdependent targets such as supply and demand in ride-hailing services. Traditional machine learning techniques approach the targets independently, while deep learning strategies may use joint learning with shared representations, both neglecting inter-target causal relationships and potentially compromising the models’ generalization capabilities. Our novel CausalTrans model introduces a framework to define and leverage the temporal causal interplay between supply and demand, incorporating both temporal and spatial causality into the forecasting process. Additionally, we enhance computational efficiency by introducing an innovative fast attention mechanism that reduces the time complexity from quadratic to linear without sacrificing performance. Our comprehensive experiments show that CausalTrans significantly surpasses contemporary forecasting methods, achieving up to a 15 \(\% \) reduction in error, thus setting a new benchmark in the field.

Exploring the impact of rider–driver ethnicity match/mismatch in ride-hailing

PurposeDrawing on the social identity theory, this paper explores the impact of rider–driver ethnicity match on the driver’s expected ride satisfaction and willingness to perform, and rider’s trust on the driver.Design/methodology/approachThe study relies on scenario-based online experiments with 291 ride-hailing drivers and 282 riders in the USA.FindingsThe findings indicate that ethnicity match between ride-hailing drivers and riders positively impact driver’s ride satisfaction and willingness to perform, and rider’s trust in the driver. The study also revealed a significant positive moderation effect of ethnic identity on the relationship of ethnicity match and those constructs.Practical implicationsWhile it may be challenging to influence an individual’s level of ethnic identity, managers can take steps to educate and train their employees regarding the impact of ethnic identity and discrimination, with a particular focus on those individuals who possess a strong sense of ethnic identity.Originality/valueThe findings of this research provide theoretical contributions to the existing literature on ride-hailing services and adds to the limited stream of logistics research that examines the impact of ethnicity on ride-hailing operations.

Coordinating last-train timetabling with app-based ride-hailing service under uncertainty

Since urban rail transit (URT) service is normally not running on 24-hour operation in most cities, last-train timetabling is a prominent problem and challenges URT managers constantly. The rise of app-based ride-hailing (ARH) service opens up new opportunities and challenges for last-train operators to better serve late-night passengers. Specifically, when passengers cannot reach their destinations only through URT services during the last-train operation period, passengers could make good use of feasible train-to-train transfers to reach stations closer to their destinations, and transfer to flexible ARH services to reach their final destinations. However, uncertain road conditions and varying passenger travel preferences complicate the coordination of URT services with ARH services. By considering different passengers’ traveling preferences, various travel path choices, and uncertain ARH travel times, we formulate a two-stage mixed-integer stochastic optimization model to achieve an optimal last-train timetable design for getting more passengers to their destinations in a cost-effective and efficient way. In addition, we propose a genetic algorithm-based solution strategy which outperforms commercial solvers with its computational performance and has its practicability assured. Through our numerical experiments, we reveal insights about how different customers’ preferences and cost components affect the optimal results and provide operational suggestions accordingly for achieving better timetable performance.

Anomalous ride-hailing driver detection with deep transfer inverse reinforcement learning

The rapid expansion in group size of online ride-hailing drivers has made anomalous driver detection become a critical issue, which substantially affects the safety and operation aspects of ride-hailing services Existing studies mainly focus on the identification of abnormal trajectories, while none of them investigate anomalous driver detection. The former evaluates a specific trajectory, while the latter comprehensively evaluates a driver. The driving preferences of anomalous ride-hailing drivers differ from those of most ride-hailing drivers. Inverse reinforcement learning (IRL) mines the hidden preference of an agent from its observed behavior Although existing IRL models can mine the driver’s route preference from trajectories, they depend on massive trajectories. To deal with insufficient trajectories, we propose deep transfer inverse reinforcement learning, which reuses the partial pre-trained common reward network and transfers it to be a part of the personalized reward network. Then, we use an autoencoder network to reduce the dimension of personalized route preference and combine it with K-means clustering to distinguish all the drivers into two categories (i.e., normal and abnormal). Numerical experiments using ride-hailing trajectories in Chengdu, China are conducted to verify the effectiveness of our proposed approach. The results demonstrate that precision and recall of our proposed approach are 91.7% and 97.1%, respectively.

