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The availability of ride-sourcing services, such as those provided by Uber and Lyft, can increase individuals' mobility and ease with which they can participate in out-of-home activities. Despite these benefits, ride-sourcing services could also induce additional travel demand, which may increase vehicle miles travelled and worsen congestion, especially if ride-sourcing vehicles occupy road space without transporting passengers. Given the negative externalities that may come about from induced ride-sourcing trips, it is important to understand the factors that influence whether a given trip would have been made had ride-sourcing services not been available. This study uses survey data collected from Metro Vancouver, Canada, in 2022, various geospatial data, and a binary logistic regression model to investigate the attributes of induced trips and the individuals making them. Individuals such as wheelchair-accessible taxi users and young adults with lower incomes are more likely to report having made an induced trip. The model also shows that induced trips tend to originate from home and are made to run errands. The built environment surrounding the trip origin and destination, such as sidewalk length, land-use types, and dwelling composition, are also determinants in induced ride-sourcing trips. The results of this study can help guide planning decisions that encourage these trips to be made by more sustainable modes. • Wheelchair-accessible taxi users are more likely to have made an induced TNC trip. • Paratransit users are also more likely to have reported an induced trip. • The effects of age on induced ride-sourcing trips are moderated by income level. • A 10% increase in detached dwellings leads to 19% decrease in odds of induced trip. [ABSTRACT FROM AUTHOR]

Ride-sourcing drivers, as individual service suppliers, can freely adopt their own relocation strategies, including waiting, cruising freely, or following the platform recommendations. These decisions substantially impact the balance between supply and demand, and consequently affect system performance. We conducted a stated choice experiment to study the searching behaviour of ride-sourcing drivers and examine novel policies. A unique dataset of 576 ride-sourcing drivers working in the US was collected and a choice modelling approach was used to estimate the effects of multiple existing and hypothetical attributes. The results suggest that ride-sourcing drivers' relocation strategies vary considerably between different drivers groups. Surge pricing significantly stimulates drivers to head toward the designated areas. However, the distance between the driver's location and surge or high-demand areas demotivates them from following the platform repositioning recommendations. We discuss the implications of our findings for various platform policies on real-time information sharing and platform repositioning guidance. [ABSTRACT FROM AUTHOR]

Driver fatigue is a major cause of road crashes. However, there is limited knowledge regarding the potential consequences of driver fatigue in taxi (conventional and app-based), ride-hailing, and ridesharing services. Driver fatigue is likely to be significantly exacerbated in this population due to the multi-task characteristics of their jobs; thus, conducting a comprehensive study on driver fatigue in these transportation sectors is of utmost importance. This systematic review summarises the current state of knowledge about the causes and consequences of driver fatigue. We also suggested some potential control mechanisms for driver fatigue in taxi and ride-hailing services along a fatigue risk trajectory. We included studies published prior to September 2022 in three databases (Web of Science, Scopus, and PubMed) using a predefined search strategy. Eligible studies were critically appraised using the Joanna Briggs Institute (JBI) critical appraisal checklists. A total of 18 studies met our eligibility criteria as scoped from the 414 initially identified studies. Eight contributing factors to driver fatigue were revealed including long working hours, short rest breaks, limited driving experience, job demand, poor sleep, algorithmic management, traffic congestion, and additional workload. Furthermore, our review identified risk factors for driver fatigue in taxi and ride-hailing services, including road safety, work pressure and driver's health, optimism bias, job precariousness, and lack of additional benefits. Findings to date suggest that driver fatigue in taxi and ride-hailing industries is as serious as, or more serious than, in other transportation sectors. Understanding the working conditions of these drivers is critical to establish effective policies and practices for reducing crash-related driver fatigue. [ABSTRACT FROM AUTHOR]

This study aims to explore the association between distracted driving (including cognitive, visual, operational, and auditory distractions) and multi-source features of ride-hailing drivers, and to discover frequent patterns of distracted driving. To achieve this, a natural driving experiment was conducted, and an association rule mining ('Apriori' algorithm) was used to uncover hidden rules between distracted driving and the multi-source features (including emotion, valence, arousal, driving tasks, cumulative driving hours, velocity, longitudinal acceleration, lateral acceleration, heading rate, presence of intersections, traffic status, and driving time of day). Results indicate that distracted driving is prevalent among ride-hailing drivers in specific scenarios, including trips without passengers, non-intersection sections, driving for over 4 h, and congested traffic conditions. The emotional state of drivers has also been found to have an interesting association with distracted driving. For instance, cognitive and operational distraction were highly associated with positive driving emotions, but auditory distraction caused by passenger interference was highly associated with negative driving emotions. Moreover, there are variations in distracted driving patterns across different categories. These findings can help ride-hailing platforms develop more scientific and effective distracted driving monitoring and prevention strategies. [ABSTRACT FROM AUTHOR]

Cities around the world are trying to understand if and how to regulate urban mobility in a way that stimulates innovation and supports business while also promoting public values and accelerating a sustainability transition. Service providers are also attempting to understand how to grow and thrive as a business as they challenge existing urban mobility structures and practices via new mobility services, new uses of public space, etc. Thus, this article seeks to understand the interplay between business models and public policies and, ultimately, the implications policy instruments have on shaping conditions for sustainable urban mobility. To address these questions, a qualitative approach is utilized, comprising case studies of two 'new mobility' service providers (Bolt and Tier) operating in three Northern European cities (Oslo, Stockholm, and Berlin) including interviews with these companies and local public actors. Findings show that the business models are influenced by legitimization on the national level, the local authorities' and service providers' approaches, and policy instruments related to the right to operate, including caps, geographic coverage, parking, geofencing, and data sharing. Utilizing business models and multi-level perspectives, the findings are discussed in relation to actualizing sustainable transport, e.g. interdependencies, goal alignment, and temporal and spatial considerations. The authors emphasize the importance of learning by doing, policy mixes (versus instruments), and purpose-driven collaboration among stakeholders. [ABSTRACT FROM AUTHOR]

A system manager makes dynamic pricing and dispatch control decisions in a queueing network model motivated by ride hailing applications. A novel feature of the model is that it incorporates travel times. Unfortunately, this renders the exact analysis of the problem intractable. Therefore, we study this problem in the heavy traffic regime. Under the assumptions of complete resource pooling and common travel time and routing distributions, we solve the problem in closed form by analyzing the corresponding Bellman equation. Using this solution, we propose a policy for the queueing system and illustrate its effectiveness in a simulation study. [ABSTRACT FROM AUTHOR]

