Metropolitan Segment Traffic Speeds From Massive Floating Car Data in 10 Cities

Neun, Moritz; Christian, Eichenberger,; Xin, Yanan; Fu, Cheng; Wiedemann, Nina; Henry, Martin; Tomko, Martin; Ambühl, Lukas; Hermes, Luca; Kopp, Michael

doi:10.1109/tits.2023.3291737

Cited by 1 publication

(1 citation statement)

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“…The approach combines the simulation of commuting car trips performed through floating car data (FCD) for characterizing the car trips and the traditional discrete choice modelling for the identification of the potential shift of each car trip. FCD are obtained from vehicle tracking and are currently provided by owners (or administrators) of vehicle fleets, such as taxi fleets, private vehicle tracks, or ridehailing services (Neun et al, 2023). According to Nigro et al (2022), FCD covering the entire city of Rome are assumed to be a representative sample of private mobility, at least in terms of travelled distances and times.…”

Section: Introductionmentioning

confidence: 99%

A mixed behavioural and data-driven method for assessing the shift potential to electric micromobility: evidence from Rome

Nigro,

Comi,

De Vincentis

et al. 2024

Front. Future Transp.

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Electric micromobility, both as a private option and as a shared service, can represent an alternative to cars, particularly for given user groups (market segments) and specific classes of travel distance. The paper explores the potential for shifting from cars to electric micromobility (specifically, e-bikes and e-scooters) for commuting trips, investigated through floating car data (FCD). The methodology combines the calibration of random utility models (RUMs) and the subsequent simulation through the adoption of FCD spanning the entire city of Rome (Italy). The data used for the calibration of RUM models have been sourced from an online revealed preferences and stated preferences survey carried out between November 2020 and January 2021. Socioeconomic factors, along with transport features (travel time, access time, monetary costs, and perceived safety levels), enter into the definition of the mode choice probability. The first results showed that in Rome, the potential demand for electric micromobility could range between 14% of the FCD sample in the best case (low cost, high accessibility, and road infrastructures with a high perceived level of safety) and about 2% in the worst case (high cost, low accessibility, and a low perceived level of safety).

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Section: Introductionmentioning

confidence: 99%