Short-Term Traffic Flow Prediction of Expressway Considering Spatial Influences

Shuai, Chunyan; Wang, Wencong; Xu, Geng; He, Mengchang; Lee, Jae Young

doi:10.1061/jtepbs.0000660

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…In this paper, four benchmark methods including ARIMA, LSTM, GRU, and Bi-LSTM are selected for comparison, which have the ability to mine the temporal characteristics of time series data, and have been applied to short-term traffic flow prediction in existing literature (Shuai et al, 2022 ; Zhao et al, 2021 ). The process of the model comparison part among this paper can be seen in Fig.…”

Section: Methodsmentioning

confidence: 99%

Traffic flow prediction using bi-directional gated recurrent unit method

et al. 2022

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Traffic flow prediction plays an important role in intelligent transportation systems. To accurately capture the complex non-linear temporal characteristics of traffic flow, this paper adopts a Bi-directional Gated Recurrent Unit (Bi-GRU) model in traffic flow prediction. Compared to Gated Recurrent Unit (GRU), which can memorize information from the previous sequence, this model can memorize the traffic flow information in both previous and subsequent sequence. To demonstrate the model’s performance, a set of real case data at 1-hour intervals from 5 working days was used, wherein the dataset was separated into training and validation. To improve data quality, an augmented dickey-fuller unit root test and differential processing were performed before model training. Four benchmark models were used, including the Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and GRU. The prediction results show the superior performance of Bi-GRU. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of the Bi-GRU model are 30.38, 9.88%, and 23.35, respectively. The prediction accuracy of LSTM, Bi-LSTM, GRU, and Bi-GRU, which belong to deep learning methods, is significantly higher than that of the traditional ARIMA model. The MAPE difference of Bi-GRU and GRU is 0.48% which is a small prediction error value. The results show that the prediction accuracy of the peak period is higher than that of the low peak. The Bi-GRU model has a certain lag on traffic flow prediction.

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Section: Methodsmentioning

confidence: 99%

Traffic flow prediction using bi-directional gated recurrent unit method

et al. 2022

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“…In terms of traffic flow prediction, He et al [2] analyzed the distribution law of traffic flow through ETC toll data and used the Gaussian mixture regression (GMR) model to predict short-term traffic flow in ETC lanes. Shuai et al [3] proposed a mixed two-layered model for predicting highway traffic flow and assessed its effectiveness by using data from 51 toll stations. In terms of traffic operation status judgment, Wang et al [4] used actual highway data for parameter regression analysis and evaluated the traffic operation status between the mainline and ramp systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Travel Characteristics Identification Method for Expressway Passenger Cars Based on Electronic Toll Collection Data

Cai

Zhang

Zhang³

et al. 2023

Sustainability

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Passenger cars have emerged as a substantial segment of the vehicles traversing expressways, generating extensive traffic data on a daily basis. Accurately identifying individual vehicles and their travel patterns and characteristics is crucial in addressing the issues that impede the sustainable development of expressways, including traffic accidents, congestion, environmental pollution, and losses of both personnel and property. Regrettably, the utilization of electronic toll collection (ETC) data on expressways is currently not adequate, and data analysis and feature mining methods are underdeveloped, leading to the undervaluation of data potential. Focusing on ETC data from expressways, this study deeply analyzes the spatiotemporal characteristics of travel by passenger car users. Here, we propose an advanced user classification model by combining the traditional clustering algorithm with the feature grouping recognition model based on a back propagation neural network (BPNN) algorithm. Real-world data on expressway vehicle travel are used to validate our models. The results show a significant improvement in iteration efficiency of over 26.4% and a 23.17% accuracy improvement compared to traditional algorithms. The travel feature grouping recognition model yielded an accuracy of 95.23%. Furthermore, among the identified groups, such as “Public and commercial affairs” and “Commuting”, there is a notable characteristic of high travel frequency and concentrated travel periods. This indicates that these groups have placed significant pressure on the construction of a safe, efficient, and sustainable urban transportation system.

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“…Wang et al [18] fuse vehicle detector data, long-range microwave sensor data, and toll-data and employ Deep Belief Network (DBN) to successfully predict small-scale ring roads at time intervals of 30/60/120-minute traffic flow at the toll-gate. Shuai et al [19] adopted the modified Long Short-Term Memory (LSTM) and predicted the traffic volume of the 51 screened toll-gate. These deep learning-based methods are good at capturing traffic trends in complex toll-gate environments or exploring spatial connectivity between one or more road segments in a single temporal dimension, but the spatial characteristics between each toll-gate on a large scale are not considered.…”

Section: Introductionmentioning

confidence: 99%

CPT-DF: Congestion Prediction on Toll-Gates Using Deep Learning and Fuzzy Evaluation for Freeway Network in China

Shi

Wang

et al. 2023

Journal of Advanced Transportation

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Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent.

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Short-Term Traffic Flow Prediction of Expressway Considering Spatial Influences

Cited by 7 publications

References 28 publications

Traffic flow prediction using bi-directional gated recurrent unit method

Traffic flow prediction using bi-directional gated recurrent unit method

Travel Characteristics Identification Method for Expressway Passenger Cars Based on Electronic Toll Collection Data

CPT-DF: Congestion Prediction on Toll-Gates Using Deep Learning and Fuzzy Evaluation for Freeway Network in China

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