Shared Mobility Systems Research Articles

A project and lift approach for a 2-commodity flow relocation model in a time expanded network

We deal here with a general 2-commodity flow model over a time expanded network designed for the management of a shared mobility systems. The model involves an integral flow vector that represents carriers together with an integral flow vector that represents the items transported by those carriers. It simultaneously copes with temporal and resource issues, and is difficult to handle in the practice. So we propose here a Project and Lift approach to handle it. We start by projecting the time expanded network model on the original transit network to obtain a simpler two-commodity flow projected model. In order to make this projected model consistent with the original problem, we introduce some complex constraints, prove that these constraints can be separated in polynomial time and discuss the experimental behavior of the related Branch-and-Cut algorithm. Next, we introduce the Lift issue about the way one may turn an optimal solution of our projected model into a solution of the original problem. Finally, we thoroughly study a somewhat restrictive Strong Lift setting of this Lift issue, design and test an exact mixed integer programming Strong Lift model, and discuss some flexible alternative approaches.

Generating sparse origin–destination flows on shared mobility networks using probabilistic graph neural networks

Shared mobility services, such as bike sharing, have gained immense popularity and emerged as an integral part of sustainable urban mobility solutions. The planning of such systems requires forecasting the potential origin–destination (OD) flows between mobility sites (e.g., bike sharing stations) within the proposed network. Existing methods primarily focus on mobility flows between large regions, and do not generalize well to detailed planning applications due to the high spatial resolution required, with increased uncertainty and data sparsity. This study proposes a zero-inflated negative binomial graph neural network (ZINB-GNN) to generate sparse OD flows while capturing complex spatial dependencies. To reflect sparsity, OD flows are modeled as following ZINB distributions parameterized via feed-forward networks. To capture spatial dependencies, localized graphs are constructed to represent proximity between OD pairs, with spatial features encoded using GNNs. ZINB-GNN is validated through a case study of the bike sharing system in New York City. The results verify its prowess in both prediction accuracy and uncertainty quantification under real-world network expansion scenarios. We also demonstrate its interpretability by revealing important factors affecting OD flows. These findings can directly inform the planning of bike sharing systems, and the methodology may be adapted for other shared mobility systems.

FedSM: A Practical Federated Shared Mobility System

Shared mobility leverages under-utilized vehicles to offer on-demand transport services by sharing vehicles among users. It strives to match supply with demand via a series of data-intensive operations such as supply prediction and task assignment. However, its full potential is often compromised in practice as most shared mobility platforms operate in isolation, leading to sub-optimal resource utilization. In this demonstration, we advocate a federated approach to shared mobility, which enhances its effectiveness by enabling optimizations across platforms while retaining their autonomy. We develop privacy-preserving operators and incentive mechanisms dedicated to supply prediction and task assignment in shared mobility and implement generic interfaces that support diverse prediction and assignment algorithms. We showcase the shared mobility system with real-world ride-hailing applications.

A two-stage game-theoretical framework for Pricing & Management problem of shared mobility in a competitive market

ABSTRACT With the global proliferation of shared mobility systems, the vehicle-sharing market has witnessed an influx of companies and investors, thereby intensifying competition and potentially leading to suboptimal decision-making. Consequently, there is a pressing need for a modelling framework that specifically addresses operational and strategic-level policy design for multiple vehicle-sharing companies in a competitive market. In this study, we develop a two-stage game-theoretical framework to simulate the competitive process between multiple vehicle-sharing companies, facilitating their business decisions regarding pricing and management (P&M) problem, where management mainly include service area design, vehicle deployment, redirecting, and rebalancing. In the first stage of the framework, two companies operating under the same shared mobility service form a strategic alliance to compete against another vehicle-sharing service. Subsequently, in the second stage, these two companies engage in competition to maximise their individual profits. Based on the factors such as generalised travel cost, average acceptance level and vehicle availability, the competition process of each stage is formulated as a generalised Nash equilibrium problem. A linearisation method is employed for solving the equilibrium solution. Finally, we apply our proposed models to various vehicle-sharing systems and application scenarios to demonstrate their applicability. The numerical results illustrate that our model assists companies in making rational management decisions amidst intense competition while also revealing market competition landscape of vehicle-sharing systems based on different travel distances in the shared mobility market.

