On-demand Mobility Research Articles

Quantifying the external costs of autonomous on-demand ride pooling services

Mobility On Demand (MOD) services, such as ride-pooling, provide convenient and cost-effective transportation options. While previous studies focused on operational costs and service quality, we take a broader perspective by examining the external costs associated with autonomous ride-pooling services. Incorporating external costs into the design and evaluation of MOD services enables a comprehensive understanding of their impact on the entire urban population, informing effective regulations and incentives. We present an approach for calculating space-varying external costs, accounting for factors like air pollution, climate impact, noise and accidents. These costs are integrated into FleetPy, an agent-based simulation tool for ridesharing analysis and optimization. A case study in Munich uncovers the tradeoffs between external costs, internal costs, and service quality. Our findings suggest that mid-sized vehicles with a three-person capacity strike a balance between energy efficiency and transport capacity. By applying our approach, external costs can be reduced by up to 37%.

An aggregate matching and pick-up model for mobility-on-demand services

This paper presents an Aggregate Matching and Pick-up (AMP) model to delineate the matching and pick-up processes in mobility-on-demand (MoD) service markets by explicitly considering the matching mechanisms in terms of matching intervals and matching radii. With passenger demand rate, vehicle fleet size and matching strategies as inputs, the AMP model can well approximate drivers’ idle time and passengers’ waiting time for matching and pick-up by considering batch matching in a stationary state. Properties of the AMP model are then analyzed, including the relationship between passengers’ waiting time and drivers’ idle time, and their changes with market thickness, which is measured in terms of the passenger arrival rate (demand rate) and the number of active vehicles in service (supply). The model can also unify several prevailing inductive and deductive matching models used in the literature and spell out their specific application scopes. In particular, when the matching radius is sufficiently small, the model reduces to a Cobb–Douglas type matching model proposed by Yang and Yang (2011) for street-hailing taxi markets, in which the matching rate depends on the pool sizes of waiting passengers and idle vehicles. With a zero matching interval and a large matching radius, the model reduces to Castillo model developed by Castillo et al. (2017) that is based on an instant matching mechanism, or a bottleneck type queuing model in which passengers’ matching time is derived from a deterministic queue at a bottleneck with the arrival rate of idle vehicles as its capacity and waiting passengers as its customers. When both the matching interval and matching radius are relatively large, the model also reduces to the bottleneck type queuing model. The performance of the proposed AMP model is verified with simulation experiments.

Spatial factors associated with usage of different on-demand elements within mobility hubs: a systematic literature review

ABSTRACT Mobility hubs offer a strategic opportunity to enhance multimodal transportation and reduce greenhouse gas emissions within the transport sector. Identifying the spatial factors determinants influencing the usage of mobility hub components is essential for policymakers and transport planners aiming to identify their optimal locations. This study undertakes a systematic literature review and ranking of spatial factors associated with the usage of key on-demand services within mobility hub elements, including bike sharing, scooter sharing, car sharing, ride hailing and taxi and electric vehicle charging stations. Utilising databases such as Web of Science, Google Scholar and Francis & Taylor, we evaluated 119 records, identifying 39 key factors from social and built environments that are associated with usage of the on-demand elements. Key findings highlight the significance of population density, employment density, proximity to public transport, recreational POIs and household income, along with a noted negative association with the factor slope. This research contributes to exploring how these factors align across various on-demand mobility services that can be potentially used for policymakers, and transportation planners in evaluating potential strategies for the optimal allocation and development of mobility hubs.

Beyond the last mile: different spatial strategies to integrate on-demand services into public transport in a simplified city

AbstractIntegrating on-demand services into public transport networks might be the best way to face the current situation in which these new technologies have increased congestion in most cities. When cooperating with on-demand services rather than competing with them, public transport would not risk losing users, and could attract some passengers from private modes thanks to an increased quality of service. This fact has engendered a growing literature discussing how to design such an integrated system. However, all of that research has imposed that on-demand mobility is to solve the so-called “last-mile problem”, serving only as a feeder that connects the exact origins/destinations with the traditional public transit network. As it induces a large number of transfers and it precludes some scale-effects to be triggered, in this paper we challenge that imposition and investigate if this is the best spatial integration strategy. To do so, we study a simplified linear city in a morning peak situation, where we propose seven different line structures, all of them combining a traditional fixed line with on-demand ride-pooling (ODRP): three direct structures, where ODRP can serve full trips, three semi-direct, where a single ODRP vehicle can serve the largest part of a trip, and a base case in which ODRP is restricted to the first and final legs only. Our results show that the base case is optimal only under very specific demand patterns, or when transfer penalties are disregarded. Our analytical approach reveals relevant operational aspects of such integrated systems: namely, that the base case can help increase directness (diminishing detours), and that ODRP can help shorten the routes of the fixed services to decrease operator costs.

