TY - JOUR
T1 - Network Inefficiency
T2 - Empirical Findings for Six European Cities
AU - Hamm, Lisa S.
AU - Loder, Allister
AU - Tilg, Gabriel
AU - Menendez, Monica
AU - Bogenberger, Klaus
N1 - Funding Information:
The authors acknowledge support from the German Federal Ministry of Transport and Digital Infrastructure (BMVI) and the NYUAD Center for Interacting Urban Networks (CITIES). As a data source, the pNEUMA data set was used: open-traffic.epfl.ch.
Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Lisa S. Hamm acknowledges support from the German Federal Ministry of Transport and Digital Infrastructure (BMVI) for the funding of the project TEMPUS (Test Field Munich—Pilot Test Urban Automated Road Traffic). Allister Loder acknowledges support from the German Federal Ministry of Transport and Digital Infrastructure (BMVI) for the funding of the project KIVI (Artificial Intelligence in Ingolstadt’s Transportation System), grant no. 45KI05A011. Gabriel Tilg acknowledges support from the German Federal Ministry of Transport and Digital Infrastructure (BMVI) for the funding of the project LSS (Capacity increase of urban networks). Monica Menendez acknowledges the support from the NYUAD Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award CG001 and by the Swiss Re Institute under the Quantum Citiesä initiative.
Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2022.
PY - 2022/8
Y1 - 2022/8
N2 - 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.
AB - 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.
KW - operations
KW - traffic control
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U2 - 10.1177/03611981221082588
DO - 10.1177/03611981221082588
M3 - Article
AN - SCOPUS:85127792469
SN - 0361-1981
SP - 99
EP - 111
JO - Transportation Research Record
JF - Transportation Research Record
IS - 8
ER -