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

The rapid expansion of ride-hailing service has imposed a huge amount of additional traffic on urban transportation systems. To reduce congestion, different regulatory policies have been announced to control the number of ride-hailing vehicles in metropolitan areas around the world. However, these measurements, shooting at ride-hailing vehicles only, deprive non-car owners of their private-car mobility rights (endowed by ride-hailing services), therefore are debatable in themselves. Instead of imposing specialized restrictions on ride-hailing vehicles, we propose to cure the deteriorating traffic conditions with classic traffic regulation policies implemented on all road space users. In this study, in a simple one corridor scenario with two different types of ride-hailing services, i.e., ride-sourcing and ride-sharing, we model travelers' mode choices at user equilibrium under no policy intervention to reveal the negative impacts of ride-hailing on urban transportation efficiency, and compare the different system optimum states with and without ride-hailing to demonstrate the great potential of ride-hailing in mitigating congestion. Based on the model, we then examine the impacts of three traffic regulation policies, i.e., road space rationing, congestion pricing and tradable credit schemes, and investigate the existence of traffic regulation policies that can achieve system optimum in the presence of ride-hailing. • This paper studies the impacts of two ride-hailing services on traffic condition. • This paper shows that ride-hailing services may increase or decrease congestion. • This paper studies the effectiveness of three classic traffic regulation policies. • This paper reveals the existence and properties of system optimal policies. [ABSTRACT FROM AUTHOR]

AbstractThe World Bank’s International Finance Corporation has produced three research reports on gender equality and ride-hailing, in collaboration with ride-hailing companies. This article examines these reports in light of wider conversations about the growing corporate co-production of development knowledge. Focusing on research claims about the gender and development potential of ‘sharing economy’ firms like Uber, it argues that the shortage of women drivers has been successfully framed as a gender and development problem, rather than a labour supply problem to be resolved by ride-hailing companies themselves. Multi-stakeholder solutions are proposed, involving licensing actors, charities, government development agencies, banks, and insurance providers. Gender equality research contributes to a new development common sense involving expanding the numbers of indebted ‘independent contractors’ in the gig economy; reducing ‘regulatory burdens’ on platforms; and binding nongovernmental organizations (NGOs) ever tighter to corporate interests. This article shows the centrality of gender research to the Bank’s broader reorientation towards business-led development, and highlights some distinctive lessons of work on ride-hailing. [ABSTRACT FROM AUTHOR]

Algorithms take decisions that affect humans, and have been shown to perpetuate biases and discrimination. Decisions by algorithms are subject to different interpretations. Algorithms' behaviors are basis for the construal of moral assessment and standards. Yet we lack an understanding of how algorithms impact on social construction processes, and vice versa. Without such understanding, social construction processes may be disrupted and, eventually, may impede moral progress in society. We analyze the public discourse that emerged after a significant (five-fold) price-surge following the Brooklyn Subway Shooting on April 12 2022, in New York City. There was much controversy around the two ride-hailing firms' algorithms' decisions. The discussions evolved around various notions of fairness and the algorithms' decisions' justifiability. Our results indicate that algorithms, even if not explicitly addressed in the discourse, strongly impact on constructing fairness assessments and notions. They initiate the exchange, form people's expectations, evoke people's solidarity with specific groups, and are a vehicle for moral crusading. However, they are also subject to adjustments based on social forces. We claim that the process of constructing notions of fairness is no longer just social; it has become a socio-algorithmic process. We propose a theory of socio-algorithmic construction as a mechanism for establishing notions of fairness and other ethical constructs. [ABSTRACT FROM AUTHOR]

Sustainable development goals (SDGs) are both a guide for growing more sustainably and a useful tool for engaging in corporate social responsibility (CSR) that can boost sustainable economic models. Hence, in this paper, we shed light on how companies' application of SDG practices affects perceived quality (PQ) and consumer loyalty based on the resource-advantage theory of competition (RAT). Exploratory analysis was used to create a scale of SDG practices, which was confirmed through confirmatory analysis. Structural equation modeling was used to analyze the mediation between the constructs. Our findings indicate that PQ mediates the relationship between SDG practices and loyalty, confirming a previous result on CSR. Hence, PQ retains paramount importance; only through PQ can SDG practices affect loyalty. We also propose the theoretical foundations for conceptualizing SDG practices among ride-hailing companies and empirically demonstrate the mediation effect of PQ. [ABSTRACT FROM AUTHOR]

The introduction of ride-sourcing services often has a disruptive impact on transportation networks. The availability of these services tends to produce negative externalities, including the replacement of trips made by more sustainable modes, increases in trip rates, and increases in vehicle-kilometers travelled. However, these services can also improve the accessibility of individuals without access to a private vehicle and help supplement spatial and temporal gaps in the transit network. Consequently, it is essential to understand the determinants of ride-sourcing adoption and usage to inform policies that mitigate the negative externalities of these services while capitalizing on their benefits. This study uses data from a web-based survey to examine the adoption and utilization of ride-sourcing in Toronto. Specifically, this study combines descriptive analysis with the estimation of econometric models to analyze ride-sourcing use and the relationship between ride-sourcing and conventional modes. The results offer insights into the nature of ride-sourcing use in Toronto and highlight how the relationship between ride-sourcing and conventional modes can vary across the population and trip purposes. This study aims to contribute to the literature by offering insights into the use of exclusive and shared ride-sourcing and examining the extent to which the relationship between ride-sourcing and conventional modes differs based on trip purposes. • The relationship between ride-sourcing and existing modes in Toronto is examined. • SP data are used to estimate mode choice models for commute and non-commute trips. • The impacts of travel time and cost on ride-sourcing use differ by trip purpose. • Ride-sourcing demand was found to be more sensitive to travel time than travel cost. [ABSTRACT FROM AUTHOR]

Ride-hailing, a newly emerging mobility service that is popular worldwide, has become an efficient new mode of transportation. Nonetheless, the use and value of ride-hailing remain unclear for newly developed areas in the suburbs. We crawled through the usage data of 10 ride-hailing apps from Wuhan, China, and used Spatial Autocorrelation and Geographic Weighted Regression (GWR) to explore the role of ride-hailing in suburban new towns. We found that: (1) There is variability between urban and suburban in the use of ride-hailing, and residents in suburban new towns are more inclined to complete travel activities by ride-hailing. (2) In suburban new towns, ride-hailing has a complementary effect on public transportation, and this complementary role has differences in regional and demographic attributes. This effect is greater for high-tech industrial areas and is more in women and young people than in men and elderly people. Overall, this study confirms from a geospatial perspective that residents of suburban new towns are more likely to use ride-hailing compared to central urban areas and that ride-hailing can supplement the lack of public transportation services (PTS) in suburban areas and improve transportation services in such new towns where development and construction are not yet complete. Therefore, the integration of online taxis with traditional public transportation is expected to promote multi-modal transportation options in newly developed areas and help realize the development of suburban new towns. In addition, the study also found the effectiveness of using big data from mobile phones in studying residents' temporal and spatial behavior. [ABSTRACT FROM AUTHOR]

