Expressway Exit Traffic Flow Prediction for ETC and MTC Charging System Based on Entry Traffic Flows and LSTM Model

Abstract

The Expressway (controlled-access highways) of China is the longest in the world and plays an important role in people's daily life. Accurate short-term traffic prediction is essential for travel schedule and active traffic management. There are two coexisting charging systems for expressway in China, Electronic Toll Collection (ETC) and Manual Toll Collection (MTC), which have different passing capacity and variation pattern. In this work, we demonstrate that the exit traffic flow prediction at Shanghai Xinqiao toll station using entry traffic flows from multiple close-related stations with Long Short-Term Memory (LSTM) model. Based on the origin-destination (OD) traffic data of a month, we present a new method to predict the exit station's traffic flow in the future 5 minutes. After deleting abnormal data, we select 12 of the 109 entry toll stations for the experiment. The traffic flow of these 12 entry stations account for 86% of the total exit traffic flow. This method uses the spatial-temporal matrix to deal with different three scenes that are ETC and MTC charging systems individually, the mix of ETC and MTC. We use the LSTM model with various lengths of flow sequence and amounts of hidden layer neurons for three different scenes. Lastly, we validate our model and carefully select the hyperparameters for better prediction accuracy by three evaluation metrics. The experimental results demonstrate that predicting the ETC is the best in the three scenes.

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

“…For the training, validation, and testing process, 9.8×10 4 , 2.1×10 4 , and 2.1×10 4 information packets were respectively processed, thus making a total of 1.4×10 5 . The LSTM architecture is configured with 1 hidden layer (100 Neurons), 'Initial Learn Rate' equal to 0.0001, RMSE loss function, maximum 50 epochs [62], [63], and using the Adam optimizer [64]. Note that the LSTM's learning algorithm is local in space and time, while its computational complexity per time step and weight is O(1) [61], thus, leading to a system complexity O(2 × 100).…”

The Fast Detection of Abnormal ETC Data Based on an Improved DTW Algorithm

“…Through the statistical analysis of the trajectory data for Fujian Province in the early stage, the statistical data of the incidence of abnormal trajectory windows and the dynamic step length r of built windows are shown in Table 1. According to the statistics of the existing ETC trajectory data, the value range of r is in [3,5]. Compared with the path length N of ODLJ reaching more than 40,000, the value of r is much smaller than N, which means its influence is almost negligible; that is, the time complexity of O r 2 is about equal to O(1).…”

“…There are more than 200 million ETC OBU devices, with average daily ETC transaction data of nearly 1 billion [1]. The ETC transaction data record almost all vehicles' traffic conditions on expressways and can be used for expressway traffic flow prediction [2,3], transit time estimation [4,5], traffic demand visualization [6], etc. The data are expected to provide important information services for intelligent driving on expressways.…”

Convolutional Long-Short Term Memory Network with Multi-Head Attention Mechanism for Traffic Flow Prediction

“…The advanced development of sensing technology makes big data more affordable and accessible, and thus, data-driven methods have been increasingly adopted for the predictive modeling of traffic flow. Data-driven methods can be classified into two categories: machine learning methods and deep learning methods [ 6 , 7 , 8 , 9 , 10 ]. In comparison with machine learning methods, deep learning methods have gained more attention from both academia and industry in traffic flow predictions due to their extraordinary prediction fidelity and robustness.…”