A scalable combinatorial benders decomposition for the Stochastic Dial-a-Ride Problem

Nowadays, Demand-Responsive Transport companies can help the elderly or disabled citizens in their everyday journeys as well as provide an important link between big cities and their outer suburbs. In practice, many of those trips are scheduled in advance but many others are asked on the fly (online) by the users while the drivers are already on their routes. To answer those online requests, dispatching centers often rely on fast heuristics based on some myopic objectives. However, this deteriorates the flexibility of the dispatch over time. This paper shows the potential of a thorough optimization of the planning of the vehicles at the start of the day under the hypothesis that the transport company is able to forecast the requests that will arrive during the service. Our objective is to design an initial set of routes for the vehicles that maximizes the expected acceptance rate of future requests, since the ones scheduled in-advance are mandatory. In this prospect, we propose a Combinatorial Benders Decomposition framework to solve the so-called Stochastic Dial-a-Ride Problem. A key ingredient for the success of this solver is the use of a clustering-based initialization of the Master Problem. The computational results show the effectiveness of this approach when applied to instances extracted from the literature as well as from actual services provided by Padam Mobility, an international company in Shared Mobility Systems. The proposed method provides a substantial performance gain though traditional heuristics are kept to manage online insertions afterward.

Planning shared mobility hubs in European cities: A methodological framework using MCDA and GIS applied to Barcelona

In the move towards sustainable urban mobility through seamless intermodality, European cities are faced with the possibility of implementing shared mobility systems. They constitute an opportunity to create new urban hubs, considered as nodes of intermodality and places of urban intensity. In order to effectively plan the future locations of shared mobility hubs, this paper outlines the methodological framework that sustains the design of a new decision-support tool, the so-called Mcda2MobilityHub (M2MHub),11Link to the M2MHub tool, which is available as an open plugin for QGIS: https://github.com/natrouk/Mcda2MobilityHub-desicion-support-tool_QGIS-plugin. using Multi-Criteria Decision Analysis (MCDA) and Geographic Information Systems (GIS). For this purpose, the analysis of existing literature and interviews with local authorities and transport operators, led to the identification of six main location criteria (mobility, compactness, diversity of uses, profile of potential users, spatial configuration and environment). The criteria weights were calculated using the Analytic Hierarchy Process (AHP) and the framework was applied to the case study of the city of Barcelona. The results obtained are presented in the form of heat maps, highlighting the flexibility of the decision-support tool to transform open data into appropriate indicators and detailed maps. This output can be used to support future policies for planning shared mobility hubs as places for convenient multimodal transfers, which also enable social activities and improve the quality of life in European cities.

Platform for Stop-based Carpooling

This paper introduces a transformative stop-based carpooling app designed to address the limitations of existing solutions. By strategically placing designated stops, such as bus stops, our innovative model restricts ride creation and participation exclusively between these stops, ensuring consistent availability, mitigating privacy concerns, and providing users with alternative transportation options. Through a comprehensive study in a representative urban setting, we examine the impact on car occupancy rates, traffic reduction, and user satisfaction, highlighting the model's ability to cater to diverse user needs. The integration with existing public transport systems extends its eco-friendly footprint, while strategically placed stops enhance coverage in areas with fewer active drivers, overcoming the limitations of conventional carpooling solutions. This paper contributes a blueprint for a sustainable and efficient shared mobility system, showcasing the potential of the stop-based approach in revolutionizing urban transportation.

Linking dynamics in consumer behavior and product life cycles in environmental assessments of shared mobility systems: A literature review

Sharing economy businesses (SEB) have attracted attention for their potential to reduce environmental impacts compared to conventional business models. While it is often assumed that SEB enable more sustainable modes of consumption, it is still a challenge to clarify the desirable linkage between consumers and business providers by means of services, products, and digital platforms, due to complexity and uncertainty in consumer behavior. To address this, a systematic review of literature that evaluates the environmental impacts of SEB, using shared mobility systems (SMS) as a case study, was conducted. An analysis is presented on how studies have addressed the above challenges, extracting key factors identified in literature to determine the net environmental effects of SMS, which can be useful in re-designing them for environmental sustainability.

