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 9 publications

(8 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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“…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%

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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Public Transport Occupancy Estimation using WLAN Probing and Mathematical Modeling

Cited by 9 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

Automatic Passenger Counting on the Edge via Unsupervised Clustering

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