Land Use Transport Research Articles

Geospatial modelling of housing rents from TOD using MGWR and implications on integrated transportation-land use planning

Revealing the effect of transit-oriented development (TOD) on housing rents is critical for supporting transportation financing and sustainable urban development. However, existing research has not thoroughly examined the effects of land use type and TOD type on housing rents, crucially lacking geospatial modelling. This study investigates the spatial effect of TOD on housing rents using multi-scale geographically weighted regression (MGWR) and the relationship of land use type and TOD type to this effect. The results show that MGWR accurately quantifies the spatial effect. Meanwhile, the important TOD variables (integrated transit index, land use entropy, overall walkability) have positive effects on housing rents, which show spatial heterogeneity. Moreover, the premium effect of TOD station areas with a smaller proportion of residential and commercial land and a larger proportion of educational and cultural facilities is higher. Furthermore, in TOD station areas where transportation supply capacity is high but urban development is lagging, land use entropy and overall walkability have a strong influence on housing rents. Based on the study's results, we offer three recommendations for implementing TOD practices into integrated transportation and land use planning. This study renews our understanding of the outcomes of TOD both perspectively and methodologically.

Impact of autonomous vehicles on discretionary activities: an agent-based model with space–time accessibility constraints

AbstractThe extensive development of autonomous vehicles (AVs) is set to revolutionise the way of travelling. Research suggests that the introduction of AVs may affect travel behaviour and choices, resulting in long-term changes in land use. Accessibility is an important concept that connects transportation and land use, providing a holistic performance measure for the transport-land use system. However, this concept has not been adequately capitalised in studies that attempt to understand the impact of AVs on location choice decisions. To explore this knowledge gap, we proposed an agent-based simulation framework that integrates with accessibility constraints to study how AVs influence behavioural and location choices. The framework consists of an activity-based travel demand model with accessibility constraints and a dynamic transport assignment model. The accessibility constraints are derived from individuals’ travel time budgets based on activity-travel survey data. We applied the agent-based simulation framework to Clayton, Australia, and focused on discretionary activity location choices. Various values of travel time and vehicle running costs underpinned by the use of AVs were examined. While most studies have concluded that AVs can significantly increase trip lengths for daily activities, our results demonstrate that even when AVs are used, the movement of individuals is still limited by spatio-temporal constraints of accessibility. As a result, we predict that the increase in discretionary trip lengths and their impact on traffic congestion is modest.

Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach

In this study, we aimed to investigate the influence of the built environment (BE) on commuter mode choice using machine learning models in a dense megacity context. We collected 10,150 home-based commuting trips data from Dhaka, Bangladesh. We then utilized three machine learning classifiers to determine the most accurate prediction model for predicting the mode of transportation chosen for commuting in Dhaka. Based on the predictive performance of the classifiers, we identified that the Random Forest (RF) algorithm performed the best. Using the RF model, this study also explored the relative importance of BE factors in predicting commute mode choice, identified nonlinear relationships between the BE factors and mode choice, and examined the interaction effects of these factors on mode selection. Our results reveal that, compared to socio-demographic factors, the BE substantially influence commuter travel behavior. The BE characteristics have a specific nonlinear threshold limit at which they can have a notable impact on lowering private car use, and private car use does not display a constant return of scale with BE. Their interaction effects illustrate the potential optimal combination of BE interventions to lower private car use for commuting. These findings hold substantial implications for urban environmental policy, emphasizing the need for transit-oriented development, travel demand management, and integrated land-use transportation planning to foster low-carbon transportation systems in cities like Dhaka.

Exploring the influence of transportation on urban spatial structure using the spatial Durbin model: evidence from 265 prefecture-level cities in China

The interactive relation between transportation and urban spatial structure remains a significant yet challenging issue in transport engineering and urban planning. Most previous studies indicate that the coordination of transportation and urban structure is conducive to solve urban diseases and promote urban sustainable development. Grounded in the theory of city-region spatial structure, this study examines the spatiotemporal dynamics of urban spatial structure from 2006 to 2019 and investigates the impact of transportation on shaping urban spatial structure in prefecture-level cities in China using spatial Durbin model. Major findings include: first, the nighttime light remote sensing data is employed to characterize urban spatial structure with the mono-centricity index ranging from 0.26 to 0.48. The coastal cities tend to exhibit the polycentric structure, while the cities in western region often display the monocentric structure. Second, there is a gradual decline in mono-centricity structure in these cities. Spatial heterogeneity in urban spatial structure is evident in eastern, central, western and northeastern China. Third, transportation significantly and positively influences spatial structure, however, the impact varies across regions and city sizes. Finally, policy implications are proposed based on these findings, such as promoting the integrated land use-transportation development, implementing targeted regional policies, and enhancing land use spatial planning.