A Self-Adaptive Meta-Heuristic Algorithm Based on Success Rate and Differential Evolution for Improving the Performance of Ridesharing Systems with a Discount Guarantee

One of the most significant financial benefits of a shared mobility mode such as ridesharing is cost savings. For this reason, a lot of studies focus on the maximization of cost savings in shared mobility systems. Cost savings provide an incentive for riders to adopt ridesharing. However, if cost savings are not properly allocated to riders or the financial benefit of cost savings is not sufficient to attract riders to use a ridesharing mode, riders will not accept a ridesharing mode even if the overall cost savings is significant. In a recent study, the concept of discount-guaranteed ridesharing has been proposed to provide an incentive for riders to accept ridesharing services through ensuring a minimal discount for drivers and passengers. In this study, an algorithm is proposed to improve the performance of the discount-guaranteed ridesharing systems. Our approach combines a success rate-based self-adaptation scheme with an evolutionary computation approach. We propose a new self-adaptive metaheuristic algorithm based on success rate and differential evolution for the Discount-Guaranteed Ridesharing Problem (DGRP). We illustrate effectiveness of the proposed algorithm by comparing the results obtained using our proposed algorithm with other competitive algorithms developed for this problem. Preliminary results indicate that the proposed algorithm outperforms other competitive algorithms in terms of performance and convergence rate. The results of this study are consistent with the empirical experience that two people working together are more likely to come to a correct decision than they would if working alone.

The Future Role of Shared E-Scooters in Urban Mobility: Preliminary Findings from Portugal

E-scooter sharing systems are a new mobility solution that is emerging in cities all throughout the world. Its rapid diffusion and disruptive nature have gained immediate attention from scholars, decision-makers, the public and the media (with strong arguments in favor of or against being put forward). To date, the scientific community has focused mostly on studying the determinants for adoption by end-users, the trip patterns, the safety, and the sustainability of such systems. This research work provides preliminary insights on the future role that these shared mobility systems can play in urban mobility. The study focuses on recent experiences in Portugal (more than a dozen cities and a dozen service providers) drawing on a literature review and an expert survey (N = 23) with local decision-makers, scholars, service providers and civic associations. The analysis provides a positive outlook on the future of shared e-scooters, observes that deployment has been taking place through waves of launch–test–withdraw/expand, and highlights which policies and actions should be prioritized so that these systems can make a truly significant contribution towards sustainable urban mobility and livable cities.

DCA: Delayed Charging Attack on the Electric Shared Mobility System

DCA: Delayed Charging Attack on the Electric Shared Mobility System

Dynamic pricing for shared mobility systems based on idle time data

In most major cities today, various shared mobility systems such as car or bike sharing exist. Maintaining these systems is challenging, and, thus, public and private providers strive to improve operational performance. An important metric which is regularly recorded and monitored in practice for this purpose is idle time, i.e., the time a vehicle stands unused between two rentals. Usually, it is available for different temporal and spatial granularities. At the same time, dynamic pricing has been shown to be an efficient means for increasing operational performance in shared mobility systems, but data necessary for traditional dynamic pricing approaches, like unconstrained demand, is much less available in practice. Thus, dynamic pricing based on idle time data appears promising and first ideas have been proposed. However, the existing approaches are based either on simple business rules or on myopic optimization. In this work, we develop a novel dynamic pricing approach that determines prices by online optimization and thereby anticipates future profits through the integration of idle time data. The core idea is quantifying the remaining profitable time by using idle times. With regard to application in practice, the developed approach is generic in the sense that different types of readily available historical idle time data can be seamlessly integrated, meaning data of different spatio-temporal granularities. In an extensive numerical study, we demonstrate that the operational performance increases with higher granularity and that the approach with the highest one outperforms current pricing practice by up to 11% in terms of profit.

What motivates the use of shared mobility systems and their integration with public transit? Evidence from a choice experiment study

Shared mobility, including bike-sharing, shared e-scooter, and ride-hailing, could improve transportation sustainability when substituting private car use and integrating with public transit. However, if shared mobility competes with other green modes, it cannot guarantee sustainability benefits. The competing and synergistic relationships between conventional modes and shared mobility are complex and not well-studied to date. Understanding users’ preferences in mode choice decisions among shared mobility, conventional modes, and multimodal systems can help better evaluate the impact of shared mobility adoption and support related policies. This paper presents the design and results of a stated-preference choice experiment study conducted in Indianapolis, Indiana. An integrated choice and latent variable (ICLV) model was estimated to identify attributes that affect travelers’ mode choices for both non-commuting and commuting purposes. Results show that (1) travel cost and travel time are significant variables and their impact on mode choice for system integration and competition is elastic; and (2) user heterogeneity can be observed through three latent variables (perceptions of shared mobility, travel attributes importance, and social values) to identify traveler’s preferences and concerns for mode choice. The contributions from this study include: (1) developing quantitative models to estimate mode choice behavior with regard to shared mobility use, and (2) identifying a set of policy guidelines for system development to encourage multimodal usage and decrease car dependency.

