This study developed a shock waved diagram based deep learning model (SW-DLM) to predict the occurrence of public events in real-time... Show moreThis study developed a shock waved diagram based deep learning model (SW-DLM) to predict the occurrence of public events in real-time according to their impacts on nearby highway traffic. Specifically, using point traffic volume data as a boundary condition, shock wave analysis is first conducted to understand the impacts and features of a public event on a nearby highway-ramp intersection. Next, this analysis develops the SWG algorithm to efficiently generate and expand shock wave diagrams in real-time according to the data collection rate. Built upon that, this study contributes a novel approach, which encodes a shock wave diagram with an optimal grid of pixels balancing resolution and computation load. Using the features extracted from encoded time-series shock wave diagrams as inputs, a deep learning approach, Long-short term memory (LSTM) model, is applied to predict the occurring of a public event. The numerical experiments based on the field data demonstrate that using encoded shock wave diagrams rather than point traffic data can significantly improve the accuracy of the deep learning for predicting the occurring of a public event. The SW-DLM presents satisfied prediction performance on the average as well as on an individual day with or without traffic accident interference, happening nearby the venue of a public event. The implementation of this approach to real-time traffic provision tools such as GPS will alert travelers en route on-going events in a transportation network and help travelers to make a smart trip plan and avoid traffic congestion. Moreover, it promotes smart city development by providing a strong capability to monitor the transportation system and conduct real-time traffic management intelligently. Show less
