Shared Mobility Systems Research Articles

Factors affecting the adoption of shared mobility systems: Evidence from Australia

The purpose of this paper is to examine the current impact and potential of the various forms of shared mobility, with a focus the shire of Wanneroo in Western Australia. Shared mobility options included in the study are car-pooling, ride-sourcing, car-sharing and bike-sharing. Residents of Wanneroo were asked about their use of shared mobility, their intended behaviour in the future, their views on the benefits, barriers to adoption, and their shared mobility use in travel to/from rail stations. Results show that a significant percentage of respondents had used shared mobility options, and the primary use of shared mobility was for social activities and secondly, work-related activities; a higher percentage of respondents said they were positive about shared mobility and would use it in the future. The findings indicate that gender is the most important driver to predict whether an individual will or will not use shared mobility. Employment status, annual income, attitude towards benefits of shared mobility, the capability to access shared mobility, and reasons for shared mobility have a positive correlation to the possibility of shared mobility use. Finally, the study reports that respondents are positive about the use of shared mobility options for travelling to and from the rail station.

Public Acceptability of Low Emission Zones: The Case of “Madrid Central”

Cities have intensified the adoption of Low Emission Zones (LEZs) to improve urban livability. Despite the high social controversy caused by LEZs in many cities, the scientific literature has paid little attention to study their public acceptability. This paper conducts a modelling approach exploring the impact of four groups of variables on the public acceptability of LEZs: (i) socio-economic and demographic characteristics; (ii) personal attitudes; (iii) travel-related variables; and (iv) perceptions and mobility habits linked to LEZs. The city of Madrid, Spain, is a case study of great interest because a LEZ called “Madrid Central” has been recently implemented. A total of 799 individual questionnaires were used to calibrate an ordered logit model. Results indicate that socio-economic and demographic variables are weakly related to the level of public acceptability towards the LEZ. On the contrary, the political ideology of individuals, their environmental awareness, their primary transport mode, the use of shared mobility systems, and the frequency of access to “Madrid Central” have a higher explanatory power. The results may be useful for policy-makers to understand the factors that increase the public acceptability of LEZs.

Quantifying economic benefits from free-floating bike-sharing systems: A trip-level inference approach and city-scale analysis

Despite many qualitative discussions about the benefits of free-floating bike-sharing systems (FFBS), high-resolution and quantitative assessments about the economic benefits of FFBS for users are absent. This study proposes an innovative trip-level inference approach for quantifying the economic benefits of FFBS, leveraging massive FFBS transaction data, the emerging multimodal routing Application Programming Interface from online navigators and travel choice modeling. The proposed approach is able to analyze the economic benefit for every single bike-sharing trip and investigate the spatiotemporal heterogeneity in the economic benefits from FFBS. An empirical analysis in Shanghai is conducted using the proposed approach. The estimated saved travel time, cost, and economic benefit due to using FFBS per trip are estimated to be 9.95 min, 3.64 CNY, and 8.68 CNY-eq, respectively. The annual saved travel time, cost, and economic benefits from FFBS in Shanghai are estimated to be 17.665 billion min, 6.463 billion CNY, and 15.410 billion CNY-eq, respectively. The relationships between economic benefits from FFBS and built environment factors in different urban contexts are quantitatively examined using Multiple Linear Regression to explain the spatial heterogeneity in the economic benefits of FFBS. The outcomes provide a useful tool for evaluating the benefits of shared mobility systems, insights into the users’ economic benefit from using FFBS from per-trip, aggregated and spatial perspective, as well as its influencing factors. The results could efficiently support the scientific planning, operation and policy making concerning FFBS in different urban contexts.

