Chiara Calastri

• PhD in Transport Studies (Choice Modelling)
• Professor (Assistant) at University of Leeds

It is widely accepted that the COVID-19 pandemic has dramatically changed travel patterns since 2020, largely due to restrictions on people’s movement and work-from-home practices. A large number of studies have been conducted to understand such changes from a trip maker’s perspective, using different types of mobility data collected across the wor...

Background The COVID-19 pandemic has been linked to a sharp drop in ED attendance, but the exact reasons for this are unclear. The aim of this study was to investigate differences between individuals attending the ED before and during the pandemic and the reasons for their choices. Methods Two population-based online surveys were conducted before...

Travel-based multitasking, i.e. using travel time to conduct enjoyable and/or productive activities, is the subject of an increasing number of theoretical and empirical studies. Most existing studies focus on modelling the choice of which activities people conduct while travelling, and a limited number of papers also focuses on their duration. The...

Road risk analysis is one of the key research areas in transport, where the impact of perceived risk on choices, especially in a dynamic setting, has been long recognised. However, due to the lack of dynamic data and the difficulty in capturing risk perception, the existing studies typically resort to static and stated approaches to infer the exper...

The increased interest in time use among transport researchers has led to a search for flexible but tractable models of time use, such as Bhat's Multiple Discrete Continuous Extreme Value (MDCEV) model. MDCEV formulations typically model aggregate time allocation into different activity types during a given period, such as the amount of time spent...

The use of virtual reality (VR) in transport research offers the opportunity to collect behavioral data in a controlled dynamic setting. VR settings are useful in the context of hypothetical situations in which real-world data does not exist or in situations which involve risk and safety issues making real-world data collection infeasible. Neverthe...

In the past few decades, the travel behaviour literature has devoted increasing attention to understanding the demand for leisure and social travel and the engagement in leisure activities. Some of the studies in this field have adopted a social network perspective, acknowledging that it is mainly the people involved motivating such activities and...

Immersive technologies in transport research are gaining popularity, allowing for data collection in a controlled dynamic setting. Nonetheless, their ecological validity is still to be established hence their use in mathematical modelling in a transport setting has been scarce. We aim to fill this gap by conducting a study of cycling behaviour wher...

Recent work in transport research has increasingly tried to broaden out beyond traditional areas such as mode choice or car ownership and has tried to position travel decisions within the broader life context. However, while important progress has been made in terms of how to capture these additional dimensions, both in terms of detailed tracking o...

The MDCEV modelling framework has established itself as the preferred method for modelling time allocation, with data very often collected through travel or activity diaries. However, standard implementations fail to recognise the fact that many of these datasets contain information on multiple days for the same individual, with possible correlatio...

This chapter encompasses the different themes and contributions presented in the Ph.D. thesis that was awarded the 2017 Eric Pas dissertation Prize at the 15th International Conference on Travel Behavior Research. Four main topics are discussed: performing choice modeling with semi-ubiquitous data, modeling decisions related to social networks, est...

Social networks have attracted attention in different fields of research in recent years and choice modellers have engaged with their analysis by looking at the role that social networks play in shaping decisions across a variety of contexts. The incorporation of the social dimension in choice models creates the need for understanding how social ne...

A considerable amount of studies in the transport literature is aimed at understanding the behavioural processes underlying travel choices, like mode and destination choices. In the present work, we propose the use of evolutionary game theory as a framework to study commuter mode choice. Evolutionary game models work under the assumptions that agen...

An understanding of activity choices and duration is a key requirement for better policy making, in transport and beyond. Previous studies have failed to make the important link with individuals' social context. In this paper, the Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) model is applied to the choice of activity type and duration...

Over the last two decades, passively collected data sources, like Global Positioning System (GPS) traces from data loggers and smartphones, have emerged as a very promising source for understanding travel behaviour. Most choice model applications in this context have made use of data collected specifically for choice modelling, which often has high...

This special issue collects six papers that were presented at the 2015 IATBR conference, which took place in Windsor, UK. It also includes a resource paper from one of the conference workshops. All regular papers were selected by the guest editors and subsequently peerreviewed in line with the European Journal of Transport and Infrastructure Resear...

Communication patterns are an integral component of activity patterns and the travel induced by these activities. The present study aims to understand the determinants of the communication patterns (by the modes face-to-face, phone, e-mail and SMS) between people and their social network members. The aim is for this to eventually provide further in...