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.     

Creating bus timetables with maximal synchronization

This paper addresses the problem of generating a timetable for a given network of buses so as to maximize their synchronization. It attempts to maximize the number of simultaneous bus arrivals at the connection (transfer) nodes of the network. Transit schedulers, taking into account the satisfaction and convenience of the system's users, appreciate the importance of creating a timetable with maximal synchronization, which enables the transfer of passengers from one route to another with minimum waiting time at the transfer nodes. In this paper, the problem is formulated as a mixed integer linear programming problem, and a heuristic algorithm is developed to solve the problem in polynomial time. The efficiency of this algorithm, compared to optimal solutions, is illustrated through a series of examples.     

Generation of synthetic datasets for discrete choice analysis

Despite the widespread use of synthetic data in discrete choice analysis, little is known about how the methodology used to generate synthetic datasets influences the properties of parameter estimates and the validity of results based on these estimates. That is, there are two potential sources of biases when using synthetic discrete choice data: (1) bias due to the method used to generate the dataset; and, (2) bias due to parameter estimation. The primary objective of this study is to examine bias due to the underlying data generation method. This study compares three methods for generating synthetic datasets and uses design of experiments and analysis of variance methods to investigate the ability to recover estimates for “true” logsum parameters for nested logit models. The method that uses nested logit probabilities to generate the chosen alternative results in unbiased parameter estimates. The method that is based on Gumbel error component approximations reveals that while the error components themselves are unbiased, subtle empirical identification problems can arise when these error components are combined with synthetically generated utility functions. The method that is based on normal error component approximations reveals that all logsum coefficients are biased upwards; the bias dramatically increases for those nests that have a low choice frequency and is most pronounced for those nests with high correlations among alternatives. Based on the results of the analysis, several recommendations for the generation of synthetic datasets for discrete choice analyses are provided.     

Creating complex congestion patterns via multi-objective optimal freeway traffic control with application to cyber-security

This article presents a study on freeway networks instrumented with coordinated ramp metering and the ability of such control systems to produce arbitrarily complex congestion patterns within the dynamical limits of the traffic system. The developed method is used to evaluate the potential for an adversary with access to control infrastructure to enact high-level attacks on the underlying freeway system. The attacks are executed using a predictive, coordinated ramp metering controller based on finite-horizon optimal control and multi-objective optimization techniques. The efficacy of the control schemes in carrying out the prescribed attacks is determined via simulations of traffic network models based on the cell transmission model with onramps modeled as queue buffers. Freeway attacks with high-level objectives are presented on two illustrative examples: congestion-on-demand, which aims to create precise, user-specified pockets of congestion, and catch-me-if-you-can, which attempts to aid a fleeing vehicle from pursuant vehicles.     

ATCEM: a synthetic model for evaluating air traffic complexity

Air traffic complexity, which measures the disorder of air traffic distribution, has become the critical indicator to reflect air traffic controller workload in air traffic management (ATM) system. However, it is hard to assess the system accurately because there are too many correlated factors, which make the air traffic complexity nonlinear. This paper presents an air traffic complexity evaluation model with integrated classification using computational intelligence (ATCEM). To avoid redundant factors, critical factors contributing to complexity are analyzed and selected from numerous factors in the ATCEM. Subsequently, to construct the mapping relationship between selected factors and air traffic complexity, an integrated classifier is built in ATCEM. With efficient training and learning based on aviation domain knowledge, the integrated classifier can effectively and stably reflect the mapping relationship between selected factors and the category of air traffic complexity to ensure the precision of the evaluation. Empirical studies using real data of the southwest airspace of China show that the ATCEM outperforms a number of state‐of‐the‐art models. Moreover, using the critical complexity factors selected in ATCEM, the air traffic complexity could be effectively estimated. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

Changes in transport risk perception in urban populations in Norway from 2004 to 2013

Urban populations transport risk perception is interesting because it is associated with travel mode choices and use. This study investigates changes in transport-related risk constructs in the urban population in Norway in 2004 and 2013, and describes whether people perceive private or public to be associated with the highest risk. The results are based on self-completion questionnaire surveys conducted in two independent representative samples living in the same urban areas in 2004 (n?=?592) and 2013 (n?=?1035). Overall, the respondents perceived the risk as lower in 2013 than in 2004. For both time periods, people consistently assessed the risk constructs related to private motorized transportation as higher than corresponding risk in public transportation. The findings suggest that while transportation risk perception in urban populations may change over time, the pattern that private motorized transportation is associated with a higher perceived risk than public transportation remains stable.     

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.     

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.