Constraints in household relocation: Modeling land-use/transport interactions that respect time and monetary budgets

Moeckel, Rolf

doi:10.5198/jtlu.2015.810

Cited by 40 publications

(36 citation statements)

References 42 publications

(17 reference statements)

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“…The population synthesizer is incorporated with the open-source land use model SILO [43] and is available at the GitHub repository: https://github.com/msmobility/silo. The pseudocode for the optimization phase is in…”

Section: Discussionmentioning

confidence: 99%

Population Synthesis Handling Three Geographical Resolutions

Moreno

Moeckel

2018

IJGI

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Abstract:In this paper, we develop a synthetic population as the first step in implementing an integrated land use/transport model. The model is agent-based, where every household, person, dwelling, and job is treated as an individual object. Therefore, detailed socioeconomic and demographic attributes are required to support the model. The Iterative Proportional Updating (IPU) procedure is selected for the optimization phase. The original IPU algorithm has been improved to handle three geographical resolutions simultaneously with very little computational time. For the allocation phase, we use Monte Carlo sampling. We applied our approach to the greater Munich metropolitan area. Based on the available data in the control totals and microdata, we selected 47 attributes at the municipality level, 13 attributes at the county level, and 14 additional attributes at the borough level for the city of Munich. Attributes are aggregated at the household, dwelling, and person level. The algorithm is able to synthesize 4.5 million persons in 2.1 million households in less than 1.5 h. Directions regarding how to handle multiple geographical resolutions and how to balance the amount and order of attributes to avoid overfitting are presented.

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Section: Discussionmentioning

confidence: 99%

Population Synthesis Handling Three Geographical Resolutions

Moreno

Moeckel

2018

IJGI

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“…Land use may have significant interactions with transport (Moeckel, 2016;Miller & Evans, 2011). Land use mainly considers commercial area (F10), residential area (F11) and transportation area (F12), while actors like education and hospital have been involved before.…”

Section: F7mentioning

confidence: 99%

Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data

Zhang

Sun

Shen

et al. 2017

JTLU

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Abstract:With the development of in-vehicle data collection devices, GPS trajectory has become a priority source to identify traffic congestion and understand the operational states of road network in recent years. This study aims to investigate the relationship between traffic congestion and built environment, including traffic-related factors and land use. Fuzzy C-means clustering was used to conduct an exhaustive study on the 24-hour congestion pattern of road segments in an urban area, so that the spatial autoregressive moving average model (SARMA) could be introduced to analyze the output from the clustering analysis to establish the relationship between built environment and the 24-hour congestion pattern. The clustering result classified the road segments into four congestion levels, while the regression explained the impact of 12 traffic-related factors and land-use factors on the road congestion pattern. The continuous congestion was found to mainly occur in the city center, and the factors, such as road type, bus station in the vicinity, ramp nearby, commercial land use, and so on, had large impacts on congestion formation. The Fuzzy C-means clustering is proposed to be combined with quantitative spatial regression, and the overall evaluation process will assist to assess the spatial-temporal levels of service regarding traffic from the congestion perspective.Keywords: Congestion pattern, taxi GPS data, fuzzy C-means clustering, spatiotemporal regression, built environment factor IntroductionIn urban road network, the recurrent or current congestion of a certain road segment may largely impact the local network and reduce travel efficiency. Consequently, it is important to identify the Compared with mobile phone data, floating car data, cargo transport vehicle record and navigation system, taxi GPS trace data is one of the easiest available sources for accurate travel route and travel time records for a wider area with more road details. Data mining based on taxi trip can be traced back to the 1970s (Goddard, 1970), which has been applied to a wide range of studies, mainly including activity-based and infrastructure-based fields. The activity-based studies mostly focus on driver behavior, supply-demand pattern, and traffic state analysis, while the infrastructure-based studies mainly focused on lanes channelization (Tang, Yang, Kan, & Li, 2015) and signal-timing estimation (Yu & Lu, 2016).From driver behavior perspective, Zhang, Qiu, Duan, Du, and Lu (2015) proposed a space-time visualization method to demonstrate taxi daily trajectories by GIS-T to recognize working time, operating range, and residence location without time division. Qing, Parfenov, and Kim (2015) compared direct extracted datas like travel distance, speed, demand, and supply mismatch of taxi trip between fair weather and extreme storm using Manhattan GPS data, and discovered the reduction in trip distance and supply of drives during the extreme storm. Meanwhile, Hwang, Wu and Jian (2006) used structural equation modeling technique...

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“…To account for land use/transport interactions through auto ownership, the model described here has been integrated with a transportation model and a land use model. The MSTM (Maryland Statewide Transportation Model) [15] models travel behavior and the microscopic SILO (simple integrated land use orchestrator) land use model [16] simulates land use changes.…”

Section: Links Between Land Use and Transportmentioning

confidence: 99%

Car Ownership and the Impact of Built Environment, Demographics and Transport Systems

Moeckel¹,

Yang²

2016

JCEA

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Auto ownership is one of the most important linkages between travel demand and land use. Residents in denser, urban or more transit accessible neighborhoods tend to own fewer cars. Car ownership influences almost all aspects of travel behavior, including travel frequency, travel distances, mode choice and time-of-day choice. At the same time, car ownership affects residential location choices, as households owning cars are less likely to choose urban neighborhoods than households without cars. This paper describes a new microscopic auto-ownership model that has been estimated with survey data. The model is fully integrated with a land use and a transportation model to capture: (1) how owning a car affects travel behavior and location choice; and (2) how the built environment and the transportation needs affect auto-ownership decisions. The model has been validated against census data and is fully operational.

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Constraints in household relocation: Modeling land-use/transport interactions that respect time and monetary budgets

Cited by 40 publications

References 42 publications

Population Synthesis Handling Three Geographical Resolutions

Population Synthesis Handling Three Geographical Resolutions

Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data

Car Ownership and the Impact of Built Environment, Demographics and Transport Systems

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