Shared autonomous micro-mobility for walkable cities

The integration of autonomy into micro-mobility systems has given rise to a new solution known as shared autonomous micro-mobility (SAmM)11SAmM = Shared autonomous micro-mobility.. This paper delves into the opportunities that emerge from this concept, which is driven by the resurgence of walkable, human-centric cities and the advancements in autonomous vehicles. While most autonomous vehicle research has focused on cars and taxis, SAmM proposes more lightweight and environmentally friendly alternatives that align with the vision of more human-centered, walkable cities. At the same time, the integration of autonomy into micro-mobility systems holds the potential to enhance efficiency by reducing fleet sizes and increase convenience by transforming them into on-demand mobility services. This study presents a roadmap for the successful implementation of SAmM, covering essential aspects such as vehicle design, infrastructure requirements, and operational strategies. This research sheds light on the transformative potential of SAmM and provides insights for mobility operators, urban planners, policymakers, and researchers.

Complements or competitors? Equity implications of taxis and ride-hail use in Chicago

Taxis have provided on-demand mobility for decades, including to people earning the lowest incomes. However, studies reveal gaps in taxi service across many US cities due to factors such as fleet size limitations, information asymmetry, and discriminatory practices. Since 2010, the emergence of ride-hail services such as Uber and Lyft has revolutionized the transportation landscape, offering faster and more affordable alternatives to traditional taxis. Yet it remains unclear the extent to which ride-hail services may fill spatial-temporal gaps in taxi services or how car access shifted after the arrival of ride-hail services. Additionally, taxis may still play an important role in car access for people who rely on cash payments. This study uses longitudinal trip-level ride-hail and taxi data in Chicago, IL, to examine the shifting trip patterns across the two modes. We compared the spatial-temporal pattern of taxi and ride-hail trips, as well as the share of taxi trips paid for in cash across three periods; we then developed spatial regression models to investigate what factors relate to taxi cash payments. Results show that ride-hail services may, directly and indirectly, fill taxi service gaps. Additionally, population density, land use mix, and the proportion of people of color and zero-car households are positively related to the share of taxi trips paid in cash, while the number of ride-hail trips and banking density are negatively associated with the percentage of cash payments. Policymakers should consider the evolving interplay between taxi and ride-hail services and the significance of cash payments to ensure equitable access to transportation options for individuals without cars.

On-demand mobility-as-a-Service platform assignment games with guaranteed stable outcomes

Mobility-as-a-Service (MaaS) systems are two-sided markets, with two mutually exclusive sets of agents, i.e., travelers/users and operators, forming a mobility ecosystem in which multiple operators compete or cooperate to serve customers under a governing platform provider. This study proposes a MaaS platform equilibrium model based on many-to-many assignment games incorporating both fixed-route transit services and mobility-on-demand (MOD) services. The matching problem is formulated as a convex multicommodity flow network design problem under congestion that captures the cost of accessing MOD services. The local stability conditions reflect a generalization of Wardrop's principles that include operators’ decisions. Due to the presence of congestion, the problem may result in non-stable designs, and a subsidy mechanism from the platform is proposed to guarantee local stability. A new exact solution algorithm to the matching problem is proposed based on a branch and bound framework with a Frank-Wolfe algorithm integrated with Lagrangian relaxation and subgradient optimization, which guarantees the optimality of the matching problem but not stability. A heuristic which integrates stability conditions and subsidy design is proposed, which reaches either an optimal MaaS platform equilibrium solution with global stability, or a feasible locally stable solution that may require subsidy. For the heuristic, a worst-case bound and condition for obtaining an exact solution are both identified. Two sets of reproducible numerical experiments are conducted. The first, on a toy network, verifies the model and algorithm, and illustrates the differences local and global stability. The second, on an expanded Sioux Falls network with 82 nodes and 748 links, derives generalizable insights about the model for coopetitive interdependencies between operators sharing the platform, handling congestion effects in MOD services, effects of local stability on investment impacts, and illustrating inequities that may arise under heterogeneous populations.

Could the next generation of bike-sharing with autonomous bikes be financially sustainable?

