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Predicting Carsharing Station-Based Trip Generation Using a Growth Model

Authors:
Marlène Ménoire
Marlène Ménoire
Marlène Ménoire
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Grzegorz Wielinski at Polytechnique Montréal
Grzegorz Wielinski
Catherine Morency at Polytechnique Montréal
Catherine Morency
Martin Trépanier at Polytechnique Montréal
Martin Trépanier
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Abstract

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.
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Transportation Research Procedia 48 (2020) 1466–1477
2352-1465 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the World Conference on Transport Research – WCTR 2019
10.1016/j.trpro.2020.08.192
10.1016/j.trpro.2020.08.192 2352-1465
© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientic committee of the World Conference on Transport Research – WCTR 2019
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Marlène Ménoire et al. / Transportation Research Procedia 48 (2020) 1466–1477 1467
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1468 Marlène Ménoire et al. / Transportation Research Procedia 48 (2020) 1466–1477
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I8E4:8F<M8B9;BHF8;B?7F     
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Marlène Ménoire et al. / Transportation Research Procedia 48 (2020) 1466–1477 1469
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-45?88F6E<CG<I84A4?LF<FB94??I4E<45?8FHF87<AG;<FFGH7L
/4E<45?8 &<A &4K ,G4A74E7
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'H@58EB9@87<4AI8;<6?8FC8EFG4G<BA      -EH8
,;BEG8FG7<FG4A68GB49E889?B4G<A:4E84@      -EH8
)EBCB9C8BC?858GJ88ALB?<I<A:<AG;<F4E84     
)EBCB9C8BC?858GJ88ALB?<I<A:<AG;<F4E84      -EH8
)EBCB9C8BC?858GJ88ALB?<I<A:<AG;<F4E84     
)EBCB9C8BC?858GJ88ALB?<I<A:<AG;<F4E84     
)EBCB9@4?8F?<I<A:<AG;<F4E84     
)EBCB998@4?8F?<I<A:<AG;<F4E84      -EH8
)EBCB9C8BC?8J<G;4A4AAH4?<A6B@8B9
4CCEBK.,4A7HC?<I<A:<AG;<F4E84
    
)EBCB9C8BC?8J<G;4A4AAH4?<A6B@8B9
4CCEBK.,4A7HC?<I<A:<AG;<F4E84
    
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)EBCB9;BHF8;B?7FJ<G;C8EFBA     
)EBCB9;BHF8;B?7FJ<G;C8BC?8      -EH8
)EBCB9;BHF8;B?7FJ<G;C8BC?8     
)EBCB9;BHF8;B?7FJ<G;C8BC?8     
)EBCB9;BHF8;B?7FJ<G;C8BC?8     
)EBCB9;BHF8;B?7FJ<G;C8BC?84A7@BE8      -EH8
I8E4:8F<M8B9;BHF8;B?7F     
I8E4:8AH@58EB96;<?7E8A<A;BHF8;B?7     
)EBCB9C8BC?8FC84><A:E8A6;     
)EBCB9C8BC?8FC84><A:A:?<F;      -EH8
)EBCB9C8BC?8FC84><A:5BG;?4A:H4:8F     
)EBCB94A47<4A6<G<M8A     
)EBCB9C8BC?8J<G;4AHA78E:E477<C?B@4BE;<:;8E     
)EBCB9HA8@C?BL87C8BC?8      -EH8
4E4668FFE4G8<AF<784@E4A:89EB@FG4G<BAF      -EH8
'H@58EB9JBE>GE<CFJ<G;478FG<A4G<BA<AF<784@
E4A:89EB@FG4G<BAF
     -EH8
04?>45<?<GL<A78KJ<G;<A@4EBHA7FG4G<BAF      -EH8
<FG4A68GBG;8A84E8FG@8GEBFG4G<BAHCGB@9EB@
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<FG4A68GBG;8A84E8FGGE4<AFG4G<BAHCGB@9EB@
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'H@58EB95HFC4FF4:8FC8E@BAG;J<G;<A@B9
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4C8E<@8G8EB94A7@8GE8F4EBHA7FG4G<BAF&B78?F;4I8588AG8FG87J<G;BA?LBA8B9G;8F8I4E<45?8F4G
