On-demand Transportation Service Research Articles

Mobility-as-a-Service and unsustainable travel behaviour: Exploring the car ownership and public transport trip replacement side-effects of the MaaS paradigm

Car-centred development adds significantly to the climate change threat our planet faces. Mobility-as-a-Service (MaaS) is a concept looking to generate modal shift via the digital and physical integration of alternative on-demand transport services. Recent research raises concerns, however, that contrary to the hype, MaaS may result in unsustainable travel behaviour. On the one hand, future MaaS users that are now primarily drivers may not be ready to shift away from their cars, while, on the other hand, people regularly using public transport may substitute some of these journeys with MaaS-enabled car-sharing, ride-sharing and ride-hailing services. Herein, we study the effects of MaaS on intentions to own a car and to substitute public transport trips with car-centric services, via a UK-based quantitative survey. We use Principal Component Analysis and Ordinal Regression Modelling to see how MaaS-related perceptions referring to Added Value, Car Use Convenience and Enjoyment, Human Element Externalities, Trust in Functionality, Cost Incentives, Car Use Morality, Trust in Enabling Technology and Car Ownership Necessity affect usage intentions. Our results highlight some, typically less discussed, grey areas of MaaS and inform the design of policies, including car service disincentivitisation, responsible user benefits, MaaS rebranding and public transport investment prioritisation.

Driving sustainable transportation: insights and strategies for shared-rides services

The concept of sharing, enabled by emerging technologies, is playing an increasingly important role in contributing to a transformation toward more sustainable transportation. This study aimed to contribute to the growing body of literature on on-demand transportation services, with a particular emphasis on sharing or pooling a ride when using services such as transportation-network companies (TNCs) and microtransit. The study conducted a shared mobility survey of over 2,500 respondents from selected locales across Texas—ranging from large urban areas to small cities and rural areas. We analyzed the survey data in detail using extensive statistical analysis and inferential techniques and adopted an analysis approach toward implementation-oriented research to address the gap between theory and practice. Demographic, as well as geographic and built-environment, factors were found to play an important role in determining whether users will opt for a shared or pooled service and/or how they perceive these alternatives. The findings highlight the importance of improving safety and security, increasing awareness of the benefits of ride-sharing, and designing appropriate policy measures to promote sustainable mobility. We identified potential operational improvements, government policies, and employer programs to improve shared-ride services and encourage their use, such as reducing uncertainty in shared rides and minimizing inconvenience for passengers. A critical finding was the need to prioritize operational improvements in shared-ride trips over solely relying on financial incentives to induce behavior change. Enhanced public awareness and education were also determined to be crucial regardless of the nature of improvements, policies, or programs that are implemented.

Exploring Deep Learning Approaches for Short-Term Passenger Demand Prediction

An accurate short-term passenger demand forecast makes a contribution to the coordination of traffic supply and demand. Forecasting the short-term passenger demand for the on-demand transportation service platform is of utmost significance since it might incentivize empty cars to relocate from over-supply regions to over-demand regions. Yet, because spatial, temporal, and exogenous dependencies need to be evaluated concurrently, short-term passenger demand forecasting may be rather difficult. This article aims to investigate several methods that can be utilized to forecast short-term traffic demand, with a primary emphasis on deep learning approaches. We examine varying degrees of temporal aggregation and how these levels affect various architectural configurations. In addition, by analyzing 22 models representing 5 distinct architectural configurations, we illustrate the influence of varying layer configurations within each architecture. The findings indicate that the long-term short memory (LSTM) structures perform the best for short-term time series forecasting, but more complex architectures do not significantly enhance the outcomes. Moreover, considering the spatiotemporal aspects results in an improvement in the prediction of more than fifty percent. In addition, we investigate the vectorization of time, also known as Time2Vec, as a way of embedding to make it possible for a selected algorithm to recognize periodic characteristics in time series, and we show that the outcome is improved by fifteen percent.

