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ABSTRACTThis study introduces a survey instrument to collect retrospective life-course events, focusing on residential relocation, and it utilizes the survey to evaluate determinants of residential mobility. The survey consists of seven modules collecting information about household structure and household demographics, latest residential relocation, current and previous home, employment, education, vehicle ownership, and travel behavior. The time window of the life-course calendar in the survey is customized according to the latest residential relocation as an anchor point to assist with memory recollection and balance the required input from participants. The survey is used to collect data from a sample of 514 respondents in Sydney, Australia, and another sample of 404 respondents in Chicago, Illinois, in the US. The Cox proportional hazard model is used to analyze residential mobility. The results show primary school commencement is a salient determinant of residential relocation, but its impact is significantly higher in Chicago compared to Sydney.

The growing trend of children being driven to school has become a cause of concern in developed countries, with parents' perception of trust in neighbourhoods emerging as a key determinant. Previous research has mainly investigated the influence of either the mother's or the father's perception on children's school travel. While the contribution of each parent's perception to the final decision, and the factors affecting this contribution, remains unclear.

•Trust and distrust are not the two ends of a continuum, but they serve as separate constructs with distinct causes and consequences.•Trust and distrust have significant effects on traffic information app usage for mandatory trips, but the magnitudes of their effects are different.•Distrust is non-significant in traffic information app usage for non-mandatory trips.•The difference between the effects of trust for mandatory and non-mandatory trips was minor.

•Residents’ satisfaction increases after the completion of transport project.•During the construction satisfaction decreases among all residents.•Those staying in the same neighbourhood show higher satisfaction during construction.•Socio-demographic factors significantly impact satisfaction responses.

Transit-oriented development (TOD) advocates sustainable development and the building of livable neighborhoods. Several measures are proposed to evaluate TOD principles; however, the suitability of these measures is not fully explored in the context of developing countries. This paper provides a systematic method for multivariate analysis of TOD measures in the context of developing countries. A bivariate analysis is conducted to simultaneously study all TOD measures against the distance to transit measure because of its essential role in TOD. A dynamic zoning system is introduced to capture a realistic reflection of neighborhood that is walkable for its residents. In the proposed zoning system, neighboring zones can overlap, as the walkable areas for the nearby residences may overlap. The adherence of Tehran’s Master Plan to TOD principles is examined with a focus on TOD measures and multidimensionality in assessment. The results highlight the curse and the blessing of the high-density urban sprawl in sustainable development.

There are numerous studies on traffic volume prediction, using either non-parametric or parametric methods. The main shortcoming of parametric methods is low prediction accuracy. Non-parametric methods show higher prediction accuracy, but they are criticised due to lack of support from theory. The innovation of this paper is to combine bootstrap with the conventional parametric ARIMA model with the aim of improving prediction accuracy while maintaining theory adherence. The outcome of this process is an ensemble of ARIMA models (E-ARIMA) where each model is developed using a random subsample of data. The validity of the proposed model is examined by comparing E-ARIMA with ARIMA and Long Short-Term Memory (LSTM) as representatives for parametric and non-parametric methods respectively. One year of traffic count data on four main arterial roads in Sydney, Australia is used for calibration and validation purposes. The results suggest that creating an ensemble of models improves prediction accuracy.

Residential relocation decision making is a complicated process, and modelling this complex course of actions requires careful scrutinisation of different aspects. The relocation decision comprises several different decisions, including the reason for the relocation, relocation timing, and attributes of the desired residence. Among these decisions needing to be taken, the reason for relocation and its timing are decided earlier than others. Depending on the variant reasons and motivations for relocating, its timing may be accelerated or decelerated. Relocation usually occurs because of a multiplicity of reasons, which necessitates using a multivariate model for relocation decision making that is jointly modelled with the timing decision. A competing accelerated failure model to jointly formulate these decisions. The housing search literature emphasizes on the importance of considering financial risk acceptance level of decision makers in residential relocation decision models. Therefore, a binary logit model is used to model whether the decision maker is financially risk averse or not. This paper used longitudinal data collected in Australia from the Household, Income, and Labour Dynamics in Australia Survey. Further, the impact of group decision making on residential relocation is captured in this paper through the information provided in Household, Income, and Labour Dynamics in Australia Survey regarding the manner in which decisions are made within households.

Calibration is a critical aspect of model development that has long been recognized by researchers as a challenging issue. In particular, difficulties arise when the observed data used for calibration do not match the model output, which is the case in the majority of transport planning models. In the traditional calibration process, the origin–destination (OD) matrices are the key interface between demand and supply models, which could lead to issues when observed traffic link counts are used to update the OD matrix, causing a loss of key demand characteristics in the process. Developing a unified structure for modeling both demand and supply requires a calibration process that meets the requirements of both types of models, a serious issue which has received less attention in the literature. In this paper, the existing processes of developing and integrating demand and supply models are discussed and then examined using a case study in the Melbourne area. The numerical results show that the standard OD calibration procedure causes unrealistic changes in the OD matrix. Finally, some possible solutions to address the current limitations in development of a unified structure are discussed.

Modeling travel-related decisions of transport system users is the core of many behavioral travel demand models. It is of great significance to use these models for planning purposes in which decisions of individuals are simulated for specific time intervals or on a continuous dimension until the target year. Discrete choice and survival analysis methods are two popular econometric structures to model and forecast time-dependent outcomes. This paper elaborates the conceptual and practical differences between these two methods in the context of vehicle ownership modeling. There are meaningful differences between these two methods including data preparation approaches, interpretations of the variable of interest in the model, and the simulation procedures. Further, this paper shows how negligent application of discrete choice methods for modeling time-to-event variables results in specification bias. This discussion paves the path for using hazard-based models in travel demand modeling, as the application of these models have been quite limited compared with their capacity.