Transport-land use systems of sustainable London city

<p>Transportation and land use are interrelated and should be investigated simultaneously for sustainable urban. This paper investigates the interaction between transport and land-use systems using TRANUS model, to support the sustainable development of the London city, provide implicative information for London Mayor’s Transport Strategy (MTS), and reduce transport energy use and CO<sub>2</sub> emissions. Three infrastructure improvements scenarios for 2025 for MTS are examined. Results show that the trips will increase from 2012 to 2025 by over 1 million. High-occupancy car, bike, rail and tube are still the main transit, and CrossRail will be increasingly recognized. The transport energy use in 2025 high scenario is the smallest compared to 2025 baseline and low scenario. The transport CO<sub>2</sub> emissions show difference for these three 2025 scenarios, with low and high scenarios having smaller transport CO<sub>2</sub> emissions than baseline. These have informative implications for UK national infrastructure plans, and suggest that accounting environmental benefits of infrastructures will contribute to reduce the underinvestment in infrastructure.</p>

Providing climate change resilient land-use transport projects with green finance using Z extended numbers based decision-making model

As the climate change enforces decision-makers (DMs) to change established policies and strategies, investors are motivated to finance the development of green projects. Because of their environmental effects, land-use transport projects are of special importance in this process. Different types of environment-friendly land-use transportation projects are financed by the private sector or regulatory authorities and institutions. However, the choice of the correct project is a complex issue. In the decision-making process, social concerns are crucial as well as financial, technical and environmental ones. In order to solve the multi-criteria decision-making problem, we propose a novel group decision support model using the Logarithm Methodology of Additive Weights (LMAW) and Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) based on the fuzzy Z extension (ZE)-numbers. The proposed group decision support model has a unique capability for decision. The opinions of two groups of the DMs and experts are used to consider the decision reliability in two different stages to reach an optimal decision. To illustrate the use of model, we create a scenario that considers four small-scale green finance planning alternatives that are evaluated using twelve criteria that reflect the choice problem's economic, environmental, technical, and political aspects. According to the findings, the optimum plan should be both inclusive and equitable, as well as economically efficient. Selection of the best green finance planning involves consideration of socioeconomic variables.

A land-use transport-interaction framework for large scale strategic urban modeling

We introduce a family of land use transportation interaction (LUTI) models which enable future employment, population and flows or trips between these activities to be explained and predicted. We begin by focusing on the generic spatial interaction model, noting the ways in which its components reflect demand and supply at different locations measured in terms of employment and working population. This suggests an equilibrium structure which is our starting point in developing a simplified version of the model which we extend to deal with four different activity sectors – housing, retail activities, schools, and health facilities. We use this generic structure to develop four related versions of the generic LUTI model equations for residential populations, retailing, education and hospitals which are all driven by employment in terms of where people live and work. This constitutes our integrated framework that we use in calibrating, that is fine-tuning the model to three urban areas (cities) in Europe: to Oxford and its county, Turin and its region, and Athens in its hinterland of Attica reflecting population volumes from 700,000, 1.7 million and 3.8 million persons respectively. In each case, we use the models to predict the impact of different scenarios – new housing developments in Oxfordshire, new universities and metro lines in Turin, and economic development in the Athens region. We describe the details of these scenarios in Supplementary Information (SI) which shows the versatility of using the models to examine such impacts and we conclude with directions for improving the various models and nesting them at different scales within the land use-transport planning process.

