On the Relationship Between Accuracy of Bus Position Estimated by Crowdsourcing and Participation Density

Aihara, Kenro; Bin, Piao; Imura, Hajime

doi:10.1007/978-3-030-21935-2_9

Cited by 1 publication

(1 citation statement)

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“…In [7,8], they developed a bus query application based on the crowdsourcing model, using Bluetooth as a crowd counting device and location conversion trigger. Let the users' smartphones provide the number of passengers and traffic conditions instead of the equipment that will be installed on the bus to achieve these proposes.…”

Section: Related Workmentioning

confidence: 99%

Using Mobile Sensing Technology for Capturing People Mobility Information

Song

Ito

Nishiyama

et al. 2019

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility

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The detection and analysis of human crowds have been widely used from urban design and traffic management to disaster evacuation and mobility prediction. Currently, several common methods of crowd flow detection have different performances in terms of accuracy, cost and scope of application. One of the main reasons for the difference is that the technology or equipment used to detect crowd flows are different. For example, focus on accuracy, use the camera to record in real-time, and analyze the flow of people with the relevant algorithms. Or pay more attention to the scope of application, using the GPS information uploaded by the user when using the map service to obtain the direction and speed information of the crowd. In this paper, we propose a clientserver system based on Bluetooth scanning to obtain crowd information. Our proposed system has the advantages of low cost and location flexibility. The system can detect any area without pre-deployed, as long as there is a sufficient number of users involved.

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Section: Related Workmentioning

confidence: 99%