Monitoring Public Transit Ridership Flow by Passively Sensing Wi-Fi and Bluetooth Mobile Devices

Pu, Ziyuan; Zhu, Meixin; Li, Wenxiang; Cui, Zhiyong; Guo, Xiaoyu; Wang, Yinhai

doi:10.1109/jiot.2020.3007373

Cited by 43 publications

(18 citation statements)

References 49 publications

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“…As the various data of transit systems are being collected, for example, smart card, Wi-Fi, and Bluetooth mobile data, some researchers have estimated travel behavior based on a data-driven approach (8)(9)(10). Especially, many researchers have sought to estimate travel behavior using machine learning techniques (11,12).…”

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confidence: 99%

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

Lee

Kim

Kho

et al. 2021

Transportation Research Record: Journal of the Transportation R

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As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

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confidence: 99%

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

Lee

Kim

Kho

et al. 2021

Transportation Research Record: Journal of the Transportation R

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“…This is consistent with the increased use of cell phones in general. Recently Pu et al [48] estimated transit ridership by passive sensing of Wi-Fi and Bluetooth mobile devices. A summary of few publications related to travel time studies is presented for understanding in Table 1.…”

Section: Big Data and Transportationmentioning

confidence: 99%

Efficacy of Bluetooth-Based Data Collection for Road Traffic Analysis and Visualization Using Big Data Analytics

Kulkarni

Kumar

Rao

2023

Big Data Min. Anal.

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Effective management of daily road traffic is a huge challenge for traffic personnel. Urban traffic management has come a long way from manual control to artificial intelligence techniques. Still real-time adaptive traffic control is an unfulfilled dream due to lack of low cost and easy to install traffic sensor with real-time communication capability. With increasing number of on-board Bluetooth devices in new generation automobiles, these devices can act as sensors to convey the traffic information indirectly. This paper presents the efficacy of road-side Bluetooth scanners for traffic data collection and big-data analytics to process the collected data to extract traffic parameters. Extracted information and analysis are presented through visualizations and tables. All data analytics and visualizations are carried out off-line in R Studio environment. Reliability aspects of the collected and processed data are also investigated. Higher speed of traffic in one direction owing to the geometry of the road is also established through data analysis. Increased penetration of smart phones and fitness bands in day to day use is also established through the device type of the data collected. The results of this work can be used for regular data collection compared to the traditional road surveys carried out annually or bi-annually. It is also found that compared to previous studies published in the literature, the device penetration rate and sample size found in this study are quite high and very encouraging. This is a novel work in literature, which would be quite useful for effective road traffic management in future.

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“…Wi-Fi or Bluetooth sensors have emerged in transport planning literature since the years 2010s and seems to be a promising way to capture mobility (Blogg et al, 2010;Dunlap et al, 2016;Ji et al, 2017;Malinovskiy et al, 2012). In these studies, Wi-Fi sensors detect the unique Media Access Control (MAC) addresses of connected objects if their Wi-Fi function is turned on (Pu et al, 2021). This way of detecting active Wi-Fi interfaces is easy to implement because only one sensor by bus is needed and no specific calibration is required (Michau et al, 2013).…”

Section: The Potential Of Wi-fi Sensors To Gather Mobility Datamentioning

confidence: 99%

The Potential of Wi-Fi Data to Estimate Bus Passenger Mobility

Fabre¹,

Bayart²,

Bonnel³

et al. 2022

SSRN Journal

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Monitoring Public Transit Ridership Flow by Passively Sensing Wi-Fi and Bluetooth Mobile Devices

Cited by 43 publications

References 49 publications

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

Efficacy of Bluetooth-Based Data Collection for Road Traffic Analysis and Visualization Using Big Data Analytics

The Potential of Wi-Fi Data to Estimate Bus Passenger Mobility

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