An automatic methodology to measure drivers’ behavior in public transport

Catalán, Hernán; Löbel, Hans; Herrera, Juan Carlos

doi:10.1080/15472450.2022.2129019

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…This augmentation is directly obained through the consideration of KS among other common transportation modes. Indeed, the most popular TMD classes are tramway, see Byon, Ha, Cho, Kim, and Yeun (2017), bus, see Liang, Zhang, Wang, and Xu (2020), Catalán, Lobel, and Herrera (2022) and metro (or subways), see Fang et al (2017). Bikes and e-bikes were considered in Xiao, Juan, and Zhang (2015), whereas one study, Prentow et al (2015), has considered 10 minutes of KS data, in a specific hospital environment, resulting into a KS-F1-Score of 56%.…”

Section: Ks As a Novel Class In Tmdmentioning

confidence: 99%

Kick-scooters identification in the context of transportation mode detection using inertial sensors: Methods and accuracy

Alaoui

Fourati

Kibangou

et al. 2022

Journal of Intelligent Transportation Systems

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This work presents a novel transportation mode detection algorithm that handles the recognition of kick-scooters. In 2015, 10 minutes of data from a kick-scooter were considered in a transportation mode detection study, yielding a 56% F1-score. Since then, kick-scooters were not given much attention. Yet, kick-scooters are now very present in the urban transportation ecosystem, and their consideration in transportation studies has become a must. To fill this gap, 4 hours of kick-scooter signals were collected by 18 participants, with a set of 6 different kick-scooters, using 3 body-worn inertial measurement units. Obviously, kick-scooter patterns are classified in contrast with other modes of transportation. Two classification scenarios are considered in order to gradually increase the classification model complexity. The first scenario includes walking, biking, and kick-scooter, while the second considers public transport (tramway and bus) in addition to the former transportation modes. Results show that kick-scooters can be detected with an F1-score of 80% in the first scenario. Walking and public transport samples were still accurately classified in the second scenario, with an F1-score above 80% for both classes. However, bike and kick-scooter samples were both classified with lower F1-scores, equal to 59% and 64% respectively. Therefore, the main focus of future works should be directed towards the separability of kick-scooters and bikes when public transport is considered. The findings also suggest to place preferably the sensors in the trouser's pocket, allowing for leg motion to be finely captured.

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