Free-floating Service Research Articles

Optimal Rebalancing Strategy for Shared e-Scooter Using Genetic Algorithm

Shared e-scooters are provided as a free-floating service that can be freely rented and returned within the service area. Although this has a positive effect in terms of convenience for users of shared e-scooters, it is creating new urban problems, such as undermining the aesthetics of the city and obstructing the passage of pedestrians. Therefore, this study developed an optimal rebalancing algorithm to mitigate these problems and proposed an efficient operation plan. Complete relocation was performed to match the demand and supply for an efficient operation by reducing the unnecessary oversupply of shared e-scooters. The optimal rebalancing algorithm that reflects the attributes of e-scooters was developed through genetic algorithms and subsequently applied to actually used cases. The results indicate that when 20% of the potential demand was considered, an optimal solution could be derived with two relocation vehicles; however, when the potential demand was not considered, three relocation vehicles were required. Therefore, it is anticipated that the results of this study can serve as basic data for solving various urban problems caused by the recent rapid increase in the use of shared e-scooters.

Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeService ScienceVol. 15, No. 1 Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1Guest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarPublished Online:13 Mar 2023https://doi.org/10.1287/serv.2023.0317Rapid developments in city infrastructure and technologies are creating numerous opportunities and inspiring innovative and emerging urban services. Among these innovations, complex systems of urban transportation and logistics have embraced advances and have been reshaped significantly. They enable innovative new urban services, which are now booming and changing everyday life for urban residents.This special issue of Service Science explores perspectives on innovation in transportation-enabled urban services. We hope that the special issue will enhance the understanding of the planning, operation, and management of such services. Contributions are expected to demonstrate rigorous model development, economic/econometric analysis, and decision-making tools based on optimization and/or data-driven approaches. Research in this special issue addresses the novel challenges and opportunities that arise in these transportation/logistics systems and relevant transportation-enabled urban services.The articles in the special issue represent an exciting diversity in research settings, methodologies, and applications. The articles are composed into two parts. Part 1 of the special issue includes four papers. In what follows, we provide a brief description of the papers in Part 1. The introduction of papers in Part 2 is rendered to the journal issue scheduled for September 2023.Yan et al. (2023) study an incentive mechanism for private parking-sharing programs in an imperfect information setting. They propose a matching-and-pricing mechanism for a driver’s demand-reporting problem in parking-sharing programs in which owners share their private parking slots with other drivers. They generate a driver-slot matching solution and show that, under the assumption that drivers do not know with certainty whether other drivers will show up to compete for the parking slots, the mechanism proposed induces drivers to truthfully report their private information about the travel plans and guarantees some desirable properties.Schwamberger et al. (2023) study the dynamic customer contacting for e-fulfillment in times of crisis. Specifically, the outbreak of the coronavirus disease 2019 pandemic increased demand for online grocery orders for both click & collect (C&C) and attended home delivery (AHD) and outstripped delivery capacity by a wide margin. They investigate a new demand management concept of proactively contacting customers and develop a decision policy for the C&C fulfillment method to address the problem of when to contact customers. They also extend this approach to the AHD setting and propose a three-step procedure to solve this problem.van der Gaast and Arslan (2023) explore the last-mile logistics operations of instant grocery delivery services. They introduce an instant delivery problem to replicate and examine two widely adopted strategies in the rapid delivery market: the personal shopper system (PSS) and the inventory owned delivery (IOD) system. They propose a tailored rolling horizon framework that utilizes column generation to browse updated delivery plans for arriving customer orders. Computational studies show that the PSS is a highly competitive strategy compared with IOD, particularly when dealing with small-sized customer orders.Ke and Mo (2023) study a one-way car-sharing system with both station-based and free-floating service types, which apply the two types in different parts of the service area. They establish a mixed-integer linear-programming model to maximize the operator profit, considering both strategic decisions of service type and operational decisions of relocation and trip selection. They propose a heuristic algorithm to effectively solve the model. Computational results demonstrate that the integrated system achieves a larger profit and higher demand fulfillment rate compared with the single station-based or free-floating system.ReferencesKe H, Mo Y (2023) Station-based or free-floating? An integrated carsharing system with dual service types. Service Sci. 15(1):58–76.Google ScholarSchwamberger J, Fleischmann M, Strauss A (2023) Feeding the nation—dynamic customer contacting for e-fulfillment in times of crisis. Service Sci. 15(1):22–40.Google Scholarvan der Gaast JP, Arslan AM (2023) Personal shopper systems in last-mile logistics. Service Sci. 15(1):41–57.Google ScholarYan P, Cai X, Chu F, Ni D, He H (2023) An incentive mechanism for private parking-sharing programs in an imperfect information setting. Service Sci. 15(1):3–21.Google Scholar Back to Top Next FiguresReferencesRelatedInformation Volume 15, Issue 1March 2023Pages 1-78, C3 Article Information Metrics Information Published Online:March 13, 2023 Copyright © 2023, INFORMSCite asGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif Benjaafar (2023) Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1. Service Science 15(1):1-2. https://doi.org/10.1287/serv.2023.0317 PDF download

