Review and evaluation of methods in transport mode detection based on GPS tracking data

Sadeghian, Paria; Håkansson, Johan; Zhao, Xiaoyun

doi:10.1016/j.jtte.2021.04.004

Cited by 32 publications

(12 citation statements)

References 44 publications

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“…With the development of mobile computation, various sensors carried in smart phones are becoming novel data sources for crowd behavior identifying and environment perception [34][35][36][37]. Thus, smartphone-based methods have gradually emerged as a popular way to categorize different travel modes [11].…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors

Guo

Huang

et al. 2022

Applied Sciences

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Nowadays, large-scale human mobility has led to increasingly severe traffic congestion in cities, how to accurately identify people’s travel mode has become particularly important for urban traffic planning and management. However, traditional methods are based on telephone interviews or questionnaires, which makes it difficult to obtain accurate and effective data. Nowadays, numbers of smartphones are equipped with various sensors, including accelerometers, gyroscopes, and GPS, providing a novel social sensing data source to detect people’s travel modes. The fusion of multiple sensor data is a promising way for travel mode detection. However, how to use these sensor data to accurately detect travel mode is still a challenging task. In this paper, we presented a light-weight method for travel mode detection based on four types of smartphone sensor data collected from an accelerometer, gyroscope, magnetometer, and barometer, and a prototype application was developed. Then, a novel convolutional neural network (CNN) was designed to identify five representative travel modes (walk, bicycle, bus, car, and metro). We compared the overall performance of the proposed method via different hyperparameters, and the experimental results show that the F value of the proposed method reaches 97%, which verified the effectiveness of the proposed method for travel mode classification.

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Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors

Guo

Huang

et al. 2022

Applied Sciences

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“…Even though KS are new to TMD research, multiple TMD methods have been developed to recognize common transportation modes such as private cars, bikes, and PT. Recent TMD surveys can be found in Sadeghian, Håkansson, and Zhao (2021), Kamalian, Ferreira, and Jul (2022), Ahmed and Diaz (2022). Historically, the main devices used were first GPS loggers Biancat, Brighenti, and Brighenti (2014), Roy, Fuller, Nelson, and Kedron (2022) and then smartphones Carpineti, Lomonaco, Bedogni, Di Felice, and Bononi (2018), Sharma, Singh, Udmale, Singh, and Singh (2021), Wang, Luo, Zhao, and Qin (2021), Liu (2022), which are still in use at the present, although a recent study has explored the use of smartwatches to perform TMD, see Hasan, Irshaid, Alhomaidat, Lee, and Oh (2022).…”

Section: Sensorsmentioning

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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“…parents, school representatives or transport organizers. In the past, solutions of this type were most often based on GPS technology (as described in [7,29]) and, slightly less frequently, on passive RFID technology [30]. Commercial applications of this type include Track-SchoolBus app.…”

Section: Using Iot Solutions For Monitoring Children Travels To and F...mentioning

confidence: 99%

Intelligent mobility: A model for assessing the safety of children traveling to school on a school bus with the use of intelligent bus stops

Murawski¹,

Szczepański²,

Jacyna-Gołda³

et al. 2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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The aim of the article is to develop a model for assessing the safety of children’s travel. Safety is the most important indicator describing the mobility system of children, even more important than the costs of operating it. Due to the dynamic development of intelligent solutions, it is possible to undertake additional activities supporting the improvement of children’s safety when traveling to and from school. However, their implementation requires an adequate assessment of a children’s mobility system. Currently, there are no solutions that could comprehensively support the decision-making process in this sphere. The article presents the issues of children’s mobility, a literature review in this area, mathematical model for assessing school bus travel, and a computational example. The presented approach is an original solution allowing for evaluation of the existing systems and their development scenarios. In addition, it enables the comparison of children mobility systems of different complexity and scale.

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Review and evaluation of methods in transport mode detection based on GPS tracking data

Cited by 32 publications

References 44 publications

Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors

Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors

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

Intelligent mobility: A model for assessing the safety of children traveling to school on a school bus with the use of intelligent bus stops

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