Operational Impacts of On-Demand Ride-Pooling Service Options in Birmingham, AL

Transportation Network Companies (TNCs) use online-enabled apps to provide on-demand transportation services. TNCs facilitate travelers to connect with drivers that can offer them rides for compensation using driver-owned vehicles. The ride requests can be for (a) individual or (b) shared rides. The latter, also known as ride-pooling services, accommodates requests of unrelated parties with origins and destinations along the same route who agree to share the same vehicle, usually at a discounted fare. Uber and Lyft offer ride-pooling services in select markets. Compared to individual ride requests, ride-pooling services hold better promise toward easing urban congestion by reducing the number of automobiles on the road. However, their impact on traffic operations is still not fully understood. Using Birmingham, AL as a case study, this research evaluated the impact that ride-pooling services have on traffic operations using a Multi-Agent Transport Simulation (MATSim) model of the Birmingham metro area. Scenarios were developed to simulate baseline conditions (no TNC service) and ride-pooling availability with two types of ride-pooling services, namely door-to-door (d2d) and stop-based (sB) service and three fleet sizes (200, 400, and 800 vehicles). The results indicate that when TNC vehicles are added to the network, the Vehicle Kilometers Traveled (VKT) decrease by up to 5.78% for the door-to-door (d2d) service, and up to 2.71% for stop-based (sB) services, as compared to the baseline scenario (no TNC service). The findings also suggest that an increase in the size of the ride-pooling fleet results in a rise in total ride-pooling service VKT, network-wide total VKT, and detour distance. However, increasing the size of the ride-pooling fleet also results in a decrease in the ride request rejection rates, thus benefiting the customers and decreasing the vehicle empty ratio which, in turn, benefits the TNC drivers. The results further suggest that a fleet of 200 ride-pooling vehicles can meet the current demand for service in the Birmingham region at all times, thus it is the optimal ride-pooling TNC fleet size for a medium-sized city such as Birmingham.

PRYORTIE: Public on-demand smart transportation service via a scalable fog-based platform

Over the past few years, the on-demand transport services (e.g., Uber) have witnessed an unprecedented popularity due to its easy-to-adopt model (i.e., a passenger orders a taxi, a request is sent to all drivers in the vicinity, drivers accept/reject the order). Nonetheless, such a service is affected directly by multiple urban factors such as traffic, number of existing drivers en-route, and local weather which may cause a huge delay. Dial-A-Ride Problem with Transfers (DARPT) allows passengers to change vehicles during their trip in a bid to minimize the delay. However, DARPT relies solely on using the existing Global Positioning System (GPS) that suffers from delay in propagating raw traffic data (e.g., route coordinates, geodesic distance, and time stamp) on time and ultimately causes traffic disruption and increases ride time constraint. To this end, this work introduces a novel interactive Fog-based navigation simulator, PRYORTIE. PRYORTIE uses roadside smart units in the form of fog nodes as a means for collecting and sharing real-time traffic data comprising of vehicles’ location and speed within the area, they each cover. These nodes can calculate an optimal route for a vehicle using a novel path recommender algorithm that utilizes a vehicle's current position, its destination, and current traffic data. The nodes can update the route in real-time using the same algorithm if traffic conditions within their radius change. A package delivery case study has been implemented and simulated in PRYORTIE to demonstrate the impact of using fog nodes in routing and navigation. Compared with the state-of-the-art, the proposed approach shows on average 46.34% improvement in saving travel time. The proposed method clearly demonstrates tangible benefits of communicating real-time data to drivers and lays the ground for subsequent future work in smart transportation and navigation especially in the context of last mile delivery. It can easily be extended to autonomous vehicles and can be easily integrated into existing transportation frameworks that employ a fog-cloud paradigm for smart transportation.

Gender Gaps in Improvements to Shared-Ride Services: Insights from a Shared Mobility Survey

This study analyzed data from a 2022 shared mobility survey to investigate the impact of gender on the potential improvements to on-demand transportation services, particularly shared-ride services such as ridesharing through transportation network companies (TNCs) and microtransit. Results revealed that male respondents were more inclined to opt for financial incentives such as pretax benefits, direct subsidies, and subsidizing certain trips. Employer-related programs such as parking cash-out programs and flexible working hours were also found to be more appealing to male respondents. In contrast, female respondents placed a greater emphasis on safety-related measures, indicating that safety concerns may be too significant for them to overlook, even when presented with financial incentives.

