This chapter proposes an overview of the main urban goods transport movement approaches in more than 40 years of research in the field. A state of the art on urban goods transport modelling is proposed in the form of a chronology to examine the main four periods in urban goods transport modelling, the main approaches, and the main schools of thinking. After that, the main dominant approaches are defined and presented. The author observes that, although in literature there is the feeling that no unified approach is dominating the field, several dominant approaches can be found: the first concerns demand generation models, the second the way generation is linked to route definition and construction, the third to the units used, and the fourth to the use of origin-destination synthesis when few data are available and classical models are not able to be deployed. Finally, as a conclusion, the paper shows on the current state of the field, which has achieved various advances, shows the capacity to innovate in the near future.
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It is usually considered that urban goods transport is a complex system (Gonzalez-Feliu & Routhier, 2012), which needs to be understood and modelled (Taniguchi and Thompson, 2015). Therefore, modelling and assessment methods take an important place in urban logistics research (Gonzalez-Feliu, 2018b). The main goals and issues of urban goods transport and logistics can be considered from various viewpoints and scales (Gonzalez-Feliu, 2018b): reliability and efficiency; economic (and financial) issues; greenhouse gas and global heating; environmental nuisance; congestion and noise; social and societal issues; spatial and land use consequences, etc. To quantify those issues, and make diagnoses and assessments of the impacts of urban goods transport on urban space (or on the actions of stakeholders on the urban goods transport system, among others), various data collection methods can be deployed1 (Gonzalez-Feliu et al., 2013; Allen et al., 2014). From those methods, modelling remains a major issue in urban logistics, as the models can be transferred and adapted to different contexts, sometimes with small quantities of data (see chapter 2 for big/no data issues). Although for decades the main discourse in urban goods transport modelling research was that a standard was not imposed or even possible (Ambrosini et al., 2008; Comi et al., 2012; Anand et al., 2015), we observe recently that unified methods start to be deployed and used (Holguin-Veras, 2016; Gonzalez-Feliu, 2018a,b).
A model can be defined as a representation of a reality (Bonnafous, 1989), but since that reality is observed and then conceptualized by a human (Wright, 1983), known here as “modeler”), we can state that the model is relative to the vision the modeller has of the reality (Gonzalez-Feliu, 2018b), but also to the way he or she represents that reality (Putnam, 1991). Then, that representation needs to be related to the aims, goals and scopes of both the modeller and the users of the model, and eventually be modified if the representation is not suitable for those purposes (Ackoff, 1977). Therefore, we can define a model as a representation of the observed reality as seen, approximated and interpreted by the modeller. We observe thus a plethora of models and approaches, most of them of applied scope, which follow or can follow unified patterns and methodological frameworks (Gonzalez-Feliu, 2018a), are usually related to standard or known mathematical or physical representations (Ortuzar & Willumsen, 2001) and take into account of a given complexity (Zbilut & Giuliani, 2007). Moreover, the analysis of the evolution in the way of modelling goods transport is interesting. As there are different classifications and typologies of those models (Comi et al., 2012; Gonzalez-Feliu & Routhier, 2012; Holguin-Veras et al., 2012; Anand et al., 2015; Gonzalez-Feliu, 2018b) this chapter does not aim to re-propose or build a new typology or analysis grid (to do that, readers are suggested to see Gonzalez-Feliu & Routhier, 2012 and Anand et al., 2015), but to analyse how those models have been deployed to represent a reality and with which approximations and representations they did it.