Estimating bus passenger volume based on a Wi-Fi scanner survey

Hidayat, Arief; Terabe, Shintaro; Yaginuma, Hideki

doi:10.1016/j.trip.2020.100142

Cited by 15 publications

(6 citation statements)

References 22 publications

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“…In the same applicative context, the work of [3] faces the problem of classifying MAC addresses of passengers and pedestrians by collecting probe messages with GPS information acquired by the scanner. This information is then coupled with static information as bus stop zoning and bus route circulation.…”

Section: Related Workmentioning

confidence: 99%

“…These types of data are becoming a standard for developing concepts related to smart cities both for transport companies and authorities. Due to the increasing spread of low-cost video cameras and sensors, new technologies have been introduced to complement traditional methods based on manual counting, interviews and questionnaires; see e.g., [1][2][3].…”

Section: Introduction 1background Motivations and Challengesmentioning

confidence: 99%

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Automatic Passenger Counting on the Edge via Unsupervised Clustering

Delzanno,

Caputo,

D’Agostino

et al. 2023

Sensors

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We present a device- and network-based solution for automatic passnger counting that operates on the edge in real time. The proposed solution consists of a low-cost WiFi scanner device equipped with custom algorithms for dealing with MAC address randomization. Our low-cost scanner is able to capture and analyze 802.11 probe requests emitted by passengers’ devices such as laptops, smartphones, and tablets. The device is configured with a Python data-processing pipeline that combines data coming from different types of sensors and processes them on the fly. For the analysis task, we have devised a lightweight version of the DBSCAN algorithm. Our software artifact is designed in a modular way in order to accommodate possible extensions of the pipeline, e.g., either additional filters or data sources. Furthermore, we exploit multi-threading and multi-processing for speeding up the entire computation. The proposed solution has been tested with different types of mobile devices, obtaining promising experimental results. In this paper, we present the key ingredients of our edge computing solution.

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Section: Related Workmentioning

confidence: 99%

Section: Introduction 1background Motivations and Challengesmentioning

confidence: 99%

Automatic Passenger Counting on the Edge via Unsupervised Clustering

Delzanno,

Caputo,

D’Agostino

et al. 2023

Sensors

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show abstract

“…The results are promising, but the MAC randomization problem of mobile devices might need further attention. In [88], the authors proposed a MAC address cleaning and processing scheme that considers speed, bus stop zoning, and route circulation to obtain the Origin-destination (OD) matrix and passenger volumes for bus route sections. Although the results showed that the estimated passenger volume would usually be smaller than the actual value, the former follows the same trend as the latter.…”

Section: Wi-fi-based Traffic Parameter Extractionmentioning

confidence: 99%

A Review of Wi-Fi-Based Traffic Detection Technology in the Field of Intelligent Transportation Systems

Lin

Lyu

et al. 2022

Buildings

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With the increasing innovation and development of Wi-Fi technology, its penetration in the various fields of industry and academia is becoming more and more profound. As the core infrastructure of traffic data collection in the field of Intelligent Transportation Systems (ITS), Wi-Fi-based traffic detectors have great potential for use in traffic target positioning, perception, and pattern recognition due to their low-cost and extensive infrastructure deployment. This paper conducts a comprehensive review of three major Wi-Fi-based traffic detection applications in the field of ITS: target positioning, traffic parameter extraction, and travel mode identification. Among these, target positioning is one of the most widespread applications of Wi-Fi technology, which is also the basis for two other research aspects. Moreover, Wi-Fi-based positioning can be divided into two categories: ranging-based positioning and range-free one; in the field of transportation, it can also be categorized into pedestrian positioning and vehicle positioning based on travel mode. To further demonstrate the effectiveness of Wi-Fi-based ITS applications in practice, this study compares with the various Wi-Fi-involved models and algorithms around the world, as well as provides some ideas and inspiration along with this direction.

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“…In addition, the estimation using WiFi risks violating privacy because it obtains a unique MAC address for each device. Hidayat et al proposed a new data cleaning procedure to characterize bus passenger volume using a combination of media access control (MAC) address and global positioning system (GPS) data for estimating the number of passengers [ 24 , 25 ]. Their approach shows that the correlation between WiFi estimation and ground truth is 0.78.…”

Section: Related Workmentioning

confidence: 99%

Estimating Congestion in a Fixed-Route Bus by Using BLE Signals

Kanamitsu

Taya

Tachibana

et al. 2022

Sensors

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Information on congestion of buses, which are one of the major public transportation modes, can be very useful in light of the current COVID-19 pandemic. Because it is unrealistic to manually monitor the number of riders on all buses in operation, a system that can automatically monitor congestion is necessary. The main goal of this paper’s work is to automatically estimate the congestion level on a bus route with acceptable performance. For practical operation, it is necessary to design a system that does not infringe on the privacy of passengers and ensures the safety of passengers and the installation sites. In this paper, we propose a congestion estimation system that protects passengers’ privacy and reduces the installation cost by using Bluetooth low-energy (BLE) signals as sensing data. The proposed system consists of (1) a sensing mechanism that acquires BLE signals emitted from passengers’ mobile terminals in the bus and (2) a mechanism that estimates the degree of congestion in the bus from the data obtained by the sensing mechanism. To evaluate the effectiveness of the proposed system, we conducted a data collection experiment on an actual bus route in cooperation with Nara Kotsu Co., Ltd. The results showed that the proposed system could estimate the number of passengers with a mean absolute error of 2.49 passengers (error rate of 38.8%)

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Estimating bus passenger volume based on a Wi-Fi scanner survey

Cited by 15 publications

References 22 publications

Automatic Passenger Counting on the Edge via Unsupervised Clustering

Automatic Passenger Counting on the Edge via Unsupervised Clustering

A Review of Wi-Fi-Based Traffic Detection Technology in the Field of Intelligent Transportation Systems

Estimating Congestion in a Fixed-Route Bus by Using BLE Signals

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