Conditional Freight Trip Generation modelling

摘要

Freight Trip Generation (FIG) in general and FTG modelling in particular are fields that are not concentrated upon as much as passenger trip generation. Therefore, the main objective of this work was to improve the understanding of the underlying processes that generate freight trips and through this understanding, to improve the modelling of FIG. To achieve this goal, the authors first had an extensive literature review to understand the reasons for the weaknesses of the current FTG modelling approaches. After identifying these weaknesses, some of them were brought to a focus in this work. One of the main weaknesses was the inadequacy of the classification system which was used to group commercial establishments in a set of standardized classes. Hence, firstly an experiment was conducted to create groups of logistical sites that had homogeneous FTG characteristics. It was observed that one of these segments had too many zero trips for a particular vehicle category, namely tractor trailers. Then, to solve this problem, a new 'conditional' modelling approach for FTG modelling of this group and this vehicle category was proposed and tested using the data obtained from Kocaeli City Logistics Master Plan. This new hypothesised conditional approach aimed to find the probability of the segment generating tractor-trailer trips using the binary logit model and the generated trips given that the sites produced tractor trailer trips using the regression technique. Afterwards, the models developed using the new approach were compared with the models obtained using only the common modelling approach of the regression analysis. The results indicated that creating homogeneous groups of logistical sites was possible and the new conditional modelling approach which was applied to one segment of the logistical sites for FTG of tractor-trailers, performed better than the regular regression modelling. Lastly, some recommendations for further improvement of this modelling approach were provided. (C) 2016 Elsevier Ltd. All rights reserved.