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COVID-19 has delivered an unintended positive consequence through working from home (WFH). While it may be some time until we are able to indicate, with some confidence, the impact that WFH will have on traffic congestion and crowding on public transport, there is a sense already that it is a game changer, and indeed is one of the most effective policy levers that the transport sector has had for many years in ‘managing’ the performance of the transport network. This paper draws on multiple ways of survey data that have been collected since March 2020 when the pandemic first resulted in severe restrictions in Australia. We present the evidence up to December 2021 on the incidence of WFH and how it has been received by employees and employers from the height of restrictions up to a period when restrictions were relaxed, followed by further lockdowns throughout Australia. We show what this might mean for work productivity, lifestyle, and the changing preferences for passenger modes. With a growing preference, within some occupation classes, to WFH 1 to 2 days a week, and a good spread through the weekdays, we discuss what this means for the way we analyse the impact of transport initiatives on the performance of the transport network with a particular emphasis on the growth in suburbanisation of transport improvements, less costly service and infrastructure improvements, and the changing role of public transport.

The COVID-19 pandemic has had a significant impact on the amount of weekly commuting activity, with a commensurate increase in remote working, especially from home. The reduction in the amount of commuting time has resulted in time released for other activities. In this paper we identify the incidence of released time to paid work, unpaid work and leisure, and investigate the key drivers of this allocation. The findings are important in obtaining estimated time benefits from reduced commuting activity with such travel time being traded against work and against leisure, and what this might mean for the future travel, activity location, and lifestyle landscape.

Since the start of 2020, we have seen major changes in the way communities operate. Mobility behaviour has been drastically impacted by work from home (WFH) and by lockdowns and restrictions in different jurisdictions. This study investigates the influence of WFH and different lockdown patterns on commuting and non-commuting trips in Australia by workers between early 2020 and late 2021. The data includes three waves of data collection to represent different lockdown periods. A multiple discrete-continuous extreme value (MDCEV) model is estimated to represent the number of one-way trips undertaken weekly with different purposes (commuting, work-related, education, shopping, personal business/social recreation), and by different modes (car, public transport, active modes). Explanatory variables include socioeconomic characteristics, location, the time period during the pandemic (i.e., waves), and latent variables. The results suggest that across all waves and jurisdictions, respondents that WFH more often are more likely to undertake relatively more shopping trips and personal business/social recreation trips, perhaps substituting these trips in replacement of their lesser commuting trips. Interestingly, all other influence held constant, individuals who are more concerned about the use of public transport are more likely to undertake commuting trips by all modes, more likely to do shopping trips, and less likely to undertake personal business/social recreation trips – suggesting they are prioritising essential trips rather than social/personal trips.

Freight delivery modelling has made significant progress in the past few decades. In this study we propose to use an aggregate multi-step approach to gain a better understanding of the tour-based trips of light commercial vehicles in Sydney, Australia. The paper identifies preference differences in destination choice-making given by the number of stop and the tour-based trip type, defined by the total number of stops in the trip. The findings suggest that estimating a separate model for each number of stops provides a better understanding on how destination choices are made. Simulation results show how the probability of choosing a certain destination in the different tour-based trip types and number of stop changes due to variations in travel time and distance. Results show that light commercial vehicles are more sensitive to the generalised cost (defined by travel time and distance) in the first stop, and the sensitivity decreases as the trip is completed.

Travel Demand Management (TDM) initiatives are widely applied by transport planners to establish and enable appropriate use of critical transport infrastructure. This paper considers the specific case of TDM in an education precinct (university) context drawing on international experience to date, with examples of TDM measures that have been introduced in university settings. The important role of regular surveys of staff and student travel behaviour is highlighted, particularly as part of a Sustainable Travel Plan (STP). A comparison of TDM Measures implemented as part of University STPs is discussed using recent experience from Australia as a case study. An important message of this paper is the need to propose a new focus on Education precinct TDM in the light of the COVID-19 pandemic. The paper thus explores the international evidence on the influence of changing travel and study practices in response to COVID-19 before considering, with reference to Australia, the role of Return to Campus plans which have been developed to encourage COVID-Safe Travel in a university context. The paper concludes with a call to learn more about the emerging and intended commuting patterns of university staff and students “post-pandemic” or under “living with COVID-19” and the requirements of TDM implementation plans in the developing university environment.

