Journal: IET Intelligent Transport Systems

Abbreviation

IET Intell. Transp. Syst.

Publisher

Institution of Engineering and Technology

Journal Volumes

ISSN

1751-956X
1751-9578

Description

Filters

2016 - 201912020 - 20232
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Publications 1 - 3 of 3
  • Ride-pooling in the light of COVID-19
    Item type: Journal Article
    Zwick, Felix; Fraedrich, Eva; Axhausen, Kay W. (2023)
    IET Intelligent Transport Systems
    The mobility provider MOIA operates Europe's largest contiguous electric ride-pooling service in Hamburg, representing a testbed of how shared and digitized transport can help foster the transformation of urban mobility. The on-demand service has been in operation since 2019 and was thus affected by the COVID-19 pandemic in 2020. This study shows real-world insights into travel behavior before and during the pandemic, contributing to the empirical evidence on recent mobility behavior. After the application of descriptive statistical analyses, several (spatial) regression models are estimated to understand the relationship between spatial variables and demand. MOIA trip data from three different time periods are used: (a) before the COVID-19 pandemic in summer and autumn 2019, (b) during the time of the first lockdown in Germany in spring 2020, and (c) after the first lockdown in summer and autumn 2020. A significant positive effect on ride-pooling demand is observed for number of inhabitants, workplaces, gastronomic facilities, and at the airport in all time periods. In the course of the pandemic, the main travel patterns remained stable. However, the positive influences of gastronomy and the airport on ride-pooling demand diminished in 2020. In contrast, the impact of hospitals on ride-pooling demand increased in the course of the pandemic. In areas with high car ownership, ride-pooling demand declined compared to pre-pandemic times.
  • Robust cooperative train trajectory optimization with stochastic delays under virtual coupling
    Item type: Journal Article
    Wang, Pengling; Zhu, Yongqiu; Zhu, Wei (2023)
    IET Intelligent Transport Systems
    Virtual coupling technology was recently proposed in railways, which separates trains by a relative braking distance (or even shorter distance) and moves trains synchronously to increase capacity at bottlenecks. This study proposes a real-time cooperative train trajectory planning algorithm for coordinating train movements under virtual coupling by considering stochastic initial delays. The algorithm uses mixed-integer programming models to estimate the delay propagation among trains, detect feasible coupled-running locations, and optimize the trajectories of the two trains such that they coordinate their speeds to achieve energy-efficient, punctual movements, as well as a safe coupled-running process. A robust optimization method is proposed to capture the stochastic delays as an uncertainty set, which is reformulated to its dual problem. Case studies of planning train trajectories for the classical virtual-coupling scenario suggest that (1) the coupled-running distance is greatly affected by the coordination of train timetables, delays, and safe separation constraints at switches; (2) the coordination of train movements for a coupled-running process imposes extra energy costs; and (3) the proposed method can detect feasible coupled-running locations and produce cooperative speed profiles in short computational times.
  • Comparison of adaptive traffic control benefits for recurring and nonrecurring traffic conditions
    Item type: Journal Article
    Stevanovic, Aleksandar; Dakic, Igor; Zlatkovic, Milan (2016)
    IET Intelligent Transport Systems
Publications 1 - 3 of 3