The heterogeneity in commuters' travel behaviors toward different types of ride-hailing services: The case of Hanoi, Vietnam

This study addresses the lack of knowledge regarding the diversity in commuter travel behavior, particularly the effects of different types of ride-hailing services (RHS). It focuses on the distinct impacts of motorcycle-based RHS (MRHS) and car-based RHS (CRHS) on user behaviors. By applying multinomial logit and bivariate probit regression analyses to data from 500 RHS users gathered in March 2023, the study reveals that increases in income and personal vehicle ownership have contrasting effects on MRHS and CRHS usage. Notably, MRHS usage is positively associated with employing RHS as a feeder mode to metro stations, while CRHS usage demonstrates a negative correlation. Furthermore, the study establishes a user preference for MRHS over CRHS during peak traffic times. These insights highlight the importance of creating specific policies for different types of RHS, which is vital for the development of successful urban transportation strategies.

Leading the Horse to Water? Investigating the Impact of Ride-Hailing Services on Hate Crimes

Hate crimes, which stem from prejudiced attitudes, have a distributionally detrimental impact on societal stability. Although inter-group contacts are potentially an effective means for reducing prejudice and subsequently decreasing the number of hate crimes, scholars have recently recognized the possibility of negative contacts that might actually amplify prejudice. As a result, the question of whether intergroup contacts truly possess the ability to effectively decrease hate crime numbers remains inconclusive. In addition, prior contact research primarily relies on laboratory experiments because the establishment of intergroup contacts in a field setting is challenging. Examination of the effectiveness of intergroup contacts hence merits further investigation in a real-world setting. In this article, we propose that ride-hailing services, which naturally connect individuals from different backgrounds, offer an avenue to facilitate intergroup contacts in practice, which could potentially reduce prejudice and the volume of hate crimes. Leveraging the staggered introduction of this technology into counties in the United States, we conducted a series of analyses to empirically evaluate the contact effects in the open field. Our analysis reveals a notable decrease in the number of hate crimes (particularly a 5.75% reduction in racial hate crimes) after the introduction of ride-hailing services. These findings remained consistent across various robustness tests. Additional moderation analysis suggests that the increased interaction between different groups, facilitated by ride-hailing services, is the most likely explanation for the observed decrease in hate crimes. We further conducted an extensive survey involving real ride-hailing drivers and passengers. The results from our survey provide direct evidence that ride-hailing services create natural and constructive environments where positive interactions and mutual understanding can develop among diverse groups of people. This, in turn, helps mitigate prejudice and hate crimes within society, as observed in our analysis. This study not only extends the existing body of literature on contact theory but also sheds light on how modern technologies can play a pivotal role in curtailing hate crime, yielding both theoretical and practical implications.

ОЦІНЮВАННЯ ЯКОСТІ ПОСЛУГ РАЙДХЕЙЛІНГ-КОМПАНІЇ «УКЛОН» ЗА ДОПОМОГОЮ МОДЕЛІ SERVQUAL

The paper presents the results of a study of the quality of passenger taxi services (case study for Ukrainian ride-hailing service "Uklon"), which was conducted by applying the SERVQUAL tool. The ride-hailing service is an innovative product in the rapidly developing market of taxi services and a business model, a taxi aggregator. Its peculiarities identified via the literature review are the on-demand provision of paid prearranged passenger transportation services combined with smartphone mobile applications or online platforms for immediate booking of trips, making electronic payments, and rating drivers and passengers. The study aimed to obtain the quality measures for the service − the integrated indicator and indicators for the partial characteristics. The theoretical basis of SERVQUAL offered by A. Parasuraman, V. A. Zeithaml, and L. L. Berry is the gap concept and taxonomy of service quality determinants (model RATER). The instrument allows the measurement of service quality as a gap between the consumers' expectations regarding the service characteristics and their perceptions based on consumption experience. Quantitative marketing research in the form of a survey of 50 clients of the "Uklon" was implemented. The method provided the following stages: determination of essential characteristics of a ride-hailing service; development of two questionnaires; collection of primary data by surveying customers aimed at revealing their pre-consumption expectations; surveying customers aimed at revealing their perceptions regarding the quality of services of "Uklon" based on the experience gained; calculation of the general and partial indicators for service quality. The service quality accounted for -0,69, and quality indicators for partial characteristics range from 0.02 to -1.46. The ride-hailing service "Uklon" is close to fully satisfying clients, but there is room for improvement. Two unique value propositions differentiating the company from competitors are identified. Recommendations for improving the services are provided, and prospects for further research are defined.

