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Article

Sustainability Assessment Framework for Urban Transportation Combining System Dynamics Modeling and GIS; A TOD and Parking Policy Approach

by
Ahad Farnood
1,*,
Ursula Eicker
2,*,
Carmela Cucuzzella
3,
Govind Gopakumar
4 and
Sepideh Khorramisarvestani
5
1
School of Graduate Studies, Concordia University, Montreal, QC H3G 1M8, Canada
2
Canada Excellence Research Chair Next Generation Cities, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC H3G 1M8, Canada
3
Faculté de l’aménagement, Université de Montréal, Montreal, QC H3T 1J4, Canada
4
Centre for Engineering in Society, Concordia University, Montreal, QC H3G 1M8, Canada
5
Department of Geography, Planning and Environment, Concordia University, Montreal, QC H3G 1M8, Canada
*
Authors to whom correspondence should be addressed.
Smart Cities 2025, 8(4), 107; https://doi.org/10.3390/smartcities8040107
Submission received: 31 March 2025 / Revised: 22 May 2025 / Accepted: 30 May 2025 / Published: 30 June 2025
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Abstract

Highlights

What are the main findings?
  • The integrated System Dynamics and GIS framework can measure temporal feedback loops while considering spatial metrics (transit accessibility, parking supply) as input and output data to assess urban planning indicators.
  • Scenario analyses indicate that transit-oriented development and parking policies can reduce car dependency and emissions. Also, limiting parking availability around transit nodes while investing in active transportation infrastructure significantly shifts travel behavior toward more sustainable modes.
What is the implication of the main finding?
  • This paper’s model shows where planning policies should intervene in urban development. It combines TOD, parking limits, and active travel improvements to create feedback loops that support low-carbon mobility.
  • The SD–GIS framework offers a scalable decision-support tool for urban planners, enabling them to model location-specific interventions and their long-term impacts on sustainability.

Abstract

Urban transportation systems face increasing pressure to reduce car dependency and greenhouse gas emissions while supporting sustainable growth. This study addresses the lack of integrated modeling approaches that capture both spatial and temporal dynamics in transport planning. It develops a novel framework combining System Dynamics (SD) and Geographic Information Systems (GIS) to assess the sustainability of Transit-Oriented Development (TOD) strategies and parking policies in two brownfield redevelopment sites in Montreal. The framework embeds spatial metrics, such as proximity to transit, parking availability, and active transportation infrastructure into dynamic feedback loops. Using scenario analysis, the study compares a baseline reflecting current norms with an intervention scenario emphasizing higher density near transit, reduced parking ratios, and improved walkability and bike infrastructure. The results suggest that aligning TOD principles with targeted parking limits and investments in active mobility can substantially reduce car ownership and emissions. While primarily conceptual, the model provides a foundation for location-sensitive, feedback-driven planning tools that support sustainable urban mobility.

1. Introduction

Urbanization has accelerated rapidly in recent decades, posing significant challenges for sustainable transportation planning [1,2]. As cities expand, the interplay between urban form and travel behavior becomes increasingly complex, influencing accessibility, mode choice, and environmental outcomes [3,4]. Transit-Oriented Development (TOD) has emerged as a widely endorsed strategy to reduce car dependency by promoting compact, mixed-use, and walkable neighborhoods centered around high-capacity transit infrastructure [5,6]. However, the effectiveness of TOD and other sustainable transportation strategies depends on a comprehensive understanding of how urban form interacts with transportation policies [7].
System Dynamics Modeling (SDM) has been instrumental in capturing the dynamic, feedback-driven nature of urban systems, allowing for the simulation of policy interventions over time [8]. Yet, conventional SDM approaches often lack spatial features, limiting their ability to account for geographic variations in land use, transit accessibility, and infrastructure availability. On the other hand, Geographic Information Systems (GIS) provide essential spatial insights but are seldom integrated into system dynamics frameworks. This disconnect restricts the capacity of planners to develop data-driven, location-sensitive strategies that optimize urban mobility and sustainability [9]. The lack of an integrated approach that considers both system dynamics and spatial attributes in transportation modeling remains a fundamental gap in urban planning research.
To address this gap, this study develops an integrated System Dynamics-GIS framework for assessing the sustainability of urban transportation systems. By integrating GIS-based spatial metrics into SDM, this approach enhances traditional system dynamics modeling by incorporating location-based variables. The framework is designed to evaluate key policy interventions, including TOD strategies and parking regulations, which play a critical role in shaping travel behavior [6,8,10]. Parking supply, in particular, remains an underexplored yet powerful determinant of vehicle ownership and mode choice, influencing the extent to which cities can transition away from car dependence [11,12]. This research applies the proposed framework to two case studies in Montreal, Bridge-Bonaventure and Lachine-Est, both undergoing brownfield redevelopment with a focus on sustainable mobility. Through scenario simulations, this study examines how variations in density distribution, transit investment, and parking policies influence travel behavior, vehicle ownership, and greenhouse gas (GHG) emissions. By incorporating both dynamic system feedback and spatial features, the proposed framework offers a more comprehensive understanding of how urban form and transportation policies interact to support sustainable development.
This paper proceeds as follows. First, the literature on sustainable urban mobility and integrated SD–GIS modeling is reviewed to establish the study’s conceptual foundation. This is followed by a detailed explanation of the methodological framework. It then introduces the methodological framework and outlines the research questions and objectives in Section 3.1. The subsequent sections describe the model structure, present the two Montreal-based case studies, and analyze scenario outcomes. The discussion reflects on the policy implications of the findings, and the paper concludes with limitations and future research directions.

