Estimating service related traffic demand from trip chain data

Kurzfassung

Commercial traffic constitutes a significant part of total traffic. While on long distances goods traffic prevails, particularly in metropolitan areas service related traffic (i.e. traffic resulting from services delivered to customers at home, offices, constructions sites etc.) takes the lead – and is continuously growing. It is common practice to forecast transport demand for passenger traffic and goods traffic. Dedicated models for service related traffic are still rare. This is not only due to the recognition of the particularities of this special part of traffic among transport planners, it is also due to the complexity of analyzing and depicting service related traffic. One characteristic of service related traffic is that it consists of tours connecting sometimes more than one destination with the origin of the tour. Furthermore, many trips are trips with commercial vehicles, registered by a company or organization, but also private vehicle owners use their cars to some extent for commercial trips. This poses the opportunity to use trip chain data available for private and commercial vehicles to derive total transport demand of service related trips from a sample population. This article introduces such a methodology to extrapolate traffic demand from trip chain data. It is not only applicable to service related traffic, but to all tour based traffic, if empirical tour patterns are available. A regular survey of the German Federal Motor Transport Authority together with detailed spatial and demographic data was used as input to the model. The city of Berlin served as a case study.