Microscopic Demand Modeling of Urban and Regional Commercial Transport

Commercial transport is an intrinsic part of the evaluation of traffic volumes. However, it is often limited to freight transport, and while this is a significant element, it disregards the share of trips contributed by plumbers, electricians, care services, and the like. These businesses add a significant part to the commercial traffic volume, especially in urban areas. The reasons, commercial passenger transport lacks behind are wide-ranging, one of the leading causes being difficulties in gathering sufficient data. In this paper, the authors present a microscopic approach to model commercial travel demand, including but not limited to freight traffic, based on data from a national survey and open data. They differentiate between vehicles of businesses that have a fixed daily schedule, with only small variations of their trip purposes and vehicles of businesses that can predict their daily schedules only to a certain degree. The latter have varying trip purposes and decide on a short-term base if and what sort of trip is to be pursued. Vehicles with fixed daily schedules include plumbers, electricians, care services, and delivery trucks. Due to their database, the authors produced a model for these vehicles exemplary for delivery by determining the number of trips for a day and assigning destinations to those trips afterward. They also take the number of private trips into account, laying the foundation of being able to incorporate the commercial transport model into a passenger transport model. The authors show that their model can overcome the lack of regional data. Based on generic data, the application of their approach shows promising results for the urban and regional commercial travel demand of a model region. By basing their model on generic data, the authors introduced an opportunity to model commercial travel demand not only in one model region but also for other urban areas in Germany and possibly in various areas in Europe, assuming that structural data is similar.


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  • Accession Number: 01676941
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 26 2018 9:38AM