2. Literature Review

Efforts to create more sustainable urban transportation systems have gained momentum in recent decades, driven by growing concerns about traffic congestion, air pollution, and rising greenhouse gas emissions [13,14]. Initially, urban development patterns were characterized by suburban sprawl, which encouraged extensive car use and created long travel distances [15,16]. In the late twentieth century, researchers and policymakers began to advocate for strategies like “smart growth” and “new urbanism”, emphasizing compact, walkable neighborhoods with mixed land uses and access to transit [13,15,17,18]. These movements aimed to reduce reliance on cars, promote public transit, and encourage more walking and cycling [17,18,19,20,21]. Over time, additional frameworks emerged; like TOD and the complete streets concept, aiming to design more livable cities by organizing growth around transportation hubs [15,22]. While these approaches have pushed the conversation on sustainable mobility forward, the complexity of urban environments still poses challenges for planners and researchers, which underscores the need for integrated models and tools that account for the many factors shaping travel behavior across both time and space [17,21].
One of the most widely recognized insights in transportation research is that urban form plays a crucial role in shaping mobility behavior [23,24]. Studies have repeatedly shown that higher density, a diverse land-use mix, and pedestrian-oriented street designs are associated with lower car dependency, more active travel, and increased transit ridership [25,26,27]. Density, for instance, can reduce the average distance between origins and destinations, making walking or cycling more feasible for everyday trips [2,25]. Similarly, a well-balanced mix of land uses enhances accessibility, making it easier for people to access daily needs, often without needing a private vehicle [25,28]. However, these relationships are not linear, as they involve complex feedback loops wherein parking availability, perceived safety, and infrastructure quality all shape individuals’ mode choices [16,26]. Understanding how these variables intersect, and how policy interventions like parking regulations or transit fare changes can shift travel behavior, is crucial for sustainable urban mobility [8,26].
SDM offers a valuable framework for analyzing the dynamic and interrelated components of urban transportation systems, especially where feedback loops play a decisive role in shaping long-term outcomes [29,30]. Originating from systems theory, SDM uses stocks, flows, and causal loop diagrams to represent how changes in one part of a system affect other interconnected variables over time [31]. In transportation research, SDM has been employed to explore phenomena such as congestion growth, mode-shift processes, and transit funding [8,29,32]. By capturing these cyclical interactions, SDM facilitates scenario testing, enabling researchers to forecast how specific interventions, like increasing road capacity or investing in public transit, could unfold over decades [33,34]. Despite its strengths, traditional SDM commonly lacks spatial resolution, treating location-specific factors (e.g., proximity to transit stations, land-use configuration) as uniform or aggregated [17,35]. This gap suggests the potential for enriching SDM by incorporating geographic data that reflect the differing spatial realities within and between urban regions [17].
Geographic Information Systems (GIS) have become essential for understanding and managing the spatial complexities of urban areas, particularly in the realm of transportation [35]. By offering georeferenced data on road networks, land-use patterns, and proximity to amenities, GIS tools reveal how small variations in location can substantially influence travel choices [33,36]. For example, a neighborhood’s walkability score or the percentage of residents within a given distance of a transit station can be accurately quantified through GIS-based spatial analysis [37]. Studies in this domain highlight how seemingly modest geographic shifts (such as an additional two or three blocks from a rail stop) can dramatically reduce the likelihood of public transit use [38]. Moreover, GIS approaches have been used to identify “transit deserts”, areas where residents lack practical access to frequent or reliable public transportation, and to propose targeted interventions, including new bus routes or bicycle-sharing stations [36,39]. While these spatially explicit methods provide valuable policy insights, they often act as static snapshots, identifying where resources are needed but failing to capture the evolving feedback loops, such as how transit improvements might gradually reshape land use and travel behavior over time. [33,34]. This gap underscores the potential synergy between GIS-based metrics of accessibility and land use, and the more dynamic feedback-oriented analyses facilitated by system dynamics modeling [35,40].

Integration of System Dynamics and GIS in Urban Sustainability Assessment

Recent advancements in urban sustainability assessment emphasize the need for integrated modeling approaches that capture both temporal dynamics and spatial variations. SDM and GIS have emerged as powerful tools to achieve this integration, allowing researchers to explore complex feedback mechanisms and spatial interactions in urban systems.
Pokharel and colleagues employed a two-stage SD modeling approach to investigate car dependency and sustainable mobility policies [41]. Their work highlights the importance of understanding feedback loops and leveraging path dependencies to shift urban transport systems towards sustainability. This approach aligns with the current study’s objective of exploring the dynamic interactions between urban form, travel behavior, and sustainability indicators, particularly in brownfield redevelopment projects [41].
Z. Xu & Coors proposed a GISSD (Geographical Information System System Dynamics) system that integrates SD modeling, GIS spatial analysis, and 3D visualization for urban sustainability assessment. Their study demonstrates how spatial metrics, dynamic simulations, and visualizations can provide a holistic view of urban systems. This integrated framework directly informs the present study’s methodology, which utilizes SD modeling to capture temporal dynamics and GIS tools to quantify spatial variations in urban form and accessibility [34].
Additionally, the work by AlKhereibi and colleagues highlights the significance of feedback loops between land-use policies and travel behavior, influencing sustainability outcomes [42]. Their findings underscore the necessity of system-level thinking and dynamic modeling to capture the interdependencies among urban form, policy interventions, and travel patterns. These insights are particularly relevant to the present study’s focus on TOD principles and proximity planning [42].
Collectively, these studies provide a solid theoretical foundation for integrating SD and GIS in urban sustainability assessment, validating the current study’s methodological framework. They highlight the value of feedback loops, dynamic causal relationships, and spatial–temporal analysis, supporting the development of policy interventions for sustainable urban mobility and development. Table 1 presents a comprehensive compilation of indicators derived from the literature, categorized across key dimensions of sustainable urban development, transportation systems, and mobility patterns. By synthesizing data from multiple studies, this table establishes a foundation for assessing the interactions between urban form, travel behavior, and sustainability outcomes, guiding the development of the proposed SDGIS framework.
Table 1 summarizes key indicators of sustainable urban mobility as identified in a range of recent studies [43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64]. These references, drawn from the literature review, covering built environment, transportation, and socio-economic dimensions.
Table 1. Table of indicators extracted from the literature. An “X” indicates that the respective study included or discussed the corresponding indicator.
Table 1. Table of indicators extracted from the literature. An “X” indicates that the respective study included or discussed the corresponding indicator.
CategoryIndicatorsReferences
[43][42][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64]
Built
Environment		DensityXXXXXXXXXXXXXXXXXXXXXX
Mixed land usesXX XXXXXXXXXXXXXXXXXXX
DesignXXX X X X XXX X X
AccessibilityXXXX XXXXXXXXXXXXXXXXX
ProximityXXX X X XXXXXXXXXXX
WalkabilityXX XXXXXXX X XXXXXXXX
BikeabilityX X X XXX
Block size X X X X X X
Green space/public space ratioXX XXXX XXXX X
TransportationTransportation networkXX XXXXXX XXXXXXXXXXX
Distance to transitXXXXXXXXXX XXXXX XXXX
Public transit accessibility (travel time to key destinations)XXXXXXXXXX XXXXXXXXXXX
Frequency and reliability of transit servicesXX XX XXXX X X X X
Number of transit stations/stops nearbyXX X XXXX XXXXX XXX
Ridership levelsXX X XXX X XXX
Parking facilities and demand managementXX XX X X X X X
Walking infrastructure (walking routes)XX XX X XXX X XXXX
Cycling infrastructure (bike lanes, bike-sharing systems)XX XXXX X XX X XXXX
Traffic congestion and road safetyXXXXXXXX XX XXX XX X
Socio-
economic		Age distribution XX X X X
Ethnicity and cultural diversity X X X
Vehicle ownership ratesXXX X X XX X
Education level X X
Household income and affordabilityXXXXX XXX XX XXX
Employment accessibility X XX X XXXXXXX XXX
Social equity in transport services X X X

3. Methodological Framework

Understanding the complexities of urban systems and their sustainability requires a robust and integrative approach. This section presents the methodological framework developed in this study, combining SD modeling with GIS analysis. The framework is designed to capture the temporal dynamics and spatial characteristics of urban form, travel behavior, and sustainability outcomes. By integrating these two complementary methods, the framework provides a powerful tool for exploring causal relationships, assessing policy impacts, and informing urban planning decisions.

