•Ridesplitting can significantly reduce the vehicle hours traveled (VHT) by 22%.•The percentage of ridesplitting trips is fairly low (6–7%) in current ridesourcing services.•Most ridesplitting trips ...(more than 90%) consist of two shared rides, and used mainly for non-commuting trips.•Single rides and shared rides have different spatiotemporal patterns and travel characteristics.•Ridesplitting leads to 10 min average delay, 1.55 km average detour distance, and degraded travel time reliability.
With the development of mobile internet technology, on-demand ridesourcing services have rapidly spread across the world and have caused debates in the transportation industry. While many researchers have begun to study the characteristics and impacts of ridesourcing, there are few published studies specifically on ridesplitting, a ridesourcing service that matches riders with similar origins or destinations to the same ridesourcing driver and vehicle in real time. This paper aims to explore the characteristics and effects of ridesplitting using observed ridesourcing data provided by DiDi Chuxing that contain complete datasets of the ridesourcing trajectories and orders in the city of Chengdu, China. First, a ridesplitting trip identification (RTI) algorithm is developed to separate the shared rides from the single rides (non-ridesplitting orders) and derive ridesplitting scales. Second, a ridesplitting trajectory reconstruction (RTR) algorithm is proposed to estimate the ridesplitting effects on delays and detours. Then, we analyze and compare the scales, spatiotemporal patterns and travel characteristics between shared rides and single rides, which are very different. The results show that the current percentage of ridesplitting in ridesourcing is still low (6–7%), which may be explained by the extra delay (about 10 min on average), detour (about 1.55 km on average), and degraded travel time reliability caused by ridesplitting. In addition, built environment factors, such as density, diversity, and development, are also correlated with ridesplitting demand and delay. The findings of this study can help better understand the features of ridesplitting and develop strategies for improving its use in emerging ridesourcing services.
•Shared ride services with autonomous vehicles result in lower user waiting times.•Sharing also results in lower operational costs for operators than no sharing.•Trade-offs examined from perspective ...of operators, users and policymakers.•Benefits accrue only when demand pool is sufficiently large.•Role for public sector to incentivize shared ride offering and use.
This paper presents a quantitative analysis of the operations of shared-ride automated mobility-on-demand services (SRAMODS). The study identifies (i) operational benefits of SRAMODS including improved service quality and/or lower operational costs relative to automated mobility-on-demand services (AMODS) without shared rides; and (ii) challenges associated with operating SRAMODS. The study employs an agent-based stochastic dynamic simulation framework to model the operational problems of AMODS. The agents include automated vehicles (AVs), on-demand user requests, and a central AV fleet controller that can dynamically change the plans (i.e. routes and AV-user assignments) of AVs in real-time using optimization-based control policies. The agent-based simulation tool and AV fleet control policies are used to test the operational performance of AMODS under a variety of scenarios. The first set of scenarios vary user demand and a parameter constraining the maximum user detour distance. Results indicate that even with a small maximum user detour distance parameter value, allowing shared rides significantly improves the operational efficiency of the AV fleet, where the efficiency gains stem from economies of demand density and network effects. The second set of scenarios vary the mean and coefficient of variation of the curbside pickup time parameter; i.e. how long an AV must wait curbside at a user’s pickup location before the user gets inside the AV. Results indicate that increases in mean curbside pickup time significantly degrade operational performance in terms of user in-vehicle travel time and user wait time. The study quantifies the total system (user plus fleet controller) cost as a function of mean curbside pickup time. Finally, the paper provides an extensive discussion of the implications of the quantitative analysis for public-sector transportation planners and policy-makers as well as for mobility service providers.
Sharing rides in on-demand systems allow passengers to reduce their fares and service providers to increase revenue, though at the cost of adding uncertainty to the system. Notably, the uncertainty ...of ride-pooling systems stems not only from travel times but also from unique features of sharing, such as the dependency on other passengers' arrival time at their pick up points. In this work, we theoretically and experimentally analyse how late arrivals at pick up locations impact shared rides' performance. We find that the total delay is equally distributed among sharing passengers. However, delay composition gradually shifts from on-board delay only for the first passenger to waiting delay at the origin for the last passenger. Sadly, trips with more passengers are more adversely impacted. Strategic behaviour analysis reveals Nash equilibria that might emerge. We analyse the system-wide effects and find that when lateness increases passengers refrain from sharing and eventually opt-out.
