On the modeling of passenger mobility for stochastic bi-modal urban corridors

Dakic, Igor; Ambühl, Lukas; Schümperlin, Oliver; Menéndez, Mónica

doi:10.1016/j.trpro.2019.05.015

Cited by 3 publications

(1 citation statement)

References 58 publications

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“…Castrillon and Laval (2018) extended the stochastic approximation for MFDs (Laval and Castrillón, 2015) to apply to bi-modal traffic on homogeneous urban corridors. Based on unimodal semi-analytical MFD estimation methods (Leclercq and Geroliminis, 2013;Tilg et al, 2020), Dakic et al (2019) developed a VT-based method to estimate the passenger MFD for bi-modal corridors. Thereby, they accounted for the stochastic nature of bus operations, moving bus bottlenecks, and traffic state dependency of bus arrivals.…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based Design of Urban Bi-modal Transport Systems

Tilg¹,

Abedin²,

Amini³

et al. 2020

Front. Future Transp.

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The three-dimensional passenger macroscopic fundamental diagram (pMFD) describes the relation of the network accumulation of public transport and private vehicles, and the passenger production. It allows for modeling the multi-modal traffic dynamics in urban networks and deriving innovative performance indicators. This paper integrates this concept into a multi-modal transport system design framework formulated as a simulation-based optimization problem. In doing so, we consider the competition for limited road space and the operational characteristics, such as congestion occurrences, at the strategic design level. We evaluate the proposed framework in a case study for the Sioux Falls network. Thereby, we deliver a proof of concept, and show that the proposed methodology indeed designs a transport system which benefits the overall system's performance. This paper further advances the integration of sequential model-based optimization techniques, macroscopic traffic flow concepts, and traffic simulation to design multi-modal transport systems. This supports transport planners and local authorities in composing efficient and robust transport networks.

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Section: Introductionmentioning

confidence: 99%

Simulation-Based Design of Urban Bi-modal Transport Systems

Tilg¹,

Abedin²,

Amini³

et al. 2020

Front. Future Transp.

View full text Add to dashboard Cite

show abstract

Simulation-based joint optimization framework for congestion mitigation in multimodal urban network: a macroscopic approach

2019

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Understanding traffic capacity of urban networks

Loder

Ambühl

Menéndez

et al. 2019

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Traffic in an urban network becomes congested once there is a critical number of vehicles in the network. To improve traffic operations, develop new congestion mitigation strategies, and reduce negative traffic externalities, understanding the basic laws governing the network’s critical number of vehicles and the network’s traffic capacity is necessary. However, until now, a holistic understanding of this critical point and an empirical quantification of its driving factors has been missing. Here we show with billions of vehicle observations from more than 40 cities, how road and bus network topology explains around 90% of the empirically observed critical point variation, making it therefore predictable. Importantly, we find a sublinear relationship between network size and critical accumulation emphasizing decreasing marginal returns of infrastructure investment. As transportation networks are the lifeline of our cities, our findings have profound implications on how to build and operate our cities more efficiently.

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On the modeling of passenger mobility for stochastic bi-modal urban corridors

Cited by 3 publications

References 58 publications

Simulation-Based Design of Urban Bi-modal Transport Systems

Simulation-Based Design of Urban Bi-modal Transport Systems

Simulation-based joint optimization framework for congestion mitigation in multimodal urban network: a macroscopic approach

Understanding traffic capacity of urban networks

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