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

Abstract

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.