Land use change in Dhaka City Corporation Area and its impact on transportation: A way forward towards integration into national policies

Land use and transport integration has been highlighted as one of the most important policy objectives considering the interrelationship between the two concepts and can be used as a means of planning through intervention. From the standpoint of revealing the interplay, the study's objective is to assess the land use change of Dhaka City Corporation Area and determine its impact on transportation to assist the national policymaking and achieve Sustainable Development Goals (SDGs). The methodology directed the determination of the land use change from 2010 to 2018, and then the consequent change in traffic flow while the necessary data in this regard were collected from secondary sources. The study shows that mixed-use development generates fewer trips. This is the study's finding in regard to integration among the national policies regarding land use and transport planning. This study is a methodological approach to analyse the impact of land use change on transportation and can be a role model for solving complex urban problems, e.g., traffic congestion by integrating the Land Use-Transport interaction. The study provides a background of the policymakers in their policymaking process, and an integrated policy will enable the nation to fulfil its Sustainable Development Goals.

Last Mile Connectivity in the Indian Scenario: A literary review

The last few decades have witnessed several dynamics in the global transportation scenario like shifting toward a sustainable transport system, implementing integrated land-use transport planning concepts, a strengthened transportation system with stable Artificial Intelligence (AI) and Internet of Things (IoT), and a sturdy structure combining Information Technology (IT) and sustainable parameters. Last Mile connectivity (LMC) is a globally acclaimed sustainable measure in urban transportation. It discourses the issues commonly faced in recent times concerning environmental, economic, and social aspects of transit systems. In this paper, the authors have attempted to review various articles and literature documents concerning the global and contextual scenarios to understand the status of LMC in India. Published methodologies by multiple researchers are followed to analyze the literature documents, and subsequently, the descriptions and synthesis are summarized in outlined sections. Thus, this paper aims to understand the policy and implementation approaches for LMC in Indian cities and analyze the impact on the current transportation scenario. Finally, it outlines the status of LMC and briefly promotes the future scope toward building a sustainable environment holistically.

The Impacts of Road Traffic on Urban Carbon Emissions and the Corresponding Planning Strategies

Carbon emissions increase the risk of climate change. As one of the primary sources of carbon emissions, road traffic faces a significant challenge in terms of reducing carbon emissions. Many studies have been conducted to examine the impacts of cities on carbon emissions from the perspectives of urbanization, population size, and economics. However, a detailed understanding of the relationship between road traffic and urban carbon emissions is lacking due to the lack of a reasonable set of road traffic metrics. Furthermore, there have been fewer studies that have conducted cluster analyses of the impact factors, which will be supplemented in this research. We established 10 impact metrics, including the highway network system, city road network system, public transit system, and land use system of streets and transportation, using 117 county-level cities in Hebei Province as the study area, which is one of the regions in China with the most acute conflicts between economic development and the environment. We built an ordinary least squares (OLS) model, a spatial lag model (SLM), a spatial error model (SEM), a spatial Durbin model (SDM), and a geographically weighted regression (GWR) model, and performed a cluster analysis on the key metrics. The results are as follows: (1) The difference in spatial distribution of urban land-average carbon emissions is obvious, highly concentrated in the areas surrounding Beijing and Tianjin. (2) The GWR model has a higher R2 and a lower AICc than global models (OLS, SLM, SEM, and SDM) and performs better when analyzing the impact mechanism. (3) Highway network density, city road length, and density of the public transit network have significant effects on urban land-average carbon emissions, whereas the street and transportation land use systems have no significant effect, which indicates that the highway network and public transit systems should be prioritized. (4) The GWR model results show that the impact of the four metrics on the urban land-average carbon emissions exhibits clear spatial heterogeneity with a significant piecewise spatial distribution pattern. The highway network density has a relatively large impact on the northern region. The northwest is more affected by the density of the public transit network. The southwest is most impacted by the length of city roads. (5) The study area is divided into four distinct characteristic areas: the highway network dominant impact area, the public transit dominant impact area, the city road network dominant impact area, and the multi-factor joint impact area. Different traffic optimization strategies are proposed for different areas.

Extensibility of a Machine Learning Model for Stormwater Basin Design and Retrofit Optimization Through a User-Friendly Web Application

