2019 · 31 citations · 7 references
ComputingComputer ScienceTransportation PlanningMachine LearningTransportTransportationDecision TreesIntelligent Transportation SystemsPassenger TransportTransport ModelingTransport ModellingTransportation AnalyticsBus OperationsBus StopsBus TransportRandom Forest ClassificationTraffic Count PredictionTraffic Surveillance
In recent years, some bus companies have raised revenue by reviewing the route plan using the number of passengers. The company has a system that can automatically counts the number of passengers on an ongoing basis. But they are costly because they use cameras and sensors those are dedicated for counting. It is too expensive for bus companies that really need to reconsider their route planning to introduce the system. In order to solve this problem and realize efficient operation, we propose a method to count passengers by using a drive recorder and sensors those are already equipped with buses. Drive recorders and various sensors will be obliged by the government to be set up by bus operators in the future. We constructed a model using Random Forest Regression with the position of the bus from the GPS module in the buses, the position of the bus stop used for operation management, and the number of passengers estimated from the image processing method combining YOLOv3 and Deep SORT. As a result, the average correct answer rate when the passengers get on and off are 96.2% and 70.1% respectively. Our method which utilized non-dedicated camera achieved higher correct answer rate than the conventional method which utilizes dedicated camera for counting passenger.
7
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu et al. · 2018 · 25.4K citations
Simple online and realtime tracking
Alex Bewley, Zongyuan Ge, Lionel Ott et al. · 2016 · 3.9K citations · Full text
Multiple Hypothesis Tracking Revisited
Chanho Kim, Fuxin Li, Arridhana Ciptadi et al. · 2015 · 681 citations