Dynamic priority rules for combining on-demand passenger transportation and transportation of goods

Urban on-demand transportation services are booming in both passenger transportation and the transportation of goods. The types of service differ in timeliness and compensation and, until now, providers operate larger fleets separately for each type of service. While this may ensure sufficient resources for lucrative passenger transportation, the separation also leaves consolidation potentials untapped. In this paper, we propose combining both services in an anticipatory way that ensures high passenger service rates while simultaneously transporting a large number of goods. To this end, we introduce a dynamic priority policy that uses a time-dependent percentage of vehicles mainly to serve passengers. To find effective time-dependent parametrizations given a limited number of runtime-expensive simulations, we apply Bayesian optimization. We show that our anticipatory policy increases revenue and service rates significantly, whereas a myopic combination of service may actually lead to inferior performance compared to using two separate fleets.

Communication-wise Comparison of the Online Resource Allocation Methods in CAV Fleets

Deploying fleets of Connected Autonomous Vehicles (CAVs), with limited peer-to-peer communication ranges, in order to provide On-demand Transport (ODT) services, requires a careful choice and evaluation of solution methods for resource allocation problems. We adopt in this paper a multiagent approach consists of: 1) defining a generic model to ODT's dynamic resource allocation problem in connected autonomous vehicle fleets, taking into account the limited connectivity and communication constraints, 2) behavior abstraction of AV Agents, and 3) abstracting the solution methods as Coordination Mechanisms (CMs) to define the characteristics of a solution method and its requirements to implement the corresponding planning sub-behavior. Using this methodology, we were able to compare the performance of a variety of solution methods with a set of evaluation criteria, namely for the solution Quality of Service (QoS) and Quality of Business (QoB). In this work, we focus on the communication-wise evaluation criteria, such as connectivity and network load.

Accounting for the Influence of Attitudes and Perceptions in Modeling the Adoption of Emerging Transportation Services and Technologies in India

Many rapidly developing countries around the world are at a crossroads when it comes to transportation, air quality, and sustainability. Indeed, the challenges presented by vehicular growth in India have motivated the search for sustainable transportation solutions. One solution constitutes ridehailing services, which are expected to reduce car ownership and provide affordable means of transportation. Another key solution is the rise of electric vehicles (EVs), which are expected to reduce greenhouse gas emission and address the growing demand for sustainable urban mobility. Using a unique survey data set collected in 2018 from a sample of 43,000 respondents spread across 20 cities in India, this paper attempts to shed light on the factors that affect adoption of on-demand transportation services and EVs in India. In particular, not only does this paper consider the socio-economic and demographic variables that affect these behavioral choices, but the modeling framework adopted in this study places a special emphasis on representing the important role played by attitudes, values, and perceptions in determining adoption of on-demand transportation services and EVs. It is observed that attitudes and values significantly affect the use of on-demand transportation services and EV ownership, suggesting that information campaigns and free trials/demonstrations would help advance the adoption of sustainable transportation modes. The model results help in the identification of policy options and infrastructure investments that can advance a sustainable transportation future in India.

Assessing the Implementation of On-Demand Transportation Services for People with Disabilities

As there are many barriers to the effective operation of paratransit services required by the Americans with Disabilities Act (ADA), both riders and transit agencies have incentives to shift some paratransit ridership to more effective transit services. To address these barriers, transit agencies have started partnering with technology-enabled third-party companies (e.g., transportation network companies, taxi companies) to provide new on-demand mobility options for people with disabilities. Previous studies identified the benefits and challenges of implementing on-demand services through a public-private partnership, but did not fully investigate the implementation of this emerging on-demand service from the perspective of serving individuals with disabilities. This study conducted online surveys and semi-structured interviews with 10 transit agencies across the United States to understand how this new on-demand mobility option has complemented ADA paratransit services, and what challenges and outcomes the partnerships have experienced over time. Findings indicate that despite the positive impact of implementing the on-demand service on overall service operation and delivery, transit agencies still experienced challenges in providing accessible and reliable on-demand services for riders. Transit agencies and partnering mobility service companies should work together to increase technology adoption, availability and reliability of wheelchair accessible services, and driver sensitivity to the needs of individuals with disabilities, as well as to support the sustainability of the program through continuous evaluation.