Modeling of shared mobility services - An approach in between aggregate four-step and disaggregate agent-based approaches for strategic transport planning

Shared mobility services are slowly penetrating European cities. Hence, transport models that are capable of modeling them are fundamental, to support policy-makers for making informed decisions. Many European cities, especially the small- and the medium-sized ones, continue to use the traditional four-step modeling approach. This approach does not have the necessary capacity to model the shared mobility services and there is a need to extend them. Hence, this research proposes an extended framework, which includes modules for synthetic population generation and fleet management. Furthermore, modules are also suggested for the estimation of emissions, car-ownership and induced demand, as such measures are increasingly expected by cities. Multiple equilibrium checks between the aforementioned modules are avoided in the design of this development, to reduce model complexity and convergence issues. The models and the algorithms used for the additional modules in this study can be replaced with alternative equivalent models, provided the inputs and outputs are consistent with the framework. As such, the framework is software agnostic. Based on a case study on the city of Thessaloniki, a proof of concept is provided. The intermediate modeling framework, proposed in this study, provides an opportunity for cities to evaluate and integrate shared mobility systems and form long term planning strategies. Besides, it acts as a bridge between the worlds of simple and complex modeling approaches and pave the way for reducing the reservations of the cities toward complex approaches, while preparing them for a smoother transition in future.

The Use of Artificial Intelligence in the Assessment of User Routes in Shared Mobility Systems in Smart Cities

The use of artificial intelligence in solutions used in smart cities is becoming more and more popular. An example of the use of machine learning is the improvement of the management of shared mobility systems in terms of assessing the accuracy of user journeys. Due to the fact that vehicle-sharing systems are appearing in increasing numbers in city centers and outskirts, and the way vehicles are used is not controlled by operators in real mode, there is a need to fill this research gap. The article presents a built machine learning model, which is a supplement to existing research and is updated with new data from the existing system. The developed model is used to determine and assess the accuracy of trips made by users of shared mobility systems. In addition, an application was also created showing an example of using the model in practice. The aim of the article is therefore to indicate the possibility of correct identification of journeys with vehicles from shared mobility systems. Studies have shown that the prediction efficiency of the data generated by the model reached the level of 95% agreement. In addition, the research results indicate that it is possible to automate the process of evaluating journeys made in shared mobility systems. The application of the model in practice will facilitate management and, above all, it is open to further updates. The use of many machine learning models will allow solving many problems that will occur in an increasing number of smart cities.

Evaluating the travel impacts of a shared mobility system for remote workers

Given the rapid rise of remote work, there is an opportunity for new shared mobility services designed to meet the needs of passengers with multiple possible work locations. This paper develops a new optimization model to enable shared mobility systems to match drivers and passengers when passengers have flexible destinations. Constraints representing employer policies, such as mandatory co-location of colleagues and limited capacity of satellite offices are introduced in order to explore the impact of employer remote work policies on travel demand. A case study using historical demand data demonstrates that incorporating flexible work locations can increase ride-pooling participation by up to 6.7% and reduce vehicle-kilometers traveled by 4.9%. Outcomes are found to be significantly affected by employer policies. The implications of the results for shared mobility business models, employer remote work plans and local transportation policy are discussed.

Agent-Based Mobility Simulation for Potential Cost and Benefits of Subscription Services with Associated Cost Structures

This study aims to present a method to analyze the problems that arise from the increasing use of shared mobility systems to solve the current problems of congestion. The use of shared and/or autonomous vehicles (SAVs) is beneficial since it could reduce the number of vehicles on the roads. On the other hand, the increased usage of SAVs to fulfill the same transportation needs would affect the total life of those vehicles. Current cost models are based on average values and life cycles of 15 years; they may not be useful in the case of increased daily travel miles of vehicles when subscription-based shared mobility systems become prevalent. In this paper, we present a comprehensive framework which considers the interactions of new cost structures and the system performance under new mobility schemes. This paper envisions the scenarios that will exemplify the importance of considering the changing costs because of the increased mileage of vehicles. Cost functions are properly designed to assess the new scenarios, a pricing scheme is introduced from the service provider’s point of view to adjust the demand for the services and the profits, an agent-based simulation platform is presented, and a framework is demonstrated to consider the interactions and optimize the system.

