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A non‐recurrent road traffic anomaly refers to a sudden change in the capacity of a road segment, which deviates from the general traffic patterns, and is usually caused by abnormal traffic events such as traffic accidents and unexpected road maintenance. Timely and accurate detection of non‐recurrent road traffic anomalies facilitates immediate handling to reduce the wastage of resources and the risk of secondary accidents. Compared with other types of traffic anomaly detection methods, prediction algorithms are suitable for detecting non‐recurrent anomalies for their potential ability to distinguish non‐recurrent anomalies from recurrent congestion (e.g., rush hours). A typical prediction algorithm detects an anomaly when the difference between the predicted traffic parameter (i.e., speed) and the actual one is greater than a threshold. However, the subjective setting of thresholds in many prediction algorithms greatly affects the detection performance. This study proposes a novel framework for non‐recurrent road traffic anomaly detection (NRRTAD). The temporal graph convolutional network (T‐GCN) model acts as the predictor to learn the general traffic patterns of road segments by capturing both the topological effects and temporal patterns of traffic flows, and to predict the "normal" traffic speeds. The hierarchical time memory detector (HTM‐detector) algorithm acts as the detector to evaluate the differences between the predicted speeds and the actual speeds to detect non‐recurrent anomalies without setting a threshold. In the experiments with traffic datasets of Beijing, NRRTAD outperformed other methods, not only achieving the highest detection rates but also exhibiting higher resilience to noise. The main advantages of NRRTAD are as follows: (1) adopting the T‐GCN with a weighted graph to integrate differentiated connection strengths of multiple types of topological relations between road segments as well as temporal traffic patterns improves the prediction performance; and (2) utilizing a flexible mechanism in the HTM‐detector to adapt to changing stream data not only avoids subjective setting of a threshold, but also improves the accuracy and robustness of anomaly detection. [ABSTRACT FROM AUTHOR]

We estimate economic impacts from decaying water infrastructure in the United States. Using water main breaks in Washington, DC, and a yearlong panel of hourly traffic speeds, we estimate causal effects of water main failures on traffic congestion. We use k‐means clustering to create clusters of streets that are similar to each other: treated observations are compared to other units in the cluster. We identify heterogeneous treatment effects algorithmically while retaining straightforward standard error calculations. We find strong evidence of heterogeneous treatment effects across clusters but small welfare impacts of water main breaks on traffic patterns overall. [ABSTRACT FROM AUTHOR]

Traffic speed prediction is an emerging paradigm for achieving a better transportation system in smart cities and improving the heavy traffic management in the intelligent transportation system (ITS). The accurate traffic speed prediction is affected by many contextual factors such as abnormal traffic conditions, traffic incidents, lane closures due to construction or events, and traffic congestion. To overcome these problems, we propose a new method named fuzzy optimized long short‐term memory (FOLSTM) neural network for long‐term traffic speed prediction. FOLSTM technique is a hybrid method composed of computational intelligence (CI), machine learning (ML), and metaheuristic techniques, capable of predicting the speed for macroscopic traffic key parameters. First, the proposed hybrid unsupervised learning method, agglomerated hierarchical K‐means (AHK) clustering, divides the input samples into a group of clusters. Second, based on parameters the Gaussian bell‐shaped fuzzy membership function calculates the degree of membership (high, low, and medium) for each cluster using Takagi‐Sugeno fuzzy rules. Finally, the whale optimization algorithm (WOA) is used in LSTM to optimize the parameters obtained by fuzzy rules and calculate the optimal weight value. FOLSTM evaluates the accurate traffic speed from the abnormal traffic data to overcome the nonlinear characteristics. Experimental results demonstrated that our proposed method outperforms the state‐of‐the‐art approaches in terms of metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). [ABSTRACT FROM AUTHOR]