PShare: Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route

Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In this paper, we mainly study the privacy and utility of the ride-sharing system, which enables multiple riders to share one driver. To solve the privacy problem and reduce the ride-sharing detouring waste, we propose a privacy-preserving ride-sharing system named pShare. To hide users’ precise locations from the service provider, we apply a zone-based travel time estimation approach to privately compute over sensitive data while cloaking each rider’s location in a zone area. To compute the matching results along with the least-detouring route, the service provider first computes the shortest path for each eligible rider combination, then compares the additional traveling time (ATT) of all combinations, and finally selects the combination with minimum ATT. We designed a secure comparing protocol by utilizing the garbled circuit, which enables the ORH server to execute the protocol with a crypto server without privacy leakage. Moreover, we apply the data packing technique, by which multiple data can be packed as one to reduce the communication and computation overhead. Through the theoretical analysis and evaluation results, we prove that pShare is a practical ride-sharing scheme that can find out the sharing riders with minimum ATT in acceptable accuracy while protecting users’ privacy.

Utility and mechanism design in multi-agent systems: An overview

Future cities promise to be more autonomous than ever, largely owing to our ability of coordinating complex systems in real time: fleets of self-driving cars will offer on-demand transportation services, delivery drones will fly parcels in our skies, power plants will provide renewable energy reliably. In many of these systems, there is no single decision-maker with full information and authority. Instead, the system performance greatly depends on the decisions made by interacting entities with local information and limited communication capabilities. Game theory, intended as the study of multi-agent decision-making, is a fitting paradigm to tackle many of the associated challenges. Moving from this observation, in this paper we review how tools and ideas from game theory can be brought to bear on the coordination of multi-agent systems. At the heart of the proposed approach is the design and influence of agents’ preferences so that their local optimization induces a desirable system behavior. Its applicability spans a variety of settings irrespective of whether the decision makers are strategic (e.g., drivers in a road network), or not (e.g., delivery drones). Along the way, we also discuss future research directions and connections with related research areas including algorithmic game theory, incentive and mechanism design, economics, computational complexity, and approximation algorithms.

On the scalability of private and pooled on-demand services for urban mobility in Amsterdam

ABSTRACT The emergence of on-demand transport services is set to change the mobility landscape in urban areas. This study investigates the potential scalability of an on-demand mobility system to substitute motorised trips performed by private cars and public transport in Amsterdam, The Netherlands. We adopt an agent-based simulation framework and investigate scenarios where either private and pooled on-demand services replace private car trips, public transport trips, or both private car and public transport trips. Service performance in terms of level of service offered and operational efficiency are analysed. Results indicate that pooled on-demand services fare better than private on-demand in terms of veh-km travelled and the empty drive ratio. Private on-demand services generate 43%, 38%, and 44% more veh-km than pooled on-demand services when on-demand services replace car trips, public transport trips, or car and public transport trips, respectively. However, private on-demand services offer shorter total travel times than pooled on-demand for all scenarios.

Decentralised cooperative cruising of autonomous ride-sourcing fleets

As transportation network companies and automobile manufacturers continue to invest in the development of self-driving vehicles, it can be expected that autonomous taxi (a-taxi) fleets will become a major component of on-demand transport services in the foreseeable future. The majority of existing automated fleet management systems focus on central dispatch strategies that rely on real-time information and communication. This paper proposes a novel decentralised cooperative cruising method for offline operation of a-taxi fleets, which serves as a contingency plan during a full communication shutdown. The proposed method acts as an emergency plan for the system to continue serving passengers with the objective of maximising the total number of served passengers by the fleet. The method uses historical trip data to estimate PageRank centralities of roads as a proxy of long-term likelihood of finding waiting passengers over a series of trips. The proposed method uses this metric to (i) compute weighted shortest paths for vacant a-taxi cruising route planning, and (ii) partition the network into homogeneous regions for effective cruising destination choice (mission planning). The movements of vacant a-taxis between regions are modelled as a Markov chain such that a transition probability matrix is computed to achieve the optimal spatial distribution of vacant a-taxis to maximise the total expected pick-ups by the fleet, estimated based on bilateral meeting functions. Compared to benchmark strategies which select destinations randomly and cruise along the shortest travel time path, the proposed method shows significant improvements in service performances for different fleet sizes.