The COVID-19 pandemic has reshaped the way we live and travel, possibly for many years to come. The ‘New Normal’ seems to be one that is best associated with living with COVID-19 rather that ‘after COVID-19’. After a year or more since the pandemic spread throughout the world, we have amassed a significant amount of evidence on what this is likely to mean for patterns of commuting activity in a setting where working for home (WFH) is becoming a more popular and legitimate alternative to choosing a commuting mode. With WFH continuing to some extent, non-commuting travel is also likely to change as workers and their families have greater flexibility in when and to what extent they conduct their shopping, social-recreation and other non-commuting trip activity. This paper recognises that all trip purpose activity is being impacted by the pandemic and that the drivers of changing number of trips by each and every trip purpose need to be identified as a way of establishing likely future levels of frequency of all trip making. In this paper we develop a series of trip making models for workers and non-workers in New South Wales and Queensland in a metropolitan and a regional setting, using data collected in late 2020. The influence of the number of days WFH is identified as an important influence on the number of one-way weekly trips for various trip purposes, which together with socioeconomic, geographic and attitudinal variables enable us to gain an understanding of what is driving levels of trip-purpose-specific travel during the pandemic. Elasticities and simulated changes are presented as a behaviourally rich way to understand the sensitivity of influences on the frequency of travel.

The pace of electrification for public transit bus fleets is accelerating internationally. Clear targets have been established by transport policymakers to achieve a zero-emission bus target as early as 2030 in some jurisdictions. Two prominent choices are battery-driven electric buses and fuel-cell electric buses. We draw on evidence on these current and future development to provide an assessment of the two types of technology on emission reduction, capital, maintenance and energy costs, and other aspects. We apply a decision support system to compare a number of scenarios for different electrification plans of bus fleets using Australian data. Comparing scenarios such as slow versus fast take up and different mixture of energy technology in future zero emission fleets provides evidence on the reductions in carbon dioxide emissions and costs in converting a diesel bus fleet to a fully green, at tailpipe, fleet.

New public transport projects are expensive and long lived and their financing often is a challenge. As a result, cities are often concerned to understand the economic impacts induced by these new public transport projects, and in particular whether increases in land value which may have followed the investment can be ‘captured’ as a potential source of finance. However, land value uplift might occur at different stages of a project and might not be only due to the improved accessibility of the project. This study focusses on understanding the complexity of value uplift on residential properties resulting from three public transport projects in Sydney, Australia: two light rail projects and one heavy rail. Multilevel models are presented comparing the affected or ‘catchment’ areas of the projects with what is referred to as a ‘control area’ where the investment has not had an impact. Four different methods are used to define these control areas. The results show that, despite controlling for other attributes, such as property and neighbourhood, and using different methodologies for the control areas, there are still quite different results for the amount of uplift brought about by the different investments. What is however clear from the results is that increases to land value can come from the early stages of the project (i.e., during announcement and construction) when the accessibility improvements have not been realised.

Discrete choice studies are increasingly used in urban planning to understand preferences and to make informed decisions based on its outcomes. Traditional discrete choice modelling approaches have evolved in a setting in which some very specific behavioural assumptions are made in specifying decision-making. These assumptions have given rise to the study of alternative process strategies in decision-making, such as majority of confirming dimensions (MCD), attribute non-attendance (ANA), or value learning (VL). In this paper, a stated choice experiment was designed to understand business location decisions, where a location specialist had to compare their current location with two alternative locations. After each choice task, respondents were asked whether they used ANA in processing the choice tasks, and at the end of the experiment a number of questions were asked to identify whether specific process heuristics were used such as MCD and VL. Choice models were estimated to compare the influence of including different stated heuristics responses. The results show that the model which included the stated heuristics responses is superior in terms of the goodness of fit and in the estimates’ significance levels. The willingness to pay estimates derived from a traditional model were statistically equivalent to the ones derived from the stated multiple heuristics model. However, the median WTP derived from the stated multiple heuristics model was slightly higher and the confidence intervals lower than in the traditional model.

Hensher and Ho (2017) proposed a way to condition the utility of each alternative in a choice set on experience with the alternatives accumulated over previous periods. The paper found that the overall statistical performance of the mixed logit model improved significantly, suggesting that this conditioning idea has merit. Experience was treated as an exogenous influence linked to the scale of the random component, and to that extent it captures some amount of the heterogeneity in unobserved effects, purging them of potential endogeneity. The current paper continues to investigate the matter of endogeneity versus exogeneity. The proposed approach implements the control function method in the representative component of the relative utility expressions in a choice model. These methods have been investigated by Wooldridge (2005), Train and Petrin (2010), Train and Wilson (2009) and Guevara and Hess (2019), amongst other authors. We develop two choice models, both using stated preference data. The paper extends the received contribution in that we allow for the endogenous variable to have an impact on the attributes through a two stage method, called the Multiple Indicator Solution, also implemented in a different context by Guevara et al. (2019) for a single (crowding) attribute, in which stage two is the popular control function method. In the first stage, the entire utility expression associated with all observed attributes is conditioned on the prior experience with an alternative. Hence, we are capturing possible correlates associated with each and every attribute and not just one selected attribute. We find evidence of potential endogeneity. The purging exercise however, results in both statistical similarities and differences in time and cost choice elasticities and mean estimates of the value of travel time savings. We are able to identify a very practical method to allow for possible endogeneity under heteroscedastic conditioning that will encourage researchers and practitioners to use such an approach in more advanced non-linear discrete choice models as a matter of routine.