# Exploring a System Dynamics Approach to Develop Shared-Mobility Services Models: A Literature Review

Danilo Ribeiro, Lúcio Galvão Mendes, Fernando Antônio Forcellini, Mauricio Uriona-Maldonado

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Danilo Ribeiro, Lúcio Galvão Mendes, Fernando Antônio Forcellini, Mauricio Uriona-Maldonado. Exploring a System Dynamics Approach to Develop Shared-Mobility Services Models: A Literature Review. 19th IFIP International Conference on Product Lifecycle Management (PLM), Jul 2022, Grenoble, France. pp.463-473, 10.1007/978-3-031-25182-5_45. hal-05420949

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# Exploring a System Dynamics Approach to Develop Shared-Mobility Services Models: A Literature Review

Danilo Ribamar Sá Ribeiro<fnref n="1" />✉, Lúcio Galvão Mendes<fnref n="1" />✉, Fernando Antônio Forcellini<fnref n="1" />,<fnref n="2" />✉, and Mauricio Uriona-Maldonado<fnref n="1" />✉

**Abstract.** Shared Mobility Services (SHMS) are business models (BMs) that act as sharing economy platforms commonly based on mobile applications. They are conceived in a complex landscape characterized by the presence of information and the widespread presence of smart devices. In this sense, decision-makers, system developers, maintainers, and providers try to understand the interrelationships within this framework in terms of policy evaluation and/or impact on its successful adoption, analysis of all aspects of SHMS that affect the environment such as understanding patterns, user behavior, and improving the quality of this BM. To do this, models are built to capture the complex dependencies and interactions of these variables to guide decision-making. In this context, System Dynamics (SD) emerges as a useful approach to manage and capture the complexity/causality relationships of SHMS variables. Based on this, it developed a literature review to verify which research has applied SD to explore SHMS, analyzing existing models, their methods, variables, and impacts, as well as directions for future research. From a practical point of view, it is hoped that this research can be used as a guideline for urban planners and SHMS providers.

**Keywords:** Shared mobility services · System dynamics · Modeling

## 1 Introduction

Shared Mobility Services (SHMS) act as sharing economy platform commonly based on mobile applications, allow on-demand usage [16], and are conquering urban centers due it is beneficial for the Sustainable Development Goals (SDG 11) by promoting collective consumption behavior [37]. SHMS is part of the transportation system and also has some mutual influences, but it is difficult to estimate its impact on this system [17, 37]. Conceived in a complex scenario characterized by the presence of information and the wide presence of intelligent devices, understanding the interrelationships within the

<footnote n="1">¹ Graduate Program in Production Engineering, Federal University of Santa Catarina, Florianópolis, SC 88040-900, Brazil
danilo_saribeiro@hotmail.com</footnote>

<footnote n="2">² Department of Mechanical Engineering, Federal University of Santa Catarina, Florianópolis, SC 88040-900, Brazil</footnote>
structure of this system provides an aggregate strategic view on the analysis of all aspects of SHMS that affect the total environment, which is crucial for the respective decision-makers, system developers, maintainers and providers [5, 34]. The use of simulation and modeling techniques is therefore considered necessary to conduct a consistent analysis and evaluation of different SHMS business models (BMs) in urban environments [9]. These can guide city planners to develop responsive policy interventions and service providers to develop SHMS [34]. In this context, System Dynamics (SD) emerges as a useful approach to manage and capture the complexity/causality relationships of variables [5, 6] that characterize many SHMS-related problems [34].

The interest in SD in the context of mobility is due to the growing challenges of intelligent configuration to understand and analyze the interactions between a set of dynamic factors that shape the patterns, behaviors, and impacts of these BMs [38]. [7] states that SD modeling aids in the construction of causal feedback relationships and consequent conceptual models, which can elucidate the behavior of various complex problems of a system or subsystems. This approach has been used in various applications and studies related to SHMS, as it is a potential tool for explaining the complex relationships of these systems [15, 29]. To understand the complexity of SHMS, new holistic and dynamic models of transportation demand must be developed [24].

In order to fill this gap, this paper aims to verify how SD has been used as an analysis tool for SHMS. More specifically, this paper answers the following questions: 1) Which studies have proposed dynamic models to understand the complexity of SHMS? 2) What are the main causal relationships and impacts studied by the models? 3) What are the main research directions on the application of SD in SHMS?