1470 Marlène Ménoire et al. / Transportation Research Procedia 48 (2020) 1466–1477
"E9:4=00?,7)=,9>;:=?,?4:9'0>0,=.3%=:.0/4,G
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Marlène Ménoire et al. / Transportation Research Procedia 48 (2020) 1466–1477 1471
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Marlène Ménoire et al. / Transportation Research Procedia 48 (2020) 1466–1477 1473
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Marlène Ménoire et al. / Transportation Research Procedia 48 (2020) 1466–1477 1475
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1476 Marlène Ménoire et al. / Transportation Research Procedia 48 (2020) 1466–1477
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Marlène Ménoire et al. / Transportation Research Procedia 48 (2020) 1466–1477 1477
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... Single Station De et al., 2013, Cheng et al., 2019Wang et al., 2020;Ménoire et al.,2020Station Cluster Stillwater et al., 2009, Hu et al., 2018Location Schmöller et al., 2014Becker et al., 2017Land Parcel Kim, 2015, Kang et al., 2016, Seign et al. 2015, Khan et al., 2017, Klemmer et al.,2016, Schmöller et al., 2015, Celsor et al., 2007Cocca, 2020;Abbasi, 2021Explained Variable Usage Frequency Katzev, 2003, Costain et al., 2012, Kang et al., 2016, Schmöller et al., 2014Klemmer et al., 2016Becker et al., 2017;Cheng et al., Kim et al., 2017, Kim 2015Booking Density Wagner et al., 2016, Klemmer et al., 2016, Schmöller et al., 2015, Hu et al., 2018 Kim, 2017, Costain et al., 2012, Klemmer et al., 2016, Multiple Linear Model De et al., 2013, Kim, 2015, Kang et al., 2016, Seign, 2014, Schmöller et al., 2014, Stillwater et al., 2009 Chen et al., 2018 relationship network of the location-based units. Hence, the results obtained using location-based analysis units are limited to a specific OWC system and cross-section of the development process of the OWC system. ...
... As the basic analysis unit, almost all the existing studies have considered the location-based demand. Different levels of spatial scales have been adopted in these studies, including a single station (De et al., 2013, Cheng et al., 2019, Wang et al., 2020, Ménoire et al., 2020, a station cluster (Stillwater et al., 2009, Hu et al., 2018, and land parcels (Kim, 2015, Kang et al., 2016, Seign, 2014, Khan et al., 2017, Klemmer et al., 2016, Schmöller et al., 2015, Cocca, 2020, Abbasi, 2021. However, on adopting a smaller spatial scale for the analysis unit, the model of demand estimation enables a more precise estimation of the demand in the local area. ...
... Several studies have adopted different versions of the usage intensity (such as the vehicle usage rate and the number of bookings) as the explained variables. These studies, however, failed to distinguish between pick-up and drop-off (Kim et al., 2017, Katzev, 2003, Costain et al., 2012, Kim, 2015, Kang et al., 2016, Schmöller et al., 2014, Klemmer et al., 2016, Hu et al., 2018Ménoire et al., 2020, Abbasi, 2021. Completing a one-way carsharing trip requires two stations or spatial units in the OWC system. ...