As ride-hailing becomes more common in cities, public agencies increasingly seek transportation network company (TNC) service data to understand (and potentially regulate) demand and service ...response. Despite the increase in ride-hailing or TNC demand and subsequent research into its determinants, there remains little research on shared TNC trips and the spatial distribution of trip demand across demographic and land use variables. Using Chicago as a case study, shared TNC trip data from 2019 was used to estimate the count and ratio of shared ride services, based on built environment, demographic, location, time of day, and trip details. Findings reveal that trip length, day of week designation, density of pedestrian and multi-modal infrastructure, and underlying socioeconomic characteristics of the origin zones influence the proportion and count of shared ride-hail trips. Of concern is that those using transit or active modes may be taking more ride-hailing trips, but these Chicago-region results indicate that the provision of pedestrian infrastructure and remoteness to transit stops result in fewer shared trips.
The constraints placed by the transport options available to job-seekers are key factors for the accessibility of employment locations and therefore social inclusion. The present paper investigates ...the importance of these constraints and the potential appeal of an employer-subsidised Demand Responsive Transport (DRT) service to job-seekers at risk of social exclusion. Mainly quantitative questionnaire data were obtained from a survey (n = 254) of jobseekers attending three ‘Jobcentre Plus’ government agency offices in Bristol (UK) during September 2017. The offices, which integrate the provision of social security benefits with support to secure work, were in inner-city, intermediate, and peripheral locations. Comparative spatial analysis was conducted both within and between the locations. The respondents emerged as having high public transport dependence for the commute, and transport-related perceived barriers emerged as second in importance only to ‘qualifications and skills’ and were reported as having inhibited attendance at job interviews and jobs. The preferences identified from the literature for finding work near home or in the city centre was confirmed. Reaching employment locations on the periphery of the city was particularly problematic. Job-seekers interviewed at the intermediate location reported the widest geographical scope of search. Logistic regression modelling confirmed the perceived options for public-transport commuters were somewhat different. Gender and the type of work sought also influenced spatial perceptions. Respondents were more willing to share the commute with ‘people they knew’, and strongly supported the concept of employer-subsidised DRT, with some statistically significant gender differences in attractiveness regarding the specific nature of the service offer. It is concluded that employer-subsidised DRT services would be most appropriate for remote sites, in situations in which the labour force is likely to be drawn from areas hard to connect with public transport, and where car use is either low, or being reduced by car use restraint policies. Future research into the context of real-world applications is required to examine whether benefits to employers, including staff recruitment and retention, would be sufficient to justify employer subsidies.
•Accessibility and transport do have important implications for job searching.•Searches by residents of intermediate suburbs had the broadest spatial scope.•Public transport-dependent job-searchers showed a restricted spatial search scope.•Employer-subsidised DRT perceived to enhance job-site accessibility.•Women more attracted to an Employer-subsidised DRT than men.
Shared mobility services are evolving globally. However, the first-mile and last-mile (FMLM) shared-ride taxi service poses a complex problem due to its large-scale nature and mixed-type variables ...(numeric and categorical features). As the input size of the problem increases exponentially, the absence of a known polynomial-time algorithm further complicates the finding of an optimal solution. Consequently, exploring potential solutions becomes computationally infeasible for more significant instances. Thus, this paper proposes using the k-prototype algorithm, an unsupervised learning approach, to cluster passengers' requests for FMLM shared-ride taxi service, which can reduce the problem's complexity via feasible clustering. Notably, the k-prototype algorithm is suitable for data sets with both numeric and categorical variables. It demonstrates a promising ability to handle large data sets effectively. As presented in this paper, the FMLM shared-ride taxi service prototypes and their unique characteristics could be optimally identified using the k-prototype algorithm with the Silhouette coefficient (as a performance index). By examining an illustrative case study with ten mixed-type variables of 946 passengers' requests, the results demonstrate the effective clustering of passengers' requests into three distinct prototypes, which can be characterized uniquely based on the temporal factors (pickup time of individual requests) and trip characteristics (including traveled distance, taxi type, as well as pickup and drop-off locations) that are significant in operating a competitive shared-ride taxi service. This paper is anticipated to reveal useful practical implications for the relevant stakeholders, especially the taxi service providers, in managing the FMLM shared-ride taxi services optimally to ensure an efficient and effective operating system.