The ‘Sharing Trap’: A Case Study of Societal and Stakeholder Readiness for On-Demand and Autonomous Public Transport in New South Wales, Australia

Focus groups on shared, autonomous vehicles (SAVs) in New South Wales expressed “sharing anxiety”—an intense concern about the prospect of sharing their mobility journey with strangers, without a driver or authority figure present. This presents a significant barrier to the acceptance of SAVs, particularly autonomous public and on-demand transport (ODT), which is a major focus for Transport for New South Wales (TfNSW). Given this potential barrier, we interviewed (N = 13) operators, academics, and regulators with TfNSW to assess their role and abilities in overcoming sharing anxiety. However, our findings revealed a relative lack of awareness from experts in the mobility industry about the existence of sharing anxiety in users, suggesting additional barriers to adoption. We make suggestions for policy considerations for stakeholders that could mitigate sharing anxiety: promoting dynamic ridepooling products in commercial services, using tax breaks as incentivization; requiring ODT services and operators in jurisdiction to use a standardized, unified interface for users (“single-app”); shared, on-demand transport services likely need longer incubation/pilot periods in order for the sharing behavior to become culturally established. We conclude with a reflection on how COVID-19 has impacted the development of shared mobility and suggest further exploration in policy implementation.

Not my usual trip: Ride-hailing characterization in Mexico City

The literature on ride-hailing has experienced rapid growth in recent years, with an accent on industrialized cities, mainly in the United States and Europe. Previous research has identified the characteristics and preferences of ride-hailing adopters in a handful of cities. However, given their marked geographical focus, whether such findings are relevant and applicable to the practice of transport planning and regulation in cities in the Global South remains largely untested.This paper examines ride-hailing in the Metropolitan Area of Mexico City. We build on statistical modelling informed by the Mexico’s household travel survey from 2017 to determine the main drivers for ride-hailing adoption, unpack ride-hailing user characteristics, and understand how they differ from other transport users in the local context. We use findings to discuss the implications of ride-hailing for urban mobility in one of the largest cities in Latin America.Recognizing that the trajectory of adoption and development of app-based urban transport services differs from those followed in the United States and Europe, the paper hypothesizes that ride-hailing usage in a context such as Mexico may be mediated by social issues such as the perception of crime, risk of sexual harassment in public transportation, and lack of flexibility and quality in other modes. Such challenges are frequently experienced by women in this and similar contexts as documented by the literature.Our findings shed light on the complex role of gender and care relationships play in the adoption of on-demand transportation services. Relevant findings suggests that variables such as age, education and income have a positive effect on ride-hailing adoption, in line with the existing literature. Also in line with current literature, we find that ride-hailing in Mexico City is instrumental for leisure and health trips. However, when considering gender, and the links between gender and care responsibilities, findings show that women in households with a higher number of elders depend more on on-demand transport. These results are novel in the context of the ride-hailing literature and suggest areas for further exploration in similar contexts to inform discussions about the role of these travel alternatives for women and their ability to navigate the city.

PRide: Privacy-Preserving Online Ride Hailing Matching System With Prediction

Online Ride-Hailing(ORH) service enables riders to enjoy on-demand transportation service with mobile devices. Despite the convenience of the service, privacy leakage could be caused because riders need to submit their locations during ride matching. To protect users' privacy, much effort has been done to construct a privacy-enhanced ORH system in recent years. However, almost all of current privacy-preserving schemes match the nearest driver to the requesting rider, it is not the optimal matching strategy and unnecessary waste of travel distance may be caused in this way. To solve the problem, we propose a privacy-preserving ORH scheme named pRide, which minimizes the overall travel distance by matching the best driver in global perspective instead of the nearest driver in the local region. To find the best driver, we utilize a deep learning model to predict emergence of ride requests in various regions, and enables the ORH server to perform ride matching leveraging prediction results. Based on the framework, we also propose optimizations to enhance pRide. Moreover, to further defend inference attacks from riders, we propose a private comparing algorithm at the cost of a little communication overhead. Through theoretical analysis and experiments, we prove that pRide is able to decrease the overall travel distance securely and efficiently in ORH matching scenarios.

