Assessing dynamic responses of freight operators to government policies: a latent curve modelling approach

Governments have long attempted to minimise the negative effects of freight transport by introducing policies that attempt to restrict or change how freight firms operate. Despite this, freight transport still contributes disproportionately to emissions and congestion relative to its proportion of the vehicle fleet. A rapid expansion in the number of freight vehicles being used (in part due to a dramatic increase in internet shopping) in recent years has further necessitated policies that can reduce the negative effects whilst still allowing freight transport to provide its substantial benefit to the economy. Doing this effectively requires an understanding of how firms adapt to government policies as well as other measures not only at a particular point in time but over a longer period of time. Generally, behavioural models for freight have focused on parameterising attributes for a specific choice (e.g., mode) but have made the implicit assumption that these attributes do not change over time (Danielis and Marcucci, 2007; Fowkes et al., 2004). Although these models provide a reasonable basis for behavioural models for freight, the lack of a temporal component means they cannot be used to assess how the influence of each attribute on decisions changes over time. Furthermore, they are unable to be used to investigate how quickly firms adapt to changes in government policies. To address these limitations, this paper explores the dynamic (change over time) responses of freight operators to two government policies, a Low Emission Zone (LEZ) and a cordon-based congestion charge, in terms of the choice of number of routes, toll road use, vehicle class and emissions standard, and departure time. Using a unique dataset collected from Australian (urban) freight operators using an adaptive-dynamic simulation method (Ellison et al., 2012), firms’ dynamic responses are modelled using latent curve models to investigate firms’ adaptation strategies in response to new government policies. The data collected using the simulation include characteristics of the firms (e.g., size, fleet mix and primary business, etc.) and repeated choices for five (simulated) time periods as well as responses to some attitudinal questions. The latent curve models, a form of structural equation modelling (SEM), are used to identify if there is a change in each of the underlying choices captured by the survey as a result of the policies, the magnitude and timing of the changes as well as what factors influence the changes. The results of the models show that both an LEZ and a congestion charge result in significant changes in how freight firms say they will operate. In addition, the timing and magnitude of the resulting changes are shown to be a result of not only the policy itself (and its effects on the attributes) but also some of the characteristics of the firm and firms’ previous decisions. Among the firm characteristics that are important to how firms adapt to new policies are the size of the firm, their existing vehicle mix and their primary business. Crucially, it is the combination of these factors that lead to specific changes being made rather than only one of the characteristics or the policy acting alone. The use of latent curve models means that the influence of each of the attributes on the decisions made by firms can be evaluated not only at a specific point in time but also throughout the adaptation process. The models show that the influence of each of the attributes, including various measures of cost, differ depending on both the relevant decision and the time period. In addition, the influence of the attributes includes the past and future (forecast) values of the attributes as well as the value of the attributes in each time period. These results suggest that although the (changing) values of the attributes are important to the decisions that are made by freight operators, the influence of any particular attribute is not generally constant throughout the adaptation process. Furthermore, the importance of the values of the attributes are most important when decisions are made in the “base” period (i.e., before any new policies are announced) and in the time period immediately preceding the policy coming into effect. Interestingly, although when changes are made varies somewhat between the different decisions with some changes being made earlier (e.g., use of toll roads) and others being made later (e.g., number of routes), the importance of these two time periods is repeated in many of the decisions being modelled. The results presented in this paper have a number of important implications for policy makers. Paramount among these are that firms may respond to a policy before it comes into effect and that even substantial changes to the values of the attributes (e.g., costs) will not necessarily lead to as large a change as would otherwise be expected because of the importance of a firm’s previous decisions on its adaptation strategies. References Danielis, R. and Marcucci, E. (2007), “Attribute cut-offs in freight service selection”, Transportation Research Part E: Logistics and Transportation Review, Vol. 43 No. 5, pp. 506–515. Ellison, R.B., Greaves, S.P. and Hensher, D.A. (2012), “Capturing Freight Operators’ Behavioural Responses to Government Policies Using an Adaptive-Dynamic Simulation Method”, 13th International Conference on Travel Behaviour Research, Toronto, Canada. Fowkes, A.S., Firmin, P.E., Tweddle, G. and Whiteing, A.E. (2004), “How highly does the freight transport industry value journey time reliability—and for what reasons?”, International Journal of Logistics: Research and Applications, Vol. 7 No. 1, pp. 33–43.

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