General conclusions applying to service trips

The aim of this section was to develop a basis of reliable information on service traffic. With the help of data of the German study Motor vehicle traffic in Germany 2010 / Kraftfahrzeugverkehr in Deutschland 2010 (KiD 2010) we could show that the share of service traffic in traffic at all in Germany is 11.8% in terms of trips and 19.9% in terms of kilometres travelled. The share of service traffic in commercial transport is 42% in terms of trips and 43% in terms of kilometres travelled.

Service trips are mostly done with passenger cars and light duty vehicles. Economic sectors with high shares in total service trips are Construction sector (NACE F) and Human health and social work activities (NACE Q). Out of all service trips 10% are done with vehicles registered for private persons. Economic sectors with high shares in terms of kilometres travelled in total service traffic are the Construction sector (NACE F), Professional, scientific and technical activities (NACE M), the Manufacturing sector (NACE C), and the Wholesale and retail trade sector (NACE G). In terms of kilometres travelled 8.3% of all service traffic is done with vehicles registered for private persons.

High shares of service traffic are done in city centre areas in agglomeration areas, in dense areas near urbanized areas, and in highly dense areas in agglomeration areas. In terms of kilometres travelled high shares of service traffic is done in city centre areas in agglomeration areas, highly condensed areas in agglomeration areas, and condensed areas near urbanized areas.

Vehicles used for service traffic make on average 3.2 service trips per day. On average vehicles travel 73.8 kilometres while doing service trips. Number of trips and kilometres travelled vary in terms of vehicles used, economic sector and spatial type. Economic sectors with high shares in service trips (NACE C, F, G, M) make between 2.5 and 3.2 trips per day on average. The average trip length in these economic sectors varies between 25 kilometres and 62 kilometres. Accordingly daily tour length varies between 69 kilometres and 200 kilometres. The highest average trip rate per day in service traffic is observed in highly condensed areas in agglomeration areas. The lowest average trip rate per day is observed in rural areas near urbanized areas. The average trip length varies between 16 kilometres and 26 kilometres per trip.

The analysis of the German study KiD 2010 gives no information on the factors influencing the trip patterns of companies in the service sector. Further studies as used by Hebes (2011) can give information on this. The empirical results of Hebes show that there are four typical travel patterns. Following the four identified clusters there are on the one hand vehicles which are characterized by only a few stops and little road performance per day. On the other hand many cars visit numerous customers and participate a lot in traffic. Hebes shows that

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four corporate factor groups, namely internal structures and internal processes as well as external structures and external processes, play a role in travel behaviour in service traffic. Our research on other surveys and studies in Europe on service traffic revealed that there are only few of them. Furthermore they are different in terms of definition of service traffic, in terms of spatial coverage and in terms of observed objects. Nevertheless they add further knowledge on trip patterns in service traffic.

To set up a proper observatory on service traffic we need to take identified limitations and learning from our previous analysis into account. Crucial is the question of the observed object of studies and surveys on service traffic. Both establishment based analysis as well as vehicle based analysis can give detailed insight into the research topic. Combined studies could link knowledge ideally but often exceeds restrictions set by resources. Ideas for better observing service traffic include the connection of trips and stop point in the tour context. This will enable synthetic tour planning and will lead to a better theory on modelling. Trip chain data could thus serve as estimator for service-related traffic. This will build up further knowledge on traffic behaviour. The vehicle based observation of KiD 2010 is a good basis for the observation of service traffic but background knowledge on the reasons behind the trip patterns are missing. Thus an observation of the behaviour of companies and their whole vehicle fleets is needed. Decision makers need to be asked on the rationales behind vehicle deployment, on determining factors for fleet composition as well as decision-making processes in fleet use. At the moment we are only observing the derived behaviour but do not know anything about the genesis and generation of service traffic. There is no information on choice behaviour. As we have seen from the analysis service traffic is partly done with vehicles registered for private persons. Thus they must be included in any kind of analysis. Furthermore the observation needs to take into account non-road-trips as not all service traffic is done in road transport.

Understanding the generation of service traffic, we will be able to give better information on how to influence service traffic in a sustainable manner. But as in commercial traffic, the premise will be not to avoid traffic rather to improve traffic. As there is always an economic reason behind service traffic it will not be possible to avoid service trips at all. But there is room for improvements. Common strategies in commercial transport are the use of advanced vehicle technology, the temporal shift of traffic as well as the consolidation of trips.

At the moment we cannot determine exactly the difference in service traffic between countries. There are no studies and surveys available which allow a comparison of countries. From the analysis in Germany we can at least see that there are no big differences between different spatial types. Our findings do show relatively homogeneous results on that. The set- up of studies and surveys on service traffic should therefore be coordinated on European level. A starting point for a better observation of service traffic may be to conduct surveys as KiD 2010 in Germany in other countries on national basis as well. Furthermore this must be enhanced with local or regional establishment based surveys where whole companies and their fleets are included. Such surveys should take into account economic sectors, which have been identified as main producers of service traffic. This will provide a solid foundation for the definition of policy measures for the sustainable development of urban transport including service traffic.

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