Search results

Purpose – Departure time choice not only depends on the desire to carry out activities at certain times and places; it is a complex decision making process influenced by travel conditions, congestion levels, activity schedules, and external trip factors. To estimate departure time choice models capturing the factors influencing it in appropriate form, a complex data collection procedure allowing to obtain detailed input data from different sources and at different time periods is required.

The main aim of this chapter is to describe and discuss the survey methodology we used in a time-of-day choice project, involving the collection of revealed preference (RP) and stated preference (SP) data to estimate hybrid discrete departure time choice models incorporating latent variables. Preliminary model results are also presented as an example.

Methodology/approach – Data was obtained from 405 workers at different private and public institutions located in the centre of Santiago, Chile. The survey process had three different stages and used various collection methods (e-mail, web-page, and personal interviews at the workplace) in order to satisfy efficiency, reliability and cost criteria.

The RP component survey design was based on the last origin-destination survey implemented in Santiago (i.e. a travel diary filled under an activity recall framework). Relevant level-of-service measures at different time periods were obtained from GPS data measured from instrumented vehicles in the public and private transport networks. A SP-off-RP optimal design considering dependence among attribute levels was also developed. Finally, several 1–7 Likert scale questions were included to incorporate the latent variables.

Findings – The survey methodology described in this chapter represents a successful experience in terms of collecting high quality data, from different sources, with the aim of estimating appropriate time-of-day choice models. The data collection process was carried out in different stages, by means of web pages, email, and personal interviews. The data was further enriched with level-of-service attributes measured at different times of the day with unusual precision. Preliminary results reported in this chapter show that data obtained through this methodology are appropriate to model time-of-day choices.

Originality/value of chapter – The novelty of the survey methodology described in this chapter is the collection of data of a different nature for time-of-day choice modelling through the integration of different collection techniques.

Acquisition of very precise information about preferred departure/arrival times, level of service at different times of the day, detailed information about flexibility in schedules, employment information and attitudes towards departure times, should allow practitioners to estimate hybrid time-of-day choice models incorporating latent variables.

The effects of the COVID-19 pandemic on cities have transformed the lives of urban societies across the globe. One of such effects has been the redefinition of access and urban mobility patterns, exposing divides and inequalities along the lines of class, gender and social positions. In Latin America, long-term lockdowns and widespread containment-oriented restrictions have deepened already acute conditions of poverty and deprivation. Low-income and socially vulnerable households and individuals in countries such as Colombia find themselves unable, or in a disadvantaged position, to work from home, access goods and services securely and avoid transport modes that increase exposure to contagion. This chapter examines inequalities in urban mobility and access to essential opportunities in urban settings in Colombia, through data collected from 3,900 respondents to a web survey organised during the national lockdown in the country in April 2020. The chapter presents a Latent Class Analysis model exploring how intersecting differences in class, gender, ethnicity, age and other relevant socioeconomic characteristics, influence the degree of adaptability and capacity to adapt to the challenging conditions posed by COVID-19 for physical travel and carrying out everyday activities. Building on three distinct classes of mobility and access-related conditions, the chapter reflects on structural inequalities associated with Colombian cities’ urban form, functional and productive structures and its wide social gaps. The chapter builds on empirical findings to reflect on urban policy and discuss avenues for addressing social and spatial inequalities worsened by the pandemic.

Transport and pandemics are interlinked given the ubiquitous nature of modern transport systems. The COVID-19 pandemic has provided much evidence for both virus contagion but also containment and how transport plays a role in both. As the world and its cities experienced lockdowns, there were travel restrictions, physical social distancing rules, transport systems shut down, changed operations, a re-opening with lower demands in some sectors (e.g., air transport and urban public transport services) and an increased demand in others (e.g., freight and home deliveries). These changes brought about a series of reactions at all levels, from governments and local authorities, operators of all transport modes but also personal and individual behaviour. This volume provides evidence on an array of transport and pandemic experiences through a collection of works from around the world, each chapter discussing a mode, a region and possible future outcomes. This introductory chapter provides the context for this volume with an overview of literature that looks at transport and pandemics, a timeline of events that marked the COVID-19 pandemic developments across different parts of the world, and finally an overview of the chapters in the volume. It concludes with some insights from the editors on the future of transport in a post-COVID world.