Simulation-Based Design of Urban Bi-modal Transport Systems

Tilg, Gabriel; Abedin, Zain Ul; Amini, Sasan; Busch, Fritz

doi:10.3389/ffutr.2020.581622

Cited by 7 publications

(2 citation statements)

References 72 publications

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“…1. Multimodal vehicle interactions: So far, most MFD literature has focused on car traffic, although in the last years, interactions between different modes have been receiving increasing attention: for example, bimodal interactions, that is, between cars and buses (13)(14)(15) or cars and pedestrians (16), but also trimodal interactions between cars, buses, and bicycles (17). As the latter show, interactions between different modes have different effects on the overall pace of the vehicles compared with cases where only unimodal interactions are considered.…”

Section: The Macroscopic Fundamental Diagrammentioning

confidence: 99%

Network Inefficiency: Empirical Findings for Six European Cities

Hamm

Loder

Tilg

et al. 2022

Transportation Research Record: Journal of the Transportation R

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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.

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Section: The Macroscopic Fundamental Diagrammentioning

confidence: 99%

Network Inefficiency: Empirical Findings for Six European Cities

Hamm

Loder

Tilg

et al. 2022

Transportation Research Record: Journal of the Transportation R

Self Cite

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“…So far, literature has been primarily focused on a single mode: cars (Daganzo and Geroliminis, 2008;Geroliminis and Daganzo, 2008;Mahmassani et al, 2013;Loder et al, 2019a). 1 Some studies are already investigating interactions between cars and buses (Geroliminis et al, 2014;Loder et al, 2017;Castrillon and Laval, 2018;Dakic et al, 2020;Tilg et al, 2020), and cars and pedestrians (Daganzo and Knoop, 2016), but no approach exists to derive multi-modal MFDs for a vector of vehicle accumulations with more than two modes (in most cases buses and cars). In this paper, we contribute with such a vector-based approach that is based on the idea of quantifying pairwise the interaction delays of interacting modes and link them to the MFD shape like a copula function.…”

Section: Introductionmentioning

confidence: 99%

How Many Cars in the City Are Too Many? Towards Finding the Optimal Modal Split for a Multi-Modal Urban Road Network

Loder

Bressan

Wierbos

et al. 2021

Front. Future Transp.

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Interactions among different modes or vehicle classes in urban road networks affect the network performance in different and complex ways. Thus, an answer to the question of “how many cars are too many for a city?” is not trivial. However, multi-modal macroscopic fundamental diagrams (MFD) offer a novel opportunity to answer this question. So far, no methodology exists to estimate multi-modal MFDs resulting from arbitrary multi-modal interactions. In this paper, we propose a methodology to capture additional delays in the shape of the MFD and derive an approach for estimating multi-modal MFDs thereof. The influence on the MFD shape is established using the two-fluid theory of urban traffic by defining pairwise copula functions between travel times of each mode. In contrast to many existing approaches, the presented approach retains individual mode's speed information. We show the applicability of the approach with a tri-modal case of bicycles, buses, and cars with empirical data from Amsterdam (The Netherlands) and London (United Kingdom). Although the approach is not limited to this specific tri-modal case, we use the example to discuss the initial policy question by deriving optimal modal splits for a given accumulation of travelers. Last, we compare the new approach to existing estimation methods for bi-modal MFDs describing car and bus traffic.

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