Flow-based unit is better: exploring factors affecting mid-term OD demand of station-based one-way electric carsharing
Article
  • Sep 2021
  • TRANSPORT RES D-TR E
Station-based one-way carsharing (OWC) serves as a flexible method to enjoy the benefits of car travel, while also demonstrating the potential to mitigate environmental challenges and traffic congestion in cities. In the previous studies on OWC, location-based units such as a single station, station cluster, and land parcel were generally used as the basic analysis units for demand; however, these studies failed to consider the association among the operating areas. This led to results that were pertinent to a specific OWC system and cross-section of the development process. The objective of this study is to explore the significant factors related to flow-based demand (i.e., four-weekly bookings from the origin spatial unit to the destination spatial unit (OD bookings)). Four groups of explanatory variables are adopted: carsharing spatial unit attributes, built environment, transportation facilities, and OD trip attributes (such as public transportation and car travel distance between the OD). A combination model integrating machine learning and a generalized linear model is also developed to address the zero-inflation issue of the data. Moreover, an approach of Shapley additive explanations is implemented to determine the considerable effects of the factors. The results show that (1) the OD trip attributes play an important role in estimating the carsharing OD demand; (2) taxi and carsharing demands exhibit a non-significant partial overlap, and carsharing may compete with buses and supplement to the metro; and (3) the travel purpose in carsharing is diverse for any land use over a four-week period.
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... The evolution of carsharing services is expected to follow an asynchronous temporal and spatial evolutionary path (Hansen/Coenen 2015;Meelen/ Frenken/Hobrink 2019), which should result in processual and spatial patterns, different for each city. In contrast, from a business perspective, the diffusion of carsharing services is mainly determined by demand and supply factors, such as population density (Hu/Chen/Lin et al. 2018;Hjorteset/Böcker 2020) or public transportation availability (Ménoire/Wielinski/Morency et al. 2020;Abbasi/Ko/Kim 2021). Thus, we should be able to observe spatial patterns that reflect particularities in terms of economies of scale and scope relative to the structural characteristics of the cities, but with similar tendencies everywhere. ...
Mobility transition or just another business? Temporal and spatial analysis of the diffusion of station-based carsharing services in five German municipalities
Article
Full-text available
  • Jul 2023
Station-based carsharing systems are regularly seen as key building blocks for a successful sustainability transition of municipal mobility systems. While numerous studies deal with the optimal distribution of carsharing stations from a business perspective, none explores which spatial and temporal patterns shape the development of carsharing station networks, even though sustainability transitions and their patterns are perceived as strictly place-specific. This study uses a comparative cross-case analysis of five southwest German cities to reveal similarities in spatial and temporal patterns. The analysis differentiates between ‘efficient causes’, described by structural and social demographic characteristics of cities, and ‘final causes’, described by the causal pathway development of municipal carsharing policy. Results reveal that despite the different city characteristics and causal pathways, the spatial evolution of station-based carsharing systems follow similar growth paths, tending towards consolidation of carsharing patterns regardless of municipal support. The study adds to our understanding of mobility transitions and casts doubt on the ability of current station-based carsharing systems to shift municipal mobility systems towards ecological sustainability.
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... The effect of accessibility to other modes of transport with respect to the demand for station-based car sharing is not clear either and possibly highly influenced by public transport service quality, cultural and social differences. In a study conducted in the U.S., Stillwater et al. (2009) do not find a clear relation with respect to access to public transport, in Seoul Kang et al. (2016) find that low accessibility to subway has a positive effect on demand, whereas in Montreal Ménoire et al. (2020) find that high accessibility to busses has a positive effect. ...
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Free-Floating Carsharing: City-Specific Growth Rates and Success Factors
Article
Full-text available
  • Dec 2016
Free-floating carsharing, a relatively new market segment within carsharing, is expanding through Europe and North America. This type of system allows users to book a car at any point and any time within a specified area. This type of carsharing currently exists in about 34 cities across nine countries, in cities of highly varied demographics and urban form. Shared vehicles could be part of new mobility services that foster inter- and multimodal travel and serve as an essential part of energy and climate strategies in the transport sectors. However, empirical data on use of free-floating carsharing is usually unavailable for research purposes. New data collection methods have to be developed to evaluate the effects of carsharing systems. For five years, InnoZ (Innovationszentrum für Mobilität und gesellschaftlichen Wandel) has been using web mining to acquire a robust set of data about free-floating carsharing vehicles and movements. Since 2011, about 50 million movements have been recorded by using a web-mining script. This paper provides a first look at this dataset, showing that use of the services is generally increasing over time. It also confirms previous research that household size and residential density are key drivers of free-floating carsharing use.