# 2 Research Background

## 2.1 Shared Mobility Services as a System

SHMS is a representation of a sharing economy, characterizing a BM that is linked to an emerging transportation strategy, smart urban infrastructure, and sustainable from social, economic, and environmental perspectives [5, 28]. It allows users to have short-term access to a transport mode as needed [37]. This new paradigm of a technology-mediated exchange system that promotes the sharing of underutilized assets, with no direct purchase and maintenance costs involved, and reduced fuel and parking expenses, is thus considered an economical alternative to car ownership [1].

SHMS are classified as a usage-driven product-service system (PSS), in which service providers sell the accessibility and use of specific products, retain ownership, and are responsible for the maintenance and performance of the products [14]. PSS is understood as a BM that combines products and services into a system that provides functionality for consumers [32]. Today, PSS become challenging in the context of Industry 4.0, leading to the integration of these BMs with digital technologies promoting digitalization and servitization [23]. Followed by the rapid progress of digitalization, SHMS has become an important and multifaceted topic [28, 37]. PSS and smart connected products are, from the companies' side, driving a market transition from the sale of products towards the sale of the use of solutions, and, from the customers' side, reshaping the concept of value [26]. Given that SHMS has a wide range of benefits, such as: reducing private
vehicle use and congestion, providing cost savings through economies of scale, reducing emissions through the deployment of clean technology and fuels, facilitating more efficient land use (e.g. by reducing the number of required parking spaces), and increasing mobility options and connectivity between other modes of transportation [3, 12].

SHMS includes sharing of a vehicle, a passenger ride, or a delivery ride [27]. Car-sharing, scooter sharing, and bike-sharing are services that enable the sharing of a vehicle. Ridesharing, on-demand ride services (including taxi e-Hail and ride-sourcing or transportation network companies - TNCs), and micro transit facilitate the sharing of a passenger ride. Finally, CNS enables the sharing of a delivery ride (i.e., a ride for cargo) [27, 37]. In addition, it can include sequential sharing, simultaneous sharing, and essential services [28]. The sharing modes are classified into Round-trip, One-way (one-trip, with an undetermined end), Peer-to-peer (users interact with each other), Business-to-peer, Station-based (the user can pick up and return the vehicle at fixed stations), and Free-floating [27].

## 2.2 System Dynamics

SD was developed by Jay Forrester in the 1960s at the Massachusetts Institute of Technology (MIT). It is defined as a set of conceptual tools used to study and understand the structure and dynamics of highly complex systems based on the foundation of feedback control theory [7, 29]. This approach deals with internal feedback loops and attractions that affect the behaviour of the system as a whole. Through various types of diagrams (causal; stock and flow), it is possible to graphically express a system (a well-delimited piece of an event) making it possible to see more clearly the dynamic complexity (over time) and the relationships between stakeholders [7].

Thus, SD seeks to simulate the behavior of the system over time, representing the relationship between identified key variables and is used for improvement-oriented decision-making or a better understanding of complex systems [29]. As a methodology, SD relies on both quality (e.g., survey and interview methods) and quantitative techniques (e.g., computer programming and simulation), emphasizes stakeholder involvement (to define mental models within the system), and encourages the researchers themselves to adopt a non-linear mental model (to seek and describe the feedback processes of a dynamic problem) [33].

[29] proposes a procedure that comprises five steps: (i) Identification and definition of the problem, representing the historical pattern to be described with the mathematical equations, what are its boundaries, the time horizon of the analysis, and the expected behavior of the system over time; (ii) Construction of the causal loop diagram (CLD), allow to map the mental models when analyzing systems, generate hypotheses about the causes of the dynamics of the system and communicate feedbacks considered responsible for the problem under analysis; (iii) construction of the simulation model to test the dynamic hypothesis, the stock and flow diagram (SFD) is drawn up and validation tests are performed; (iv) analysis of the experiments; (v) policy formulation and evaluation, used to better understand the roles and relative importance of model parameters in generating historical trends by creating entirely new strategies, structures, and decision rules.
## 3 Methodology

