Network Inefficiency: Empirical Findings for Six European Cities. Issue 8 (August 2022)

• Record Type: Journal Article
• Authors: Hamm, Lisa S.; Loder, Allister; Tilg, Gabriel; Menendez, Monica; Bogenberger, Klaus
• Is Part Of: Transportation research record. Volume 2676:Issue 8(2022)
• Page Start: 99
• Page End: 111
• Publication Date: 2022-08
• Subjects: operations -- traffic control; Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981221082588
• Languages: English
• ISSNs: 0361-1981

Abstract

When planning road networks, inhomogeneous traffic conditions and the effects of multimodal interactions are often neglected. This can lead to a substantial overestimation of network capacities. Empirical macroscopic fundamental diagrams (MFDs) or volume delay relationships show considerable scatter, reflecting a reduction in network performance and an inefficient use of infrastructure. The implication is that the external costs of vehicular (car) traffic get underestimated, when planning traffic capacities and speeds based on optimal rather than on real estimates. In this paper, we contribute with an explorative and empirical approach to analyze network inefficiency and quantify its drivers. We propose to measure network efficiency by introducing the idea of excess delays for the MFD. We define excess delays as the difference between the observed speed and the optimal network speed at a given density. We apply the concept to traffic data sets of six European cities that differ in the data collection method and we use quantile regression methods for analysis. We find that excess delays are present in every data set and increase with the road network's traffic load. We further confirm the intuition that traffic signal control, network loading, and multimodality influence the level of network inefficiency. The excess delay formula allows quantifying this information in a simple way and provides additional insights apart from the standard MFD model. The approach supports planners to obtain better real-world and less optimistic speed predictions for traffic analyses and suggests shifting urban transport to more spatially and temporally efficient modes.