Capturing network properties with a functional form for the multi-modal macroscopic fundamental diagram

•New functional form for multi-modal MFDs.•Estimation of function based on network topology and traffic data.•Validation with simulation and empirical data sets. In urban road networks, the interactions between different modes can clearly impact the overall travel production. Although those interact...

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Veröffentlicht in:Transportation research. Part B: methodological 2019-11, Vol.129, p.1-19
Hauptverfasser: Loder, Allister, Dakic, Igor, Bressan, Lea, Ambühl, Lukas, Bliemer, Michiel C.J., Menendez, Monica, Axhausen, Kay W.
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container_issue
container_start_page 1
container_title Transportation research. Part B: methodological
container_volume 129
creator Loder, Allister
Dakic, Igor
Bressan, Lea
Ambühl, Lukas
Bliemer, Michiel C.J.
Menendez, Monica
Axhausen, Kay W.
description •New functional form for multi-modal MFDs.•Estimation of function based on network topology and traffic data.•Validation with simulation and empirical data sets. In urban road networks, the interactions between different modes can clearly impact the overall travel production. Although those interactions can be quantified with the multi-modal macroscopic fundamental diagram; so far, no functional form exists for this diagram to explicitly capture operational and network properties. In this paper, we propose a methodology to generate such functional form, and we show its applicability to the specific case of a bi-modal network with buses and cars. The proposed functional form has two components. First, a three dimensional lower envelope limits travel production to the theoretical best-case situation for any given number of vehicles for the different modes. The lower envelope’s parameters are derived from topology and operational features of the road network. Second, a smoothing parameter quantifies how interactions between all vehicle types reduce travel production from the theoretical best-case. The smoothing parameter is estimated with network topology and traffic data. In the case no traffic data is available, our functional form is still applicable. The lower envelope can be approximated assuming fundamental parameters of traffic operations. For the smoothing parameter, we show that it always hold similar values even for different networks, making its approximation also possible. This feature of the proposed functional form is an advantage compared to curve fitting, as it provides a reasonable shape for the multi-modal macroscopic fundamental diagram irrespective of traffic data availability. The methodology is illustrated and validated using simulation and empirical data sets from London and Zurich.
doi_str_mv 10.1016/j.trb.2019.09.004
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In urban road networks, the interactions between different modes can clearly impact the overall travel production. Although those interactions can be quantified with the multi-modal macroscopic fundamental diagram; so far, no functional form exists for this diagram to explicitly capture operational and network properties. In this paper, we propose a methodology to generate such functional form, and we show its applicability to the specific case of a bi-modal network with buses and cars. The proposed functional form has two components. First, a three dimensional lower envelope limits travel production to the theoretical best-case situation for any given number of vehicles for the different modes. The lower envelope’s parameters are derived from topology and operational features of the road network. Second, a smoothing parameter quantifies how interactions between all vehicle types reduce travel production from the theoretical best-case. The smoothing parameter is estimated with network topology and traffic data. In the case no traffic data is available, our functional form is still applicable. The lower envelope can be approximated assuming fundamental parameters of traffic operations. For the smoothing parameter, we show that it always hold similar values even for different networks, making its approximation also possible. This feature of the proposed functional form is an advantage compared to curve fitting, as it provides a reasonable shape for the multi-modal macroscopic fundamental diagram irrespective of traffic data availability. 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subjects Automobiles
Bus
Buses
Congestion
Curve fitting
Economic models
MFD
Multi-modal
Network topologies
Parameter estimation
Roads
Smoothing
Topology
Traffic
Traffic information
Travel
title Capturing network properties with a functional form for the multi-modal macroscopic fundamental diagram
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