Quantifying the impacts of the modifiable areal unit problem in travel demand 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.

Quantifying the impacts of the modifiable areal unit problem in travel demand 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.

Population synthesis in activity-based travel demand

A Synthetic Population is first part of creating travel demand model by using activity-based approach. Population synthesis is application of algorithms that expanded representative samples of people or household with characterises (such as gender, car ownership, age or ethnicity etc.) to entire area of researching. Because of complexity of people decisions before or during travel, one attribute is not enough to fully describe what factors have impact on them. Population synthesis iterate a set of attributes for each person in the sample and after expansion and assigning weights create simulated people or household with their characteristic. Basic components are marginal distribution targets of household and person attributes, household and person samples and algorithm for selecting the sample records into a synthetic population such that the attributes of that population match the marginal targets. Goal of this paper is to present population synthesis and her importance for activity-based approach in travel demand modelling. The paper will consist of introduction, literature overview, presenting benefits and complexity of population synthesis, discussion and conclusion.

The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas

Recently, the explosive growth of ridesourcing, or on-demand ridesharing, has attracted a great deal of attention from researchers and planners. Despite its transformative impacts on mobility, limited studies have examined how built environment affects its use. In this study, we investigate the impacts of built environment on ridesourcing demand. We employ structural equation modelling to account for the complex relationships among study variables, and investigate the impacts at census block group level by using RideAustin data in Austin, Texas. Findings reveal strong impacts of built environment on ridesourcing demand and significant temporal heterogeneity. The models show that greater population/employment/service job densities, road density, pavement completeness, land use mix and job accessibility by transit produce more ridesourcing demand. Access to the commuter rail (MetroRail) also leads to greater demand. Furthermore, time-of-day (TOD) models demonstrate that these effects vary significantly according to the time of day. Our research has implications for policy making and for travel demand modelling of ridesourcing.

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

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.