Graph Neural Network-Based Design Decision Support for Shared Mobility Systems

Abstract Emerging shared mobility systems are gaining popularity due to their significant economic and environmental benefits. In this paper, we present a network-based approach for predicting travel demand between stations (e.g., whether two stations have sufficient trips to form a strong connection) in shared mobility systems to support system design decisions. In particular, we answer the research question of whether local network information (e.g., the network neighboring station’s features of a station and its surrounding points of interest (POI), such as banks, schools, etc.) would influence the formation of a strong connection or not. If so, to what extent do such factors play a role? To answer this question, we propose using graph neural networks (GNNs), in which the concept of network embedding can capture and quantify the effect of local network structures. We compare the results with a regular artificial neural network (ANN) model that is agnostic to neighborhood information. This study is demonstrated using a real-world bike sharing system, the Divvy Bike in Chicago. We observe that the GNN prediction gains up to 8% higher performance than the ANN model. Our findings show that local network information is vital in the structure of a sharing mobility network, and the results generalize even when the network structure and density change significantly. With the GNN model, we show how it supports two crucial design decisions in bike sharing systems, i.e., where new stations should be added and how much capacity a station should have.

Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility

Understanding the usage demand of shared mobility systems in different areas of a city and its determinants is crucial for planning, operation and management of the systems. This study leverages an unbiased data-driven approach called accumulated effect analysis for examining the complex (nonlinear and interactive) effects of correlated built environment factors on the usage of shared mobility. Special research emphasis is given to unraveling the complex effects using an unbiased and data-driven approach that can overcome the impacts of correlations among built environment factors. Based on empirical analysis of synthetic data and a field dataset about dockless bike sharing systems (DLBS), results demonstrate that the method of partial dependency analysis prevalent in the relevant literature, will result in biases when investigating the effects of correlated built environment factors. In comparison, accumulated local effect analysis can appropriately interpret the effects of correlated built environment factors. The main effects of many built environment factors on the usage of DLBS present nonlinear and threshold patterns, quantitively revealed by accumulated local analysis. The approach can reveal complex interaction effects between different built environment factors (e.g., commercial service and education facility, and metro station coverage and living facility) on the usage of DLBS as well. The interactions among two built environment factors could even change with the values of the factors rather than invariant. The outcomes offer a new approach for revealing complex influences of different built environment factors with correlations as well as in-depth empirical understandings regarding the usage of DLBS.

A Review of the Promotion of Sustainable Mobility of Workers by Industries

The evolution and growth of cities present considerable challenges to the promotion of sustainable mobility, namely in commuting trips. In the present and recent past, many industries and companies of the economy’s productive sector have had to move as far as possible from urban areas, to minimize the impact of their activities on people’s health and quality of life. In more dispersed and low-density territories, working and residential areas are very far from each other, and there is typically poor public transport service and a lack of cycling networks for commuting purposes. This scenario encourages the use of private automobiles, not only as a necessity but also often as an obligation, making the mobility system nearly unsustainable. Therefore, it has become clear that companies can play an important role in promoting more sustainable mobility by reducing car use on commuting trips and by offering employees clean and more efficient transport alternatives to promote workers’ well-being and quality of life. Through an extensive literature review, a selection of scientific articles in the last 13 years was analyzed and discussed. The results highlighted that the location of industrial areas, the supply of public transport, the usage of active modes, and shared mobility systems are key factors to reduce car usage in workers’ commuting trips. Therefore, any sustainable mobility strategies that companies adopt will minimize the respective negative externalities, helping promote more environment-friendly ways of transportation, accessibility, social equity, and inclusion in workers’ communities. This justifies the need and urgency for the development of specific sustainable mobility plans oriented for companies/industries, instead of addressing this as just another element of a conventional urban mobility plan. Since this mobility represents a high volume of trips, repeated according to very regular patterns, it must be sustainability-oriented, allowing the improvement of system, trip, and vehicle efficiency.