Modeling Supply and Demand Matching for Free-Floating Shared Mobility System Optimization

Modeling Supply and Demand Matching for Free-Floating Shared Mobility System Optimization

Alternative futures for shared mobility systems: impacts on transport planning tools and techniques

On-demand mobility services are changing the way we move in cities, fostered by digitalisation, vehicle automation and Mobility-as-a-Service (MaaS) platforms. Transport planning tools and techniques are expected to keep up to date to support decision-makers on the successful integration of these new mobility options in the urban transport mix. However, the evolution of the emerging mobility solutions is highly uncertain, as it is a function of complex factors that go beyond the influence of transport stakeholders. This poses a challenge for the definition of the requirements that data analysis techniques and modelling tools will face. This paper presents a series of explorative scenarios that provide different contexts for the evolution of urban mobility in Europe, in order to grasp the plausible pathways that shared mobility services will follow in the upcoming decades. The scenarios are adapted from those developed by the climate change research community and are used in a Delphi poll to gather expert visions on how shared mobility services will evolve. The availability of a range of possible futures for shared mobility facilitates the identification of the capabilities that transport decision-makers will demand from data analysis techniques and modelling tools.

Customer-Centric Dynamic Pricing for Free-Floating Shared Mobility Systems

Customer-Centric Dynamic Pricing for Free-Floating Shared Mobility Systems

A Holistic Approach to Electric Shared Mobility Systems Development—Modelling and Optimization Aspects

Due to the development of the shared economy, increasingly more shared mobility providers have launched services based on the use of electric vehicles. The increasing growth of electric shared mobility services has produced various types of problems that do not occur (or occur with a limited effect) under conventional shared mobility systems. This increase in electric shared mobility problems has led to many effects, including limitations of the system zones or going out of business. To avoid difficulties in the functioning of electric shared mobility systems, various scientific studies have been undertaken to model and optimize the operation of these systems. Modeling and optimization mainly relate to one category of the system—for example, only to bike sharing. However, to understand the system of electric shared mobility holistically, there is a need to define the criteria generally as stimulants or destimulants. Based on these assumptions, we conducted research on the identification of factors influencing the development of electric shared mobility services. We conducted our own expert research based on the Social Network Analysis method. The aim of this study was to determine the factors that influence the development or recession of services in the entire electric shared mobility market in reference to selected stakeholders. The obtained results indicate a non-standard approach to the modeling and optimization of electric shared mobility services. This study could be used as support for creating electric shared mobility models and could also be helpful for service providers or local mobility managers through the developed recommendations.

Estimation of the shared mobility demand based on the daily regularity of the urban mobility and the similarity of individual trips.

Even if shared mobility services are encouraged by transportation policies, they remain underused and inefficient transportation modes because they struggle to find their customer base. This paper aims to estimate the potential demand for such services by focusing on individual trips and determining the number of passengers who perform similar trips. Contrary to existing papers, this study focuses on the demand without assuming any specific shared mobility system. The experiment performed on data coming from New York City conducts to cluster more than 85% of the trips. Consequently, shared mobility services such as ride-sharing can find their customer base and, at a long time, to a significantly reduce the number of cars flowing in the city. After a detailed analysis, commonalities in the clusters are identified: regular patterns from one day to the next exist in shared mobility demand. This regularity makes it possible to anticipate the potential shared mobility demand to help transportation suppliers to optimize their operations.

Economic Aspects of Driving Various Types of Vehicles in Intelligent Urban Transport Systems, Including Car-Sharing Services and Autonomous Vehicles

Nowadays, the concept of new mobility solutions like shared mobility systems is becoming more and more popular in current transport systems. The next technological step will be the idea of replacing traditional vehicles with autonomous ones. Because autonomous vehicles are a new concept in the automotive market, we dedicated this article to the idea of using autonomous vehicles as a part of car-sharing systems in intelligent, urban transport systems. The research herein is focused on the economic aspects of using autonomous vehicles in comparison to the classic car fleet available in car-sharing systems and to vehicles that belong to individual owners. We present our method for appropriate fleet selection based on the Delphi method and the calculations made through a scientific experiment performed based on Hartley’s plan. The results indicate the relation of travel parameters (including vehicle type) to the total cost of travel in urban transport systems. We also present the main terms related to autonomous vehicles. This article provides support for people who want to deepen knowledge about autonomous vehicles and new mobility solutions used in urban transport systems.