The field of transportation has undergone changes due to the advancements in technology and the pressing need for mobility solutions. As cities worldwide struggle with the challenges posed by population growth and environmental sustainability, it becomes imperative to introduce adaptive transportation options. The increasing traffic congestion in areas and concerns regarding air quality and carbon emissions emphasize the importance of finding sustainable solutions. In this regard we present the "On demand shared use bike sharing system (OSABS)" as an eco-friendly on-demand mobility alternative that has the potential to revolutionize transportation from its core. Self-driving bikes provide a solution by reducing traffic congestion and promoting sustainable modes of transport. Customers can easily reserve a bike through their smartphones and it comes directly to their location within the given time. The user will ride it manually and release it after usage. The bike then drives autonomously to the next destination. This service allows an environment-friendly door-to-door mobility solution.This scientific paper focuses on examining the viability of autonomous cargo bike-sharing as a solution for urban transportation. A simulation model has been built to reproduce the OSABS operation using a case study for the city of Magdeburg. Through a set of experiments with different demand scenarios, we analysed and discussed the profitability and feasibility of this new service. These findings would be valuable, in the conversation about the future of transportation through providing important insights into how autonomous bike-sharing systems could be effectively implemented.

Mobility on demand in the United States – Current state of integration and policy considerations for improved interoperability

The concept of Mobility on Demand (MOD) aims to create a safe and convenient ecosystem for transportation and goods delivery. Despite offering various services, the integration and seamless data exchange among different modes of transportation remain challenging. To address this, research was conducted to examine the current state of integration in the MOD ecosystem, identify gaps in data exchange, and propose policy considerations. The research highlighted the limited integration and inconsistent data structures in the United States (U.S.), attributed to the absence of policy directives and high costs of traditional standards development. Industry-led initiatives are driving progress in standards and specifications. Deep linking and deep integration were distinguished, emphasizing the importance of a truly user-centric MOD solution. Emerging mobility models leveraging blockchain technology show promise in facilitating efficient data sharing and collaboration among various operators. Transportation policy makers play a crucial role in supporting open-source standards and integration efforts. An initial framework utilizing open-source efforts such as Transport Operator Mobility-as-a-service Provider (TOMP), Mobility Data Specification (MDS), General On-demand Feed Specification (GOFS), Transactional Data Specification (TDS), Operational Data Standard (ODS), and City Data Standard for Mobility (CDS-M) was proposed to drive the advancement of scalable and user-centric MOD systems.

Service area delineation to support on-demand mobility for the transport disadvantaged

Given the significance of technology to enhance public transit services and its potential for improving access to and the delivery of transportation services, this paper addresses strategic neighborhood mobility improvements to aid the transportation-disadvantaged. A spatial optimization model is presented to identify underserved areas for which transit can be better tailored to meet their mobility needs. The introduction of mobility-on-demand in the Cincinnati metropolitan area to augment and complement existing fixed-route transit is reported. The use of spatial analytics to support targeted neighborhood system design is detailed. The study illustrates how new mobility-on-demand services will substantially enhance travel options for the transport disadvantaged. Further, the use of spatial analytics offers insights for transit service plan development and implementation by more precisely targeting mobility needs in service area delineation and operationalization.

Enhancing On-demand Transportation: Prioritizing Travel Costs with Hybrid Evolutionary Algorithms

Objective: Enhancing sustainability in on-demand transportation is a challenging task by offering flexible and optimized mobility solutions. This study focuses on on-demand mobility systems. We introduce a novel evolutionary computing method designed to address a bi-objective customized on-demand transportation problem, aiming to minimize total travel cost and total waiting time. Methods: The method incorporates specific optimization techniques, along with an efficient dominance sorting approach, using an intelligent candidate list to reduce computational time. Results: Comparative results demonstrate the effectiveness of this hybrid computing method, particularly when prioritizing total travel cost from the service provider’s perspective. Conclusion: These findings present a promising framework for decision-makers, empowering them to navigate favorable compromises between conflicting objectives and make informed choices aligned with their preferences and customer-oriented constraints.