CARE-Share: A Cooperative and Adaptive Strategy for Distributed Taxi Ride Sharing

Given the fast growth of on-demand transportation services and ride-sharing platforms, the concept of private vehicle ownership is rapidly declining. Although there are multiple fully-grown ride-sharing systems, they are proprietary and centrally controlled. Facilitating ride-sharing using a localized distributed coordination between the riders and the drivers is in need. However, fully distributed systems deal with a large number of variables and objectives and are often sub-optimal. In this paper, we propose a distributed ride-sharing system with multiple objectives which are often conflicting to each other. Therefore, we model it as a multi-objective optimization problem and solve it using the Ant Colony optimization technique which sports a multi-agent behavior. We critically analyze the spatio-temporal challenges posed by the ride sharing problem and define novel performance metrics to capture the underlying subtlety of the distributed system performance. An in-depth experimentation with recent large-scale single-ride taxi trip data from Chicago shows that our solution can ensure up to 79.65% success rate of ride sharing. We have shown that ride sharing is more successful during non-peak traffic hours due to less contention and a healthy balance in passenger and taxi numbers. Further, it has been observed that ride-sharing always reduces the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">total distance travelled</i> by all the taxis and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">total number of taxis</i> on-road; both of which positively impact road congestion and environment. The results obtained from the experiments are very much comparable to real time behaviour of taxi networks. Finally, a revenue framework is proposed to analyse nuances of the operating environment.

What difference does the gig mobility service make in the workers’ human needs structure?

This paper aims to compare and contrast the perceived psychological experience of the traditional versus gig mobility service workers with respect to their human needs structure. Gig mobility service here refers to on-demand transportation service enabled by an online platform. Both qualitative and quantitative research methods were used. Using the perspective of ERG (Existence, Relatedness and Growth) theory, we first explored the field using in-depth interviews and observations of both worker groups. Based on the qualitative findings, we formulated the quantitative measures for the corresponding constructs. The constructs’ relationship and paths differences between the two groups were analyzed statistically using partial least square path modeling (PLS-PM) method. The differences between the two groups were categorized into four quadrants, highlighting what both groups have and do not have, as well as what one group has but not the other. It was found that the gig mobility service resulted in not only more relationships among the constructs, but also generally higher perceived well-being for the workers. It seems that the gig mobility service has so far been promising in meeting workers human needs despite its long-term uncertainty. This study is among the few studies on understanding in which way the technology has changed the mobility service actors seen as workers, rather than sellers or users.

On the inefficiency of ride-sourcing services towards urban congestion

The advent of shared-economy and smartphones made on-demand transportation services possible, which created additional opportunities, but also more complexity to urban mobility. Companies that offer these services are called Transportation Network Companies (TNCs) due to their internet-based nature. Although ride-sourcing is the most notorious service TNCs provide, little is known about to what degree its operations can interfere in traffic conditions, while replacing other transportation modes, or when a large number of idle vehicles is cruising for passengers. We experimentally analyze the efficiency of TNCs using taxi trip data from a Chinese megacity and an agent-based simulation with a trip-based MFD model for determining the speed. We investigate the effect of expanding fleet sizes for TNCs, passengers’ inclination towards sharing rides, and strategies to alleviate urban congestion. We observe that, although a larger fleet size reduces waiting time, it also intensifies congestion, which, in turn, prolongs the total travel time. Such congestion effect is so significant that it is nearly insensitive to passengers’ willingness to share and flexible supply. Finally, parking management strategies can prevent idle vehicles from cruising without assigned passengers, mitigating the negative impacts of ride-sourcing over congestion, and improving the service quality.