Opus (the Open Platform for Urban Simulation) and UrbanSim 4

Waddell, Paul; Borning, Alan; Ševčíková, Hana; Socha, David

doi:10.1145/1146598.1146702

Cited by 21 publications

(7 citation statements)

References 3 publications

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“…An open source framework is needed for integrated land-use/ transport modeling (compare section 3.2). OPUS (Open Platform for Urban Simulation) (Waddell, Borning, Ševíková, & Socha, 2006) was an attempt to create an open source platform for land-use, activity-based and dynamic traffic assignment models. Even though some implementations in the U.S. and Europe were built using OPUS (Waddell, Wang, & Liu, 2008;Waddell, Wang, Sevcikova, & Borning, 2015), it was not used widely.…”

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confidence: 99%

Trends in integrated land use/transport modeling: An evaluation of the state of the art

Moeckel

Garcia

Chou

et al. 2018

JTLU

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Integrated land-use/transport models have five decades of history of both widely recognized successful implementations and implementations that remained far behind their originally stated goals. This paper summarizes the state of the art of integrated land-use/transport modeling and reports on findings from the Symposium for the Integration of Land-Use and Transport Models in Raitenhaslach, which is near Munich, in 2016. From these sources, the paper identifies major challenges in integrated land-use/transport modeling and proposes paths that support successful implementations. Particular attention is given to the coordination of short-and long-term decisions, the technical integration of models, microscopic versus macroscopic frameworks and appropriate levels of model complexity. The paper concludes with five themes that require further research to ensure that integrated landuse/transport models will keep up with modeling needs in the future.

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confidence: 99%

Trends in integrated land use/transport modeling: An evaluation of the state of the art

Moeckel

Garcia

Chou

et al. 2018

JTLU

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“…[13] David Socha has studied on building simulations of land use, activity-based travel demand, and dynamic traffic assignment. [14] An interactive agent-based behavioral modeling system as well as adaptive geometry generation algorithms for roads, parcels, and building envelopes.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulating Land Prices and Jobs in Aurangabad City through OPUS: Open Platform for Urban Simulation

Shingne¹,

Meshram²

2015

IJERT

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The paper present study on the land use model using UrbanSim in Aurangabad city of India country, as a case study on urban planning project. This project aims at advancing the modeling of interactions between land use and transport and adapting the software OPUS (Open platform for Urban Simulation) under the Indian context. UrbanSim is open source software-based tool intended to be used by Washington University, governmental institutions and planning agencies. The report documents the data acquisition, processing and definitions of the Aurangabad case study. The technical approach to create a base year for runing a "first run" of UrbanSim. The study area was divided into gridcell system. The basic model of spatial analysis is used as residential location, employment location, real estate development, land price models. The models were simulated for the period 2010 to 2020. This has provided information regard to changes in population, employment, land prices, population density etc..

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“…While most of the travel forecasting models treat the long-term choices and hence the land-use as exogenous to travel behavior, there have been recent attempts to model the longer-term and shorter-term choices in an integrated manner, including OPUS/Urbansim (Waddell et al, 2006), ILUTE (Salivini and Miller, 2005), and ILUMASS (Strauch et al, 2003). There have also been models studying the relationships between individual elements of land-use related choices and travel behavior choices.…”

Section: Connecting Long-term and Short-term Choicesmentioning

confidence: 99%

Activity-based Travel Demand Analysis

Pinjari

Bhat²

2011

A Handbook of Transport Economics

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INTRODUCTIONThe primary focus of transportation planning, until the past three decades or so, was to meet long-term mobility needs by providing adequate transportation infrastructure supply. In such a supply-oriented planning process, the main role of travel demand models was to predict aggregate travel demand for long-term socio-economic scenarios, transport capacity characteristics, and land-use configurations.Over the past three decades, however, because of escalating capital costs of new infrastructure, and increasing concerns regarding traffic congestion and air-quality deterioration, the supply-oriented focus of transportation planning has expanded to include the objective of addressing accessibility needs and problems by managing travel demand within the available transportation supply. Consequently, there has been an increasing interest in travel demand management strategies, such as congestion pricing, that attempt to change transport service characteristics to influence individual travel behavior and control aggregate travel demand.The interest in analyzing the potential of travel demand management policies to manage travel demand, in turn, has led to a shift in the focus of travel demand modeling from the statistical prediction of aggregate-level, long-term, travel demand to understanding disaggregate-level (i.e., individual-level) behavioral responses to shortterm demand management policies such as ridesharing incentives, congestion pricing, and employer-based demand management schemes (alternate work schedules, telecommuting, etc.). Individuals respond in complex ways to such changes in travel conditions. The limitation of the traditionally used statistically-oriented trip-based travel modeling approach in capturing these complex individual responses has resulted in the development of behaviorally-oriented activity-based approaches to modeling passenger travel demand. 1The origin of the activity-based approach dates back to the 1960's from Chapin's (Chapin 1974) research on activity patterns of urban population. Chapin provided a motivational framework in which societal constraints and inherent individual motivations interact to shape activity participation patterns. This framework, however, ignored the spatial context (or geography of) activity participation and did not address the relationship between activities and travel. During the same time, the first explicit discussion in the literature on activity participation in the context of time and space appears to have been proposed by Hagerstrand (1970).2 While Hagerstrand's work 1 The reader will note here that the activity-based approach has emerged in the context of modeling passenger travel demand, not for freight travel modeling. 2 In his presidential address at a regional science association congress in 1969, Hagerstrand identified three types of constraints that shape individual activity patterns: (1) authoritative constraints, (2) capability constraints, and (3) coupling constraints. Authoritative constraints refer to the constraints impose...

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Opus (the Open Platform for Urban Simulation) and UrbanSim 4

Cited by 21 publications

References 3 publications

Trends in integrated land use/transport modeling: An evaluation of the state of the art

Trends in integrated land use/transport modeling: An evaluation of the state of the art

Simulating Land Prices and Jobs in Aurangabad City through OPUS: Open Platform for Urban Simulation

Activity-based Travel Demand Analysis

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