Vertical Equity Analysis of Parking Reservation Based on the Auction Strategy

As an on-demand mobility service, parking reservations can greatly alleviate the issue of parking challenges. There are currently three primary strategies for parking reservation: first-come-first-served, permit reservation, and auction. In contrast to the first-come-first-served and permit-reserved strategies, the auction strategy uses dynamic pricing to allocate parking supplies efficiently based on the auction, which attracts more scholars for the research. However, parking reservations based on the auction process may have an inequity issue because drivers’ age, gender, income level, and location of residence fluctuate. This inequity may limit the growth of reserved parking by influencing parking drivers’ acceptance of reserved parking. But currently, very few scholars focus on the issue of reserved parking equity, and even fewer measure this nebulous and personal issue. In consideration of this, the Lorenz curve of parking reservation and the vertical equity index of parking reservation are proposed in this paper along with the calculation method for the index, which enables the problem of reserved parking vertical equity to be visualized and made concrete. The numerical experimental method is used to analyze the vertical equity of drivers with varying income levels, utilizing the Vickrey–Clarke–Groves (VCG) auction process as an example. According to the research, loss-averse drivers are more than gain-neutral and gain-seeking drivers when the income levels of the drivers using reserved parking are the same. With the increasing number of high-income drivers involved in the parking reservation, medium to low-income drivers would lose their chances of successful reservations because of their uncompetitive bid price which leads to inequity issues when the number is less than the number of parking spaces. In contrast, the vertical equity index changes more for gain-seeking drivers while being generally steady when loss-averse and gain-neutral drivers participate. For instance, when the profit and loss coefficient is 3 and 15% of drivers with high-income levels use the parking reservation platform, the vertical equity index rises from 0.09128 to 0.45434. The reference price has a moderating influence on the vertical equity index when the number of driver participants at high-income levels remains constant. In general, within a reasonable range, the higher the reference price, the more equitable the parking reservation procedure and the lower the vertical equity index.

A multi-task deep learning framework for forecasting sparse demand of demand responsive transit

Demand Responsive Transport (DRT) is a flexible transportation service that adjusts its routes and schedules in real time based on user requests. Real-time demand forecasting has become a timely issue since the resolution of transportation planning has increased to provide flexible services to users. Although many previous studies have been conducted to design the demand forecasting model using the mobility service historical data, there are more considerations when predicting real-time demand for on-demand mobility services. Therefore, this study aims to develop a demand forecasting model for DRT services addressing two issues. First, repeated calls for service and abandoned requests are converted into the unit of demand to capture served and potential demand. Second, given the sparsity of DRT historical data, a multi-task deep learning model is proposed to predict both the probability and volume of demand simultaneously to mitigate the data sparsity problem. A zero-inflated loss function was introduced to consider the skewed demand distribution. To attain our goals, the historical dataset of the I-MOD service trial in Yeongjongdo, Inchon, Korea was used. The performance of the proposed model is compared against several baseline models, demonstrating its superiority in forecasting demand for mobility services. The results show that a multi-task structure can alleviate the data sparsity problem by excluding where demand is not generated from the forecasting problem. The proposed models can provide insights into real-time demand forecasting with sparse demand.

Challenging the Gender Neutrality of On-Demand Mobility Platforms

In this article we chart a conceptual path to challenge the presumed gender neutrality of on-demand mobility platforms (ODMPs) as an effort to unlock an entry point for feminist critiques and interventions. Drawing on the Foucauldian notion of dispositif, the ODMP is theorized as a “platform assemblage”—a complex sociotechnical system encompassing the digital and the nondigital stack that are mutually constituted in a contingent, relational, and contextual way. We employed a quantitative feminist approach to unpacking the “black-boxed” gender neutrality of the sociotechnical system. We argue that the gender nonneutrality is more than merely a symptom of the immanent masculinist propensities of ODMPs, but also the origin and organ that simultaneously represent the phenomena, processes, and products of how the gender relations of power operate through digitally mediated mobilities, while contextually, relationally, and rhizomatically coopting into and with other always-already gendered social categories. We first argue that gendered practices and processes of digitally mediated mobilities and gender nonneutrality of ODMPs in the digital stack are relational and mutually constituted. ODMPs actively abstract extant gender inequalities in daily mobilities into “idiotic” data derivatives, feeding and propagating into the platform demand and pricing calculi that algorithmically prioritize mobile and masculine subjects, perpetuating or amplifying gender inequalities in digitally mediated mobilities. The digital presences and practices of ODMPs create new temporalities and spatialities of daily mobilities that are perceived as gender-specific time–space fixity and flexibility. Furthermore, ODMP-mediated mobilities have not fundamentally disrupted the conservative gendered power relations at home, and the masculinist form of control over familial automobility.

Оптимізація транспортно-логістичної системи задля спрощення переміщення населення сільської місцевості

