Feasibility of Analyzing Wi-Fi Activity to Estimate Transit Passenger Population

Oransirikul, Thongtat; Nishide, Ryo; Piumarta, Ian; Takada, Hideyuki

doi:10.1109/aina.2016.68

Cited by 18 publications

(13 citation statements)

References 14 publications

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“…Our previously-reported work [1], [2] estimated the number of passengers waiting at bus stops using similar techniques, but either ignored RSSI or did not attempt to provide near real-time information.…”

Section: Related Workmentioning

confidence: 99%

“…( 1 ) Max of RSSI > −40 dBm and duration > 60 seconds ( 2 ) Average of RSSI > −60 dBm and duration > 240 seconds…”

Section: Obtaining a Control Data Set Of Classificationmentioning

confidence: 99%

“…Our previous work [1], [2] focused on estimating the number of passengers waiting at bus stops in real-time, from which an upper bound on the number of passengers able to board a bus can be derived. The new contributions described in this paper relate to a real-time method for estimating the number of passengers who are travelling on board a bus.…”

Section: Introductionmentioning

confidence: 99%

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Classifying Passenger and Non-passenger Signals in Public Transportation by Analysing Mobile Device Wi-Fi Activity

Oransirikul

Piumarta

Takada

2019

Journal of Information Processing

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Quality of service is one factor passengers consider when deciding to use the public transportation system. Increasing the quality of service can be done in several ways, such as improving on-time arrival, reducing waiting time, and providing seat availability information. We believe that if passengers have better information for making decisions, that can increase the quality of service. This paper proposes estimating the number of passengers by analyzing signals from their Wi-Fi devices, classifying them as originating from passenger or non-passenger devices using a real-time filtering mechanism. Experimental validation was performed aboard busses of different types taking different routes. Our experimental results show that filtered data can classify passenger device signals from environmental ones with an accuracy of 75 percent, which is a promising basis for providing real-time information to passengers that improves the quality of their service.

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Section: Related Workmentioning

confidence: 99%

“…( 1 ) Max of RSSI > −40 dBm and duration > 60 seconds ( 2 ) Average of RSSI > −60 dBm and duration > 240 seconds…”

Section: Obtaining a Control Data Set Of Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classifying Passenger and Non-passenger Signals in Public Transportation by Analysing Mobile Device Wi-Fi Activity

Oransirikul

Piumarta

Takada

2019

Journal of Information Processing

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“…A heurística utilizada para detectar o telefone móvel de passageiros, com dispositivos que estão dentro do veículo, parte da preposição que os sinais destes serão avistados por um período maior de tempo daqueles que não estão embarcados (HANDTE et al, 2014;ORANSIRIKUL et al, 2016;DUNLAP et al, 2016). H0: A distribuição de probabilidades ocorrida (observada) é a mesma da distribuição esperada, segundo suposições baseadas na teoria.…”

Section: Algoritmo De Estimativa Da Ocupaçãounclassified

“…A seguir, é explorada as questões técnicas do protocolo (formalmente conhecido por IEEE 802.11) para compreender como ele permite o sensoriamento de pessoas. PR) emitidos pelos aparelhos móveis(ORANSIRIKUL et al, 2016). Uma vez que o endereço MAC contido na mensagem tem a garantia de ser único e anônimo, pode ser utilizado para identificar o mesmo dispositivo em diferentes locais e momentos nas imediações das estações móveis.…”

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Estimativa da ocupação utilizando sensoriamento wi-fi de telefones móveis: uma aplicação no transporte público urbano de passageiros por ônibus.

Nunes¹

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o período do experimento -indicar os trechos com maior lotação. Por outro lado, o método não apresentou bons resultados saindo dos terminais e quando a ocupação mudou repentinamente, requerendo estudos posteriores para refinar o algoritmo apresentado. Da mesma maneira, ainda não há como generalizar o método para toda operação, pois, o espaço amostral estudado foi pequeno, deixando como sugestão para pesquisas futuras o aprimoramento deste sistema. Palavras-chave: Transporte Público. Ônibus. Sistemas Inteligentes de Transportes. Contador Automático de Passageiros. Sensoriamento por Wi-Fi. ABSTRACT NUNES, Edson Hilios Marques. Estimating the occupancy using Wi-Fi sensing of mobile phones: An application on the urban public transportation by bus. 2019. 102 p. Dissertation (Master in Transportation Engineering) -Escola Politécnica, University of São Paulo, São Paulo, 2019.The crowdedness of a vehicle is one of the main quality factor of the public transportation (TP) quality, since, besides the comfort, also affect the reliability of the line, changing its average speed. However, the city of São Paulo, Brazil, one of the biggest bus operations of the world, it is difficult to find any automatic passenger counter (APC) equipment deployed, probably due the costs of current technologies.This research shows the results of an APC technology by the sensing of the Wi-Fi signal of smartphones that are boarded in a vehicle on TP by bus. Therefore, a prototype was developed and deployed on the line 6500-10 between the Terminal Santo Amaro and the Terminal Bandeira, during 7 working days of the week on peak and off-peak hours.was analyzed with statistical and geographical tools. During the experiment, it was observed that the Wi-Fi APC was capable of estimate the occupancy of the vehicle 85% of the cases, with a significance level up to 20% and an error up to 22 passengers per estimation. Through the geographical analysis it was generated load matrices, that allow to infer the origin and destination of the passenger in each trip, indicating the sections along the route where were more boarding and alighting. On the other hand, the method did not perform well when coming out of the final stops and when the crowdedness changed suddenly, requiring further analysis to understand how to deal better under these circumstances. Likewise, due to the limited sample utilized in this research, we cannot generalize the results presented, pressing the need for new researches on different operation scenarios.

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Investigating Feature Selection for Predicting the Number of Bus Passengers by Monitoring Wi-Fi Signal Activity from Mobile Devices

Oransirikul

Takada

2020

Advanced Information Networking and Applications

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Feasibility of Analyzing Wi-Fi Activity to Estimate Transit Passenger Population

Cited by 18 publications

References 14 publications

Classifying Passenger and Non-passenger Signals in Public Transportation by Analysing Mobile Device Wi-Fi Activity

Classifying Passenger and Non-passenger Signals in Public Transportation by Analysing Mobile Device Wi-Fi Activity

Estimativa da ocupação utilizando sensoriamento wi-fi de telefones móveis: uma aplicação no transporte público urbano de passageiros por ônibus.

Investigating Feature Selection for Predicting the Number of Bus Passengers by Monitoring Wi-Fi Signal Activity from Mobile Devices

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