To sequester particulate matter (PM) and chemical loads, stormwater basins are a common infrastructure component of transportation land use systems in a planning/design or a retrofit phase. Basin design relies on residence time (RT), capacity–inflow ratio (CIR), and continuous stirred-tank reactor (CSTR) concepts to provide presumptive guidance for load reduction. For example, 14-day (or 21-day) RT is presumed to provide load reduction for total suspended solids (TSS) (e.g., commonly 80%), and for total phosphorus (TP) (e.g., commonly 60%). Despite such guidance, most existing basins are impaired—not meeting presumptive guidance, whether for TSS, nutrients, or chemicals (e.g., metals). RT guidance results in high initial basin design costs (land and construction). For impaired basin retrofit, RT guidance generates a significantly higher cost (area/volume increase), if such a retrofit is even feasible considering proximate infrastructure constraints. This study presents a computational fluid dynamics (CFD)-machine learning (ML) augmented web application, DeepXtorm, for cost–benefit optimization of basin design and retrofit. DeepXtorm is examined against basin costs from as-built data, RT, CIR, and CSTR (deployed in the Storm Water Management Model [SWMM]) over a range of load reduction levels of TSS (60%–90%) and TP (40%–60%). For a given load reduction requirement, DeepXtorm demonstrates up to an order of magnitude (or more) lower basin cost compared with RT, CIR, and CSTR models, and greater than an order of magnitude compared with as-built cost data. DeepXtorm represents a more robust and cost-effective tool for stormwater basin design and retrofit.

RESTORATION AND MODERNIZATION OF THE AIRPORT NETWORK OF UKRAINE IN THE CONTEXT OF CURRENT SOCIO-ECONOMIC AND POLITICAL CHANGES

A comprehensive analysis of the provisions of the Aviation Transport Strategy of Ukraine until 2030 was carried out. A number of negative consequences for the airport infrastructure and the aviation industry as a whole after the beginning of the full-scale invasion of Russian troops on the territory of Ukraine have been identified. The need to improve the structure of the national aviation transport system is substantiated in the theory of transport systems. The main tasks with the step-by-step implementation of the necessary measures for the restoration and modernization of the aviation industry of Ukraine in accordance with the draft Plan for the Restoration of Ukraine have been revealed. The main areas of adaptation of the general plans of cities to the modern needs of the region, taking into account the restoration of landscaping and rational spatial development, have been determined. The fact that the regulatory and legal regulation of air transport land use should be adapted to the standards of the European Union is burdened.

Transport Infrastructure and Regional Development: A Survey of Literature on Wider Economic and Spatial Impacts

The main purpose of the paper is to provide an overview of methods used to research the relationship between transport infrastructure investments and regional development, with a focus on wider impacts both in the traditional economic context (wider economic impacts or WEIs) and—which is a methodological novelty proposed by this paper—in the spatial context (wider spatial impacts or WSIs). In terms of methodology, research may be conducted using the following methods: cost benefit analysis (CBA) and multi-criteria analysis (MCA), accessibility and potential models, econometric production function estimates, regional modelling, trade and input-output (IO) modelling, Land Use-Transport Interaction (LUTI) or spatial computable general equilibrium (SCGE) models. In the European Union, approaches based on cost-benefit analysis (CBA), or possibly multi-criteria analysis (MCA), continue to prevail. Notwithstanding the availability of a wide range of methods for quantifying socio-economic impacts, the European Union recommends that wider economic impacts at the regional and national levels be assessed by means of qualitative approaches. This being the case, it is recommendable for the European Commission to take broad-ranging action towards ensuring that the effects of transport infrastructure investments are assessed by means of a broader range of approaches.

Streamlining urban forest monitoring based on a large-scale tree survey: a case study of highway vegetation in Hong Kong.

Through the analysis of an urban tree inventory with the aid of machine learning, this study brought together different aspects of urban forestry. Urban tree monitoring is essential to successful urban forestry. Transport land use accommodates huge tree stock which requires substantial monitoring efforts. In Hong Kong, more research is needed to take into consideration how monitoring works can be improved in response to variations in tree stand characteristics. This case study aimed to illustrate the usefulness of a large-scale tree survey in mainstreaming future tree monitoring and management in transport land use. A total of 7209 trees were found in a large-scale tree survey conducted in 53 slopes and 52 verges along San Tin Highway in Hong Kong. Dominance by Corymbia citriodora (72%) was observed, especially for the highway verges. Using chi-square tests, significant associations were found between monospecific stands, habitat type, and tree risk rating. A logistic regression model was constructed to predict the occurrence of monoculture. Every metre increase in maximum tree height, the odds of a stand being monospecific would be 1.22 times greater. Stands on verges had 5.26 times greater odds of being monospecific against the slope. The associations and relationships were attributed to the dominance of C. citriodora. By boosting the logistic model, model reliability increased as kappa rose from 0.51 to 0.63, while balanced accuracy improved from 0.72 to 0.85. The occurrence of monospecific stands could be reliably predicted using maximum tree height and habitat type of tree stands. These quantitative findings monitoring can guide urban forest monitoring. Through a better understanding of urban forest structure and composition, future monitoring can aid the mainstreaming of urban forestry in transport planning.

