Article
Version 1
This version is not peer-reviewed
Detecting Urban Traffic Anomalies with Traffic Monitoring Data
Version 1
: Received: 26 July 2024 / Approved: 26 July 2024 / Online: 29 July 2024 (10:49:22 CEST)
How to cite: Mao, Y.; Shi, Y.; Lu, B. Detecting Urban Traffic Anomalies with Traffic Monitoring Data. Preprints 2024, 2024072229. https://doi.org/10.20944/preprints202407.2229.v1 Mao, Y.; Shi, Y.; Lu, B. Detecting Urban Traffic Anomalies with Traffic Monitoring Data. Preprints 2024, 2024072229. https://doi.org/10.20944/preprints202407.2229.v1
Abstract
Traffic anomaly detection is crucial for urban management, yet current research is often confined to small-scale endeavors. This study collected nine months of real-time Wuhan traffic monitoring data from Amap. We proposed Traffic-ConvLSTM, a multi-scale spatial-temporal technique based on long and short-term memory networks (LSTM) and convolutional neural networks (CNN) to effectively achieve long-term anomaly detection at the city level. First, we convert the traffic flow data into image representation, which enables the capture of the spatial correlation between traffic flow and roads and the correlation between traffic flow and roads, as well as the surrounding environment. Second, The model utilizes convolution kernels of different sizes to extract spatial features at road-level, regional-level, and city-level scales, while incorporating temporal features of different time steps to capture hourly, daily, and weekly dynamics. Additionally, varying weights are assigned to the convolution kernels and temporal features of varying spatio-temporal scales to capture the heterogeneous strengths of spatio-temporal correlations within patterns of traffic anomalies. The proposed Traffic-ConvLSTM model exhibits improved performance over existing techniques in the task of identifying long-term and large-scale traffic anomaly occurrences. Furthermore, the analysis reveals significant traffic anomalies during holidays and urban sporting events. The diverse travel patterns observed in response to various activities offer insights for large-scale urban traffic anomaly management, providing recommendations for city-level traffic control strategies.
Keywords
traffic anomalies; real-time traffic data; deep learning; artificial intelligence; multiple spatio-temporal scales
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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