Chiara Calastri

A reduction in diesel and petrol vehicles and a shift to electric vehicles (EVs) are part of many governments’ plans towards transport decarbonisation. To encourage such a shift, governments need to implement effective policies. In a stated adaptation experiment, 444 respondents were presented with four scenarios with hypothetical pricing strategies concerning EV purchase subsidies and prices for fuel, electricity, and public transport. The scenarios involved the presentation of values specifically calculated depending on the cost and composition of actual mobility tools in the household, and respondents were asked to adapt their household fleet in response. For example, they could remove current vehicles, add new ones or acquire/relinquish public transport (PT) passes. The effect of cost-related interventions on their decisions was modelled in an integrated choice and latent variable (ICLV) model. Our results suggest that the decision to remove a conventional vehicle and/or replace it with an electric vehicle can be effectively promoted by increasing fuel prices, lowering electricity prices and lowering PT fares. Providing subsidies for the purchase of EVs was found to be ineffective. An analysis of attitudes revealed that people with greater intention to buy an EV are less affected by any pricing strategies. Incentives for removing a conventional vehicle are only effective for people who are more concerned about the environment.

A reduction of diesel and petrol vehicles and a shift from conventional to electric vehicles (EV) is part of many governments’ plans towards transport decarbonization. To encourage such a shift, governments need to implement effective policies. In a stated adaptation experiment, 444 respondents were presented with four scenarios with hypothetical pricing strategies concerning EV purchase subsidies, and prices for fuel, electricity, and public transport. The scenarios involved the presentation of values specifically calculated depending on the cost and composition of actual mobility tools in the household, and respondents were asked to adapt their household fleet in response. For example, they could remove current or add new vehicles or public transport (PT) passes. The effect of such cost-related interventions on their decisions was modelled in an integrated choice and latent variable (ICLV) model. Our results suggest that the decision to remove a conventional vehicle and/or replace it with an electric vehicle can be effectively promoted by increasing fuel prices, lowering electricity prices, and lowering PT fares. Providing subsidies for the purchase of EVs was found to be ineffective. An analysis of attitudes revealed that people with greater intention to buy an EV are less affected by any pricing strategies. Incentives for removing a conventional vehicle are only effective for people who are more concerned about the environment.

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 (2019) and during (2020) the pandemic. Participants were recruited by a survey panel to be representative of the UK population aged 18–45 years. Both surveys asked about the circumstances and reasons for the last ED attendance, with specific pandemic-related questions in the second one. Comparisons of characteristics and symptoms of individuals attending during the pandemic were compared with those attending in prior years using χ ² tests. We determined the proportion of patients who had symptoms during the pandemic but did not attend, and the reasons for that choice. Results: Young and high-income people, those with chronic illnesses and those with influenza-like symptoms were more likely to attend the ED during lockdown than before. 18% of respondents had experienced urgent symptoms during the pandemic; 60% of these individuals chose not to go to the ED. While about 30% of this group stated they believed their symptoms were not serious enough, 85% of these individuals mentioned fear of infection or worry about overburdening the system as a reason for not attending. Individuals attending during the pandemic were more likely to consider their visit unnecessary compared with those attending previously. Conclusions: The study suggests that the decision to use the ED has a discretionary component. This could potentially contribute to unnecessary visits, and raises concerns that some patients who should present at the ED do not go. More effective communication about who should visit EDs during a pandemic, and the safety of doing so, is needed.

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 novelty of this study with respect to this literature is two-fold. Firstly, we specifically study the engagement in different online activities while travelling, and apply the state-of-the-art Multiple Discrete-Continuous Extreme Value (MDCEV) model to jointly model the choice and duration of multiple activities. We apply this model to data collected face-to-face from train passengers in the UK. We find that activity choice and duration is explained by both passenger and trip characteristics, especially trip purpose, ticket type and day/time of the trip. Secondly, we show how such modelling can assist in investment appraisal, in particular by providing insights into lower- and upper- bound estimates of the proportion of the entire travel time spent working, itself of importance in, for example, valuation of business travel time using the so-called Hensher Equation. We present a detailed discussion of how the findings from our work contribute to the broader discourse around the nature of travel time and its valuation.

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 working and shopping in a day. While these applications provide valuable insights into activity participation, they ignore disaggregate activity-episodes, that is the fact that people might split their total time spent working in multiple separate blocks, with breaks or other activities in between. Insights into this splitting into episodes are necessary for predicting trips and understanding time use satiation. We propose a modified MDCEV model where an activity-episode, rather than an activity type, is the basic choice alternative, using a modified utility function to capture the reduced likelihood of individuals performing a very large number of episodes of the same activity. Results from two large revealed preference datasets exhibit equivalent forecast accuracy between the traditional and proposed approach at an aggregate level, but the latter also provides insights on the number and duration of activity-episodes with significant accuracy.