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Modeling Station-Based and Free-Floating Carsharing Demand: Test Case Study for Berlin
Article
Full-text available
  • Dec 2014
Carsharing, in any form, is still growing around the world. One of the effects is the increasing number of cities in which multiple carsharing operators are competing. The carsharing industry has never been as competitive as it is now: the present is a good time for researchers to invest efforts in providing tools for the assessment and planning of carsharing programs. Nevertheless, efforts in this direction are still scarce, in particular for some of the newest forms in which carsharing has been implemented, such as free-floating carsharing. This paper reports on a study that made use of MATSim, an agent-based simulation software that had already been used to model station-based carsharing, to evaluate different carsharing scenarios for the city of Berlin. The main findings are the existing high potential to extend carsharing services further in Berlin and the apparent complementarity of station-based and free-floating carsharing. On the methodological level, the work introduces a new tool for the modeling of free-floating carsharing along with improvements of the previously existing station-based carsharing model.
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Modeling free-floating car-sharing use in Switzerland: A spatial regression and conditional logit approach
Article
  • Aug 2017
  • TRANSPORT RES C-EMER
Free-floating car-sharing has been one of the latest innovations in the car-sharing market. It allows its customers to locate available vehicles via a smartphone app and reserve them for a short time prior to their rental. Because it is available for point-to-point trips, free-floating car-sharing is not only an alternative to private cars, but also to public transportation. Using spatial regression and conditional logit analysis of original transaction data of a free-floating car-sharing scheme in Switzerland, this research shows that free-floating car-sharing is mainly used for discretionary trips, for which only substantially inferior public transportation alternatives are available. In contrast to station-based car-sharing, it does not rely on high-quality local public transportation access, but bridges gaps in the existing public transportation network.
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Implementation of free-floating and station-based carsharing in an agent-based travel demand model
Article
  • Feb 2017
The number of carsharing cars and users is growing throughout the world. Although there is a comparatively small share in modal split, carsharing is becoming more important as a supplemental transport mode, especially in metropolitan areas. This implies a need to further develop planning tools, so that this “new mode” can be considered in the planning process. This paper illustrates the integration of carsharing in an agent-based travel demand model that simulates the travel behavior of the population in the greater Stuttgart area for one week. Since it is the first time that carsharing usage is simulated for more than one day, this model permits longitudinal analysis of the intensity and variability of carsharing usage.
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A Spatial Hazard-Based analysis for modelling vehicle selection in station-based carsharing systems
Article
  • Nov 2016
  • TRANSPORT RES C-EMER
Carsharing, as an alternative to private vehicle ownership, has spread worldwide in recent years due to its potential of reducing congestion, improving auto utilization rate and limiting the environmental impact of emissions release. To determine the most efficient allocation of resources within a carsharing program, it is critical to understand what factors affect the users’ behavior when selecting vehicles. This study attempts to investigate the importance of users’ attributes and fleet characteristics on choice set formation behavior in selecting vehicles using a Spatial Hazard Based Model (SHBM). In the SHBM model, “distance to a vehicle” is considered as the prospective decision criteria that carsharing users follow when evaluating the set of alternative vehicles. This variable is analogous to the duration in a conventional hazard-based model. In addition, user socio-demographic attributes, vehicle characteristics, land use type of the trip origin, etc., collected from the Australian carsharing company GoGet are utilized to parameterize the shape/scale/location parameter of the hazard function. A number of forms of parametric SHBMs are tested to determine the best fit to the data. The accelerated failure time model with a Log-logistic distribution was found to provide the best fit. The estimation results of the coefficients of the parameters can provide a starting point for carsharing organizations to optimize their pod locations and types of cars available at different pods to maximize usage.
