Empirics of multi-modal traffic networks – Using the 3D macroscopic fundamental diagram

Allister Loder, Lukas Ambühl, Monica Menendez, Kay W. Axhausen

    Research output: Contribution to journalArticle

    Abstract

    Traffic is multi-modal in most cities. However, the impacts of different transport modes on traffic performance and on each other are unclear – especially at the network level. The recent extension of the macroscopic fundamental diagram (MFD) into the 3D-MFD offers a novel framework to address this gap at the urban scale. The 3D-MFD relates the network accumulation of cars and public transport vehicles to the network travel production, for either vehicles or passengers. No empirical 3D-MFD has been reported so far. In this paper, we present the first empirical estimate of a 3D-MFD at the urban scale. To this end, we use data from loop detectors and automatic vehicle location devices (AVL) of the public transport vehicles in the city of Zurich, Switzerland. We compare two different areas within the city, that differ in their topology and share of dedicated lanes for public transport. We propose a statistical model of the 3D-MFD, which estimates the effects of the vehicle accumulation on car and public transport speeds under multi-modal traffic conditions. The results quantify the effects of both, vehicles and passengers, and confirm that a greater share of dedicated lanes reduces the marginal effects of public transport vehicles on car speeds. Lastly, we derive a new application of the 3D-MFD by identifying the share of public transport users that maximizes the journey speeds in an urban network accounting for all motorized transport modes.

    Original languageEnglish (US)
    Pages (from-to)88-101
    Number of pages14
    JournalTransportation Research Part C: Emerging Technologies
    Volume82
    DOIs
    StatePublished - Sep 1 2017

    Fingerprint

    empirics
    public transport
    traffic
    Railroad cars
    Switzerland
    travel
    Topology
    Detectors
    performance

    Keywords

    • Macroscopic fundamental diagram
    • Mode share
    • Multi-modal traffic
    • Public transport
    • Urban traffic

    ASJC Scopus subject areas

    • Civil and Structural Engineering
    • Automotive Engineering
    • Transportation
    • Computer Science Applications

    Cite this

    Empirics of multi-modal traffic networks – Using the 3D macroscopic fundamental diagram. / Loder, Allister; Ambühl, Lukas; Menendez, Monica; Axhausen, Kay W.

    In: Transportation Research Part C: Emerging Technologies, Vol. 82, 01.09.2017, p. 88-101.

    Research output: Contribution to journalArticle

    @article{ff934ca713cf4929b8b3952fc2261d44,
    title = "Empirics of multi-modal traffic networks – Using the 3D macroscopic fundamental diagram",
    abstract = "Traffic is multi-modal in most cities. However, the impacts of different transport modes on traffic performance and on each other are unclear – especially at the network level. The recent extension of the macroscopic fundamental diagram (MFD) into the 3D-MFD offers a novel framework to address this gap at the urban scale. The 3D-MFD relates the network accumulation of cars and public transport vehicles to the network travel production, for either vehicles or passengers. No empirical 3D-MFD has been reported so far. In this paper, we present the first empirical estimate of a 3D-MFD at the urban scale. To this end, we use data from loop detectors and automatic vehicle location devices (AVL) of the public transport vehicles in the city of Zurich, Switzerland. We compare two different areas within the city, that differ in their topology and share of dedicated lanes for public transport. We propose a statistical model of the 3D-MFD, which estimates the effects of the vehicle accumulation on car and public transport speeds under multi-modal traffic conditions. The results quantify the effects of both, vehicles and passengers, and confirm that a greater share of dedicated lanes reduces the marginal effects of public transport vehicles on car speeds. Lastly, we derive a new application of the 3D-MFD by identifying the share of public transport users that maximizes the journey speeds in an urban network accounting for all motorized transport modes.",
    keywords = "Macroscopic fundamental diagram, Mode share, Multi-modal traffic, Public transport, Urban traffic",
    author = "Allister Loder and Lukas Amb{\"u}hl and Monica Menendez and Axhausen, {Kay W.}",
    year = "2017",
    month = "9",
    day = "1",
    doi = "10.1016/j.trc.2017.06.009",
    language = "English (US)",
    volume = "82",
    pages = "88--101",
    journal = "Transportation Research Part C: Emerging Technologies",
    issn = "0968-090X",
    publisher = "Elsevier Limited",

    }

    TY - JOUR

    T1 - Empirics of multi-modal traffic networks – Using the 3D macroscopic fundamental diagram

    AU - Loder, Allister

    AU - Ambühl, Lukas

    AU - Menendez, Monica

    AU - Axhausen, Kay W.

    PY - 2017/9/1

    Y1 - 2017/9/1

    N2 - Traffic is multi-modal in most cities. However, the impacts of different transport modes on traffic performance and on each other are unclear – especially at the network level. The recent extension of the macroscopic fundamental diagram (MFD) into the 3D-MFD offers a novel framework to address this gap at the urban scale. The 3D-MFD relates the network accumulation of cars and public transport vehicles to the network travel production, for either vehicles or passengers. No empirical 3D-MFD has been reported so far. In this paper, we present the first empirical estimate of a 3D-MFD at the urban scale. To this end, we use data from loop detectors and automatic vehicle location devices (AVL) of the public transport vehicles in the city of Zurich, Switzerland. We compare two different areas within the city, that differ in their topology and share of dedicated lanes for public transport. We propose a statistical model of the 3D-MFD, which estimates the effects of the vehicle accumulation on car and public transport speeds under multi-modal traffic conditions. The results quantify the effects of both, vehicles and passengers, and confirm that a greater share of dedicated lanes reduces the marginal effects of public transport vehicles on car speeds. Lastly, we derive a new application of the 3D-MFD by identifying the share of public transport users that maximizes the journey speeds in an urban network accounting for all motorized transport modes.

    AB - Traffic is multi-modal in most cities. However, the impacts of different transport modes on traffic performance and on each other are unclear – especially at the network level. The recent extension of the macroscopic fundamental diagram (MFD) into the 3D-MFD offers a novel framework to address this gap at the urban scale. The 3D-MFD relates the network accumulation of cars and public transport vehicles to the network travel production, for either vehicles or passengers. No empirical 3D-MFD has been reported so far. In this paper, we present the first empirical estimate of a 3D-MFD at the urban scale. To this end, we use data from loop detectors and automatic vehicle location devices (AVL) of the public transport vehicles in the city of Zurich, Switzerland. We compare two different areas within the city, that differ in their topology and share of dedicated lanes for public transport. We propose a statistical model of the 3D-MFD, which estimates the effects of the vehicle accumulation on car and public transport speeds under multi-modal traffic conditions. The results quantify the effects of both, vehicles and passengers, and confirm that a greater share of dedicated lanes reduces the marginal effects of public transport vehicles on car speeds. Lastly, we derive a new application of the 3D-MFD by identifying the share of public transport users that maximizes the journey speeds in an urban network accounting for all motorized transport modes.

    KW - Macroscopic fundamental diagram

    KW - Mode share

    KW - Multi-modal traffic

    KW - Public transport

    KW - Urban traffic

    UR - http://www.scopus.com/inward/record.url?scp=85025467284&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85025467284&partnerID=8YFLogxK

    U2 - 10.1016/j.trc.2017.06.009

    DO - 10.1016/j.trc.2017.06.009

    M3 - Article

    AN - SCOPUS:85025467284

    VL - 82

    SP - 88

    EP - 101

    JO - Transportation Research Part C: Emerging Technologies

    JF - Transportation Research Part C: Emerging Technologies

    SN - 0968-090X

    ER -