3.1. Research Question and Objectives

This study seeks to answer the following research questions:
How can an integrated System Dynamics (SD) and Geographic Information Systems (GIS) framework be utilized to assess the sustainability of urban transportation systems, particularly in brownfield redevelopment projects?
To address this question, the study pursues the following objectives:
  • Develop a conceptual SD-GIS framework that combines dynamic feedback modeling with spatial analysis to evaluate the impacts of urban form, transit accessibility, and parking policies on travel behavior and sustainability outcomes.
  • Identify key sustainability indicators relevant to urban mobility by extracting and re-fining variables from the literature and case studies.
  • Explore the interactions between land use, parking regulations, and transit investments in shaping travel behavior and greenhouse gas (GHG) emissions, using a qualitative system modeling approach.
  • Apply the proposed framework to two case studies (Bridge-Bonaventure and Lachine-Est) to conceptually assess how variations in density, transit accessibility, and parking policies influence mode share and vehicle ownership trends.
  • Generate qualitative policy recommendations based on the framework’s insights, providing guidance for integrating TOD principles, parking management, and active transportation infrastructure in sustainable urban development.
While this study does not present a fully quantified simulation, it establishes a methodological foundation for future research by integrating spatial dynamics with system feedback analysis. The insights generated serve as an initial step toward a more comprehensive quantitative evaluation in future applications of the SD-GIS framework.

3.2. Design Thinking: Applying Divergent–Convergent

This study initially employed an iterative Diverge–Converge approach from Design Thinking [65] to explore and refine potential directions and model components, which ultimately led to the formulation of the final research question. The process began with a broad literature review on sustainability assessment in urban development and transportation, which identified a wide range of potential indicators (divergence). These indicators were then filtered and structured into Causal Loop Diagrams (CLD), capturing key feedback mechanisms (convergence). Figure 1 illustrates the Diverge–Converge approach used during the early stages of conceptual model development, inspired by Design Thinking.
Through multiple refinement cycles, these indicators were further adjusted to fit the Stock and Flow Diagrams (SFD), ensuring a well-calibrated SD–GIS framework. This iterative process allowed for a continuous evolution of the model, integrating both spatial and dynamic aspects of urban sustainability. The full list of spatial and behavioral indicators used in the SD–GIS framework is provided in Appendix A.

3.3. Modeling

The overall workflow of the methodological framework is depicted Figure 2. It outlines the sequential steps, starting from the identification of gaps in existing literature, followed by the selection of key variables, development of causal loop diagrams (CLDs), construction of stock and flow diagrams (SFDs), and analysis of causal relationships. The integration of SD and GIS tools forms the core of this methodology, allowing for a detailed and multi-dimensional exploration of sustainability in urban systems.
The proposed methodological framework integrates SD modeling and GIS-based spatial analysis to assess urban transportation sustainability dynamically and spatially. This framework addresses gaps identified in existing sustainability assessment studies by:
  • Capturing the temporal and feedback relationships among urban form, travel behavior, and sustainability indicators (e.g., greenhouse gas emissions, active transportation share).
  • Quantifying spatial metrics, such as accessibility and proximity, to incorporate location-specific dynamics and spatial variations.
  • Providing a tool for policymakers to evaluate the implications of urban development strategies, such as TOD and investments in active transportation infrastructure.
SD modeling captures dynamic causal relationships within urban systems, such as feedback loops between travel behavior, urban density, and sustainability metrics. It enables simulations to explore how changes in urban form and transit accessibility influence travel patterns and sustainability outcomes over time [66].

3.4. Integrating System Dynamics and GIS

GIS tools complement SD modeling by quantifying spatial variations in urban form, proximity to transportation infrastructure, and accessibility. These spatial insights enhance the framework by providing proximity-based measures (e.g., distance to transit hubs, walkability) and visualizing the spatial distribution of sustainability indicators [67,68].
Together, this framework provides a comprehensive approach to understanding the temporal and spatial dynamics of urban transportation systems, offering actionable insights for designing sustainable and transit-oriented urban environments.
Figure 3 illustrates the conceptual workflow of the proposed methodological framework, which integrates Geographic Information Systems (GIS) and System Dynamics (SD) modeling to assess urban sustainability. This workflow highlights the sequential and interconnected processes involved in analyzing urban form, travel behavior, and sustainability indicators.
  • Urban Form Data (GIS): The framework begins with the collection and processing of spatial data related to urban form metrics such as land-use mix, density, street connectivity, and proximity to transportation infrastructure. These datasets are typically obtained from GIS sources like census data, OpenStreetMap, or municipal planning databases.
  • Spatial Analysis: GIS tools are applied to analyze spatial relationships and generate metrics such as accessibility, proximity, and network connectivity [68,69]. These spatial insights provide a foundational understanding of how urban form influences transportation dynamics.
  • Dynamic Inputs: The spatial metrics derived from the GIS analysis are converted into inputs for the System Dynamics model. These inputs represent critical variables such as transit accessibility, mode shares, and infrastructure quality, enabling a dynamic exploration of urban transportation systems.
  • SD Model: The SD model simulates the causal relationships and feedback loops among urban form, travel behavior, and sustainability indicators. This dynamic analysis captures temporal changes and system interactions over time, offering a comprehensive view of urban sustainability [70,71].
  • Dynamic Outputs: The SD model generates outputs such as greenhouse gas (GHG) emissions, mode share variations, and changes in travel demand [41]. These outputs reflect the long-term impacts of urban planning policies and interventions [72].
  • Proximity Planning: The final stage involves translating the model outputs into actionable insights for proximity planning. Policymakers and urban planners can use these findings to design and implement strategies that promote sustainable urban environments, such as TOD and investments in active transportation infrastructure development.

4. Model Structure

4.1. Urban Density and TOD Policies

Urban density is widely recognized as a factor that can influence transportation behavior and sustainability outcomes, though its effects may vary depending on socio-economic and design contexts. Within the designed system, density is embedded in multiple feedback loops that influence transit accessibility, active travel, car dependency, and parking demand. However, the key aspect of density that needs to be emphasized is how implementing TOD policies while modifying urban density can amplify or counteract sustainability efforts. The interactions between urban density and transportation-related variables reveal whether urban development strategies push cities toward or away from sustainable mobility.