Abstract This study aims to explore the association between distracted driving (including cognitive, visual, operational, and auditory distractions) and multi-source features of ride-hailing drivers, and to discover frequent patterns of distracted driving. To achieve this, a natural driving experiment was conducted, and an association rule mining (‘Apriori’ algorithm) was used to uncover hidden rules between distracted driving and the multi-source features (including emotion, valence, arousal, driving tasks, cumulative driving hours, velocity, longitudinal acceleration, lateral acceleration, heading rate, presence of intersections, traffic status, and driving time of day). Results indicate that distracted driving is prevalent among ride-hailing drivers in specific scenarios, including trips without passengers, non-intersection sections, driving for over 4 h, and congested traffic conditions. The emotional state of drivers has also been found to have an interesting association with distracted driving. For instance, cognitive and operational distraction were highly associated with positive driving emotions, but auditory distraction caused by passenger interference was highly associated with negative driving emotions. Moreover, there are variations in distracted driving patterns across different categories. These findings can help ride-hailing platforms develop more scientific and effective distracted driving monitoring and prevention strategies. [ABSTRACT FROM AUTHOR]

The extensive use of smartphones by individuals has led innovators to develop application based transportation services. Ride-hailing systems have been extensively operated in more than 600 cities worldwide. With the competition between taxis and ride-hailing, the number of fleets must be regulated. Identifying factors that influence the demand of taxi and ride-hailing and how the quality transit service is very important. The aims of this research are to identify the factors that influence demand for taxis and ride-hailing and the service quality of taxis and ride-hailing service. The study identified socio-demographic and trip characteristics from 949 respondents in the Jakarta Greater area, Indonesia. Respondents interviewed about the waiting time, travel time, and travel costs for the origin destination of trips that are most often done using taxi, ride-hailing, and bus. The service quality of ride-hailing and taxi was analyzed based on the respondent's preferences from an importantperformance analysis survey. The forecasting demand of taxi and ride-hailing in the Jakarta Greater area using demand-supply model is 71,660 vehicles. The research findings are that service quality of ride-hailing is better than conventional taxis based on waiting time, travel time, and travel cost variable. [ABSTRACT FROM AUTHOR]

Abstract Shared Autonomous Vehicles (SAV), or robotaxis, are expected to be commercially available within this decade. This new transport mode has the potential to revolutionize travel, offering a level of service comparable to traditional taxis with much lower prices. This may attract travelers currently using other modes, impacting the economic sustainability of public transport as well as car ownership levels. We investigate this potential demand using a scalable SAV simulation framework. We do not establish a future equilibrium considering the interaction between all users on a detailed road network, but establish the potential demand for a large metropolitan area. Travelers can choose between their current mode and the new SAV mode, with fare and waiting times which depend on real-time demand. For our input data we train a statistical model on a large transport survey from Germany for an urban region, allowing us to generate a large number of trips with realistic characteristics. We conduct a sensitivity analysis to study the effect of several key parameters on the modal shift. We find that SAVs can be attractive to many active mode and public transport users unless regulations are put in place. Our results also show that due to SAV fleet constraints, changes in incentives for travelers currently using cars may have significant consequences on the behavior of other travelers. We further calculate key economic indicators for the fleet, which can inform the discussion on the fleet size and fare level that operators are likely to choose when maximizing their own profit. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to develop a high-precision forecasting method that can forecast the probability distribution of the demand value of ride hailing. Due to the influence of various complex factors, the demand time series of ride-hailing generates noise and thus affects the accuracy of the forecast. Forecastings of uncertainty can provide a valuable reference for vehicle scheduling decisions. In the present study, the application of wavelet-DGPR models to forecast the demand time series of ride hailing was investigated. The effectiveness of the model was verified by using the demand data of ride-hailing in Hangzhou. The results show that wavelet decomposition can reduce the difficulty of forecasting; DGPR can obtain a probability distribution of demand forecast values for uncertainty forecasting. Wavelet-DGPR has better forecasting accuracy, stability, and robustness than typical methods. [ABSTRACT FROM AUTHOR]

The rise of technology-enabled ride-hailing services has affected individuals’ transportation-related decisions. The impact of these ride-hailing services likely varies across traveler segments that differ in their usage of various modes of transportation. In this paper, we develop and leverage a framework that allows us to examine the impact of ride-hailing services on the transportation mode choice for three traveler segments: drivers (who primarily use a personal automobile to travel), riders (who primarily use public transit to travel), and walkers (who primarily use non-motorized modes of transport). We first develop a framework outlining how the behavior of different traveler segments would be impacted by the introduction of ride-hailing services and show how this affects traffic congestion and public transportation ridership. To test the framework, we compiled a rich dataset, combining data on public transportation ridership, traffic congestion, and individual transportation mode choice. Employing a difference-in-differences methodology, we show that the Uber entry in a market enabled those who were walkers and riders prior to the entry of Uber to travel more conveniently, leading to an increase in traffic congestion, and induced those who were drivers to substitute their use of private automobiles with a combination of Uber and public transit. We introduced urban compactness to assess the heterogeneous impact of ride-hailing services for cities that differ in their distribution of traveler segments. We found that Uber entry increases traffic congestion and reduces public transit demand more in cities with higher levels of urban compactness, i.e., where the proportion of riders and walkers is higher than that of drivers. This work provides a holistic framework to understand the mechanism underlying the impact of ride-hailing services on public transit and traffic congestion. Urban planners and policy makers can leverage our framework, methodology, and empirical results to guide city planning decisions that have implications for sustainability. [ABSTRACT FROM AUTHOR]

Ride-hailing has emerged as one of the progressive sharing economy platforms. As a digital platform, both riders and drivers are critical to achieving sustainable ride-hailing transactions. Previous studies have gained little insight into ride-hailing services from drivers' perspectives. This study investigates the salient factors that determine the usage of ride-hailing services among drivers in Malaysia by extending the technology acceptance model (TAM), introducing governmental regulations, and integrating perceived risk and trust into the model. We collected data from a total of 495 ride-hailing drivers across Malaysia. Our results suggest that a driver's intention to use ride-hailing services is determined by perceived ease of use, perceived usefulness, and governmental regulations, which lead to actual usage. However, unexpectedly enough, the results signify that perceived risk does not affect the intention to use ride-hailing unless there is trust among the drivers. Overall, this paper draws attention to the substantial contrast in its results from the majority of prior TAM literature and has thoroughly improved the exploratory power of TAM by introducing new variables into the model, particularly from the perspective of ride-hailing drivers. This study is expected to bring theoretical and practical contributions to improve the country's ride-hailing industry. [ABSTRACT FROM AUTHOR]

