Urban logistics policies have become instrumental in achieving sustainable transport systems. Developing and emerging countries still lag far behind in the implementation of such policies when compared with developed countries. This exposure gap provides an opportunity for policy transfer, but this is a complex process requiring knowledge of many contextual factors and involving multiple steps. A good understanding of those contextual factors of measures by cities may be critical for a successful transfer. Our study aimed to identify the different contexts of urban logistics measures or policies worldwide and to assess their significance for policy transferability. In this study, urban logistics measures discussed in the literature were retrieved with a systematic literature review method and then the contexts were recorded, distinguishing between economic development levels and geographical regions. The analysis revealed that the economic level and geographical location of cities both have a strong association with the type of measure implemented. Barriers and drivers were identified by assessing policy transfer between developed and developing countries. Institutional and physical barriers appeared to be highly pertinent for a range of measures, while drivers or facilitators were identified from specific problems in developing countries and the respective measures in developed countries. Thus, the analysis of contextual factors can provide a first response to the key challenges and opportunities of sustainable urban logistics policies transfer to developing countries.

Urban freight systems in developing countries present significant challenges due to their complexity. Authorities often have inadequate institutional structures, making it difficult to identify and implement relevant initiatives. This thesis aims to characterise the systems in developing economies and model freight demand using innovative approaches by considering new attributes, dimensions and alternatives. As a first modelling step, freight (trip) generation was improved by considering spatial and locational determinants, as freight activities are strongly related to spatial and locational characteristics of establishments. Spatial models were developed using a combined spatial autoregressive model (SAR) and geographically weighted regression (GWR) or multiscale GWR (MGWR) (GWR/MGWR-SAR model). This model accounted for non-linearity, spatial heterogeneity and spatial dependency and demonstrated significant improvements (R2 0.29-0.71, RMSE reduced by 71% and AIC value by 56%). Shipment size decisions related to the choice of truck type were strongly timedependent, with commodity type, activities at the trip end, truck body type and industry sector affecting the preferences. Freight demand, including shipment size choices, was influenced by economic fluctuations, with shipment size declining after an economic slowdown. In freight demand modelling, it is imperative to consider economic conditions, especially those in developing countries, which are often susceptible to strong economic fluctuations. The models were applied in ex ante testing of a policy restricting large trucks from entering a city centre, as commonly considered in many developing countries. In tests, the truck restriction was accompanied by single-tier and two-tier distribution systems. The results showed that the two-tier system had a slight advantage over the single-tier system regarding operational expenditure and emission levels. Truck restriction was generally counterproductive, even when accompanied by distribution systems with greater speed and efficiency. We conclude that the models enhance the accurate prediction of freight demand patterns. The ex ante evaluation of policy alternatives supports the decision-making process for urban freight systems of large cities in developing economies. The models allow considering relevant practical, local contextual conditions.

No abstract

No abstract