The method used in this research was conceptual-theoretical, based on a systematic literature review. This methodology promotes the collection and evaluation of knowledge through the theoretical synthesis of existing literature from a systematic sequence of steps and reproducible [31]. It used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method that consists of the following steps: a) formulation of research questions; b) selection of databases and identification of keywords; c) evaluation and selection of studies; and d) analysis and synthesis [20]. The search term used to map the articles was: ("smart mobility*" OR "Shared mobility*" OR "carsharing" OR "scooter sharing" OR "bike-sharing" OR "on-demand ride services" OR "ride-sourcing" OR "ridesharing") AND ("System Dynamics" OR "Behavio?r*" OR "dynamic model*" OR "Stock-flow diagram" OR "causal loop diagram"). The choice of words was based on [27, 29] and defined from the research questions. This SLR is based on articles collected from the following databases: Scopus™ (Elsevier), Web of Science Core Collection™ (WoS), and Compendex - Engineering Village. Search engines were delimited to verify works related to the scope of this research. The inclusion and exclusion criteria used in the databases are shown in Table 1.

Figure 1 describes the process of inclusion and exclusion of papers during the evaluation and selection of studies. A total of 18 articles met the selection criteria and represent the bibliographic portfolio of this research. The synthesis of the results is a thematic analysis, a common technique for qualitative data, which comprises the organization of the studies into guiding axes for research [21].

<table><caption>**Table 1.** Inclusion and exclusion criteria.</caption><thead><tr><th>Criteria</th><th></th><th>Explanation</th></tr></thead><tbody><tr><td>Inclusion</td><td></td><td>The research efforts are related to studies that focused on SD in SHMS<br>Document type: English peer-reviewed articles and conferences<br>Time: Until February 2022</td></tr><tr><td rowspan="5">Exclusion</td><td>CE1</td><td>Papers that are not published in peer-reviewed journals or conferences</td></tr><tr><td>CE2</td><td>Papers are written in a language other than English</td></tr><tr><td>CE3</td><td>Duplicate papers</td></tr><tr><td>CE4</td><td>Documents that are not available online</td></tr><tr><td>CE5</td><td>It is used only as an example fact/ a part of research direction and future perspective/ a cited expression/ in keywords and/or references</td></tr><tr><td>CE6</td><td>Non-adequacy to the scope of this study (papers applied other types of modeling/ simulation in SHMS)</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Fig. 1. PRISMA flowchart characterizing the search process.</figcaption></figure>

# 4 Results and Discussion

Based on the methodology adopted, included 18 studies in the analysis to answer the research questions.

**RQ1. Which Studies Have Proposed Dynamic Models to Understand the Complexity of SHMS?** In this section, it will present the results according to the SHMS taxonomy provided by [27]. Most of the papers present models focusing on vehicle sharing, such as cars and bicycles as shown below. A few of the papers have focused on passenger sharing. Others discussed the general context of the theme.

**Sharing of a Vehicle:** [5] have built a CLD that shows the interconnections between carsharing, the car manufacturing industry, the environment, and key environmental and transportation regulations. [8] constructed a CLD that includes attributes that influenced the success of carsharing in Thailand and possible policy interventions through a group modeling building approach (GMB). [15] built an SD model to evaluate the long-term effects of e-carsharing policies on CO₂ emissions, the electric vehicle market in Fortaleza, and the conventional vehicle fleet. [39] presented an SD model to simulate the effect of introducing time-sharing electric vehicles in changing the user quantities in transportation tools, including public and private sectors, under different levels of government subsidies. [2] developed an SD model to analyze the adoption of self-consistent Connected and automated vehicles (CAV). [10] developed a conceptual SD model to analyze cybersecurity in the complex and uncertain deployment of CAVs. [36] developed an SFD that presents the variables used to model a dockless bike-sharing program (DBSP) and the causal relationships or linkages between them. The analysis focused on the economic profits of DBSPs in an environment of competition and government regulation. [35] developed an evolutionary game theory model combined with SD to demonstrate the interactive conflict between bike-sharing companies and government regulation. [30] developed an SD model based on the product life extension BM structure and business practices of Free-Floating bike-sharing in Beijing. [18] developed an SD
model of commuter bicycling, through interviews and workshops with stakeholders, to simulate policy scenarios over the next 40 years in Auckland, New Zealand.