4.1.1. The Role of TOD in Urban Density and Sustainable Mobility

TOD policies aim to foster compact, high-density, mixed-use neighborhoods with strong transit accessibility. Increasing density near transit hubs has the potential to enhance accessibility to essential services, employment centers, and transit stations, reducing the need for private vehicle trips. This improved accessibility can contribute to shifts toward public transit, cycling, and walking, depending on complementary factors such as service quality and land use diversity, and GHG emissions [21,73,74].
As shown in Figure 4, this transition forms a balancing feedback loop. Reduced car dependency decreases the demand for parking, enabling more space to be allocated to pedestrian-friendly and transit-supportive development. This in turn strengthens the case for further TOD investments, creating a self-sustaining cycle that enhances urban sustainability.
Balancing Loop:
TOD Policies → Higher Urban Density near Transit → Improved Transit Accessibility → Higher Public Transit Ridership & Active Travel → Lower Car Dependency → Less need for additional TOD measures (Including Investment and Policy Implications).
This loop highlights how TOD interventions enhance the sustainability of urban transportation systems. Higher density can foster improved transit access, potentially supporting increased ridership and lower car use (though this relationship is shaped by multiple contextual factors). As car dependency declines, parking demand decreases, enabling further pedestrian-oriented, mixed-use development. The strengthened feasibility of TOD policies supports continued investments in transit and active mobility infrastructure. The influence of TOD policies on density and mobility outcomes is summarized in Table 2.

4.1.2. Policy Implication: TOD in the Three Ds (Density, Diversity, and Design)

While implementing TOD policies, urban planners must carefully consider the 3D framework—Density, Diversity, and Design—to ensure that urban density is not only increased but also well integrated with transit, land use, and active mobility infrastructure. Density alone is insufficient; without diverse land-use planning and a well-designed pedestrian environment, the full potential of TOD in reducing car dependency and improving sustainability may not be realized.

4.2. Parking Policies and Car Dependency

4.2.1. The Role of Parking Policies in Sustainable Urban Development

Parking policies are a fundamental aspect of urban planning that interact with car ownership, travel behavior, broader urban design factors, and the overall sustainability of urban developments. In many cities, excessive parking supply has reinforced private car dependency, contributing to increased congestion, reduced public transit ridership, and a car-oriented urban structure [80]. However, well-designed parking policies, including parking regulations, pricing strategies, and TOD initiatives, can serve as regulatory tools to shift travel behavior toward more sustainable alternatives such as public transit, walking, and cycling. The dynamic relationship between parking policies, car ownership, and travel behavior is illustrated in Figure 5, while the corresponding causal mechanisms and supporting literature are summarized in Table 3.
Balancing Loop:
More Parking Spaces per Capita → Higher Parking Availability → Increased Willingness to Own a Car → Higher Car Ownership → Increased Parking Demand → Lower Parking Availability → Lower Willingness to Buy Private Vehicle.
This loop highlights a key issue in urban planning: providing more parking does not necessarily satisfy parking demand in the long run. Instead, an increase in parking spaces per capita makes private car ownership more convenient and attractive, potentially contributing to higher vehicle dependency. As more people own cars, the demand for additional parking tends to increase, creating a cycle that prioritizes private vehicle use over sustainable transportation.

4.2.2. Breaking the Cycle: Parking Policies as a Regulatory Tool

To counteract the reinforcing effect of excessive parking supply on car dependency, urban planners can implement the following regulatory strategies:
  • Reducing Parking Minimums in TOD Zones: Limiting mandatory parking requirements in high-density areas promotes TODs that prioritize sustainable mobility.
  • Dynamic Parking Pricing: Adjusting parking fees based on demand discourages unnecessary car trips and increases the attractiveness of public transit.
  • Caps on Parking Spaces Per Capita in New Developments: Placing restrictions on the number of parking spaces allocated per resident prevents an oversupply that reinforces vehicle dependency.
  • Investment in Public Transit and Active Transportation: Expanding transit services and enhancing pedestrian and cycling infrastructure ensures that parking restrictions do not compromise accessibility but rather encourage multimodal transportation.

4.2.3. Policy Interference: Parking Policies

Parking policies, parking regulations, and parking costs interact within this balancing loop, affecting the long-term sustainability of urban mobility. The most critical insight from this loop is that simply increasing parking supply does not solve parking shortages, rather, it incentivizes further car ownership, increasing demand for parking and undermining sustainability efforts. By strategically integrating parking policies with TOD principles and active transportation investments, urban developments can shift away from car dependency and toward a more balanced and sustainable mobility network.

4.3. Investment in Active Transportation—Walking and Cycling

4.3.1. The Role of Active Transportation in Sustainable Urban Mobility

Active transportation, including walking and cycling, is a fundamental component of sustainable urban development. Investments in pedestrian infrastructure, bike lanes, and bike-sharing networks create an environment that supports non-motorized travel, reducing car dependency, congestion, and greenhouse gas (GHG) emissions. Without adequate infrastructure, however, walking and cycling remain unsafe, disconnected, and inconvenient, reinforcing car reliance and discouraging active travel. Breaking this cycle requires strategic investments in active transportation infrastructure, generating a balancing loop that encourages sustainable mobility. The relationship between investments in active transportation infrastructure and changes in travel behavior and sustainability outcomes is illustrated in Figure 6 and summarized in Table 4.
Balancing Loop:
Increased Investment in Cycling and Walking Infrastructure → Expanded Bike Lanes, Bike-Sharing Stations & Pedestrian Facilities → Improved Perceived Safety and Connectivity → Higher Mode Share for Walking & Cycling → Reduced Car Dependency → Lower Congestion and GHG Emissions → Further Lower Investment Needed in Active Transportation.
This loop illustrates how investments in active transportation can encourage widespread behavioral shifts, potentially reducing car dependency and contributing to greater sustainability in urban mobility over time.

4.3.2. Strategies and Policy Levers

Investments in pedestrian and cycling infrastructure are essential to reinforcing long-term shifts in travel behavior. To strengthen the role of active transportation in sustainable urban mobility, several coordinated strategies are recommended:
  • Expanding Protected Bike Lanes and Sidewalks: Ensuring a safe, direct, and connected network for cyclists and pedestrians.
  • Enhancing Bike-Sharing Networks: Increasing the availability of shared bikes to improve first-mile/last-mile connectivity.
  • Improving Pedestrian Infrastructure: Prioritizing investments in sidewalks, crossings, and traffic-calming measures to enhance walkability.
  • Integrating Active Transportation with Public Transit: Creating seamless connections between cycling, walking, and transit to reduce car dependency.
  • Long-Term Funding and Policy Support for Active Modes: Allocating consistent financial resources to ensure infrastructure maintenance and expansion.
However, policy barriers such as car-centric urban planning, lack of dedicated funding, and inadequate integration with land use planning can hinder the effectiveness of active transportation investments. By strategically integrating active transportation investments with TOD and urban land use planning, cities can create human-scaled, walkable environments that minimize reliance on private vehicles and promote healthier, more sustainable mobility patterns.

