Generating synthetic baseline populations from register data

The paper presents a population synthesiser based on the method of Iterative Proportional Fitting (IPF) algorithm developed for the new Danish national transport model system. The synthesiser is designed for large population matrices and allows target variables to be represented in several target constraints. As a result, constraints for the IPF are cross-linked, which makes it difficult to ensure consistency of targets in a forecast perspective. The paper proposes a new solution strategy to ensure internal consistency of the population targets in order to guarantee proper convergence of the IPF algorithm. The solution strategy consists in establishing a harmonisation process for the population targets, which combined with a linear programming approach, is applied to generate a consistent target representation. The model approach is implemented and tested on Danish administrative register data. A test on historical census data shows that a 2006 population could be predicted by a 1994 population with an overall percentage deviation of 5-6% given that targets were known. It is also indicated that the deviation is approximately a linear function of the length of the forecast period.     

Introducing synthetic pseudo panels: application to transport behaviour dynamics

Transportation - In this paper, a method to study travel behaviour dynamics by constructing detailed synthetic pseudo panels from repeated cross-sectional data is presented. The method is based on...     

Exposure of urban populations to road traffic noise in Hong Kong

This article examines the exposure of Hong Kong’s population to road traffic noise and whether there is class-biased inequality in this. The socio-economic characteristics of residents of public and private building are taken from census data and the noise exposure levels from 3-D noise mapping. Using decile plots and binary logistic regression analysis, we find that in general the socially deprived groups tend to live in building groups exposed to higher noise levels. This is particularly pronounced in private housing estates where older, less educated, those engaged in craftsman and elementary jobs, and non-owners of their dwellings are exposing to higher levels of traffic noise. Inequality is less conspicuous in public housing estates.     

A dynamic modeling approach to investigate impacts to protected and low-income populations in highway planning

Environmental justice (EJ) assessment has traditionally focused on identifying distributive effects to protected populations. Federal and State highway improvement programs have been established to stimulate economic development for these populations. While this issue has long been recognized as part of EJ initiatives, no quantitative comparisons of highway construction impacts on protected populations have been reported in the literature. This paper presents a dynamic modeling approach to investigate impacts to protected and low-Income populations in highway planning using an integrated Geographic Information System (GIS) and Genetic Algorithms (GAs) optimization framework. Using census and county level parcel data, the model integrates various socioeconomic factors into a GIS while generating highway alignments using GAs. Examples using county level census data from North Carolina are demonstrated to test the sensitivity of generated highway alignments with constrained distances from protected populations. The results indicate that it is important to consider local social and economic effects, in addition to regional planning objectives when measuring the effectiveness of feasibility studies associated with highway construction. Within the proposed modeling framework attention is directed on various EJ initiatives, such as environmental health and safety laws in minority and low-income areas. The model would help planners, designers, and policy-makers understand the intricate interrelationships among local communities, while facilitating more scientific and economically equitable planning for highway construction projects.     

Trip generation of vulnerable populations in three Canadian cities: a spatial ordered probit approach

This paper provides an analysis of trip generation of three vulnerable groups: single-parent families, low income households, and the elderly. It compares the mobility of these groups to that of the general population in three Canadian urban areas of Hamilton, Montreal and Toronto, based on data from large-sample metropolitan transport surveys. An ordered probit model with spatially expanded coefficients is used for the analysis. Spatial expansion shows that there are spatial mobility trends for elderly populations and low-income populations even after socio-economic attributes are accounted for. Such spatial differences are not generally found for single parent families. This novel spatial analysis provides clues as to where vulnerable populations may experience greater degrees of social exclusion. It provides information to help prioritize transportation infrastructure projects or other social programs to take into account the needs of vulnerable populations with the lowest levels of mobility.