Access distance to free-floating services considering uncertain availability and smartphone activeness

ABSTRACT The rapid growth of free-floating sharing services is seen as a key solution to urban mobility problems; however, the wider acceptance and adaptation is critically depending upon the ease of access. The aim of this research is to quantify the access distance to free-floating services considering the density and distribution of travellers and bicycles. Our main focus the effect of different information-seeking and reservation strategies on the travellers. We distinguish those who use smartphone actively during travelling from those who only check the availability before the journey. An agent-based discrete-event simulation (DES) is developed, from which we find that 1. All travellers being active in seeking feasible bicycles do not necessarily lead to better system performance. 2. Enabling reservations can lead to longer average travel times, especially longer walking time. 3. When increasing the bicycle supply or reservation rate, the reliability of accessing desired bicycles can first increase then drop.

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

The shared e-scooter is a popular and user-convenient mode of transportation, owing to the free-floating manner of its service. The free-floating service has the advantage of offering pick-up and drop-off anywhere, but has the disadvantage of being unavailable at the desired time and place because it is spread across the service area. To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area. Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters. The temporal scope was selected as October 2020, when the demand for e-scooter use was the highest in 2020, and the spatial scope was selected as Seocho and Gangnam, where shared e-scooter services were first introduced and most frequently used in Seoul, Korea. The spatial unit for the analysis was set as a 200 m square grid, and the hourly demand for each grid was aggregated based on e-scooter trip data. Prior to predicting the demand, the spatial area was clustered into five communities using the community structure method. The demand prediction model was developed based on long short-term memory (LSTM) and the prediction results according to the activation function were compared. As a result, the model employing the exponential linear unit (ELU) and the hyperbolic tangent (tanh) as the activation function produced good predictions regarding peak time demands and off-peak demands, respectively. This study presents a methodology for the efficient analysis of the wider spatial area of e-scooters.

Forecasting the carsharing service demand using uni and multivariable models

Carsharing is ana lternative to urban mobility that has been widely adopted recently. This service presents three main business models: two of these models base their services on stations while the remainder, the free-floating service, is free of fixed stations. Despite the notable advantages of carsharing, this service is prone to several problems, such as fleet imbalance due to the variance of the daily demand in large urban centers. Forecasting the demand for the service is a key task to deal with this issue. In this sense, in this work, we analyze the use of well-known techniques to forecast a carsharing service demand. More in deep, we evaluate the use of the Long Short-Term Memory (LSTM) and Prophet techniques to predict the demand of three real carsharing services. Moreover, we also evaluate seven state-of-the-art forecasting models on a given free-floating carsharing service, highlighting the potentials of each technique. In addition to historical carsharing service data, we have also used climatic series to enhance the forecasting. Indeed, the results of our analysis have shown that the addition of meteorological data improved the models’ performance. In this case, the mean absolute error of LSTM may fall by half, when using the climate data. When considering the free-floating carsharing service, and prediction for the short-term (i.e., 12 hours), the boosting algorithms (e.g. XGBoost, Catboost, and LightGBM) present superior performance, with less than 20% of mean absolute error when compared to the next best-ranked model (Prophet). On the other hand, Prophet performed better for predictions conducted on long-term periods.