Comparative analysis of CO2 emissions of electric ride-hailing vehicles over conventional gasoline personal vehicles

Ride-hailing services have become popular due to their convenience, affordability, and accessibility. Increasing cost of car ownership, traffic congestion and parking shortages are fostering this global market growth. Though four-wheeler ride-hailing vehicles are fuel efficient, researchers have determined that the emission reductions from fuel efficiency alone cannot compensate for the additional emissions caused by "deadheading," the issue of driving without passengers on ride-hailing trips. We suggest that electric vehicle-based ride-hailing services have the potential to reduce utilization CO2 emissions, which are emissions released while using (driving) the vehicle. However, skeptics challenging this hypothesis cite CO2 released during electricity generation (carbon intensity) needed for recharging batteries and the inherent issue of deadheading in ride-hailing services. We compared ride-hailing vehicles to gasoline-powered passenger cars, the largest source of carbon emissions within the transportation sector. In many cases, ride-hailing vehicles are electric and have newer models, so they emit less CO2 per kilometer. We conducted a quantitative analysis, using a mathematical model to estimate the utilization of CO2 emissions for electric vehicles per ride-hailing trip kilometer, considering carbon intensity of electricity generation, electric vehicle fuel efficiency, and deadheading. We compared our results with average estimated utilization CO2 emissions to gasoline-powered passenger cars. Additionally, we performed a sensitivity analysis, which involved adjusting the input variables within plausible ranges to assess their impact on our model results. According to our analysis, despite deadheading, improved vehicle fuel efficiency and cleaner electricity generation result in lower CO2 emissions for electric ride-hailing vehicles than gasoline personal vehicles.

A gendered perspective on ride-hail use in Los Angeles, USA

Gender differences in travel are pervasive throughout the world. While gender gaps have narrowed in many social spheres, such is generally not the case for travel. In this study, we examine whether gender differences persist on a relatively new travel mode: ride-hail. We do this by examining the use patterns, experiences, and opinions of nearly 300 travelers in metropolitan Los Angeles, California, USA. Our survey responses show that ride-hail service offers a convenient travel option for women – one that enables many of them to access needed destinations in their daily lives. Women in carless or car-deficit households in particular benefit from using ride-hail services to meet their travel needs, which differ, on average, from men’s. For example, more women use ride-hail services for household-serving trips than men. However, ride-hail is not free of challenges for women. Concerns about safety, fear of personal and financial information leakage, and limited digital literacy and access to financial resources can inhibit or prevent some women from using these services. Compared to men, a larger share of women takes ride-hail trips booked by someone else in the household and/or travels with a companion. These findings suggest that public policy should seek to ensure that the option to use ride-hail is extended to as many people – regardless of gender – as possible, including those without mobile devices with data plans and credit or debit accounts. Riders should also be able to easily report problematic behaviors by drivers or fellow passengers to public authorities, in addition to reviews of and reports to ride-hail companies.

The crucial role of motorcycle-based ride-hailing among commuters: The case of Jakarta and Bandung in metropolitan areas

In Indonesia, issues in the metropolitan transportation sector are considered severe. For instance, peripheral districts in the Jakarta metropolitan area (JMA), such as Bogor, Depok, Tangerang and Bekasi, contribute to the high mobility rate within the metropolitan area. This article examines the extent of motorcycle-based ride-hailing for commuting in the JMA and Bandung metropolitan areas (BMA). In particular, this study explores ride-hailing users’ characteristics—social, economic and spatial—and their proportion to the total commuter mobility in a metropolitan area. The study employed descriptive statistical models using commuter data in the JMA and BMA, respectively. Our results indicate that young and lower-income commuters are the dominant users of ride-hailing services. As motorcycle taxi services are relatively cheaper and accessible, young and lower-income commuters, including informal workers, prefer ride-hailing services. The study also suggests the impact of the intensification of inter-peri-urban mobilities of post-suburbanisation on the increasing demand for ride-hailing. In this sense, the study proposes that the long-overdue strengthening of metropolitan-scale governance is urgently required. It is also crucial for metropolitan governments to issue appropriate inter-district public transportation policies and engage in development.