Shifting gears on sustainable transport transitions: Stakeholder perspectives on e-bikes in Toronto, Canada

Car-centric transportation systems are widely regarded as unsustainable. E-bikes are an emerging technology with potential to strengthen sustainability transitions. Little is known about the role of governance stakeholders in shaping e-bike technology uptake/rejection. This qualitative study draws upon theories of socio-technical transitions, disruptive technology and strategic niche management to interrogate transportation governance stakeholders’ perceptions towards e-bikes and related prospects or barriers to enhancing sustainability in Canada’s most populated, congested city. E-bikes possess niche benefits that help encourage socio-technical shift away from cars. Yet their greatest potential in strengthening transition pathways may be their ability to enhance seamless, multi-modal transportation through incorporation into transit hubs and shared mobility systems. E-bikes also pose negative disruptions that undermine their uptake. The range of models on the market warrant greater regulatory distinction according to unique characteristics and hazards. E-bike governance requires further coordination, and decision-makers need more data on usage patterns and modal displacement.

Enhanced Mobility With Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems

Shared mobility can provide access to transportation on a custom basis without vehicle ownership. The advent of connected and automated vehicle technologies can further enhance the potential benefits of shared mobility systems. Although the implications of a system with shared autonomous vehicles have been investigated, the research reported in the literature has exhibited contradictory outcomes. In this paper, we present a summary of the research efforts in shared autonomous vehicle systems that have been reported in the literature to date and discuss potential future research directions.

Bi-objective autonomous vehicle repositioning problem with travel time uncertainty

We study the problem of repositioning autonomous vehicles in a shared mobility system in order to simultaneously minimize the unsatisfied demand and the total operating cost. We first present a mixed integer linear programming formulation for the deterministic version of the problem. We extend this formulation to make it easier to work with in the non-deterministic setting. We then show how the travel time uncertainty can be incorporated into this extended deterministic formulation using chance-constraint programming. Finally, two new reformulations for the proposed chance-constraint program are developed. We show a critical result that the size of one of the reformulations (in terms of the number of variables and constraints) does not depend on the number of scenarios, and so it outperforms the other reformulation. Both reformulations are bi-objective mixed integer linear programs with a finite number of nondominated points and so they can be solved directly by algorithms such as the balanced box method (Boland et al. in INFORMS J Comput 27(4):735–754, 2015). A computational study demonstrates the efficacy of the proposed reformulations.

Static Pricing Optimization in Shared Mobility Systems Under the Consideration of Network Effects

Over the last decades, shared mobility systems have become an integral part of the inner-city mobility offer – a prominent example is car sharing. In fact, this work has been motivated by the insights we gained in close collaboration with Share Now, Europe's largest car sharing provider. In car sharing as well as in shared mobility systems in general, pricing optimization has turned out to be a promising means of controlling the complex interactions between demand and supply in order to increase profitability. Practice mostly applies static price differentiation according to a rental's spatial origin and the time of day. In research, however, such approaches have not been considered in detail yet. In this paper, we consider the static origin-based, profit-maximizing pricing problem for shared mobility systems. The problem is characterized by the determination of spatially and temporally differentiated minute prices, by the prevalence of spatio-temporal network effects, and by other practice-relevant aspects, such as a limited fleet size. Based on a deterministic network flow model, we formulate the problem as a mixed-integer linear program and prove it to be NP-hard. We propose a scalable heuristic solution approach that combines the computational benefits of problem decomposition in a rolling horizon fashion with a value function approximation technique adapted from approximate dynamic programming in order to incorporate future spatio-temporal network effects. An extensive computational study demonstrates the benefits of capturing such effects in pricing in general, as well as our value function approximation's ability to anticipate them precisely. Moreover, in a case study based on Share Now data from Florence in Italy, we demonstrate potential profit increases of around 9% compared to the de facto industry standard of constant uniform minute prices.