4.4. Integrating GIS into the System Dynamics Model: The Role of Spatial Analysis in TOD Policies

In the previous sections key feedback loops and their implications have been identified. The comprehensive structure of feedback loops and variable relationships developed in this study is summarized in the causal loop diagram shown in Figure 7. The next step is to highlight the role of GIS in refining the spatial precision and policy relevance of the model. GIS serves as a crucial tool for integrating location-based insights into the dynamic relationships of the system, ensuring that TOD policies are effectively implemented.
A fundamental challenge in TOD planning is determining the optimal locations for density concentration to enhance transit accessibility and reduce car dependency. TOD success is inherently location-dependent, requiring spatial analysis to evaluate land-use configurations, accessibility patterns, and travel behavior shifts. By integrating GIS into SDM, this research provides a framework that captures both spatial and temporal dynamics, offering a more comprehensive understanding of urban mobility and sustainability outcomes.

4.4.1. GIS Contributions to System Dynamics Modeling

GIS enhances the methodological framework by addressing the spatial dependencies of TOD policies and their influence on travel behavior. The integration of GIS allows for the following:
  • Defining and Analyzing TOD Zones involves identifying optimal areas for higher-density development by assessing their proximity to existing and planned transit stations [25,28,64]. It also requires evaluating land-use configurations to ensure that mixed-use developments are well integrated with accessibility and mobility objectives [25,43,62,64].
  • Measuring Transit Accessibility Impacts includes analyzing how changes in density influence transit accessibility, thereby encouraging a shift toward public transportation [28,44,59,75,76,77]. Additionally, GIS-based network analysis is used to measure walking distances to transit stops and assess equitable access to transit services [36,103].
  • Assessing Active Transportation Infrastructure entails evaluating walkability and bikeability within TOD zones to improve last-mile connectivity [37,92,95,96]. Furthermore, it involves identifying gaps in pedestrian and cycling infrastructure that could hinder the effectiveness of TOD initiatives [19,38,75].
  • Optimizing Parking Policy Implementation requires conducting spatial analysis of parking distribution to inform strategies for reducing parking supply in TOD zones [11,12,83]. It also involves identifying areas where parking adjustments can help decrease car dependency while maintaining transit accessibility [78,82,90,91].
  • Scenario-Based Planning for Sustainable Mobility includes simulating how variations in density and land-use configurations influence mode share, vehicle ownership, and GHG emissions [4,8]. Additionally, it involves testing different TOD and parking policy interventions to determine the most effective strategies for reducing automobile reliance [5,11,12,38].

4.4.2. Using GIS to Enhance TOD Policy Design

GIS enhances decision-making by spatially defining TOD zones, analyzing accessibility changes, and optimizing density allocations [36,42]. By integrating GIS into system dynamics modeling, this research presents a comprehensive and scalable approach to evaluating the long-term effects of TOD policies on urban mobility and sustainability.
How GIS Enhances the TOD Feedback Loop:
TOD Policies → GIS-Based TOD Zone Definition → Improved Accessibility to Transit → Reduced Car Dependency → Increased TOD Justification → Further TOD Investments.
By incorporating spatial analysis into the modeling framework, this approach ensures that TOD investments are strategically implemented to maximize sustainability objectives. The integration of GIS enables data-driven decision-making, supporting urban planners and policymakers in identifying the most effective locations for TOD interventions while balancing density, accessibility, and transportation infrastructure. The complete list of variables used in the causal loop diagram (CLD), along with their categorization under built environment, transport, and socio-economic indicators, is presented in Appendix B.

5. Case Study Context

Lachine-Est and Bridge-Bonaventure are two brownfield redevelopment areas located in Montreal, Canada, undergoing urban development with a focus on creating sustainable, mixed-use districts. Both of these projects exemplify sustainable urban redevelopment by integrating public transit investments, active transportation infrastructure, green spaces, and strategic density planning to create a low-carbon, resilient, and inclusive neighborhood by transforming a former industrial site into a walkable, transit-accessible community [104,105]. The proposed redevelopment layouts for Lachine-Est and Bridge-Bonaventure, which form the spatial basis of this study’s case analysis, are shown in Figure 8.
When evaluating TOD, the presence of transit hubs including train, tram, and metro stations plays a crucial role in shaping accessibility and transportation infrastructure. These hubs serve as focal points for sustainable mobility, influencing travel behavior, land-use patterns, and multimodal integration.
Both case studies, Lachine-Est and Bridge-Bonaventure, are characterized by strategic transit connectivity, making them ideal for examining TOD principles.
  • Bridge-Bonaventure: A planned Réseau express métropolitain (REM) station will be integrated within the development area, enhancing regional transit accessibility and reinforcing multimodal travel options [105].
  • Lachine-Est: A train station is located on the eastern border of the development area, providing regional rail connectivity. Additionally, a tram station is planned along the northern boundary, further strengthening transit access and integration with active transportation networks [104,106].
The incorporation of these transit nodes highlights the importance of accessibility and transportation infrastructure in both case studies. These developments align with TOD objectives by prioritizing public transit, reducing car dependency, and fostering compact, walkable communities. The presence of these transit hubs serves as a foundation for analyzing urban mobility patterns, accessibility impacts, and policy interventions in the context of sustainable urban planning.

6. Results and Discussion

This section presents the findings from the application of the SD–GIS framework to two brownfield redevelopment contexts—Bridge-Bonaventure and Lachine-Est in Montreal. Two scenarios are qualitatively assessed: a baseline scenario reflecting current development and parking conditions and an intervention scenario involving increased density near transit nodes and reduced parking supply. While the model remains conceptual, the comparative analysis offers preliminary insights into how changes in land use and mobility policies might influence travel behavior and sustainability outcomes over time.
Under the baseline scenario, most simulated trips in both districts continued to rely on private vehicles. Bridge-Bonaventure, marked by large brownfield parcels awaiting redevelopment, showed relatively low rates of public transit use and limited walking or cycling infrastructure. As residential density in this area remained moderate and parking requirements were minimally restrictive, the simulation suggested that vehicle ownership could remain high, with minimal change to the overall mode share. A similar pattern can emerge in Lachine-Est, where distance from rail and tram stations, combined with ample parking availability, created conditions that sustained high car dependency. In both districts, the baseline model projected modest improvements in active modes only if parallel investments in bike lanes or sidewalks were made.
In contrast, the intervention scenario modified two principal factors: a greater concentration of housing and commercial activities within 500 m of transit stations, and a reduced ratio of parking spaces per dwelling unit. These alterations were associated with potential shifts in mode share and vehicle ownership. According to the model’s feedback loops, higher residential density next to transit nodes increased perceived accessibility and lowered overall travel distances, encouraging a shift to both public transit and active travel. At the same time, lower parking availability, coupled with improved public and active transportation infrastructures, helped discourage the acquisition of private vehicles. As a result, car ownership decreased at a faster rate than in the baseline scenario, and the share of trips made by walking or cycling rose substantially particularly in Lachine-Est, where the planned tram station enhances connectivity to the rest of Montreal’s transit network.
Preliminary estimates of GHG emissions, derived from changes in the average rate of car trips, also underscored the benefits of transit-oriented growth. When combined with improved pedestrian and cycling infrastructure, such as extended bike lanes, the intervention scenario led to a lower overall travel demand by private cars, correlating with a moderate but noteworthy reduction in projected GHG emissions. The qualitative results thus suggest that restricting parking supply, strengthening active-transportation routes, and concentrating density near transit nodes can work in tandem to limit automobile dependency.
In both Bridge-Bonaventure and Lachine-Est, however, outcomes remained sensitive to the degree of investment in active mobility. Merely capping parking ratios without providing safer, more direct pedestrian and bike routes had a reduced impact on travel behavior. This highlights the significance of a dual approach: while TOD principles can bring services and residences closer to transit, complementary infrastructure for walking and cycling helps reinforce non-car modes.
Overall, while no conclusive numeric targets are provided at this conceptual stage, the model results illustrate how the proposed SD–GIS approach can map out interactions among density, transit accessibility, and parking policies. The changes in GHG emissions, mode shares, and car ownership rates under these two scenarios offer policy-makers evidence that a combined strategy of compact land use, limited parking supply, and improved active-travel facilities offer potential pathways toward low-carbon, transit-focused urban districts.