A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems

Mobility in cities is a fundamental asset and opens several problems in decision making and the creation of new services for citizens. In the last years, transportation sharing systems have been continuously growing. Among these, bike sharing systems became commonly adopted. There exist two different categories of bike sharing systems: station-based systems and free-floating services. In this paper, we concentrate our analyses on station-based systems. Such systems require periodic rebalancing operations to guarantee good quality of service and system usability by moving bicycles from full stations to empty stations. In particular, in this paper, we propose a dynamic bicycle rebalancing methodology based on frequent pattern mining and its implementation. The extracted patterns represent frequent unbalanced situations among nearby stations. They are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach. Experiments performed on real data of the Barcelona bike sharing system show the effectiveness of the proposed approach.

Predicting Carsharing Station-Based Trip Generation Using a Growth Model

Carsharing is a service that allows members to rent cars for a limited time. In Montreal, Quebec, Canada, two types of services exist: a station-based and a free-floating service. This paper proposes a trip generation model for the station-based service of the Communauto carsharing operator for 2016. To better understand relations between space and time, a growth model is used, considering these factors at different levels. For example, some factors can impact all stations similarly, while other factors may impact each station differently. Thus, this model allows to consider both spatial and temporal variables allowing more precise estimations. The aim of this research is to estimate carsharing trip generation at the station level and provide insights into the impacts of implementing new stations on demand. A step-by-step approach was adopted to define the best predictive model for the use of carsharing stations. While more complex model formulations need to be tested to enhance the analysis, the final growth model obtained indicates that, in addition to the number of vehicles available at the stations, several exogenous factors have a significant impact on the trip generation rate of a carsharing station. For instance, the model shows that demographic factors, walkability level and number of bus stations have significant impacts on the use of carsharing stations.

Carsharing in the Brussels region

The aim of this fact sheet is to highlight the differences and commonalities between the user profiles of station-based and free-floating carsharing services based on the analysis of a survey of 5 375 car sharing users in Brussels. The analysis shows that free-floating users have a higher rate of car ownership, higher share of males and they are slightly younger. Both types of services cater to individuals with a high level of education and are used approximately 1 to 3 times a month. The two services clearly serve different purposes, with a greater variety of travel motives stated by the users of free-floating services and the rates of car ownership much higher. In addition, users reportedly reduce their car use once they become members, while the impact on public transport use remains unclear, with free-floating users replacing public transport trips partly with carsharing.

What about Free-Floating Carsharing?

In recent years, carsharing has become a practical, ecological, and economical alternative to private car ownership around the world. Traditional carsharing is station-based, but new types of shared services have appeared recently. One of those is free-floating carsharing. It is more flexible, but what is its impact on user behavior? This paper aims to characterize the use of the free-floating carsharing service in central Montreal, Canada. Use of the traditional, station-based service and the new service was compared. Because some people were members of both services, it was possible to examine the specific contribution of each service to meeting travel needs. The impact of the introduction of this new transportation alternative was also examined. The results showed that compared with traditional carsharing, more women were members of the free-floating service and the trip distances and durations were much shorter. Shopping was the most important activity, and there was a concentration of trip ends near the central business district in the midday period. When asked what mode users would have used in the absence of the free-floating service, people mentioned public transit, taxis, and walking; the popularity of these alternatives varied, probably in relation to seasonal changes. Further studies are required to measure the environmental impact of this new transportation mode.