An approach to modeling bike-sharing systems based on spatial equity concept

Efficient and sustainable mobility is becoming a need for emerging metropolitan areas, essential to grant their attractiveness, quality of life and economic power. Among the possible strategies to adopt, shared mobility systems have proven to be a valuable alternative to accommodate the mobility needs of citizens ensuring at the same time sustainability and transport flexibility. In particular, this paper focuses on bike-sharing systems.Despite many studies have recognized the importance of planning processes supported by equity principles, only a few have integrated spatial equity concepts in the planning of a bike-sharing system. With this research, we want to propose an original model to determine the number and layout of bike stations, as well as the number of bicycles and racks for each of them. The suggested approach aims at minimizing the costs associated with the system implementation and operation while balancing the level of service for all the users over the territory. A set of illustrative examples and a sensitivity analysis are provided.

A unified equilibrium framework of new shared mobility systems

Modeling congestion effects arising from multiple travel modes, shared mobility modes in particular, is non-trivial because of the complex interactions among diverse agents and distinct traffic flow compositions. This research aims to provide a theoretical framework of generic traffic network equilibria to unify these services and hopefully become a step stone to modeling shared mobility services in congested road network. In the proposed framework, we mainly focus on three modes: driving solo, ridesharing, and e-hailing service. The four types of traffic flows are: personal vehicle drivers, e-hailing drivers, ridesharing riders, and e-hailing passengers. The first two flows contribute to traffic congestion while the latter two do not. To capture their interactions, a super extended network is created with four copied networks each of whom represents one type of traffic flow. The equilibrium of new mobility systems can then be reformulated as a quasi-variational inequality and solution existence is discussed. The numerical results are tested in both Braess network and Sioux Falls network to illustrate the impact of different parameters on equilibrium outcomes, including modal cost, system travel time and deadhead miles. The results of this model will help assist transportation planners in making policy and regulation decisions regarding shared mobility services.

Two-stage stochastic approximation for dynamic rebalancing of shared mobility systems

Mobility systems featuring shared vehicles are often unable to serve all potential customers, as the distribution of demand does not coincide with the positions of vehicles at any given time. System operators often choose to reposition these shared vehicles (such as bikes, cars, or scooters) actively during the course of the day to improve service rate. They face a complex dynamic optimization problem in which many integer-valued decisions must be made, using real-time state and forecast information, and within the tight computation time constraints inherent to real-time decision-making. We first present a novel nested-flow formulation of the problem, and demonstrate that its linear relaxation is significantly tighter than one from existing literature. We then adapt a two-stage stochastic approximation scheme from the generic SPAR algorithm due to Powell et al., in which rebalancing plans are optimized against a value function representing the expected cost (in terms of fulfilled and unfulfilled customer demand) of the future evolution of the system. The true value function is approximated by a separable function of contributions due to the rebalancing actions carried out at each station and each time step of the planning horizon. The new algorithm requires surprisingly few iterations to yield high-quality solutions, and is suited to real-time use as it can be terminated early if required. We provide insight into this good performance by examining the mathematical properties of our new flow formulation, and perform rigorous tests on standardized benchmark networks to explore the effect of system size. We then use data from Philadelphia’s public bike sharing scheme to demonstrate that the approach also yields performance gains for real systems.

Fleet operational policies for automated mobility: A simulation assessment for Zurich

We evaluate performance of four different operational policies to control an automated mobility-on-demand system with sequential vehicle-sharing in one public open-source-accessible, agent-based, high fidelity simulation environment. Detailed network dynamics on a road level of precision are taken into account. The case study is conducted in a simulation scenario of Zurich city. The results indicate that automated vehicles’ shared mobility systems can provide approximately six times higher occupancy rates than conventional private cars, but that their costs – in Switzerland – are considerably higher than those of subsidized public transport or private cars in the short term. However, these services are predicted to be considerably cheaper than the full costs of owning and using a private car, which makes the long-term adoption of automated taxi services likely. Simulations demonstrate that choice of fleet operational policy determining customer-vehicle assignment and repositioning of empty vehicles (rebalancing) heavily influences system performance, e.g., wait times and cost. This aspect must thus be regarded as an important factor in the interpretation of existing and future simulation studies.