The purpose of the article is to examine existing data for the selection of survey areas, data collection for the mobile network at the level of travel chains, and analysis of travel patterns. Travel demand in three rural areas with different population densities is considered, and requirements for new mobility solutions based on demand and people's travel habits are established.
 Public transport plays a decisive role in the logistics of territorial communities of Ukraine. However, inadequate provision and implementation of technological advances have left rural areas behind and heavily dependent on private cars. This article describes how to improve the availability of transport services in rural areas, where the demand for travel is often underestimated. The demand for travel in three rural areas with different population densities was analyzed, and the requirements for new mobility solutions based on demand and people's travel habits were determined.
 There is a need to access a higher level of subsidies if on-demand mobility services cannot attract an adequate level of demand so that rural and semi-rural communities have access to the same level of services and opportunities available to urban areas. Most of these services are deployed in urban settings with high travel demand and population density. Research on regions with low demand is rarely considered.
 Many on-demand services are community-based, where demand is determined based on local knowledge and specific needs. Those mobility services that are more commercially oriented use qualitative market research to determine demand for specific population segments and specific locations.
 Social isolation is exacerbated by lack of accessibility, roads and public transport, but is also linked to socio-demographic factors.
 It is suggested that consideration be given to removing barriers to commercially viable new mobility services in rural areas by: using data-driven approaches to understand typical daily travel patterns; identifying new mobility services that can be deployed for commercial purposes and those that will require ongoing financial support to provide socially necessary services.

Outsourcing service price for crowd-shipping based on on-demand mobility services

Online-to-offline (O2O) parcel delivery service providers (PSPs) are facing ever-increasing pressure to provide timely and reliable delivery services. An emerging form of collaborative crowd-shipping based on on-demand mobility (COM) services, which utilizes the excess capacity of on-demand mobility vehicles (e.g., taxis and ride-hailing vehicles) to deliver O2O parcels, is proposed to alleviate PSP’s delivery pressure while improving the profitability for the on-demand mobility service provider (OMP). This study investigates an outsourcing service price problem for a collaborative COM considering the game play between PSP and OMP. A bilevel optimization model is formulated, and a customized iterative hybrid (IH) algorithm integrating two tabu search algorithms with objective-oriented granularity and a genetic algorithm is proposed to find the solution. Numerical experiments conducted on randomly generated instances and a real-life case study demonstrate that the COM model with a well-designed outsourcing service price can benefit both participants, with PSP achieving up to 17.0 % cost savings and OMP achieving up to 15.8% profit improvement.

Investigating the impact of stop network designs on DRT operation

Modern on-demand mobility concepts have the potential to drive the mobility transition. Increasing attention is being paid to DRT (Demand Responsive Transportation) services that enable the bundling of trips with the help of dynamic routing algorithms. Many providers use a stop-based operating concept for this purpose. The transport between so-called virtual stops promises a higher fraction of shared trips and shorter vehicle detours. The aim of this paper is to investigate multiple stop network designs and their impacts on the efficiency of the operation. For this purpose, three stop network generation approaches were derived from literature and applied to the simulation environment MATSim. A scenario in the city center of Berlin is simulated, where all inner car trips are replaced by DRT. Comparing the stop network design against a door-to-door service, the results suggest a trade-off between a reduction of vehicle kilometers (up to 18%) and increased walking distances (up to 250 meters on average).

Evaluating prebooked on-demand mobility services using MATSim

A common task in today’s transport simulation research is the assessment of on-demand mobility services on a large scale. A framework that is widely used for such tasks is MATSim, which has been applied frequently to study highly dynamic on-demand mobility services. To date, its efficient fleet management implementation does not yet support on-demand requests that are pre-booked in advance. This type of service, however, is seeing increased interest in the context of long-distance, rural, and inclusive mobility. Therefore, the present paper explores how prebooking can be integrated into MATSim and a use case for the city of Melun in France is presented.

A flexible approach for demand-responsive public transport in rural areas

Rural mobility research has been left aside in favor of urban transportation. Rural areas? low demand, the distance among settlements, and an older population on average make conventional public transportation inefficient and costly. This paper assesses the contribution that on-demand mobility has the potential to make to rural areas. First, demand-responsive transportation is described, and the related literature is reviewed to gather existing system configurations. Next, we describe and implement a proposal and test it on a simulation basis. The results show a clear potential of the demand-responsive mobility paradigm to serve rural demand at an acceptable quality of service. Finally, the results are discussed, and the issues of adoption rate and input data scarcity are addressed.

Design of multi-optional pickup time offers in ride-sharing systems

Ride-sharing systems strive to provide affordable on-demand mobility in urban areas by effectively consolidating incoming transportation requests. To ensure that transportation offers meet travelers’ individual time requirements and constraints, service operators offer multiple pickup times from which travelers can choose. Designing such pickup time offers is challenging due to the uncertainty of both the requirements of the requesting traveler and the efficient fulfillment of future demand. We propose a parametric cost function approximation to balance between maximizing the probability that a traveler will choose an offered pickup time and minimizing the expected vehicle routing effort. We demonstrate the effectiveness of the proposed approach in a comprehensive computational study and provide managerial insights, particularly with respect to the value of information on traveler pickup time requirements.