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    Using Machine Learning to Estimate Pedestrian and Bicyclist Count of Intersection by Bluetooth Low Energy

    Source: Journal of Transportation Engineering, Part A: Systems:;2021:;Volume ( 148 ):;issue: 001::page 04021101
    Author:
    Yaobang Gong
    ,
    Mohamed Abdel-Aty
    DOI: 10.1061/JTEPBS.0000619
    Publisher: ASCE
    Abstract: Quantifying pedestrian and bicycle traffic is important for planning, investment, and safety improvements. Traffic agencies have implemented various pedestrian/bicyclist detection systems, but the accuracy is unsatisfactory for intersections. Some studies have explored the use of media access control (MAC) address-scanning sensors such as Bluetooth and Wi-Fi scanners. However, they may suffer from low detection rates. To overcome these shortcomings, this study proposed a system based upon Bluetooth low energy (BLE) scanners. First, the feasibility was assessed by identifying the detection rate and range of BLE scanners. Evaluation experiments uncovered that the detection rate is much higher than the Bluetooth ordinary, and it is sufficiently high for traffic count studies. Moreover, the detection range could cover the whole intersection while reducing the overestimating caused by the large detection range in comparison with other MAC address–scanning sensors. A two-step framework is then proposed for identifying the pedestrians and bicyclists from stationary objects and motorized travelers using one of the popular machine-learning algorithms, one-class support vector machine. The proposed system is validated by the benchmark count data from video footage. The results show that the system can reasonably estimate the counts of pedestrians and bicyclists in a mixed-traffic environment. The average absolute percentage error is 6.35%. This study has concluded that compared to traditional Bluetooth and Wi-Fi, BLE is more suitable for estimating the counts of pedestrians and bicyclists.
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      Using Machine Learning to Estimate Pedestrian and Bicyclist Count of Intersection by Bluetooth Low Energy

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4282846
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    • Journal of Transportation Engineering, Part A: Systems

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    contributor authorYaobang Gong
    contributor authorMohamed Abdel-Aty
    date accessioned2022-05-07T20:45:05Z
    date available2022-05-07T20:45:05Z
    date issued2021-11-03
    identifier otherJTEPBS.0000619.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4282846
    description abstractQuantifying pedestrian and bicycle traffic is important for planning, investment, and safety improvements. Traffic agencies have implemented various pedestrian/bicyclist detection systems, but the accuracy is unsatisfactory for intersections. Some studies have explored the use of media access control (MAC) address-scanning sensors such as Bluetooth and Wi-Fi scanners. However, they may suffer from low detection rates. To overcome these shortcomings, this study proposed a system based upon Bluetooth low energy (BLE) scanners. First, the feasibility was assessed by identifying the detection rate and range of BLE scanners. Evaluation experiments uncovered that the detection rate is much higher than the Bluetooth ordinary, and it is sufficiently high for traffic count studies. Moreover, the detection range could cover the whole intersection while reducing the overestimating caused by the large detection range in comparison with other MAC address–scanning sensors. A two-step framework is then proposed for identifying the pedestrians and bicyclists from stationary objects and motorized travelers using one of the popular machine-learning algorithms, one-class support vector machine. The proposed system is validated by the benchmark count data from video footage. The results show that the system can reasonably estimate the counts of pedestrians and bicyclists in a mixed-traffic environment. The average absolute percentage error is 6.35%. This study has concluded that compared to traditional Bluetooth and Wi-Fi, BLE is more suitable for estimating the counts of pedestrians and bicyclists.
    publisherASCE
    titleUsing Machine Learning to Estimate Pedestrian and Bicyclist Count of Intersection by Bluetooth Low Energy
    typeJournal Paper
    journal volume148
    journal issue1
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.0000619
    journal fristpage04021101
    journal lastpage04021101-9
    page9
    treeJournal of Transportation Engineering, Part A: Systems:;2021:;Volume ( 148 ):;issue: 001
    contenttypeFulltext
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