7. Conclusions

This research proposes a novel framework integrating SD with GIS to evaluate urban transportation sustainability. Building on a gap identified in the existing literature, the insufficient inclusion of spatial (location-based) data into SD models, the framework embeds explicit geographic indicators, such as proximity to transit hubs and parking availability, into causal feedback loops. By doing so, it addresses both the temporal and spatial dimensions critical to shaping travel behavior and guiding effective urban development policies. The case studies of Bridge-Bonaventure and Lachine-Est illustrate how brownfield redevelopment projects with well-placed transit nodes can create reinforcing cycles of reduced car dependence and heightened sustainability when accompanied by strategic density allocation, parking regulations, and investments in active transportation infrastructure.
Despite its comprehensive approach, the model currently focuses on a conceptual (rather than fully quantitative) representation of the feedback mechanisms. Quantifying these relationships, and validating the model’s assumptions with real-world data, will be essential for future work. Additionally, while TOD principles, parking supply, and active transportation emerged as key leverage points, the scope does not yet include potential influences like social equity, emerging mobility services, or cultural preferences. Incorporating these factors would further enrich the dynamic analysis and expand the model’s applicability to diverse urban contexts.
Based on the SD–GIS framework and the insights gained from applying it to Bridge-Bonaventure and Lachine-Est, we propose the following targeted recommendations to enhance sustainability in these brownfield redevelopment projects:
Recommendation 1—TOD: Adopting TOD principles in both sites involve intensifying development within walking distance of major transit nodes. Higher residential and commercial densities within these areas naturally boosts transit ridership and reduce reliance on private cars. By clustering essential services and amenities, such as grocery stores, schools, or community centers around train and tram stations, residents have easier access to day-to-day needs without lengthy car trips.
Recommendation 2—Parking Supply and Management: Parking availability is a strong determinant of private car ownership and usage. Restricting the number of parking spaces per residence, particularly in high-access zones near transit, limits excess parking supply and encourages a shift toward public and active transport modes. Progressive policies could also differentiate parking regulations based on proximity, for instance, imposing stricter parking caps or higher fees for buildings located within a 500-m radius of transit hubs. This ensures that those who benefit most from transit connectivity shoulder lower costs for sustainable travel choices while being nudged away from car ownership.
Recommendation 3—Active and Public Transportation Infrastructure: Upgrading and expanding safe bike lanes, pedestrian pathways, and coherent bus routes significantly reinforces sustainable travel. Investing in a connected cycling network encompassing protected lanes, well-lit paths, and secure bike storage can motivate residents to adopt cycling as a principal travel mode. Meanwhile, improving bus frequency and reliability or establishing connecting lines to train or tram stations will enhance the overall appeal of public transit.
Recommendation 4—Land-Use Mix and Density Distribution: Encouraging a balanced land-use mix ensures that homes, workplaces, and essential services are accessible within TOD zones, reducing car dependency. However, intensifying development should not be pursued without a clear understanding of the economic and planning constraints within designated land-use zones. Prior research by Cucuzzella and colleagues has developed a TOD index that integrates development potential, economic vibrancy, and socio-economic factors to assess where intensification is most viable [75]. Future applications of the SD-GIS framework could incorporate such multi-criteria indicators to ensure that density increases align with sustainable urban growth patterns.
Recommendation 5—Integrated Policy Application: The model’s feedback loops underscore how TOD efforts and parking restrictions are most successful when implemented as an integrated policy. By densifying neighborhoods around transit stations and simultaneously capping parking availability in those areas, planners create a self-reinforcing cycle favoring sustainable modes. The policy context of Montreal, which already acknowledges parking limits in certain districts, can be a supportive backdrop for enacting tighter regulations, especially if coupled with tangible improvements in active and public transport networks.
These recommendations highlight the interplay between land-use strategies, transit connectivity, and parking regulations. When local governments, developers, and transit authorities coordinate these elements, they can significantly influence travel behavior and foster resilient, low-carbon districts. The Bridge-Bonaventure and Lachine-Est projects present opportunities to demonstrate how a combined SD–GIS modeling approach can guide integrative planning decisions that yield enduring environmental, social, and economic benefits.

8. Limitations

Several limitations define the boundaries of this study. First, the model has been developed as a conceptual tool without extensive quantitative calibration or simulation. While the feedback loops and spatial metrics draw on prior research, they would benefit from numerical validation. Second, socio-economic indicators and new mobility services (such as ridesharing and e-scooters) are omitted, potentially limiting generalizability. Third, the case studies, Bridge-Bonaventure and Lachine-Est, offer illustrative insights on brownfield redevelopment but may not mirror circumstances in cities with vastly different regulatory or economic contexts.

9. Future Research

Moving forward, the priority should be to integrate stock-and-flow diagrams (SFDs) and numerically calibrate the model using primary or secondary data. Such calibration would allow scenario testing, sensitivity analyses, and an exploration of “optimal density” thresholds around transit nodes. Additionally, the model could be extended to include equity considerations, dynamic fare or congestion-pricing schemes, and other advanced mobility technologies. A more comprehensive database of case studies, spanning varying urban forms and cultural conditions, would further reveal how an SD–GIS approach can guide policymaking and lead to robust, location-sensitive transportation solutions.