**Shared of a Passenger Ride:** [19] developed an SD model based on diffusion models to understand the influences that facilitate the growth of ridesharing by drivers and passengers. Using a sociotechnical systems-based SD model, [11] analyzed and predicted the market share between SHMS and cab services under three possible regulation scenarios to explore the relationship between innovation and information and communication technology (ICT) regulation. [22] modeled through an SFD the relationship between the dynamics of vehicle-hours-traveled (VHT), the vehicle-hours-dispatched (VHD), and the vehicle-hours-parked (VHP) using a shared mobility-on-demand Chicago cab system.

**Generic Approaches:** [25] explored the role of SHMS in activating SD of feedback loops in the context of sustainability transition. [13] analyzed the motorization process in China and then an SD model is designed for scenario analysis of urban traffic conditions and CO₂ emissions. [34] designed um a CLD based on causal feedback relationships between the various factors related to people's different mobility needs from the perspective of big data. [6] presented a decision support model using the SD method to evaluate urban mobility in Brazilian cities with policy influence. [4] evaluated the performance of several factors and attributes that influence a smart city: economy, mobility, and governance system, and recorded the stakeholders' views, for this they developed conceptual models of applied SD.

**RQ2. What are the Main Casual Relationships and Impacts Studied by the Models?** The papers included in this review are categorized into different aspects of SD, as shown in Table 2. The main variables of the models and their causal relationships from the papers are mentioned. Causality relationships are part of the representation of the relationship between two variables [29]. In addition, it classified the impacts of the models from social, economic, and/or environmental perspectives. In brief, impacts included policy/regulatory evaluation, subsidies, cost savings and convenience, reduction of vehicle miles traveled (VMT) or vehicle kilometers traveled (VKT), travel patterns and behavior as well as reduction of personal vehicle ownership as well as reduction of pollutant emissions such as CO₂. Most studies focus on the national or regional wideness of the results' impact. The main tools/ techniques for the construction and calibration of the models were research project [19], case study [4, 6, 15, 22, 34, 36], focus group and questionnaires [8, 13, 18, 19], series of searches of published literature [2, 5, 18, 19], primary datasets [2], historical data from the authoritative (secondary datasets) [6, 13, 15, 18, 22, 34]. Most studies developed the SD model and performed scenario analysis in software. In complementary SD, [13] performed a Sensitivity analysis and [35] performed an Evolutionary game analysis.

**RQ3. What are the Main Research Directions on the Application of SD in SHMS?** The authors referred to limitations and challenges associated with using SD modeling within the context of the problem being addressed. From the analysis of the final portfolio, it was possible to identify the predominant future directions:
<table><caption>**Table 2.** The main causal relationships and impact models of the studies.</caption><thead><tr><th>Authors</th><th colspan="2">Main causal relationships</th><th>Impact</th><th>Tool</th><th>Wideness</th></tr></thead><tbody><tr><td>[19]</td><td>Ridesharing adoption</td><td>Market (drivers and passengers)</td><td>Social, Economic</td><td>Vensim</td><td>Regional</td></tr><tr><td>[2]</td><td>Adoption of CAVs</td><td>Energy Implications</td><td>Social, Environmental</td><td>Stella</td><td>National</td></tr><tr><td>[18]</td><td>Increase bicycle commuting in a car-dominated city</td><td>Effects of policies</td><td>Social, Economic</td><td>Vensim</td><td>Regional</td></tr><tr><td>[13]</td><td>Urban traffic conditions</td><td>CO<sub>2</sub> emissions</td><td>Social, Economic, Environmental</td><td>-</td><td>National</td></tr><tr><td>[34]</td><td>Different needs of people's mobility (smart mobility)</td><td>Optimizing decision making</td><td>Social, Economic</td><td>Vensim</td><td>Regional</td></tr><tr><td>[6]</td><td>Urban transport system</td><td>Environmental, economic, and traffic variables</td><td>Social, Economic, Environmental</td><td>Stella, iThink, PowerSim and Vensim</td><td>Regional</td></tr><tr><td>[22]</td><td>Taxi cab system</td><td>VHP, VHT, and VHD defined as a cost</td><td>Social, Economic</td><td>-</td><td>Regional</td></tr><tr><td>[36]</td><td>DBSPs</td><td>Maximizing the revenue</td><td>Economic</td><td>Vensim</td><td>Regional</td></tr><tr><td>[4]</td><td>Development of smart mobility</td><td>Access through ICT</td><td>Social, Environmental</td><td>-</td><td>Regional</td></tr><tr><td>[5]</td><td>Carsharing Services (considering the lifecycle of a shared vehicle)</td><td>Their environmental effects</td><td>Social, Environmental</td><td>Vensim</td><td>Global</td></tr><tr><td>[15]</td><td>E-carsharing</td><td>Electric vehicle adoption and CO<sub>2</sub> emissions</td><td>Social, Environmental</td><td>Stella</td><td>Regional</td></tr><tr><td>[35]</td><td>The conflict-handling strategy of bike-sharing companies</td><td>Government regulation</td><td>Social, Economic</td><td>Vensim</td><td>National</td></tr></tbody></table>

