Estimating Service-Related Traffic Demand from Trip Chain Data

Schneider, Sebastian; Wolfermann, Axel · 2013 · Crossref

DOI: 10.3141/2343-04

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Summary

This paper addresses the lack of dedicated modeling methods for service-related traffic, a significant subset of commercial non-freight traffic that involves the transport of people (e.g., tradespersons, contractors) to deliver services. While private passenger and freight traffic are commonly modeled, service traffic is often excluded from household surveys and freight models, despite constituting approximately 20% of urban traffic volume and growing due to shifts toward service-oriented economies. The authors argue that existing macroscopic models fail to capture the tour-based nature of service trips, which often involve multiple destinations and complex spatial patterns. Consequently, the study aims to develop a methodology to estimate total service traffic demand from sample trip chain data, enabling more accurate transport planning. The proposed method is a tour-based, multi-agent simulation approach that extrapolates total demand by "cloning" observed trip chains from empirical surveys. The methodology relies on three primary data inputs: trip chain diaries (template logbooks) detailing vehicle movements, trip generation rates specifying the frequency of trips by company type and size, and high-resolution spatial and demographic data defining the study area’s land use and business locations. The model creates a synthetic simulation substrate and assigns template logbooks to specific entities (companies or households) based on matching attributes such as economic sector and firm size. An algorithm then recursively generates trips by selecting destinations that satisfy specific constraints, including destination type (e.g., residential, industrial) and trip distance, ensuring the cloned tours fit the spatial reality of the target area while preserving the original activity sequences and distances. The methodology was validated through a case study of Berlin, Germany, using data from the "Motor vehicle traffic in Germany" (KiD) survey, national spatial data, and commercial trip generation statistics. The simulation processed 133,538 vehicles, successfully cloning 98% of the trip chains into the Berlin spatial framework. The model utilized a repository of 11,757 template logbooks, with most business sectors having ample templates for selection. Validation against existing estimates for Berlin’s service traffic showed that the model produced reasonable values for traffic volumes by vehicle type and vehicle kilometers traveled. The high success rate of cloning demonstrated the algorithm’s flexibility in adapting sample data to specific spatial constraints without altering the fundamental behavioral patterns of the trips. The significance of this work lies in providing a scalable, disaggregated tool for quantifying service traffic, which is currently underrepresented in transport planning. By leveraging existing survey data and spatial information, the method allows planners to estimate service traffic demand for any given area without requiring new, costly data collection. The approach supports the integration of service traffic into broader transport models, facilitating better infrastructure planning and policy decisions. The authors conclude that while the current model relies on static cloning due to limited behavioral data, it serves as a robust foundation for future enhancements that could incorporate dynamic behavioral adjustments and forecasted land-use changes.

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