A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information

Abstract

As a key part of the method of improving traffic capacity, traffic flow prediction is becoming a research hot-spot of traffic science and intelligent technology, in which the accuracy of traffic flow prediction is particularly concerned. In this paper, a novel fuzzy-based convolutional neural network (F-CNN) method is proposed to predict the traffic flow more accurately, in which a fuzzy approach has been applied to represent the traffic accident features when introducing uncertain traffic accidents information into the CNN at the first time. First, for the sake of extracting the spatial-temporal characteristics of the traffic flow data, this paper divides the whole area into small blocks of 32 × 32 and constructs three trend sequences with inflow and outflow types. Second, uncertain traffic accident information is generated from the real traffic flow data by utilizing a fuzzy inference mechanism. Finally, the F-CNN model is realized to train the internal information of the trend sequence, the uncertain traffic accident information, and the external information. Moreover, pre-training and fine-tuning strategies are efficiently developed to learn the parameters of the F-CNN. At last, the real Beijing taxicab trajectory and the meteorology datasets are employed to show that the proposed method has superior performance compared with the state-of-the-art approaches.INDEX TERMS Traffic flow prediction, traffic accident information, fuzzy inference system (FIS), convolutional neural network (CNN), fuzzy-based convolutional neural network (F-CNN).

Short-Term Traffic Flow Prediction Using the Modified Elman Recurrent Neural Network Optimized Through a Genetic Algorithm

“…They assess the forecast architecture of the Gray-Chicago-Milwaukee Transport Corridor (GCM) information from Chicagoland in real-time. In another work, a novel fuzzy-based convolutional neural network (F-CNN) approach for predicting more accurate traffic flow is suggested by applying a fuzzy approach which reflects the features of traffic accidents when first introduced uncertain information for road accidents in the CNN [36]. Besides the mentioned NN models, other supervised learning methods are used in different studies as well.…”

“…Considering different conditions influencing short-term traffic forecasting, such as work days and weekends [23] [36], weather [36], and special days (holidays) [13], [20], [39] [57] , are crucial for accurate prediction of short-term traffic flow.…”

“…[31] proposed a hybrid algorithm using support vector regression, random forest, and genetic algorithms, while [33] proposed a LSTM method with feature enhancement to complements LSTM deficiencies at excessively long distances, but they only used weekday data and removed weekend data. In another work, a novel fuzzy-based convolutional neural network (F-CNN) approach for predicting more accurate traffic flow is suggested by applying a fuzzy approach which reflects the features of traffic accidents when first introduced uncertain information for road accidents in the CNN [36]. In addition, external factors were considered, including weather, wind speed, temperature, and weekday/weekend information, while the holiday condition was ignored in the study.…”

Spatial-Temporal Correlation Prediction Modeling of Origin-Destination Passenger Flow Under Urban Rail Transit Emergency Conditions

“…Guo et al [24] forecasted URT passenger flow under event occurrences with social media. An et al [25] proposed a novel fuzzy-based convolutional neural network to predict traffic flow with uncertain traffic accident information. These studies are mainly aimed at the prediction of inbound and outbound passenger flow under abnormal conditions, and there are relatively few studies on OD passenger flow prediction in emergencies.…”

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

“…Tang et al proposed a hybrid method that combines clustering methods and adopted type-2 fuzzy C-means and spatiotemporal correlation to predict future traffic trends based on an artificial neural network [19]. With the development of big data technology [20,21], machine learning [22,23] and deep learning [24][25][26] methods have been applied for the identification of traffic conditions and have achieved excellent results [27][28][29]. Rao et al proposed an interval databased k-means clustering method for traffic state identification at urban intersections and demonstrated the effectiveness of the proposed method in traffic state identification at urban intersections [30].…”