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Modeling multi-level mechanisms of environmental attitudes and behaviours: The example of carsharing in Berlin
Article
  • Apr 2016
Psychological research practices are often prone to individualistic biases, emphasizing individual-level mechanisms of attitudes, behaviour, and persuasion, while neglecting the dynamics of communication in social networks. We illustrate with our InnoMind simulation model how agent-based modeling as a research method can account for the multi-level interactions between information processing in individual brains and flows of information in societies. InnoMind is based on theories of emotional cognition from cognitive science, theories of attitudes and persuasion from social psychology, and theories of social networks from sociology. In a case study, we show how the model can be used to address practical research questions in environmental psychology: We describe computer simulations with InnoMind that can serve as ex-ante evaluations of suitable campaign strategies for the promotion of carsharing as an innovative means of sustainable urban transportation. We discuss how empirical/experimental versus computational/theoretical research strategies in environmental psychology can and should be regarded as mutually informative.
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Spatial Contexts and Firm Strategies: Applying the Multilevel Perspective to Sustainable Urban Mobility Transitions in Brazil
Article
  • Sep 2015
  • J CLEAN PROD
The Multilevel Perspective on Sustainable Transitions (MLP) has been widely used to explain different patterns of technological, societal, cultural and normative transitions in an integrated and systemic way. This framework has been used to analyze individual transportation based on internal combustion engine automobiles and to develop niches that can challenge the existing dominant regimes in favor of more sustainable urban mobility systems. In a recent and relevant work, Markard et al. (2012) thoroughly discussed MLP, highlighting gaps and research opportunities. One such gap is that much of the research developed using this framework was conducted primarily using European scenarios, omitting various aspects of regional or country diversity, or the role of firm strategies failed to be considered. Therefore, we propose to analyze and compare Brazilian and German case studies regarding sustainable urban mobility transitions. To accomplish this goal, we analyze the diffusion level and different characteristics that explain the current development level of four niches: electro mobility, car sharing schemes, intermodal transportation, and innovation in public transportation. Using the multiple case study method, we compare the sustainable mobility initiatives and innovations undertaken by two German automotive companies in Brazil and in Germany. The results of the research conducted with both companies show that mobility initiatives in Brazil remain very limited. Manufacturers remain much more concerned with selling traditional products (in a much faster growth market than in Germany) than with initiating more aggressive strategies oriented to mobility. Even in their mother countries, mobility innovations can be considered moderate. Our main conclusions are that mobility initiatives in Germany and in Brazil are very different for a number of reasons, such as different pre-existing infrastructures to support new mobility initiatives, public pressure for mobility solutions, different growth patterns concerning car sales and different institutional and legal conditions regarding public and private participation in mobility issues. Therefore, the MLP framework would generate different trajectories and outcomes; in addition, firm strategies should be considered in the framework, particularly in a sector such as the automotive industry, in which firms have considerable influence.
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What About Free-Floating Carsharing?
Article
  • Aug 2015
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.
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Do sharing people behave differently? An empirical evaluation of the distinctive mobility patterns of free-floating car-sharing members
Article
  • May 2015
Characteristics of users and usage of station-based car-sharing services have been discussed in various studies. First analyses of the free-floating car-sharing model DriveNow have shown that member composition and patterns of use are not very different from those of station-based car-sharing schemes. Nevertheless, free-floating car-sharing members were drawn from a new pool of travellers, they were not attracted by existing station-based car-sharing schemes. This paper goes beyond these analyses and looks not only at the usage of car-sharing services but at the overall travel behaviour of free-floating car-sharing members (FFCS). To the best of our knowledge, this is the first time that the specifics of this travel behaviour have been analysed based on substantial data that was collected specifically for this purpose with an innovative survey design based on a GPS tracking smartphone application. The goal of this study is to contrast the core group of members of the free-floating car-sharing model DriveNow (male, 25-45 years old) with people who do not use car-sharing. Key travel indicators are compared for FFCS and non-car-sharers (NCS) with a special emphasis on type and extend of multimodal travel behaviour within those two groups. The results show higher trip frequency for FFCS and differences in mode choice pattern. FFCS are more intermodal and multimodal in their behaviour. Shares of cycling are significantly higher, shares of private car trips are significantly lower for FFCS compared to NCS. The insights gained in this study can help cities and car-sharing operators to develop framework conditions and services that optimally integrate free-floating car-sharing services into the overall urban transport systems.
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