Multi-scenario based urban growth modeling and prediction using earth observation datasets towards urban policy improvement

Urbanization is a growing challenge for city planners and policymakers who are continuously focusing on computer-based statistical models, and machine learning for a sustainable and livable city. The main objectives of this article were to develop a robust artificial intelligence-based hybrid geo-simulation model to support multi-scenario urban growth modeling for urban policy improvement. In this study, earth observation datasets, Artificial Neural Network-Multilayer Perceptron coupled with Markov Chain (MLP-Markov) and Cellular Automata and Markov Chain (CA-Markov) were applied and the best performance was measured for urban growth modeling. The result shows that the urban land use was 25.79, 31.40, 45.19, 89.22 and 147.96 square km in 1971, 1981, 1991, 2001 and 2011 which has been predicted for 2021, 2031, 2041 and 2051 based on the planned and unplanned development scenarios. The predicted urban land use of the planned development scenario is 242.10, 312.69, 363.80 and 400.72 square km while 242.91, 314.31, 366.23 and 403.98 square km of the unplanned development scenario during 2021, 2031, 2041 and 2051. The uncertainty result shows that overall agreement (84.99%) and other indices are higher, and disagreement is lower (15.01%) for MLP-Markov than the CA-Markov for the urban land use prediction. The hybrid geo-simulation models were tested over multiple urban planning indicators to understand urban growth patterns and related scenarios. The result shows that the geo-simulation model is extremely sensitive to the complex pattern of urban growth and disperse indicators over space and time. This study provides a promising guideline for urban planners and conservation scientists to implement a robust artificial intelligence-based hybrid geo-simulation model for compact, organized, and integrated land use-transportation development. HIGHLIGHTS Raipur city passing through sprawling and unplanned development due to uncontrol population growth and unmanaged development practices by local government and planning authority. Master plan of the development authority of the city failed to restrict unplanned development. Proposed urban growth model promote a compact, organized, integrated land use-transportation development and sustainable urban planning. Study, highlighted complexity in the modeling and suggested simplified Machine-based hybrid geo-simulation model for future urban growth for urban policy improvement.

Bus Transit and Land Use: Illuminating the Interaction

Attracting people to public transit in urban areas has proven to be a difficult task indeed. Recent research on the transportation–land use connection has suggested that transit use can be increased through transit-friendly land use planning. While significant evidence exists that a relationship between land use and transit is apparent, the exact nature of the relationship remains ambiguous. Despite the murky nature of the relationship, many practitioners and researchers have asserted claims regarding land use policy, namely TOD, and its effect on travel. This article examines the effect of land use, socioeconomics, and bus transit service on transit demand in the Twin Cities. The findings suggest that vertical mixed-use is important close to transit access and retail plays an important role up to a quarter mile from transit service. Population density is more important at a block-group level than block level, suggesting that density adjacent to the line may not play as critical a role as density in the larger surrounding area.

Interactive mapping for public transit planning: Comparing accessibility and travel-time framings

As transport planners increasingly frame project impacts in accessibility terms, it is worth considering how this foundational land-use transport interaction concept can shape stakeholder attitudes. In this paper, we test whether framing the benefits of public transit projects in terms of increased accessibility better fosters enthusiasm among advocates, as compared to framing benefits in terms of travel-time savings. We test two versions of an interactive mapping tool in small workshops examining upgraded bus services. One version shows isochrones and accessibility indicators, and the other shows paths and travel time indicators. Results from pre- and post-surveys suggest that framing impacts in accessibility terms may encourage broader thinking and stronger dialog than framing impacts in time-savings terms. In particular, the accessibility version seems to mitigate skepticism and car users’ predispositions against upgrading bus service. An unexpected result is that many workshop participants report decreased overall enthusiasm for the bus upgrades after using either version of the tool. This disappointment may stem from an unrealistic baseline, which assumes perfect schedule adherence not aligned with lived experiences. Future research should consider tools that help stakeholders understand and deliberate about actual service and network-level reliability, and testing such tools with wider audiences.