Travel behavior has dramatically shifted during the COVID-19 pandemic as social distancing measures and stay-at-home orders are encouraged to prevent the spread of infection. Shared mobility, which includes transit, ride-hailing, and shared ride-hailing, typically involves contact with strangers and is now viewed as a potential risk. To understand current trends and predict potential modal shifts in a post-COVID-19 world, this research designed, developed, and deployed a short online survey about comfort with and usage of shared transportation. The survey was distributed through multiple recruitment methods to adults in the Atlanta metro area. Data collected from the survey (n = 787) revealed preferences related to ride-sharing environments and safety procedures, frequency of travel on different modes before and during the pandemic, and level of comfort related to the usage of different modes. Despite reopening the economy in Georgia, this research found that the actual usage of private ride-hailing and transit has dramatically decreased. In addition to the usage, the indicated comfort level with usage of shared mobility has decreased since the pandemic. Looking to the future, regression models and data analysis indicated that although there would be an increase in comfort for all shared modes when a vaccine is available, it does not return to the pre-pandemic levels. [ABSTRACT FROM AUTHOR]

Shared mobility services, such as car-share and ride-hail programs, have become increasingly popular within the sharing economy. Shared mobility services could reduce transportation inequities among marginalized populations. However, research has yet to assess how those responsible for disseminating shared mobility services perceive these resources. To address this gap, researchers interviewed 28 community agency service providers and transportation experts. Four themes emerged about providers' perceptions of shared mobility services. Overall, providers were unfamiliar with shared mobility services, which led to reluctance to recommend them. This study suggests that shared mobility services are under-utilized for increasing mobility among marginalized populations. [ABSTRACT FROM AUTHOR]

This article draws on a study conducted over a period of 17 months, including chatnography and semi-structured interviews with 30 female platform drivers working in China's hail-riding industry, and makes three important contributions to the labour process and the social reproduction process scholarship. First, it fills a gap in the burgeoning literature on the gendered experience of gig work and of work–family flexibility in an on-demand economy. Women's surplus production, as a means of subsistence for oneself and the family, is contradictory to childcare commitments in the labour process. Second, it theorises that the communicative space is a space for social reproduction in which labour-power is replenished outside the household. Labour productivity is not solely determined by algorithmic logic and platform control, but rather is organised by the social reproduction process. Third, it discusses how female platform workers negotiate technological insecurity and resist the platform's control over and sexual exploitation in the communicative space. This sheds light on how the social reproduction process creates a potential for women's solidarity. Women fight against sexual harassment and gender-based violence by utilising communication technologies, such as WeChat and TikTok. The social reproduction process organises labour resistance in a time of individual and collective crisis. [ABSTRACT FROM AUTHOR]

With the rapid advancement of wireless technology and internet services, ride-sourcing services have become an integral part of providing door-to-door connectivity to passengers. These services provide a real-time connection between drivers and users, and ubiquitously enhance the transportation sector and urban mobility. Present study explores the factors affecting service quality provided by ride-sourcing services in Delhi-National Capital Region (NCR) by conducting research separately for ride-hailing and ride-sharing services. Users' perception was collected from a total of 1260 samples (630 for each) by undertaking face-to-face surveys using a questionnaire. Service quality models were prepared for ride-hailing and ride-sharing services using Structural Equation Modelling (SEM) on 80% of the samples (504 for each). The remaining samples were used to validate both models. 'Reliability' was identified as the most critical factor affecting the service quality of ride-hailing services, while the most impactful factor regarding ride-sharing services was 'Security'. External validation test concluded that mean error for ride-hailing and ride-sharing services were ±6.9% and ±7.5%, respectively, thus indicated the model effectiveness. The outcomes of this research will be helpful for the transport planners and Transport Network Companies (TNC's) to prepare guidelines for improving the service quality provided by these services, which in turn, will improve the urban mobility, ridership and the last mile connectivity. • The study explores the factors influencing service quality of ride-sourcing services. • Both ride-hailing and ride-sharing service quality were analysed using SEM. • Five latent factors were explored to assess service quality of both the services. • Reliability was identified as the most critical factor for ride-hailing services. • Security was recognized as the most impactful factor for ride-sharing services. [ABSTRACT FROM AUTHOR]

Ikwe is a grassroots ride-sharing group founded in Winnipeg, Manitoba, in January 2016 to serve a growing number of Indigenous and non-Indigenous women—many of them poor and vulnerable—who felt unsafe taking taxis. It uses a Facebook group to connect its network of volunteer drivers with passengers. This article argues that Ikwe's technical infrastructure—Facebook and cars—is supplemented by an emotional infrastructure through which drivers share and manage the feelings elicited by their work. This gendered emotional infrastructure is based on a network of social circulations that includes sharing information over the Drivers' Log (a Facebook Messenger group that includes all Ikwe drivers) and telling funny and sad stories at the parking lot that the drivers call 'Headquarters'. In this way, drivers seek to foster a system of mutual support and protection. It also challenges the standard notion of a rational, efficiency-maximizing commercial exchange between drivers and passengers, as drivers and passengers intimately engage with each other during rides, at Headquarters or over Facebook. [ABSTRACT FROM AUTHOR]

Ride-hailing is a source of opportunity and consternation for cities, adding both expanded mobility options and added challenges such as increased congestion. In an effort to curb congestion—and to tap a new source of revenue—some US cities and states have imposed fees on ride-hail trips. Fees are far from uniform; the fee bases, amounts levied, and use of these funds once collected range considerably between cities and states. Despite previous research that transportation fee and revenue outlay structures affect equity outcomes, no research to date has examined the equity implications of ride-hail fees. This paper addresses this gap in understanding and asks: 1) what are the geographic equity implications of ride-hail fee structures; and 2) how might equity outcomes vary by how fee revenues are allocated? I answer these questions using ride-hail fee policies from 24 US cities and states, and trip-level data from over 97 million ride-hail trips taken in 2018 and 2019 in the City of Chicago. Examining trips serving low-, middle-, and high-income neighborhoods, I analyze geographic equity implications under five different fee scenarios. Variable fee structures that reflect trip factors including trip distance, pooling, location, and time of day offer the most promising geographic equity outcomes. The examined fee scenarios would raise between $30 and $122 million per year for the City of Chicago; more than three-quarters of ride-hail fee revenues are generated from trips that begin or end in high-income areas and could represent a substantial redistribution of resources from high-to low-income areas and/or travelers depending on how revenues are spent. Cities or states seeking to implement or adjust ride-hail fees should identify concrete equity goals and design fee structures and revenue streams to meet these objectives. • Ride-hail range considerably across cities and states in the US. • Five fee scenarios evaluated using 97 million ride-hail trips in Chicago. • Variable fees offer greater geographic equity outcomes vs flat fares. • Most fee revenues generated in high-income neighborhoods. • Cities should design fee structures and revenue outlays to meet equity goals. [ABSTRACT FROM AUTHOR]

