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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.

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.

The need to recognise and account for the influence of working from home on commuting activity has never been so real as a result of the COVID-19 pandemic. Not only does this change the performance of the transport network, it also means that the way in which transport modellers and planners use models estimated on a typical weekday of travel and expand it up to the week and the year must be questioned and appropriately revised to adjust for the quantum of working from home. Although teleworking is not a new phenomenon, what is new is the ferocity by which it has been imposed on individuals throughout the world, and the expectation that working from home is no longer a temporary phenomenon but one that is likely to continue to some non- marginal extent given its acceptance and revealed preferences from both many employees and employers where working from home makes good sense. This paper formalises the relationship between working from home and commuting by day of the week and time of day for two large metropolitan areas in Australia, Brisbane and Sydney, using a mixed logit choice model, identifying the influences on such choices together with a mapping model between the probability of working from home and socioeconomic and other contextual influences that are commonly used in strategic transport models to predict demand for various modes by location. The findings, based on Wave 3 (approximately 6 months from the initial outbreak of the pandemic) of an ongoing data collection exercise, provide the first formal evidence for Australia in enabling transport planners to adjust their predicted modal shares and overall modal travel activity for the presence of working from home.

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 need to recognise and account for the influence of working from home on commuting activity has never been so real as a result of the COVID-19 pandemic. Given a recognition that WFH activity during the pandemic has reduced the amount of commuting activity compared to pre-COVID-19, the inevitable question is raised as to what this might mean for some of the crucial inputs in the appraisal of transport initiatives. One critical value used in benefit-cost analysis is the value of time which converts time into monetary units in the calculation of user benefits. We are interested in whether reduced commuting activity is associated with higher or lower willing to pay to save time. We investigate this possibility with data from the Greater Sydney Metropolitan Area in late 2020 when working from home was at a high level. The findings of a higher average commuter VoT have major implications for the VoT used in transport appraisal given that time savings are the largest user benefit. We suggest a percentage adjustment required to align with the ‘new normal’ as currently known.

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.

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.