The relationship between walkability and QOL outcomes in residential evaluation

To improve social factors related to quality of life (QOL), slow travel modes, represented by walking, may enable more opportunities for physical activities/social contacts. The impacts of walking may vary by the walkability of spatial environments, namely, by how accessible activities are by walking, and how safe, comfortable, and pleasant neighborhood streets are for walking. Little is yet known about the influence of walkability on various QOL outcomes, especially in car-oriented Asian cities. This study aimed to comprehensively explore the relationship between walkability and QOL outcomes by assessing the relationship between their multiple indicators. We first defined the indicators of walkability and QOL. Then, a questionnaire-based survey was administered to 500 inhabitants of Nagoya city (Japan) for evaluations of their neighborhoods with the indicators of walkability (neighborhood street accessibility and street quality) and QOL (work, health, social relationship, and attachment). Finally, the relationships between walkability and QOL outcomes were analyzed using structural equation modeling (SEM). Results showed that, neighborhood street quality (particularly pleasurability) affected the QOL outcomes through the interrelationship among the outcomes, more significantly than accessibility. The findings suggest the importance of street quality in land-use transport planning.

Association of Traffic-Related Air Pollution with Newborn’s Anthropometric Indexes at Birth

BACKGROUND AND AIM: The evidence suggested many adverse health effects due to exposure to traffic-related air pollution (TRAP). However, the evidence on neonatal anthropometric measurements (NAPM) in low and middle-income countries is very scarce yet. Therefore, we investigate the association of prenatal exposure to indicators of traffic and ambient particulate matter (PM) with NAPM. METHODS: This cross-sectional study was based on hospital medical records of 4053 mother-neonate pairs (2016-2018). PMs were estimated at residential addresses based on validated spatiotemporal models. Moreover, total street length in 100, 300 and 500m buffers around the home, residential distance to the ring road, major roads, heavy-traffic lights, gas station, motorway junction, bank, square, bus terminal, public parking and industrial land-use were calculated as indicators of traffic. The head circumference (HC), birth weight (BW) and birth length (BL) of neonates were collected as NAPM. Multivariate regression models were applied to evaluate the relationship between PMs and indicators of traffic with NAPM, controlled for relevant covariates. RESULTS: The adjusted models revealed that higher exposure to PM2.5 and PM10 was significantly related with lower BW and BL. Similarity results were observed for total street length in a 100 m buffer around maternal home with BW and BL. Moreover, higher distance to heavy traffic lights was significantly associated with higher BW and BL. An IQR increase in PM10 was significantly related to lower HC (95% CI: -0.11, -0.01, P-value = 0.03). An increase in distance from residential address to heavy traffic lights, ring roads, bus terminal, and transportation land-use was associated with higher HC. CONCLUSIONS: This is the first study on the association of prenatal exposure to ambient PMs and different indicators of traffic with NAPM. Overall, our findings suggested that higher prenatal exposure to TRAP was related with lower BW, BL and HC. Keywords: Infant, Maternal, Neonates

FABILUT: The Flexible Agent-Based Integrated Land Use/Transport Model

Integrated land-use transport models are often accused of being too complex, too coarse or too slow. We tightly couple the microscopic land use model SILO (Simple Integrated Land Use Orchestrator) with the agent-based transport simulation model MATSim (Multi-Agent Transport Simulation). The integration of the two models is person-centric. It means, firstly, that travel demand is generated microscopically. Secondly, SILO agents can query individualized travel information to search for housing or jobs (and to choose among available modes). Consequently, travel time matrices (skim matrices) are not needed anymore. Travel time queries can be done for any time of the day (instead of for one or few time periods), any x/y coordinate (instead of a limited number of zones) and take into account properties of the individual. This way, we avoid aggregation issues (e.g., large zones that disguise local differences) and we can account for individual constraints (e.g., nighttime workers who cannot commute by public transport for lack of service). Therefore, the behavior of agents is represented realistically, which allows us to simulate their reaction to novel policies (e.g., emission-class-based vehicle restrictions) and to extract system-wide effects. The model is applied in two study areas: a toy scenario and the metropolitan region of Munich. We simulate various transport and land use policies to test the model capabilities, including public transport extensions, zones restricted for private cars and land use development regulations. The results demonstrate that the increase of the model resolution and model expressiveness facilitates the simulation of such policies and the interpretation of the results.