scholarly journals Quantifying the impacts of the modifiable areal unit problem in travel demand models

2021 ◽  
Author(s):  
Sean Nix
Keyword(s):  
Travel Demand ◽  
Traffic Assignment ◽  
Geographic Region ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Demand Models ◽  
Travel Demand Modelling ◽  
Subject Areas ◽  
Demand Modelling ◽  
Assignment Models

This project introduces new analyses of the impacts of the modifiable areal unit problem (MAUP) in traffic assignment models which are not widely available in the literature, as well as to reveal how stable the effects are in diverse models. A comprehensive review of the literature is conducted to provide an overview of MAUP, including the scale and zonal effects, as well as its recent applications in travel demand modelling and other subject areas. Particular scrutiny is made towards inappropriate methods of MAUP-analysis in travel demand models. The scale effect is tested in traffic assignment models using associated zone structures of the Greater Montreal Area (GMA), a unique geographic region involving island regions and water bodies.

scholarly journals Quantifying the impacts of the modifiable areal unit problem in travel demand models

2021 ◽  
Author(s):  
Sean Nix
Keyword(s):  
Travel Demand ◽  
Traffic Assignment ◽  
Geographic Region ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Demand Models ◽  
Travel Demand Modelling ◽  
Subject Areas ◽  
Demand Modelling ◽  
Assignment Models

This project introduces new analyses of the impacts of the modifiable areal unit problem (MAUP) in traffic assignment models which are not widely available in the literature, as well as to reveal how stable the effects are in diverse models. A comprehensive review of the literature is conducted to provide an overview of MAUP, including the scale and zonal effects, as well as its recent applications in travel demand modelling and other subject areas. Particular scrutiny is made towards inappropriate methods of MAUP-analysis in travel demand models. The scale effect is tested in traffic assignment models using associated zone structures of the Greater Montreal Area (GMA), a unique geographic region involving island regions and water bodies.

scholarly journals Introduction of car sharing into existing car fleets in microscopic travel demand modelling

2017 ◽  
Vol 21 (6) ◽  
pp. 1055-1065 ◽  
Author(s):  
Matthias Heinrichs ◽  
Daniel Krajzewicz ◽  
Rita Cyganski ◽  
Antje von Schmidt
Keyword(s):  
Travel Demand ◽  
Car Sharing ◽  
Travel Demand Modelling ◽  
Demand Modelling

scholarly journals mobiTopp – A Modular Agent-based Travel Demand Modelling Framework

2013 ◽  
Vol 19 ◽  
pp. 854-859 ◽  
Author(s):  
Nicolai Mallig ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Travel Demand ◽  
Agent Based ◽  
Modelling Framework ◽  
Travel Demand Modelling ◽  
Demand Modelling

Are Activity Patterns Stable or Variable? Analysis of Three-Year Panel Data

2018 ◽  
Vol 2672 (47) ◽  
pp. 46-56 ◽  
Author(s):  
Tim Hilgert ◽  
Sascha von Behren ◽  
Christine Eisenmann ◽  
Peter Vortisch
Keyword(s):  
Life Events ◽  
Travel Demand ◽  
Activity Patterns ◽  
Panel Survey ◽  
Travel Demand Modelling ◽  
Demand Modelling ◽  
Socio Demographic Factors ◽  
Demographic Covariates ◽  
Modelling And Forecasting ◽  
Variable Analysis

Routines and mandatory activities, such as work and school, shape the activity patterns of individuals and strongly influence travel demand. Knowledge about stability and variability of these routines could strengthen travel demand modelling and forecasting. A longitudinal perspective is required to investigate these aspects. In this study, the activity patterns of a sample of people is compared for one week in two successive years. It is analyzed whether the activity patterns of a given person vary from year to year, to what degree, and how this variability and stability can be measured. It is considered whether socio-demographic factors and life events determine stability in weekly activity patterns. The study is based on the representative panel survey, German Mobility Panel. The weekly activity patterns of the same respondents in different years is assessed, using two methods to measure stability and variability. The survey respondents are clustered into three groups according to the degree of variability in their activity patterns. A logistic regression model is also used to identify socio-economic and demographic covariates for similarity in weekly activity patterns. Results show that about one-third of the sample had the same or very similar weekly activity patterns in the two years examined. A person’s occupation status is a good predictor for the variability of activity patterns. Moreover, persons undergoing a change in occupation status are quite likely to show a greater variability in their activity patterns.

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