Author Contributions

Conceptualization, A.F., U.E., C.C. and G.G.; methodology, A.F.; software, A.F.; validation, A.F., C.C., G.G. and S.K.; formal analysis, A.F.; investigation, A.F. and S.K.; resources, U.E.; data curation, A.F.; writing—original draft preparation, A.F.; writing—review and editing, A.F., C.C., G.G. and S.K.; visualization, A.F. and S.K.; supervision, U.E., C.C. and G.G.; project administration, A.F.; funding acquisition, U.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the NSERC under the Canada Excellence Research Chair of Professor Ursula Eicker.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
CLDCausal loop diagram
GHGGreenhouse gas
GISGeographic Information Systems
SDSystem Dynamics
SDGISSystem Dynamics Geographic Information Systems
SDMSystem Dynamics Modeling
SFDStock and flow diagram
TODTransit-oriented development

Appendix A

Table A1. Table of the definitions of the indicators (Reference: Authors).
Table A1. Table of the definitions of the indicators (Reference: Authors).
CategoryIndicatorDefinition
Built Environment IndicatorsDensityThe concentration of people, jobs, or housing units within a given area, typically measured as population or employment per square kilometer or hectare.
Mixed land usesThe integration of different land uses (residential, commercial, institutional, recreational) within a given area to reduce travel distances and promote sustainable mobility.
DesignThe physical characteristics of the built environment, including street patterns, urban form, and spatial organization, which influence walkability and connectivity.
AccessibilityThe ease with which people can reach destinations such as jobs, schools, services, and transit stations within a reasonable amount of time.
ProximityThe spatial distance between key locations, such as homes, workplaces, transit stops, and amenities, affecting travel behavior and mode choice.
WalkabilityThe degree to which an area is pedestrian-friendly, based on factors such as sidewalk availability, street connectivity, land use mix, and perceived safety.
BikeabilityThe extent to which a built environment supports cycling, including the availability of bike lanes, bike-sharing systems, bike storage, and road safety measures.
Block sizeThe average area of urban blocks, which influences street connectivity, pedestrian movement, and overall urban design. Smaller blocks typically enhance walkability.
Green space/public space ratioThe proportion of land dedicated to parks, green spaces, and public open areas relative to the total urban area, affecting environmental sustainability and quality of life.
Transport
Indicators		Transportation networkThe system of roads, railways, bike paths, and pedestrian walkways facilitate movement and connectivity within and between urban areas.
Distance to transitThe physical distance between a location (e.g., residence, workplace) and the nearest public transit stop or station, affecting transit usage.
Public transit accessibility (travel time to key destinations)The efficiency and ease with which people can use public transit to reach essential destinations like workplaces, schools, and shopping areas.
Frequency and reliability of transit servicesThe consistency and availability of public transit services, including how often they run and how well they adhere to schedules.
Number of transit stations/stops nearbyThe density of public transit access points within a given area, influencing transit ridership and accessibility.
Ridership levelsThe number of passengers using public transit services over a given period, indicating the effectiveness of the transit system.
Parking facilities and demand managementThe availability and regulation of parking spaces to balance supply and demand, reduce congestion, and encourage sustainable transportation choices.
Walking infrastructure (walking routes)The presence of sidewalks, pedestrian paths, crosswalks, and pedestrian-friendly design features that facilitate safe and convenient walking.
Cycling infrastructure (bike lanes, bike-sharing systems)The presence of dedicated bike lanes, protected cycling paths, and bike-sharing programs that support and encourage cycling as a mode of transport.
Traffic congestion and road safetyThe level of traffic congestion and the safety conditions of roads, including accident rates, pedestrian safety, and overall mobility.
Socio-Economic IndicatorsAge distributionThe demographic composition of a population in terms of age groups, which influences travel behavior, housing needs, and mobility preferences.
Ethnicity and cultural diversityThe representation of different ethnic and cultural groups within a population, which can affect travel patterns, accessibility needs, and transportation equity.
Vehicle ownership ratesThe percentage of households or individuals who own one or more private vehicles, influencing mode choice and car dependency.
Education levelThe distribution of educational attainment within a population, which can correlate with employment locations, travel demand, and transit usage.
Household income and affordabilityThe economic capacity of households, affecting housing location choices, transportation affordability, and access to services.
Employment accessibilityThe ease with which individuals can reach job opportunities using various modes of transport, affecting economic participation and commute patterns.
Social equity in transport servicesThe fairness in the distribution of transportation resources and services across different demographic and socioeconomic groups, ensuring mobility access for all.

Appendix B

Table A2. List of variables of the CLD (Reference: Authors).
Table A2. List of variables of the CLD (Reference: Authors).
CategoryIndicatorVariable in the Model
Built Environment IndicatorsDensity Urban Density
Demand for Development
Mixed land usesLand Use Mix
DesignAccessibility
Transport
Indicators		Travel Mode Share—Public TransitDistance to Transit
Transit Accessibility
Public Transit Trips
Private CarTravel Mode Share—Private Vehicle
Car Dependency
Parking Facilities and Demand ManagementParking Cost
Parking Regulations
Parking Spaces per Capita
Public Parking Availability
Private Parking Availability
Travel Mode Share—WalkingWalking
Pedestrian Infrastructure
Travel Mode Share—BikingBiking
Bike Infrastructure Quality Index
Bike Lane Length
Bike Network Connectivity
Bike-Sharing Usage
Bike-Sharing Stations
Perceived Safety (Cycling)
Travel Time
Congestion
Transit Investment
GHG Emission
Socio-Economic
Indicators		Vehicle Ownership RatesWillingness to Buy Private Vehicle
Household Income and AffordabilityIncome Level

