The Integrated Dynamic Land Use and Transport Model MARS

Pfaffenbichler, Paul; Emberger, Guenter; Shepherd, Simon

doi:10.1007/s11067-007-9050-7

Cited by 63 publications

(33 citation statements)

References 10 publications

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“…International examples of urban-related dynamic models include land use transportation systems for Montgomery County, Maryland (Haghani et al, 2003), Dalian, China (Wang et al, 2008) and as applied within Europe and Asia (Pfaffenbichler et al, 2008). Others have been utilised to plan for airport zones, for example, Zhengzhou in China (Liu et al, 2012), sustainable land use and urban development in Hong Kong (Shen et al, 2009), land use and land cover change in Daqing City in China (Yu et al, 2011) and in Burkina Faso (Rasmussen et al, 2012), and urban development projects in Seoul, South Korea (Park et al, 2013).…”

Section: Understanding Urban Modellingmentioning

confidence: 99%

Key Challenges and Potential Urban Modelling Opportunities in South Africa, with Specific Reference to the Gauteng City-Region

Wray¹,

Cheruiyot²

2015

SA J of Geomatics

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Section: Understanding Urban Modellingmentioning

confidence: 99%

Key Challenges and Potential Urban Modelling Opportunities in South Africa, with Specific Reference to the Gauteng City-Region

Wray¹,

Cheruiyot²

2015

SA J of Geomatics

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“…The models in VENSIM are built using the Causal Loop Diagram (CLD) technique to improve transparency. Moreover predefined relations of the MARS model (see [3]) within VENSIM are used.…”

Section: Submodel 2: Mobilitymentioning

confidence: 99%

“…The model is built using the Causal Loop Diagram (CLD) technique to improve transparency. This model was used in various cities including Leeds (UK), Ho Chi Minh City (Vietnam) and Mulhouse (France) [3].…”

Section: Introductionmentioning

confidence: 99%

URSETA – an interdisciplinary decision support tool for sustainable energy and mobility strategies

Forster¹,

Kaufmann²,

Rab³

et al. 2015

WIT Transactions on the Built Environment

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The City of Vienna (Austria) follows a long-term initiative to become a Smart City. Efficient, affordable and low-carbon energy systems, as well as environmentfriendly transport systems are obligatory to provide a sustainable development of a city. URSETA (Urban Strategic Energy and Transportation Analyses) is a decision support tool which is able to communicate the dynamics of a city system and shows the potentials of implementing sustainable energy and transport techniques. Based on the Geographic Information System (GIS) data of two representative areas, the energy system including different types of energy carriers (natural gas, electrical, thermal) is analysed by an energy hub model. The transport behaviour of the city system is examined by implementing an integrated, dynamic transport and land-use model. The optimal energy and heat supply of the future city is computed by a mixed integer linear optimization problem. Various sustainable strategies with a time horizon of 10 and 20 years are evaluated in the URSETA model. The obtained results are implemented in a spatial 3D model. This model features dynamic information visualisation in a virtual reality environment and enables access for different types of stakeholders.

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“…The optimization aims at obtaining the best implementation values by maximizing the value of the linear additive MCA objective function. While the MARS model can incorporate both cost-benefit analysis and MCA, as the two most prominent appraisal methods (20), the MCA was preferred in this study because of its clear advantages in the transport sustainability context (e.g., 6, 10): (i) the possibility to represent a holistic view incorporating multiple-criteria that are difficult to monetize; (ii) the possibility to involve stakeholders and account for their priorities in the decision-making process.…”

Section: [Insert Figure 2 About Here]mentioning

confidence: 99%

Integrated Transport Planning Framework Involving Combined Utility Regret Approach

Wang

Monzón

Ciommo

et al. 2014

Transportation Research Record

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Sustainable transport planning requires an integrated approach involving strategic planning, impact analysis and multi-criteria evaluation. This study aims at relaxing the utility-based decision-making assumption by newly embedding anticipated-regret and combined utility-regret decision mechanisms in an integrated transport planning framework. The framework consists of a two-round Delphi survey, an integrated land-use and transport model for Madrid, and multi-criteria analysis. Results show that (i) regretbased ranking has similar mean but larger variance than utility-based ranking, (ii) the least-regret scenario forms a compromise between the desired and the expected scenarios, (iii) the least-regret scenario can lead to higher user benefits in the short-term and lower user benefits in the long-term, (iv) utility-based, regret-based and combined utility-regretbased multi-criteria analysis result in different rankings of policy packages, and (v) the combined utility-regret ranking is more informative compared with utility-based or regretbased ranking.

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The Integrated Dynamic Land Use and Transport Model MARS

Cited by 63 publications

References 10 publications

Key Challenges and Potential Urban Modelling Opportunities in South Africa, with Specific Reference to the Gauteng City-Region

Key Challenges and Potential Urban Modelling Opportunities in South Africa, with Specific Reference to the Gauteng City-Region

URSETA – an interdisciplinary decision support tool for sustainable energy and mobility strategies

Integrated Transport Planning Framework Involving Combined Utility Regret Approach

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