The increase of urbanization is associated to the rise of urban traffic, leading to the growth of environmental and health problems. Cities all over the world are starting to implement driving ...restrictions to address these issues that challenge commuter mobility, since the public transport, by itself, cannot cover all commuters’ needs. However, other flexible and collective transports such as the emerging ridesharing and shared ride-hailing services might be, in some situations, more convenient means of transport. In this paper, two pilot tests that offered a shared ride-hailing service to commuters were analyzed and compared, with the aim of finding out the usage intention of this transport, as well as the user requirements for a frequent use of the service. The first case study took place during one week in Barcelona, whereas the second was based on the service test of MOIA in Hanover. A quantitative research was conducted to participants of both service tests. Results showed a high intention of use for commuting and leisure trips, and indicated the most important design factors to attract users.
The continued growth of ride-hailing usage creates the need for policymakers to understand the factors that affect the adoption and utilization of ride-hailing services. Attitudinal and perceptual ...factors are of particular importance, both because ride-hailing services are still evolving, and a relatively small number of studies have examined the role of these factors. This paper utilizes data from a web-based survey to understand the role that latent attitudinal factors play in adopting and using ride-hailing services in Toronto. Specifically, two binary logistic regression models are used to understand the factors that influence the adoption of exclusive and shared ride-hailing services. Besides, a zero-inflated ordered probit (ZIOP) model is estimated to investigate the factors that affect the frequency with which a person uses ride-hailing. The empirical investigation reveals that the perception of ride-hailing services tends to differ between individuals with ride-hailing experience and those without, which is expected given the relative novelty of ride-hailing. The logistic regression models reveal that, although common attributes affect the likelihood that a person has adopted a ride-hailing service, the influence of these factors varies based on the specific type of service. This underscores the value of distinguishing between exclusive and shared ride-hailing services. The ZIOP model shows that attitudinal factors regarding qualitative trip characteristics, the inclination towards using ride-hailing services in certain situations, and the consideration of parking requirements affect the frequency with which a person uses ride-hailing. Also, transit pass ownership was found to influence the frequency with which a person uses ride-hailing positively. The results of this study aim to provide insights into the adoption and utilization of ride-hailing, which can help inform policies that aim to encourage the use of shared ride-hailing as an alternative to exclusive ride-hailing services.
•We introduce an innovative transportation system, FMOD.•The modeling framework integrates scheduling, routing and assortment optimization.•The trade-off between consumer surplus and operator's ...profit is considered.•The impact of the FMOD system is analyzed with simulation experiments.•Assortment optimization and dynamic allocation provide significant benefits.
This paper introduces an innovative transportation concept called Flexible Mobility on Demand (FMOD), which provides personalized services to passengers. FMOD is a demand responsive system in which a list of travel options is provided in real-time to each passenger request. The system provides passengers with flexibility to choose from a menu that is optimized in an assortment optimization framework. For operators, there is flexibility in terms of vehicle allocation to different service types: taxi, shared-taxi and mini-bus. The allocation of the available fleet to these three services is carried out dynamically so that vehicles can change roles during the day. The FMOD system is built based on a choice model and consumer surplus is taken into account in order to improve passenger satisfaction. Furthermore, profits of the operators are expected to increase since the system adapts to changing demand patterns. In this paper, we introduce the concept of FMOD and present preliminary simulation results. It is shown that the dynamic allocation of the vehicles to different services provides significant benefits over static allocation. Furthermore, it is observed that the trade-off between consumer surplus and operator’s profit is critical. The optimization model is adapted in order to take into account this trade-off by controlling the level of passenger satisfaction. It is shown that with such control mechanisms FMOD provides improved results in terms of both profit and consumer surplus.
A real-time responsive customised bus (RTRCB) provides demand-oriented and shared-ride service for passengers with random travel demands. Unlike other works in the literature, the authors developed a ...hierarchical methodology to optimise the RTRCB schedule. It involves a trade-off between the interests of the transporter and those of the passengers. After minimising the initial travel distance while maintaining a wide service range, the bus routes are planned holistically based on the main travel locations. Based on the initial routes, the buses are dispatched to satisfy the real-time travel demands. The procedure for solving the proposed problem is developed by modifying the genetic algorithm (GA) and non-dominated sorting GA with elite strategy. The proposed method is applied to a real-life problem in the city of Shenzhen, and certain extensional analyses are performed to demonstrate their feasibility. The computational results show that: (i) the travel distance limitation and tortuosity ratio of the bus route play the most important roles in planning bus routes; (ii) the designation of all the initial bus stops as the control stops results in comparatively stable service for more passengers; and (iii) a better service performance can be achieved by introducing the soft time window strategy with an acceptable delivery delay.
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