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Figure 1. The iterative Diverge–Converge process in research, inspired by Design Thinking, illustrating continuous refinement of questions and ideas until a well-defined framework is achieved [65]. The arrows represent iterative transitions between divergent and convergent phases, where choices are explored, synthesized, and refined over time.
Figure 1. The iterative Diverge–Converge process in research, inspired by Design Thinking, illustrating continuous refinement of questions and ideas until a well-defined framework is achieved [65]. The arrows represent iterative transitions between divergent and convergent phases, where choices are explored, synthesized, and refined over time.
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Figure 2. Methodological workflow showcasing the steps from literature review and gap identification to model application and case study. The arrows represent the logical and sequential flow of the research process.
Figure 2. Methodological workflow showcasing the steps from literature review and gap identification to model application and case study. The arrows represent the logical and sequential flow of the research process.
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Figure 3. Conceptual workflow of the proposed methodological framework integrating Geographic Information Systems (GIS) and System Dynamics (SD) modeling. The arrows illustrate the flow of data and analysis through the framework.
Figure 3. Conceptual workflow of the proposed methodological framework integrating Geographic Information Systems (GIS) and System Dynamics (SD) modeling. The arrows illustrate the flow of data and analysis through the framework.
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Figure 4. The balancing feedback loop of TOD) policies showing how increasing urban density and land use mixes improve accessibility, promote active and public transportation, and reduce car dependency. Arrows represent causal relationships, where a “+” indicates that variables move in the same direction (i.e., an increase in one leads to an increase in the other), and a “–” indicates that variables move in opposite directions (i.e., an increase in one lead to a decrease in the other).
Figure 4. The balancing feedback loop of TOD) policies showing how increasing urban density and land use mixes improve accessibility, promote active and public transportation, and reduce car dependency. Arrows represent causal relationships, where a “+” indicates that variables move in the same direction (i.e., an increase in one leads to an increase in the other), and a “–” indicates that variables move in opposite directions (i.e., an increase in one lead to a decrease in the other).
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Figure 5. Balancing loop for the role of parking policies in car dependency and urban mobility (Arrows represent causal relationships).
Figure 5. Balancing loop for the role of parking policies in car dependency and urban mobility (Arrows represent causal relationships).
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Figure 6. The Reinforcing Effects of Investment in Active Transportation on Cycling and Walking Mode Share (Arrows represent causal relationships).
Figure 6. The Reinforcing Effects of Investment in Active Transportation on Cycling and Walking Mode Share (Arrows represent causal relationships).
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Figure 7. Comprehensive causal loop diagram illustrating dynamic interrelationships (Arrows represent causal relationships).
Figure 7. Comprehensive causal loop diagram illustrating dynamic interrelationships (Arrows represent causal relationships).
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Figure 8. Proposed plan for the case studies: (a) Lachine-Est Development in Montreal, Canada (b) Bridge Bonaventure in Montreal, Canada [104,105].
Figure 8. Proposed plan for the case studies: (a) Lachine-Est Development in Montreal, Canada (b) Bridge Bonaventure in Montreal, Canada [104,105].
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Table 2. TOD Policies and Their Influence on Urban Density and Sustainable Mobility. Arrows (→) indicate directional relationships. A plus sign (“+”) denotes a positive relationship (an increase in one variable tends to increase the other), while a minus sign (“−”) denotes a negative relationship (an increase in one variable tends to decrease the other).
Table 2. TOD Policies and Their Influence on Urban Density and Sustainable Mobility. Arrows (→) indicate directional relationships. A plus sign (“+”) denotes a positive relationship (an increase in one variable tends to increase the other), while a minus sign (“−”) denotes a negative relationship (an increase in one variable tends to decrease the other).
SNVariables and RelationshipCausal MechanismSupporting Literature Examples
1Density near transit hubs → (+) AccessibilityIncreasing density near transit hubs enhances accessibility to public transportation.[28,75]
2Accessibility → (+) Public transit ridershipImproved transit accessibility encourages more people to use public transit.[76,77]
3More people using transit and active modes → (−) Car dependencyA higher share of transit and active modes reduces reliance on private vehicles.[4,14]
4More people using transit and active modes → (−) Car dependencyReduced reliance on private vehicles leads to a decline in car ownership.[78,79]
5More car dependency → (+) Justifies further TOD investmentMore car dependency supports the essence of further investment in TOD policies.[21,28]
Table 3. Parking Policies, Car Dependency, and Urban Mobility. Arrows (→) indicate directional relationships. A plus sign (“+”) denotes a positive relationship, while a minus sign (“−”) denotes a negative relationship.
Table 3. Parking Policies, Car Dependency, and Urban Mobility. Arrows (→) indicate directional relationships. A plus sign (“+”) denotes a positive relationship, while a minus sign (“−”) denotes a negative relationship.
SNVariables and RelationshipCausal MechanismSupporting Literature Examples
1Parking spaces per capita → (+) Parking availabilityMore parking spaces increase overall parking supply.[81,82]
2Parking availability → (+) Willingness to buy a private vehicleEasier parking access encourages car ownership.[12,83]
3Willingness to buy a private vehicle → (+) Car ownershipHigher willingness to buy a car leads to increased vehicle ownership.[84,85]
4Car ownership → (+) Mode share for private carsIncreased vehicle ownership raises the percentage of car-based travel.[14,86,87]
5Mode share for private cars → (+) Demand for parkingA higher proportion of trips made by car increases parking demand.[88,89]
6Demand for parking → (−) Parking availabilityHigher parking demand leads to lower parking spaces availability.[79,90,91]
Table 4. Investments in Active Transportation, Mode Share, and Sustainability. Arrows (→) indicate directional relationships. A plus sign (“+”) denotes a positive relationship, while a minus sign (“−”) denotes a negative relationship.
Table 4. Investments in Active Transportation, Mode Share, and Sustainability. Arrows (→) indicate directional relationships. A plus sign (“+”) denotes a positive relationship, while a minus sign (“−”) denotes a negative relationship.
SNVariables and RelationshipCausal MechanismSupporting Literature Examples
1Investment in cycling/walking infrastructure → (+) Improved pedestrian and cycling infrastructureExpanding bike lanes and pedestrian pathways enhance connectivity and safety.[92,93]
2Improved pedestrian and cycling infrastructure → (+) Perceived safety for walking and cyclingDedicated infrastructure reduces risks, making active travel more attractive.[94,95]
3Perceived safety for walking and cycling → (+) Mode share for walking and cyclingIncreased safety encourages greater adoption of non-motorized modes.[96,97]
4Higher walking and cycling mode share → (−) Car dependencyIncreased active travel reduces reliance on private vehicles.[75,98]
5Reduced car dependency → (+) Less GHG emissions & congestionFewer car trips result in lower emissions and less road congestion.[99,100,101]
6Lower emissions and congestion → (+) Further lower investment needed in active transportationPositive environmental outcomes justify that sufficient funding for sustainable mobility has been placed and lower funding is needed in the future.[5,102]
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Farnood, A.; Eicker, U.; Cucuzzella, C.; Gopakumar, G.; Khorramisarvestani, S. Sustainability Assessment Framework for Urban Transportation Combining System Dynamics Modeling and GIS; A TOD and Parking Policy Approach. Smart Cities 2025, 8, 107. https://doi.org/10.3390/smartcities8040107

AMA Style

Farnood A, Eicker U, Cucuzzella C, Gopakumar G, Khorramisarvestani S. Sustainability Assessment Framework for Urban Transportation Combining System Dynamics Modeling and GIS; A TOD and Parking Policy Approach. Smart Cities. 2025; 8(4):107. https://doi.org/10.3390/smartcities8040107

Chicago/Turabian Style

Farnood, Ahad, Ursula Eicker, Carmela Cucuzzella, Govind Gopakumar, and Sepideh Khorramisarvestani. 2025. "Sustainability Assessment Framework for Urban Transportation Combining System Dynamics Modeling and GIS; A TOD and Parking Policy Approach" Smart Cities 8, no. 4: 107. https://doi.org/10.3390/smartcities8040107

APA Style

Farnood, A., Eicker, U., Cucuzzella, C., Gopakumar, G., & Khorramisarvestani, S. (2025). Sustainability Assessment Framework for Urban Transportation Combining System Dynamics Modeling and GIS; A TOD and Parking Policy Approach. Smart Cities, 8(4), 107. https://doi.org/10.3390/smartcities8040107

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