An exact second order cone programming approach for traffic assignment problems

Demographic changes, urbanization and increasing vehicle ownership at unprecedented rates put a lot of strain on cities particularly on urban mobility and transportation and overwhelm transportation network infrastructures and current transportation systems, which are not built to cope with such a fast increasing demand. Traffic congestion is considered as the most difficult challenge to tackle for sustainable urban mobility and is aggravated by the increased freight activity due to e-commerce and on-demand delivery and the explosive growth in transportation network companies and ride-hailing services. There is a need to implement a combination of policies to ensure that increased urban traffic congestion does not lower the quality of life and threaten global climate and human health and to prevent further economic losses. This study aims to contribute to the United Nations (UN) climate action and sustainable development goals in tackling recurring traffic congestion problem in urban areas to achieve a sustainable urban mobility in that it offers a solution methodology for traffic assignment problem. We introduce an exact generalized solution methodology based on reformulation of existing traffic assignment problems as a second order cone programming (SOCP) problem and propose column generation (CG) and cutting plane (CP) algorithms to solve the problem effectively for large scale road network instances. We conduct numerical experiments to test the performance of the proposed algorithms on realistic road networks.

Analyzing the relationship between bus and ride-hailing use in a large emerging economy city: A bivariate ordered probit model application

This study analyzes the use of ride-hailing and bus services from a multi-modal user perspective by jointly modeling individuals’ monthly frequency of use of both bus and ride-hailing services. A bivariate ordered probit model is estimated to capture the influence of socio-demographic and attitudinal characteristics as well as unobserved factors that may simultaneously influence the travel frequency by both modes. We use data collected in a survey of travelers from Porto Alegre, Brazil. The results characterize the profile of local frequent ride-hailing users as young, medium-income, self-employed individuals with a propensity toward the use of technology and a low inclination for car ownership. Most importantly, we observe that after controlling for unobserved factors that simultaneously contribute to higher trip frequencies by both modes, frequent bus users demonstrate a lower propensity toward ride-hailing use.

Investigating Commuter Satisfaction in OLA: Analyzing the Effect of Real-time Pricing on Ride Expenses and Customer Loyalty

Innovative ride-hailing services have caused a transformation in the transportation industry. Ola, the pioneer of taxi aggregation services in India, has played a significant role in driving this change. In 2010, students from the esteemed Indian Institute of Technology (IIT) Bombay founded Ola. From that time on, its global presence has expanded, now operating in more than 250 cities. Ola is recognized for its user-friendly travel choices, distinguishing itself through innovative business strategies that surpass regular taxi services. Ola’s strategy for operations is founded on the dynamic pricing model, a concept that the company created and brought into the transportation industry. This model demonstrates Ola’s ability to promptly modify pricing by market supply and demand fluctuations. Ola has established itself as a major player in the highly competitive ride-hailing industry, boasting more than 125 million users. This study analyzes the complex connection between Ola’s dynamic pricing strategy and commuter satisfaction. It also aims to analyze the impact of real-time pricing on ride costs and customer retention. This study seeks to uncover the various factors impacting commuter decisions within the growing taxi-hailing industry by analyzing customer satisfaction, decision-making processes, communication openness, app usage trends, and brand reputation. This research provides Ola with important information to enhance customer satisfaction and perfect pricing strategies. To accomplish this, it evaluates how clear the communication is perceived to be, examines the factors that greatly affect commuter satisfaction with Ola’s dynamic pricing, evaluates the impact of pricing on consumer decision-making, and understands the relationship between pricing changes and user interaction. It is essential to grasp the impact of real-time pricing on commuter satisfaction and loyalty to maintain market leadership amidst ongoing transportation sector changes, highlighting Ola’s significant role.

On-Demand Ride-Hailing Service Platforms With Hired Drivers During Coronavirus (COVID-19) Outbreak: Can Blockchain Help?

During the recent COVID-19 (CoV) global outbreak, there is a sharp decline of revenue of on-demand ride-hailing (ODR) platforms because people have serious worries of infection in the shared vehicles. Blockchain, which supports cryptocurrency and creates full traceability of the service history of each car and driver, may come to rescue by allowing the platform to offer only the “safe cars” to consumers. Motivated by the real world challenges associated with the CoV outbreak for the ODR platform, we build game-theoretical models based on the M/M/n queuing system to explore if and how blockchain can help. In the basic model, the ODR platform decides the service price and special hygiene level. Comparing between the cases with and without blockchain, we find that blockchain implementation increases both the service price and hygiene level. In addition, when the consumers’ inherent worry of infection is substantially large, implementing blockchain achieves all-win for the ODR platform, drivers and consumers. In the extended models, we first consider the case when the special hygiene level is determined by the drivers under a mixed-leadership game and then explore the case when customers are risk averse. The main findings about blockchain adoption remain valid in both cases. However, when the drivers take charge of the special hygiene level, both optimal decisions are lower in most cases. It is also important to make efforts to reduce consumers’ feeling volatility toward service valuation for improving the value of blockchain adoption and related performances.