There is growing evidence of ride-hailing platforms' adverse impact on drivers. Nonetheless, hundreds of thousands of drivers continue to work on these platforms. Why? The key contribution of this paper is to show that workers in technology-mediated labour markets come to be increasingly dependent on the technology-provider in order to connect with the customers. As more and more customers choose to get various tasks done via intermediary platforms, for workers who perform such tasks for a living, this translates into growing dependencies on these infrastructuralized platforms for their livelihoods and thus increased vulnerabilities to the impact of platform design and policies. These 'new dependencies', therefore, make it critical for us not to conflate workers' continued use of platforms with their experiencing benefits. By drawing upon a qualitative study with auto-rickshaw drivers using Ola, a ride-hailing platform similar to Uber in India, the paper shows that a consequence of 'new dependencies' for drivers is that they are stuck 'between a rock and a hard place' whereby: a) on the one hand, the platform design heightens their precarity, provides them with little benefit, and often leads to tensions with customers, b) on the other, a shift of more and more customers from street-hailing to app-based hailing over time exacerbates dependencies for drivers on these very platforms, leaving them with little choice but to continue to use them for work. [ABSTRACT FROM AUTHOR]

This article discusses how Brazilian platform workers experience and respond to platform scams through three case studies. Drawing from digital ethnographic research, vlogs/interviews of workers, and literature review, we argue for a conceptualization of "platform scam" that focuses on multiple forms of platform dishonesty and uncertainty. We characterize scam as a structuring element of the algorithmic management enacted by platform labor. The first case engages with when platforms scam workers by discussing Uber drivers' experiences with the illusive surge pricing. The second case discusses when workers (have to) scam platforms by focusing on Amazon Mechanical Turk microworkers' experiences with faking their identities. The third case presents when platforms lead workers to scam third parties, by engaging with how Brazilian click farm platforms' workers use bots/fake accounts to engage with social media. Our focus on "platform scams" thus highlights the particular dimensions of faking, fraud, and deception operating in platform labor. This notion of platform scam expands and complexifies the understanding of scam within platform labor studies. Departing from workers' experiences, we engage with the asymmetries and unequal power relations present in the algorithmic management of labor. [ABSTRACT FROM AUTHOR]

Online ride-hailing has become one of the most important transportation ways. In the ride-hailing system, how to efficiently match orders with vehicles and dispatch idle vehicles are key issues. The ride-hailing platform needs to match orders with vehicles and dispatch idle vehicles efficiently to maximize social welfare. However, the matching and dispatching decisions at the current round may affect the supply and demand of ride-hailing in the future rounds since they will affect the future vehicle distributions in different geographical zones. In fact, vehicles in different zones at different times may have different values for the matching and dispatching results. In this paper, we use the vehicle value function to characterize the spatio-temporal value of vehicles in each zone and then use it to design the order matching and idle vehicle dispatching algorithm to improve the long-term social welfare. In addition, in the order matching, passengers may untruthfully report the maximum price they are willing to pay to maximize their own profits, which can affect the order matching and thus may result in the losses of the long-term social welfare. Therefore, we design a VCG based pricing algorithm to prevent the strategic behavior of passengers. We further run experiments to evaluate the proposed algorithm. The experimental results show that our algorithm can outperform the state-of-the-art algorithm in terms of social welfare by 11.7% and service ratio by 11.1%. This work can provide some useful insights for the online ride-hailing platform to design practical order matching and pricing strategies. [Display omitted] • We design a vehicle value function to optimize order matching and improve the long-term social welfare by using a reinforcement learning method. • We consider the dispatching of idle vehicles to a zone as a virtual order and process idle vehicle dispatching issue as (virtual) order matching as well. • We design a VCG based pricing method to prevent the strategic behavior of passengers and ensure positive profits for the platform. [ABSTRACT FROM AUTHOR]

• The revenue structure and work schedule difference for two kinds of drivers, freelancers and contractors, are defined. • The drivers' synchronous contractual mode and work schedule choice model is formulated and the solution algorithm is designed. • The numerical studies are conducted to clarify the heterogeneity in working patterns between two kinds of drivers and analyze the equilibrium solutions under different market states in a dual-sourcing market. • The optimal wage strategies are explored via sensitivity analysis under different market states. In the ride-hailing market, the platforms could recruit contractors subject to prescriptive schedules to reduce the labor supply uncertainty and balance the demand and supply, and the drivers are entitled to become a freelancer or a contractor and choose the schedule they would like to provide the service. This paper proposes an analytical framework to capture the drivers' decisions of becoming a freelancer and contractor as well as how to arrange their work schedules in a dual sourcing market. The utility structures for two kinds of drivers, freelancers and contractors, are defined and a time-expanded network is adopted to portray the drivers' work schedule choices. With this setting, the drivers' simultaneous contractual mode and work schedule selection model is formulated and the algorithm is designed. The numerical examples are conducted to investigate the characteristics of the equilibrium solutions. The results show that a dual-sourcing market is always advantageous to contractors and conditionally beneficial to the platform, freelancers and customers. When there is low potential demand, higher fixed salary is preferred for the platform to attract contractor and complement the labor supply. When the market is thriving, the effect of the profits' increment by dual-sourcing is not obvious. This work could provide new insights on how to manage a dual-sourcing market for the platform. [ABSTRACT FROM AUTHOR]

Ride-splitting offers significant potential for reducing travel mileage and emissions. However, the patterns of ride-splitting demonstrate both diversity and complexity, yet there remains a deficiency in systematic and in-depth investigations of these patterns. This study aims to explore multiple patterns of ride-splitting and to evaluate the associated impacts on mileage and emission savings based on the pattern diversity. Firstly, two methods that are respectively for calculating the sub-pattern types from every ride-splitting pattern in theory, and for recognizing the actual sub-patterns in reality, are sequentially proposed. Then, the adjusted passenger mileage considering ride-splitting (i.e., APM) is proposed with detailed calculation methods for various ride-splitting patterns. Furthermore, a trip-level method for estimating emission reduction is developed by integrating the comparison of APM with the shortest mileage into the MOVES model. Finally, mileage saving and emissions reduction of ride-splitting in trip-level are investigated using the data sets of Beijing and Xiamen, respectively. The pattern diversity results show that 20 ride-splitting sub-patterns in actual data can be explored in Beijing and Xiamen. The average mileage savings can be obtained at the trip-level, which is 1.32 km for Beijing and 1.36 km for Xiamen. In the 20 sub-patterns, it is indicated that more trips involved in ride-splitting may not result in a more effective reduction, and the three-group-passenger pattern shows the best performance in CO 2 reduction. The study can provide promising results for understanding the strength of ride-splitting in reducing emissions and supporting the promotion of low-carbon travel modes. • Propose the theoretical calculation method for the ride-splitting pattern. • Develop a ride-splitting pattern recognition method based on actual data. • Explore the diversity of ride-splitting from the theoretical and actual perspective. • Evaluate the environmental impact of ride-splitting in terms of mileage and emissions. [ABSTRACT FROM AUTHOR]