Beyond Rebalancing: Crowd-Sourcing and Geo-Fencing for Shared-Mobility Systems

In this paper, we propose an integrated model of two-sided stochastic matching platforms to understand the operations of free-float shared-mobility systems, wherein agents on both sides respond to incentive instruments. In particular, we address the joint design of incentives as well as spatial allocation of capacity in such systems. Addressing such a problem via both stylized models and computational studies, we obtain analytical insights in the design and operations for such shared-mobility systems. Methodology-wise, we identify an analytical paradigm that certain stochastic models can be operationalized using tractable computational framework under heavy traffic approximations. From the platform's perspective, we formulate a strategic double-ended queueing model. We identify an operational regime wherein the systems performance is competitive in almost all dimensions against alternative scenarios. We implement this optimization problem in a real case study, using a data set from a leading free-float bicycle-sharing system, and solve it via mixed-integer Second-Order Conic Programs (SOCPs). Both stylized results and computational studies generate insights about fundamental trade-offs and triangular relationships among operational costs, capacity utilization rates and service levels. Interestingly, we find the value of parking capacity design in determining the market thickness (transient service availability) in such a two-sided marketplace. We also demonstrate that this framework can be extended in multiple directions, e.g., optimal service region design, incentive externalities, and potentially a wider range of applications. Our insights guide the platform as well as the policy-maker in terms of incentive-driven demand-supply balancing, as well as the design and spatial allocation of parking spaces for shared-mobility systems.

Impacts of integrating shared autonomous vehicles into a Peer-to-Peer ridesharing system

As public perception of sharing economy in transportation has changed, mobilephone-hailed ridesharing is gaining prominence. The key aspect of capitalizing and promoting better shared-mobility systems depends on the matching rate between the supply and demand for rides. Peer-to-peer (P2P) ridesharing systems devise higher matching rate than pure ridesharing systems by attracting more drivers. Even relaxing the spatiotemporal constraints for participants could increase the chances to be matched. However, we notice that sole P2P ridesharing systems still do not guarantee matching when the number of drivers is limited. We propose the utilization of a fleet service to cover the unmatched riders in P2P ridesharing. While it can be any type of fleet services such as taxis, Uber/Lyft, or paratransit, we explore the idea of utilizing shared autonomous vehicles as a fleet, as they can be dispatched without labor. We model an integrated system for P2P ridesharing and shared autonomous fleet vehicles (SAFVs). The proposed algorithm is designed to maximize matching ratio while optimizing the number of required SAFVs. Based on a simulated study on the northern Los Angeles, the integrated shared-mobility system is shown to have high potential to serve a high fraction of riders.

An Overview of Shared Mobility

In a wider understanding, shared mobility can be defined as trip alternatives that aim to maximize the utilization of the mobility resources that a society can pragmatically afford, disconnecting their usage from ownership. Then, shared mobility is the short-term access to shared vehicles according to the user’s needs and convenience. The contributions and added value of this paper are to provide an up-to-date and well-structured review on the area of shared mobility to researchers and practitioners of the transport sector. Hence, this paper presents a bibliographical review of shared mobility and its diverse modalities, as an alternative to individual transportation, especially in cases of individual automobiles or short trips restricted to an urban city. The present literature review on shared modes of transportation has discovered that the introduction of these modes alone will not solve transportation problems in large cities, with elevated and growing motorization rates. However, it can among the strategies employed to help alleviate the problems caused by traffic jams and pollution by reducing the number of vehicles in circulation, congestions, and the urban emission of polluting gases. Thus, the implementation of shared mobility schemes offers the potential to enhance the efficiency, competitiveness, social equity, and quality of life in cities. This paper covers the fundamental aspects of vehicle and/or ride sharing in urban centers, and provides an overview of current shared mobility systems.