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Behavioural freight transport modelling

by Dr Elnaz Irannezhad on September 20, 2021

Freight vehicle movements are the result of interactions amongst various agents in the freight market.

Each agent has its own nature (e.g., shipper, carrier) and characteristics in terms of resources (e.g. fleet, employees), geographic scope, market coverage, business strategies, and preferences over various types of logistics operations.

Agents continuously adapt to the market within which they interact and coordinate with others within their respective supply chains.

Furthermore, several freight transport decisions are made at the firm level and arguably some of them are interrelated, including buyer-supplier matching and distribution channel, shipment size, and mode of transport, and the choice of route.

The results of these decisions are freight transport markets which are observable through the physical freight flows and activities.

However, freight traffic flows cannot simply be reverse-engineered to understand and replicate the agents’ decisions and desires nor their adaptations to demand-oriented policies.

Notably, ex-ante evaluation of urban freight policies and city logistic schemes requires a more fundamental investigation of the underlying behavioural mechanisms of the various actors, which result in freight traffic flows.

Accordingly, disaggregate freight transport models rest upon the realisation that these actors are heterogeneous in their decision making process and should be modelled individually.

In the last few decades, disaggregate freight transport modelling has stimulated interest by researchers and transport organisations.

The study of disaggregate decision-making has also spanned many analytic methods that range from conventional multinomial logit models to more advanced econometric models.

Disaggregate freight models have been particularly useful in reaching out to practitioners, who are interested in evaluating freight-related public policies.

Examples of these practices are regional freight transport models with disaggregate components that have been developed for Chicago (Outwater et al., 2013), Florida (Chase et al., 2013), Portland/Oregon (Donnelly, 2002), and Tokyo (Wisetjindawat, Yamamoto, & Marchal, 2012).

However, a general lack of data, the proprietary nature of freight shipment data, the wide range of commodities with various specifications, and the complex nature of goods/service delivery has caused disaggregate freight modelling to be still far behind the passenger transport.

There are a number of areas in freight surveys and modelling that need to be improved.

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Firstly, behaviours in freight transport studies have been conceptualized as choices, and choices are formalised as optimisation problems to be solved by freight agents where they are perfectly adapted to the environment, they are aware and familiar with all possible alternatives and, most importantly, they are able to determine and select the optimal choice.

However, there is a growing literature testing the validity of those assumptions and examining the role of choice anomalies and heterogeneous decision-making strategies that sometimes contradict the axioms of traditional choice models using utility maximization.

Several heuristics and biases affect decision-making such as risk attitude, projection bias, reference-dependent preference, inertia, bargaining, and oligopoly.

Hence, there is a need for behavioural research to bridge the gap between economics and psychology and to look at the process that freight transport agents adopt to assist them in reaching a decision, rather than simply analysing the outcomes of choices.

Secondly, a decision maker is often modelled as an individual taking one decision at a time, while an outcome in real life may be a result of interactions among various agents or a result of multiple interrelated decisions.

Accordingly, ignoring the interdependency of decisions may result in endogeneity problems.

Although there have been a handful of studies that have captured multiple interrelated decisions, the majority of relevant articles focused only on the combined choice of shipment size and mode choice. Moreover, research efforts to parse the distinctive roles of various agents in a decision have been quite limited.

Considering that some decisions in freight transport directly involve a variety of freight agents, it is necessary to study how such decisions are made, who makes them, and what happens as a result of the interactions between agents.

Thirdly, freight agents are reflexive actors that are situated within the context of a specific market, with specific market competitions, and technological trends.

Classic freight models rest upon an assumption that all freight agents are homogeneous in their decision-making, and as a result the freight transport system is in equilibrium. This, however, has been proved to be in contrast with reality (Friesz and Holguín-Veras, 2005).

Recent developments in agent-based modelling have made substantial moves towards a rich behavioural description of agents in dynamic environments. However, these models are still limited to a narrow interpretation of macro-economic conditions.

Moreover, the growing use of new information systems and cooperative strategies is creating significant dynamism in the freight transport market.

While conventional static modelling approaches only model the consequences of these changes, agent-based models provide an opportunity to adopt such dynamic and behaviourally rich perspectives on freight actor behaviours.

 

References:

Chase, K. M., Anater, P., Gannett Fleming, I., Phelan, T., Eng-Wong, & Associates, T. a. (2013). Freight Demand Modeling and Data Improvement Strategic Plan. Washington, D.C

Donnelly, R. (2002). Development of the TLUMIP Commercial Travel Component.

Friesz, T. L., & Holguín-Veras, J. (2005). Dynamic game-theoretic models of urban freight: formulation and solution approach Methods and Models in Transport and Telecommunications (pp. 143-161): Springer.

Outwater, M., Smith, C., Wies, K., Yoder, S., Sana, B., & Chen, J. (2013). Tour based and supply chain modeling for freight: integrated model demonstration in Chicago. Transportation Letters, 5(2), pp. 55-66. doi:10.1179/1942786713Z.0000000009

Wisetjindawat, W., Yamamoto, K., & Marchal, F. (2012). A Commodity Distribution Model for a Multi-Agent Freight System. Procedia - Social and Behavioral Sciences, 39, pp. 534-542. doi:http://dx.doi.org/10.1016/j.sbspro.2012.03.128 Retrieved from http://www.sciencedirect.com/science/article/pii/S1877042812005952

 

Topics: Research, ARRB, Intelligent Transport Systems, Transport Data, Heavy Vehicles, Road and Transport Research