An Approach to Dynamical Classification of Daily Traffic Patterns

Abstract

This article proposes a prototype of an urban traffic control system based on a prediction‐after‐classification approach. In an off‐line phase, a repository of traffic control strategies for a set of (dynamic) traffic patterns is constructed. The core of this stage is the k‐means algorithm for daily traffic pattern identification. The clustering method uses the input attributes flow, speed, and occupancy and it transforms the dynamic traffic data at network level in a pseudo‐covariance matrix, which collects the dynamic correlations between the road links. A desirable number of traffic patterns is provided by Bayesian Information Criterion and the ratio of change in dispersion measurements. In an on‐line phase, the current daily traffic pattern is predicted within the repository and its associated control strategy is implemented in the traffic network. The dynamic prediction scheme is constructed on the basis of an existing static prediction method by accumulating the trials on set of patterns in the repository. This proposal has been assessed in synthetic and real networks testing its effectiveness as a data mining tool for the analysis of traffic patterns. The approach promises to effectively detect the current daily traffic pattern and is open to being used in intelligent traffic management systems.

Deep neural network with high‐order neuron for the prediction of foamed concrete strength

“…ML and data science has shown great potential for predicting, designing, and discovering materials (Ley & Bordas, ). In civil engineering and construction, ML has been extensively used in a variety of applications such as structural heal monitoring (Gao & Mosalam, ; Rafiei & Adeli, , ; Xue & Li, ), reliability analysis (Dai & Cao, ; Grande, Castillo, Mora, & Lo, ; Nabian & Meidani, ), transportation (Dharia & Adeli, ; García‐Ródenas, López‐García, & Sánchez‐Rico, ; Yu, Wang, Shan, & Yao, ; Zhang & Ge, ), and prediction and estimation (Adeli & Wu, ; Chou & Pham, ; Rafiei, Khushefati, Demirboga, & Adeli, ; Zhao & Ren, ). In concrete‐related studies, DeRousseau, Kasprzyk, and Srubar () recently reviewed the application of ML to optimize mixture design of concrete.…”

Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree

“…where TP and TN and the true positives and true negatives respectively, and N is the total number of instances considered. For its part, K coefficient is a statistic that measures pairwise agreement between a set of categorized data, correcting for expected chance agreement (Carletta, 1996;Garcia-Rodenas et al, 2017). The kappa coefficient is expressed as:…”

A Match‐Then‐Predict Method for Daily Traffic Flow Forecasting Based on Group Method of Data Handling

“…Note that classification here denotes pattern recognition and can include unsupervised learning method such as k ‐means clustering. Pattern can be recognized by k ‐means clustering analysis with different distance performances, including Euclidean distance (Xia et al., ) and spectral distances (García‐Ródenas et al., ). There are also some other methods for pattern classification, for example, hierarchical clustering analysis (Weijermars and Van Berkum, ), wavelet analysis (Jiang and Adeli, ), SVMs (Castro‐Neto et al., ; Wang and Shi, ; Yao et al., ), k ‐nearest neighbors (Zheng et al., ; Lin et al., 2013a; Zhang et al., ; Habtemichael and Cetin, ), and NN (Celikoglu, ; Zhang et al., ).…”