Smartphone Based Detection of Vehicle Encounters

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Original languageEnglish
Title of host publicationIWCTS 2023 - Proceedings of the 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science
EditorsOlufemi A. Omitaomu, Andy Berres, Haowen Xu
Pages42-51
Number of pages10
ISBN (electronic)9798400703577
Publication statusPublished - 15 Nov 2023
Event16th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2023 - Hamburg, Germany
Duration: 13 Nov 202313 Nov 2023

Abstract

Riding a bicycle in shared traffic alongside motor vehicles causes discomfort or even stress for many cyclists. Avoiding busy or crowded roads is only possible with good local knowledge, as no data is available on the frequency of encounters with motor vehicles for most roads. Acquiring a data set that combines smartphone sensor data with known vehicle encounters can become the foundation for a smartphone based moving vehicle detector. Therefore, readings from the omnipresent smartphone sensors magnetometer and barometer can be exploited as indicators of passing vehicles.In this paper, a novel approach is presented to detect vehicle encounters in smartphone sensor data. For this purpose, a modular mobile sensor platform is first constructed and set up to collect smartphone, camera and ultrasonic sensor data in real traffic scenarios. The platform is designed to be used with various sensor configurations to serve a broader set of use cases in the future. In the presented use case, the platform is constructed to create a reference data set of vehicle encounters consisting of location information, direction, distance, speed and further metadata. To this end, a methodology is presented to process the collected camera images and ultrasonic distance data.Furthermore, two smartphones are used to collect raw data from their magnetometer and barometric sensor. Based on both, the reference and the smartphones' data set, a classifier for the detection of vehicle encounters is then trained to operate on pure smartphone sensor data. Experiments on real data show that a Random Forest classifier can be successfully applied to recorded smartphone sensor data. The results prove that the presented approach is able to detect overtaking vehicle encounters with a F1-score of 71.0%, which is sufficient to rank different cycling routes by their 'stress factor'.

Keywords

    cycling, instrumented bicycle, machine-learning, overtaking distance, traffic safety

ASJC Scopus subject areas

Cite this

Smartphone Based Detection of Vehicle Encounters. / Schimansky, Tim Peter Jörg; Wage, Oskar; Golze, Jens et al.
IWCTS 2023 - Proceedings of the 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science. ed. / Olufemi A. Omitaomu; Andy Berres; Haowen Xu. 2023. p. 42-51.

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Schimansky, TPJ, Wage, O, Golze, J & Feuerhake, U 2023, Smartphone Based Detection of Vehicle Encounters. in OA Omitaomu, A Berres & H Xu (eds), IWCTS 2023 - Proceedings of the 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science. pp. 42-51, 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2023, Hamburg, Germany, 13 Nov 2023. https://doi.org/10.1145/3615895.3628173
Schimansky, T. P. J., Wage, O., Golze, J., & Feuerhake, U. (2023). Smartphone Based Detection of Vehicle Encounters. In O. A. Omitaomu, A. Berres, & H. Xu (Eds.), IWCTS 2023 - Proceedings of the 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science (pp. 42-51) https://doi.org/10.1145/3615895.3628173
Schimansky TPJ, Wage O, Golze J, Feuerhake U. Smartphone Based Detection of Vehicle Encounters. In Omitaomu OA, Berres A, Xu H, editors, IWCTS 2023 - Proceedings of the 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science. 2023. p. 42-51 doi: 10.1145/3615895.3628173
Schimansky, Tim Peter Jörg ; Wage, Oskar ; Golze, Jens et al. / Smartphone Based Detection of Vehicle Encounters. IWCTS 2023 - Proceedings of the 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science. editor / Olufemi A. Omitaomu ; Andy Berres ; Haowen Xu. 2023. pp. 42-51
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title = "Smartphone Based Detection of Vehicle Encounters",
abstract = "Riding a bicycle in shared traffic alongside motor vehicles causes discomfort or even stress for many cyclists. Avoiding busy or crowded roads is only possible with good local knowledge, as no data is available on the frequency of encounters with motor vehicles for most roads. Acquiring a data set that combines smartphone sensor data with known vehicle encounters can become the foundation for a smartphone based moving vehicle detector. Therefore, readings from the omnipresent smartphone sensors magnetometer and barometer can be exploited as indicators of passing vehicles.In this paper, a novel approach is presented to detect vehicle encounters in smartphone sensor data. For this purpose, a modular mobile sensor platform is first constructed and set up to collect smartphone, camera and ultrasonic sensor data in real traffic scenarios. The platform is designed to be used with various sensor configurations to serve a broader set of use cases in the future. In the presented use case, the platform is constructed to create a reference data set of vehicle encounters consisting of location information, direction, distance, speed and further metadata. To this end, a methodology is presented to process the collected camera images and ultrasonic distance data.Furthermore, two smartphones are used to collect raw data from their magnetometer and barometric sensor. Based on both, the reference and the smartphones' data set, a classifier for the detection of vehicle encounters is then trained to operate on pure smartphone sensor data. Experiments on real data show that a Random Forest classifier can be successfully applied to recorded smartphone sensor data. The results prove that the presented approach is able to detect overtaking vehicle encounters with a F1-score of 71.0%, which is sufficient to rank different cycling routes by their 'stress factor'.",
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AU - Schimansky, Tim Peter Jörg