This paper considers a multimodal charging network in which charging stations and battery swapping stations are jointly built to support an electric ride-hailing fleet synergistically. Our argument is based on the observation that charging an EV is a time-consuming burden, and battery swapping faces scaling issues due to its deployment costs. However, charging stations are cost-effective, making them ideal for scaling up EV fleets, while battery swapping stations offer quick turnaround and can be deployed in tandem with charging stations to improve fleet utilization and reduce operational costs. To fulfill this vision, we consider a ride-hailing platform that jointly builds charging and battery swapping stations to support an EV fleet. An optimization model is proposed to capture the platform's planning and operational decisions. In particular, the model incorporates essential components such as elastic passenger demand, spatial charging equilibrium, charging and swapping congestion, etc. The overall problem is formulated as a nonconcave program. Instead of pursuing the globally optimal solution, we establish a tight upper bound through relaxation and decomposition, allowing us to evaluate the solution optimality even in the absence of concavity. Through case studies for Manhattan, New York City, we find that joint planning of charging and battery swapping stations outperforms deploying only one of them, yielding a total profit that is 11.7% higher than swapping-only deployment under a limited budget, and 17.5% higher than charging-only deployment under a sufficient budget. These results underscore the complementary benefit between charging and battery swapping facilities. • Investigated a multimodal charging network including charging and battery swapping stations. • Proposed a nonconvex optimization framework for optimal infrastructure planning and fleet operations. • Evaluated the synergistic value of battery swapping and plug-in charging. • Plug-in charging can accelerate fleet electrification, while battery swapping can enhance fleet utilization. [ABSTRACT FROM AUTHOR]

Although several studies have attempted to determine the mileage and time inefficiency of ridesourcing services due to concerns about traffic and environmental impacts of these services, all of them provide a point estimate, which does not reflect the uncertainties in the calculation of these estimates. As such this study aims to present the efficiencies as ranges, based on lower and upper limits by identifying drivers' daily activity schedule. Based on an analysis of 200 busiest RideAustin drivers' trips data (around 282,037 trips over a 6 months period from 1 October 2016) the study finds that the mileage efficiency ranges from 44.3% to 71.6%, while time efficiency ranges from 42.8% to 58.4%. This means that for every 100-miles of a fare-payer passenger, drivers must travel an additional 40 to 126 miles empty. The heterogeneity of the efficiency at the driver level and the efficiency in the temporal dimension were also considered. [ABSTRACT FROM AUTHOR]

Ride-hailing has redefined vehicle access and has the potential to reduce travel difficulties for transit-poor areas and people with poor access to private and public transportation resources. Due to the lack of data, current studies lack a holistic understanding of how transportation resources serve different social groups and places in the ride-hailing era in low-density areas. This study uses multiple sources of data in Austin, Texas to understand: (1) how ride-hailing usage, transit supply, and vehicle ownership distribute across neighborhoods with different densities, income, and racial and ethnic compositions; (2) who are ride-hailing users among those with and without private vehicles, and how their ride-hailing usage and attitudes towards ride-hailing versus transit differ. Our study has shown that the ride-hailing services have provided residents living in low-income, low-density neighborhoods, and neighborhoods with a majority of Hispanics with an alternative transportation mode. However, residents living in low-density and low-income neighborhoods still use ride-hailing services less frequently than those living in high-density and high-income neighborhoods. The user survey further shows that ride-hailing users without private vehicles tend to be racial and minorities or younger people with higher education attainments. Ride-hailing services provide people with a convenient and safe transportation mode, regardless of their vehicle ownership. Our study shows the importance of ride-hailing in mitigating the social and spatial disparity and the opportunity of integrating ride-hailing and transit in transportation planning in low-density areas. • This study uses multiple data sources to study neighborhood and individual-level transportatation dirstributions. • Frequent ride-hailing usage, dense transit supply, and high share of zero-vehicle owners concentrate in downtown Austin. • Ride-hailing services support daily transportation for transport disadvantaged areas and people in Austin. • Ride-hailing services have the potential to integrate with public transit systems in low-density areas. [ABSTRACT FROM AUTHOR]

Despite their promise, popularity, and rapid growth, the transit implications of ride-hailing platforms (e.g., Uber, Lyft) are not altogether clear. On the one hand, ride-hailing services can provide pooling (i.e., traffic reductions) advantages by efficiently matching customer demand (i.e., trips) with resources (i.e., cars) or by facilitating car-sharing. On the other hand, ride-hailing may also induce extra travel because of increased convenience and travel mode substitution, which may create crowding (i.e., traffic increases). We seek to reconcile these divergent perspectives here, exploring the heterogeneous determinants of ride-hailing's effects. Taking advantage of Uber's staggered entry into various geographic markets in California, we execute a regression-based difference-in-differences analysis to estimate the impact of ride-hailing services on traffic volumes. Using monthly micro data from more than 9,000 vehicle detector station units deployed across California, we show that Uber's effect (either pooling or crowding) on traffic depends on various contextual factors. We find some evidence of pooling effects on weekdays; however, Uber's entry leads to significant crowding effects on weekends. Furthermore, the crowding effect is amplified on interior roads and in areas characterized by high population density. Although ride-hailing seems to have a substitution effect on public transportation, we find ride-hailing services may have a complementary effect for carpooling users. Finally, we show that premium ride-hailing services (e.g., Uber Black) almost exclusively lead to a crowding effect. We conduct a battery of robustness tests (e.g., propensity score matching, alternative treatment approaches, placebo tests) to ensure the consistency of our findings. [ABSTRACT FROM AUTHOR]

We consider the problem of an operator controlling a fleet of electric vehicles for use in a ride-hailing service. The operator, seeking to maximize profit, must assign vehicles to requests as they arise as well as recharge and reposition vehicles in anticipation of future requests. To solve this problem, we employ deep reinforcement learning, developing policies whose decision making uses Q -value approximations learned by deep neural networks. We compare these policies against a reoptimization-based policy and against dual bounds on the value of an optimal policy, including the value of an optimal policy with perfect information, which we establish using a Benders-based decomposition. We assess performance on instances derived from real data for the island of Manhattan in New York City. We find that, across instances of varying size, our best policy trained with deep reinforcement learning outperforms the reoptimization approach. We also provide evidence that this policy may be effectively scaled and deployed on larger instances without retraining. [ABSTRACT FROM AUTHOR]

With the soaring popularity of ride-hailing, the interdependence between transit ridership, ride-hailing ridership, and urban congestion motivates the following question: can public transit and ride-hailing coexist and thrive in a way that enhances the urban transportation ecosystem as a whole? To answer this question, we develop a mathematical and computational framework that optimizes transit schedules while explicitly accounting for their impacts on road congestion and passengers' mode choice between transit and ride-hailing. The problem is formulated as a mixed integer nonlinear program and solved using a bilevel decomposition algorithm. Based on computational case study experiments in New York City, our optimized transit schedules consistently lead to 0.4%–3% system-wide cost reduction. This amounts to rush-hour savings of millions of dollars per day while simultaneously reducing the costs to passengers and transportation service providers. These benefits are driven by a better alignment of available transportation options with passengers' preferences—by redistributing public transit resources to where they provide the strongest societal benefits. These results are robust to underlying assumptions about passenger demand, transit level of service, the dynamics of ride-hailing operations, and transit fare structures. Ultimately, by explicitly accounting for ride-hailing competition, passenger preferences, and traffic congestion, transit agencies can develop schedules that lower costs for passengers, operators, and the system as a whole: a rare win–win–win outcome. [ABSTRACT FROM AUTHOR]

