Firm-specific and location-specific drivers of business location and relocation decisions

Decisions made by businesses on where to locate or relocate are typically given less consideration than residential location in integrated transport and land use modelling systems. This is surprising given the important role that businesses play in defining employment opportunities, and hence the travel patterns of workers and any travel associated with accessing firms. As part of a larger study on giving firm location choices an endogenous representation in an integrated model system, this paper reviews the existing literature on the drivers of location and relocation decisions in various geographical jurisdictions. The findings provide a starting position in the design of future firm-specific surveys and especially the attributes that are candidates for stated choice experiments and for inclusion in firm location utility equations embedded in integrated strategic model systems

Heterogeneity in decision processes: Embedding extremeness aversion, risk attitude and perceptual conditioning in multiple heuristics choice making

There is an increasing interest, in the discrete choice modelling literature, in alternative behavioural paradigms that represent ways in which individuals make choices when faced with a choice set of alternatives, under conditions defined by revealed preference, stated choice or a mixture of both data sources. Attribute processing has come of age, and we see many studies using process heuristics such as attribute non-attendance (ANA), relative advantage maximisation (RAM), extremeness aversion (EA) and value learning (VL). With some exceptions (e.g., papers by Hensher, Hess, Scarpa, Campbell and colleagues, and Balbontin et el. 2017, 2017a), the study of each heuristic has been undertaken in isolation from other candidate heuristics; the exceptions being a joint investigation into a fully compensatory model defined by a linear additive in attributes and parameters specification and one process heuristic, commonly using latent class models (reinterpreted as probabilistic decision processing). Within the set of more than one candidate heuristic, limited account has been taken of the possibility that attributes are being processed under varying levels of risk attitude (instead assuming risk neutrality), and where multiple levels of an attribute might be observed in real markets (such as travel time over repeated trips with associated occurrences) and/or designed into stated choice experiments, no account is taken of perceptual conditioning. This paper investigates the role that two behaviourally appealing heuristics or decision rules play jointly in explaining choice making, both of which reflect risk attitude in different ways, where each heuristic contributes up to a probability within a sampled population both within and between respondents’ selection of a relevant multiple-heuristic utility expression. We jointly estimate a model that accounts for (i) extremeness aversion and (ii) an extended expected utility transformation for an attribute that accounts for risk attitude and perceptual conditioning. We use a stated choice experiment associated with a commuter car choice between tolled and nontolled roads in Australia, and compare the key behavioural output, the value of travel time savings (VTTS), obtained from the joint model and two stand-alone models. The findings suggest, after accounting for the probability of choosing each heuristic by each individual, in their construction of an empirical utility expression representing each alternative tolled road, that the mean VTTS from the multiple-heuristic model ($24.32/person hour) lies between the mean estimates obtained from the stand alone models ($21.45/person hour under extremeness aversion, and $29.19 when accounting for risk attitude and perceptual conditioning). The extremeness aversion heuristic has, on average, a 0.63 probability of relevance compared to a 0.27 probability of relevance for the other heuristic. Extremeness aversion (or seeking) is an appealing way of handling degrees of attribute risk that are not explicitly conditioned on the more traditionally identified risk parameter

How to better represent preferences in choice models: the contributions to preference heterogeneity attributable to the presence of process heterogeneity

Discrete choice studies, with rare exception, assume that agents act as if sources of observed utility are captured through a linear in parameters and additive in attributes (LPAA) form, with some interactions. A growing number of transport (and other) choice studies have investigated one or more alternative processing rules adopted by agents in arriving at a choice, raising interest in how best to represent the utility expressions in a joint process and outcome choice model. Given the popular and appealing random parameter treatment of LPAA in mixed logit as a way of identifying non-systematic preference heterogeneity in a sample, this paper considers the possibility that we might be able to interact specific process heuristics with LPAA to uncover sources of systematic preference heterogeneity hidden in the standard LPAA form, and hence establish a link between the LPAA form and candidate process heuristics, offering a way to embellish and hence clarify the contributions to preference heterogeneity attributable to the presence of process heterogeneity. Specifically, we are interested in the extent to which there is a systematic relationship between the simple LPAA form and the more complex (albeit behaviourally realistic) process heuristics emerging in the transport literature which we call conditioning by random process heterogeneity (CRPH). In this paper, in addition to LPAA, we consider two process heuristics - Value Learning, and Relative Advantage Maximisation - with an overlay to account for risk attitudes, perceptual conditioning, and overt experience. The findings, using two data sets, suggest that empirically there exists a significant attribute-specific relationship between preference heterogeneity identified through specific process heuristics and through the LPAA assumption

Do preferences for BRT and LRT change as a voter, citizen, tax payer, or self- interested resident?

Interest in modal preferences remains a topic of high interest as governments make infrastructure decisions that often favour one mode over the other. An informative input into the infrastructure selection process should be the preferences of residents, since they can guide buy in to support political and bureaucratic choice making. Cost-benefit analysis (CBA) uses the self-interest preferences of individuals as the relevant interpretation of ‘individual preferences count’, which in aggregate represent the benefit to society of candidate investments. However, the CBA benefit calculations can be rather restrictive with other preference metrics often being identified and used in various ways to inform the debate on infrastructure support. In this paper we assess how the preferences for bus rapid transit (BRT) and light rail transit (LRT) change with different roles the residents may play: a citizen or altruistic resident, a self-interested resident, a tax-payer, and as a voter. We use data collected in five countries to investigate preference differences and also to establish whether there is replicability of the findings across geographical jurisdictions. The findings suggest that there are, in general, noticeable differences in preference revelation across the metrics; however there are also both similarities and differences in the role of specific attribute drivers (as represented by willingness to pay, and magnitude of support for a specific mode) within and between preference metrics across countries

Experience as a conditioning effect on choice – Does it matter whether it is exogenous or endogenous?

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

Working from home 22 months on from the beginning of COVID-19: What have we learned for the future provision of transport services?

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

What does the Quantum of Working from Home do to the Value of Commuting Time used in Transport Appraisal?

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

Relationship between commuting and non-commuting travel activity under the growing incidence of working from home and people’s attitudes towards COVID-19

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

Time Allocation of Reduced Commuting Time during COVID-19 under Working from Home

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