Public Transport Occupancy Estimation using WLAN Probing and Mathematical Modeling

Vieira, Túlio; Almeida, Paulo; Meireles, Magali Rezende Gouvêa; Ribeiro, Renato Guimarães

doi:10.1016/j.trpro.2020.08.122

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…Respondents consider congestion (P1) as the worst problem in Belo Horizonte. The second worst problem is the low quality of public transportation (P2), which could contribute to the high use of private cars in this city, as indicated by Vieira et al [32]. These results validate hypothesis (ii).…”

Section: Urban Problemssupporting

confidence: 81%

Evaluation of Urban Mobility Problems and Freight Solutions from Residents’ Perspectives: A Comparison of Belo Horizonte (Brazil) and Szczecin (Poland)

et al. 2022

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An efficient urban freight transport (UFT) system is crucial for sustainable city development. However, implementing city logistics measures still seems challenging for municipalities and decision-makers. Moreover, city authorities’ decisions depend on politics and social issues, and the city residents’ opinions seem to be very important in this context. Therefore, the primary objective of this paper was to assess the perception of urban mobility problems and freight solutions from the perspective of city users, considering the point of view of Brazilian and Polish city dwellers. The work was based on a survey realised in Belo Horizonte (Brazil) and Szczecin (Poland). The analysis identified the similarities and differences between the perceptions of different resident groups in both cities. The practical advantage of this research is the establishment of a set of recommendations for city decision-makers in the context of residents’ perceptions and their expectations regarding the implementation of urban freight measures.

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Section: Urban Problemssupporting

confidence: 81%

Evaluation of Urban Mobility Problems and Freight Solutions from Residents’ Perspectives: A Comparison of Belo Horizonte (Brazil) and Szczecin (Poland)

et al. 2022

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“…Kalman Filters (KFs) are often combined with other models, especially for combining historical models with a real-time component [38][39][40]. The primary advantage of KF models is their noise robustness and little dependence on large training data sets.…”

Section: A Forecasting Models For Passenger Load Predictionmentioning

confidence: 99%

Improving the Prediction of Passenger Numbers in Public Transit Networks by Combining Short-Term Forecasts With Real-Time Occupancy Data

Hoppe

Schwinger

Haeger

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Passengers of public transportation nowadays expect reliable and accurate travel information. The need for occupancy information is becoming more prevalent in intelligent public transport systems as people started avoiding overcrowded vehicles during the COVID-19 pandemic. Furthermore, public transportation companies require accurate occupancy forecasts to improve service quality. We present a novel approach to improve the prediction of passenger numbers that enhances a day-ahead prediction with real-time data. We first train a baseline predictor on historical automatic passenger counting data. Next, we train a realtime model on the deviations between baseline prediction and observed values, thus capturing events not addressed by the baseline. For the forecast, we attempt to detect emerging patterns in real time and adjust the baseline prediction with deviations from the patterns. Our experiments with data from Germany show that the proposed model improves the forecast of the baseline model and is only outperformed by artificial neural networks in some instances. If the training sets only cover a limited period of up to four months, our approach outperforms competing methods. For larger training sets, there are mixed results in the sense that for some test cases, certain types of neural networks yield slightly better results, but our method still performs well with less training effort, is explainable along the whole prediction process and can be applied to existing prediction methods.

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“…The works closest to our research are those that focus on counting people in spaces and that leave aside the analysis of the behavior of the people counted. These solutions, in general, are classified into two groups, those that analyze the Wi-Fi signal [ 29 , 30 , 31 , 32 ] and those that focus on the analysis of the information packets sent by the Wi-Fi devices [ 33 , 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

“…Solutions based on Wi-Fi signal analysis (the received signal strength indicator or RSSI and the channel state or CSI, above all) show promising results but for in indoor setups. Works such as [ 31 , 32 ] performed laboratory tests for highly controlled environments, with no results in open or uncontrolled spaces. Other research based on the analysis of Wi-Fi signals, such as [ 30 ] required the installation of firmware in the access points (APs).…”

Section: Introductionmentioning

confidence: 99%

AFOROS: A Low-Cost Wi-Fi-Based Monitoring System for Estimating Occupancy of Public Spaces

Vega-Barbas

Álvarez-Campana

Rivera

et al. 2021

Sensors

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Estimating the number of people present in a given venue in real-time is extremely useful from a security, management, and resource optimization perspective. This article presents the architecture of a system based on the use of Wi-Fi sensor devices that allows estimating, almost in real-time, the number of people attending an event that is taking place in a venue. The estimate is based on the analysis of the “probe request” messages periodically transmitted by smartphones to determine the existence of Wi-Fi access points in the vicinity. The method considers the MAC address randomization mechanisms introduced in recent years in smartphones, which prevents the estimation of the number of devices by simply counting different MAC addresses. To solve this difficulty, our Wi-Fi sensors analyze other fields present in the header of the IEEE 802.11 frames, the information elements, to extract a unique fingerprint from each smartphone. The designed system was tested in a set of real scenarios, obtaining an estimate of attendance at different public events with an accuracy close to 95%.

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Public Transport Occupancy Estimation using WLAN Probing and Mathematical Modeling

Cited by 7 publications

References 6 publications

Evaluation of Urban Mobility Problems and Freight Solutions from Residents’ Perspectives: A Comparison of Belo Horizonte (Brazil) and Szczecin (Poland)

Evaluation of Urban Mobility Problems and Freight Solutions from Residents’ Perspectives: A Comparison of Belo Horizonte (Brazil) and Szczecin (Poland)

Improving the Prediction of Passenger Numbers in Public Transit Networks by Combining Short-Term Forecasts With Real-Time Occupancy Data

AFOROS: A Low-Cost Wi-Fi-Based Monitoring System for Estimating Occupancy of Public Spaces

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