Digital platforms play an increasing role across socio-economic sectors in developing countries yet development research to date on this topic has been limited and under-conceptualised. To help facilitate such research in future, this paper presents and applies the "multi-level perspective" as a framework to understand platforms in development as socio-technical transitions. Analysing a successful ride-hailing platform—EasyTaxi in Colombia—it finds what was originally a niche innovation then effected a socio-technical transition within Bogotá's taxi regime. Although there are some issues in applying the framework, it is found to have a factoral, scalar and longitudinal holism that were lacking in existing conceptualisations within the literature on platforms and developing countries. The multi-level perspective offers insights into the process of innovation, rapidity of scaling, and development impacts relating to resource endowments, institutional formalisation, and shifts in power. The framework may therefore be a useful lens for development researchers seeking to better understand digital platforms. [ABSTRACT FROM AUTHOR]

This paper follows the basic principles of taxi dynamics and uses an agent-based modelling approach (ABM) to simulate the underlying road network, vehicles and passenger situations in an urban environment and to carry out evaluations. First, based on two evaluation metrics: passenger travel time and total number of passengers transported, a comparison is made between the ride-hailing system (RH) and the system with ridesharing under different supply and demand rates, revealing the impact of ridesharing on system efficiency at urban scale. Then, in the ridesharing system (RS), four levels of supply are taken as examples, and passenger travel time is disaggregated to quantify how the definition of vicinity in the matching process affects the passenger travel time, passenger waiting time and vehicle detour time in a given city. Finally, a summary of the level of supply and the range of vicinity where ridesharing can be effective is presented in terms of the total distance travelled by the system. From a city management perspective, these results can help in the analysis and planning of efficient transport services. [ABSTRACT FROM AUTHOR]

• Contrary to popular belief, many over 65 years ride-hail, often self-booking rides. • Older adults who are more comfortable with technology book via the app. • Those who are less comfortable with technology rely on others or use phone booking. • Those with health issues and disabilities are more likely to book trips using an app. • More seniors would likely use ride-hailing if the booking process is made easy for everyone. Ride-hailing services like Lyft and Uber have the potential to improve mobility for many older adults, especially those who cannot or prefer not to drive. We used survey data from 2,917 Californians 55 years and older to investigate (1) how older adults who currently ride-hail booked their trips, and (2) what personal characteristics, including attitudes towards technology, were correlated with booking trips online versus by phone or with help. We specified four binary probit models in which the outcome variables are the manner in which a respondent accessed ride-hailing services: self-booked by phone, self-booked by app, booked by a family/friend/caregiver but rode alone, or booked by others and rode with them. We controlled for two attitudinal constructs (confident and cautious about technology), residential location, general travel behavior, physical health, and standard socio-economic factors. We found that respondents who were more confident using technology booked via apps, while those who were more cautious about technology were less likely to book using apps. This latter group was more likely to book by phone or rely on others for help. Other characteristics associated with higher likelihood of booking via apps were: living in the suburbs, not relying on others for rides, having physical health issues, being college educated, and being non-Hispanic. Our findings provide a basis to think about expanding ride-hailing to other older adults, particularly those who are not comfortable with technology, through convenient access to ride-hailing booking. [ABSTRACT FROM AUTHOR]

While studies have examined institutional tactics developed by sharing platforms to change urban regulations, there has been less attention to tactics deployed by incumbents to resist regulatory change. Drawing on the theory of institutional work, we investigate tactics that the Brussels taxi industry employed to overcome Uber's quest for legitimacy. A content analysis of media outlets shows that taxi incumbents combined three categories of tactics to maintain regulations: 1) enforcement and discursive tactics; 2) tactics traditionally used by institutional creators; and 3) tactics of mobilization. We provide implications for theory and give recommendations to urban practitioners. We advise city governors to build more alertness to institutional tactics, since this knowledge may contribute to decisions going beyond interests of specific actor groups in the sharing economy ecosystem, to the benefit of society. • Institutional work by incumbents in the sharing economy is underexposed. • Abductive approach to validate theory and explore new tactics of institutional work • 3 themes of tactics: maintaining status quo; adapting to Uber; protests and threats • The longitudinal approach highlights the complexity of governance and institutional work. • Empirics: taxi industry against Uber in Brussels Capital Region [ABSTRACT FROM AUTHOR]

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. • Detect anomalous ride-hailing drivers with insufficient training trajectories. • Integrate transfer learning with IRL to mine the driver's personalized preference. • Conduct numerical experiments using real trajectory data to verify our model. [ABSTRACT FROM AUTHOR]

Ride-hailing enjoys global popularity as a door-to-door mobility service, but its pick-up and drop-off inefficiencies and resulting environmental costs are often overlooked. This research examines the potential efficiency and environmental benefits of a flexible pick-up and drop-off (PUDO) strategy, which seeks to improve the routing efficiency by shifting the PUDO locations within short walking distance. A two-stage heuristic method is developed to identify suitable PUDO locations while considering both the detour and congestion factors. Using DiDi Chuxing trip data from central Chengdu, China, we find that 8%–23% of trips can be improved, reducing driving distance and time by up to 15%, and saving 11.6–51.1 GJ of energy consumption and 0.8–3.7 tons of CO 2 emissions in a single working day. Further built environment analysis reveals that areas with one-way and narrow streets, more diverse land use, higher income population, and better public transportation accessibility would benefit more from the proposed strategy. These insights highlight the potential of integrating walking into urban mobility solutions for improved efficiency and environmental sustainability. [ABSTRACT FROM AUTHOR]

Emerging mobility technologies, such as car sharing, ride hailing, bike sharing, and telework have led to aspirations of sustainable transportation. But how quickly are emerging mobility tools adopted? Who is adopting them? And are they likely to replace private vehicle use? This study addresses these questions using descriptive statistics and inferential models with repeated cross-sectional data from the Southern Ontario, Canada. Findings indicate that emerging mobility tool use grew between 2016 and 2021 – especially for ride hailing and telework. In the wake of the pandemic, users experimented and there were market changes. However, evidence on the potential for emerging mobility tools to lead to sustainable transportation is underwhelming. In turn, this questions as to whether emerging mobility tools should be core components of climate policy or whether they are most compelling based on a choice-based rationale for transportation policy – wherein choice is of central public value. [ABSTRACT FROM AUTHOR]