AU - Wage, Oskar

AU - Golze, Jens

AU - Feuerhake, Udo

N1 - Funding Information: This work is partially funded by the German Federal Ministry for Digital and Transport (BMDV) grand 45FGU121E ’5GAPS’, German Federal Ministry for Economic Affairs and Energy (BMWi) grant 01ME19009B ’d-E-mand’ and German Research Foundation (DFG) GRK 1931 ’SocialCars’.

PY - 2023/11/15

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N2 - Riding a bicycle in shared traffic alongside motor vehicles causes discomfort or even stress for many cyclists. Avoiding busy or crowded roads is only possible with good local knowledge, as no data is available on the frequency of encounters with motor vehicles for most roads. Acquiring a data set that combines smartphone sensor data with known vehicle encounters can become the foundation for a smartphone based moving vehicle detector. Therefore, readings from the omnipresent smartphone sensors magnetometer and barometer can be exploited as indicators of passing vehicles.In this paper, a novel approach is presented to detect vehicle encounters in smartphone sensor data. For this purpose, a modular mobile sensor platform is first constructed and set up to collect smartphone, camera and ultrasonic sensor data in real traffic scenarios. The platform is designed to be used with various sensor configurations to serve a broader set of use cases in the future. In the presented use case, the platform is constructed to create a reference data set of vehicle encounters consisting of location information, direction, distance, speed and further metadata. To this end, a methodology is presented to process the collected camera images and ultrasonic distance data.Furthermore, two smartphones are used to collect raw data from their magnetometer and barometric sensor. Based on both, the reference and the smartphones' data set, a classifier for the detection of vehicle encounters is then trained to operate on pure smartphone sensor data. Experiments on real data show that a Random Forest classifier can be successfully applied to recorded smartphone sensor data. The results prove that the presented approach is able to detect overtaking vehicle encounters with a F1-score of 71.0%, which is sufficient to rank different cycling routes by their 'stress factor'.

AB - Riding a bicycle in shared traffic alongside motor vehicles causes discomfort or even stress for many cyclists. Avoiding busy or crowded roads is only possible with good local knowledge, as no data is available on the frequency of encounters with motor vehicles for most roads. Acquiring a data set that combines smartphone sensor data with known vehicle encounters can become the foundation for a smartphone based moving vehicle detector. Therefore, readings from the omnipresent smartphone sensors magnetometer and barometer can be exploited as indicators of passing vehicles.In this paper, a novel approach is presented to detect vehicle encounters in smartphone sensor data. For this purpose, a modular mobile sensor platform is first constructed and set up to collect smartphone, camera and ultrasonic sensor data in real traffic scenarios. The platform is designed to be used with various sensor configurations to serve a broader set of use cases in the future. In the presented use case, the platform is constructed to create a reference data set of vehicle encounters consisting of location information, direction, distance, speed and further metadata. To this end, a methodology is presented to process the collected camera images and ultrasonic distance data.Furthermore, two smartphones are used to collect raw data from their magnetometer and barometric sensor. Based on both, the reference and the smartphones' data set, a classifier for the detection of vehicle encounters is then trained to operate on pure smartphone sensor data. Experiments on real data show that a Random Forest classifier can be successfully applied to recorded smartphone sensor data. The results prove that the presented approach is able to detect overtaking vehicle encounters with a F1-score of 71.0%, which is sufficient to rank different cycling routes by their 'stress factor'.

KW - cycling

KW - instrumented bicycle

KW - machine-learning

KW - overtaking distance

KW - traffic safety

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M3 - Conference contribution

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BT - IWCTS 2023 - Proceedings of the 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science

A2 - Omitaomu, Olufemi A.

A2 - Berres, Andy

A2 - Xu, Haowen

T2 - 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2023

Y2 - 13 November 2023 through 13 November 2023

ER -

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