Predicting pedestrian traffic flow rate flowrate and density with deep neural networks
LE3 .A278 2021
2021
Silver, Danny
Acadia University
Master of Science
Masters
Computer Science
Pedestrian traffic information offers useful insights when developing or maintaining a business. This research combines image processing and machine learning methods to predict pedestrian traffic flowrate and density for up to two days into the future, based on weather data, calendar data, and special events. To obtain the traffic flowrate and density, we first developed a neural network model to predict the number of new people and the total number of people in each sequence of images captured by a Nova Scotia Webcams camera. These counts of people are used to calculate the pedestrian traffic flowrate and density labels for hourly intervals. These labels are then combined with hourly weather data, calendar data, and special event data from the same period to train a recurrent neural network to predict the traffic flowrate and density for up to two days in advance. We try two different approaches, CNN-LSTM and dual input CNN to predict the number of new people and the total number of people from the images and compare how well each approach performs. The results show that the dual image input CNN models are more effective at predicting the number of new people and the total number of people than the CNN - LSTM models. Tested on independent test sets of images using K-fold cross-validation, theMTL CNN model achieved a test accuracy of 72% for the number of new people and 78% accuracy for the total number of people. We trained LSTM models to predict pedestrian traffic flowrate and density using weather data, calendar data and special event data for up to two days in advance. The LSTM model has a MAPE of 33% for flowrate prediction and 33% for density prediction using observed weather data. The model also has a MAPE of 43% for flowrate and 39% for density using forecasted weather data.
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https://scholar.acadiau.ca/islandora/object/theses:3700