(continued)
<table><caption>**Table 2.** (continued)</caption><thead><tr><th>Authors</th><th colspan="2">Main causal relationships</th><th>Impact</th><th>Tool</th><th>Wideness</th></tr></thead><tbody><tr><td>[39]</td><td>Development of Time-Sharing Electric Vehicles</td><td>Subsidy implications</td><td>Social, Economic, Environmental</td><td>Vensim</td><td>Regional</td></tr><tr><td>[8]</td><td>Carsharing operation</td><td>Viewpoints and expectations of stakeholders</td><td>Social, Economic</td><td>-</td><td>Regional</td></tr><tr><td>[30]</td><td>The business practices of DBSP</td><td>The product Lifetime extension</td><td>Social, Environmental</td><td>Vensim</td><td>Regional</td></tr><tr><td>[10]</td><td>Deployment of CAVs</td><td>Cybersecurity assessment</td><td>Social, Economic</td><td>-</td><td>Global</td></tr><tr><td>[11]</td><td>SHMS and taxi services</td><td>Regulation and the social benefits of mobility users</td><td>Social, Economic</td><td>AnyLogic</td><td>National</td></tr></tbody></table>

* Discuss the role of government in sustainable mobility [15]. Also, to be conducted on the interconnections between various modes of transportation and their relevant environmental effects on SHMS [5];

* Design and implement responsive policies according to the implemented scenarios in models [34];

* Explore the role of upscaling dynamics and feedback loops for the implementation of sufficiency-oriented transport policy measures [25];

* Explore the effectiveness of the models of the final portfolio, towards further validation of the parameters, and the refinement of the models, including new variables representing other facets of the problem or additional feedback loops, could be added [2, 11, 15, 19, 34, 39];

* Use methodologies that involve stakeholders' alignment to build SHMS-related mental models [8];

* Apply questionnaires to obtain the joint probability distribution of cost-convenience for users [39];

* Need for more studies about the urban transport system using the SD approaches [6, 18]. According to this review, most SD models focus on sharing vehicles (only cars and bikes) or passengers. In this sense, there is an opportunity to develop SD models to understand scooter sharing (adoption and its impacts) and model various categories of these BMs in combination [5].

* Cover a variety of SHMS focusing on product lifetime and its five key activities (improved product design, access, maintenance, redistribution, and recovery) [30].
# 5 Conclusion

This paper provides a comprehensive review of research that has applied SD to explore SHMS. A carefully chosen set of keywords was used to discover relevant studies. Following PRISMA guidelines, it performed a content analysis based on three research questions: Which studies have proposed dynamic models to understand the complexity of SHMS, what are the main casual relationships and impacts studied by the models, and what are the main research directions on the application of SD in SHMS. To answer these questions, the included records have been reviewed, and the essential aspects of SD models and the future directions of this thematic were defined. Based on the previously mentioned perspectives, it concludes that SD presents to be an appropriate approach to capture the causal relationships among variables in SHMS that is part of such a complex system as urban transportation [5]. These studies have theoretical and practical implications by contributing to the exploration of the applicability of SD and to the identification of value and main controls for assertive decision-making to get more consistent BMs of SHMS and to analyze and assess its multiple influences on the urban environment [19]. Furthermore, most studies analyze policies focused on the adoption of positive externalities of SHMS, highlighting efforts in the social, economic, and environmental spheres. In sum, this research presents itself as a useful starting point for researchers, practitioners, and stakeholders when it comes to this topic.

**Acknowledgments.** The authors acknowledge the Coordination for the Improvement of Higher Education Personnel (Brazil) – CAPES | Finance Code 001.

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