Research has shown that automation, carpooling, and electrification combined in a mobility on demand framework have the potential for theoretically large emissions reductions. However, less research has been done regarding consumer preferences and behavioral responses to this vision of transportation of the future. In this paper we use choice experiment data collected from an online ride-hailing survey to quantify consumer preferences for these technologies. Our results suggest there are major barriers to consumers' willing to ride in such vehicles. We find that respondents require large discounts to ride in driverless vehicles or to carpool with other passengers, even holding ride time constant. Though they are open to riding in EVs and HEVs, they are not willing to pay more to do so. Safety concerns and an unwillingness to ride with strangers appear to be the primary drivers of these preferences. [ABSTRACT FROM AUTHOR]

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

Ride-hailing services can expand access to opportunities in urban areas, but out-of-pocket costs may limit its benefits for low-income individuals. This paper examines how ride-hailing shapes spatial and socioeconomic differences in access to opportunities while accounting for the trade-off between travel time and monetary costs. Using one year of aggregate Uber trip data for Rio de Janeiro in 2019 and a new multi-objective routing algorithm, we analyze the potential for ride-hailing services to improve employment accessibility when used as a standalone transportation mode and in conjunction with transit as a first-mile connection. We find that, compared to transit, standalone ride-hailing can significantly expand accessibility as a standalone mode for short trips, and as a first-mile feeder to transit in trips longer than 30 min. However, the accessibility benefits of ride-hailing accrue mostly to high-income groups due to affordability barriers. These findings suggest that policy efforts to integrate rideshare with transit are likely not going to benefit low-income communities without some form of subsidized fare discounts to alleviate affordability barriers. The paper also highlights how accounting for trade-offs between travel-time and monetary costs can importantly influence the results of transportation accessibility and equity studies. • We examine how ride-hailing shapes access to opportunities as a standalone mode and when combined with transit as a first-mile feeder service • We use multi-objective optimization routing to calculate accessibility while accounting for the trade-off between travel time and monetary costs • Compared to transit, ride-hailing significantly expands accessibility as a standalone mode for shorter trips (up to 40 min.), and as a first-mile feeder to transit in trips longer than 30 min. • When we account for different affordability thresholds, the accessibility benefits of ride-hailing services accrue mostly to high-income groups • The study shows that accounting for trade-offs between travel-time and monetary costs can importantly influence the results of transportation accessibility and equity studies [ABSTRACT FROM AUTHOR]

On-demand ride-hailing services have received widespread interest from industry and academic. However, very few researches have empirically investigated the matching policy of ride-hailing platforms with real data. This study contributes in developing multi-level regression models to evaluate the nearest-first matching policy with real-world city-wide data collected from a ride-hailing platform UCAR in China. Specifically, after observing spatial-temporal patterns and the inherently hierarchical structure of the data, we propose multi-level logistics regression models for separately predicting the matching probabilities of passengers' trip requests in the off-peak, eve-peak, and morning-peak hours, where trip requests are grouped by the origins and destinations with cross-classification. The intercepts and slopes in the models can vary with the groups. The multi-level models are verified to be more appropriate and accurate than the traditional logistics regression models. The multi-level models can identify heterogeneous effects of the predicting variables on the matching policy among the groups and allow the discovery of many new findings on matching policy. These findings provide valuable policy implications for ride-hailing platforms to design an improving matching policy. [ABSTRACT FROM AUTHOR]

With the rapid growth of the mobility-on-demand (MoD) market in recent years, ride-hailing companies have become an important element of the urban mobility system. There are two critical components in the operations of ride-hailing companies: driver–customer matching and vehicle rebalancing. In most previous literature, each component is considered separately, and performances of vehicle rebalancing models rely on the accuracy of future demand predictions. To better immunize rebalancing decisions against demand uncertainty, a novel approach, the matching-integrated vehicle rebalancing (MIVR) model, is proposed in this paper to incorporate driver–customer matching into vehicle rebalancing problems to produce better rebalancing strategies. The MIVR model treats the driver–customer matching component at an aggregate level and minimizes a generalized cost including the total vehicle miles traveled (VMT) and the number of unsatisfied requests. For further protection against uncertainty, robust optimization (RO) techniques are introduced to construct a robust version of the MIVR model. Problem-specific uncertainty sets are designed for the robust MIVR model. The proposed MIVR model is tested against two benchmark vehicle rebalancing models using real ride-hailing demand and travel time data from New York City (NYC). The MIVR model is shown to have better performances by reducing customer wait times compared to benchmark models under most scenarios. In addition, the robust MIVR model produces better solutions by planning for demand uncertainty compared to the non-robust (nominal) MIVR model. • Introduce a matching-integrated vehicle rebalancing (MIVR) model for generating better rebalancing decisions. • A robust MIVR model incorporates inherent demand uncertainty in the ride-hailing system. • Real-world simulations show the performance improvements over two benchmark models. • Compare the robust and normal MIVR models under multiple demand scenarios. [ABSTRACT FROM AUTHOR]

• A macroscopic fluid modeling framework for on-demand ride-hailing system. • Strategic decision making of a platform for operating a large-scale ride-hailing system. • An empirical study is presented to demonstrate the framework. This paper proposes a macroscopic fluid modeling framework to assist with strategic decision making of a platform for operating a large-scale on-demand app-based ride-hailing system. The framework captures the spatiotemporal characteristics of a ride-hailing system, and is flexible in representing control policies that a platform is implementing. It thus enables the analysis of a large-scale ride-hailing system with observable market responses and facilitates the optimization of the control polices. As a demonstration of the proposed framework, we customize it for a ride-hailing system operated by Didi Chuxing in a large city of China, and conduct an empirical study of the system. In doing so, we calibrate the model with a high degree of granularity using empirical data, and then investigate optimal pricing strategies to achieve various managerial objectives of the platform. To our best knowledge, the proposed model is the first of its kind that offers a tractable way to support the analysis and optimization of large-scale ride-hailing systems. The empirical study also provides one of the first testbeds for examining various operations strategies for ride-hailing services. [ABSTRACT FROM AUTHOR]

Ride-sourcing, the use of private cars to provide on-demand mobility services, first appeared in San Francisco around the year 2010. Since then, transportation network companies (TNCs) who offer ride-sourcing services have expanded all around the world. By examining three case cities (San Francisco, Mexico City, Paris) we explain what facilitated this growth and how the regulation of TNCs differs. Subsequently, an economic analysis discusses the current expansionary strategy of TNCs and their future. We show that TNCs adapt their strategies to local contexts, with first priority to establish themselves in the market, if necessary, using gray regulatory areas, even if they face resistance from city authorities, taxi drivers and other groups, and despite being unprofitable. Our economic model explains this. We show that an unregulated ride-sourcing market leads to monopolistic situations once autonomous vehicles become available. We hence conclude that city authorities need to develop a regulatory framework to maximize social welfare. [ABSTRACT FROM AUTHOR]