Multi-View Travel Time Prediction Based on Electronic Toll Collection Data

Luo, Sijie; Zou, Fang; Zhang, Cheng; Tian, Junshan; Guo, Feng; Liao, Lyuchao

doi:10.3390/e24081050

Cited by 11 publications

(8 citation statements)

References 37 publications

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“…Expressway Section QD [ 39 ]: Each ETC gantry and the entrance/exit of an expressway toll station is collectively called a node

, and two adjacent nodes constitute an expressway section, referred to as QD :

where

and

are the start and end points of QD .…”

Section: Methodsmentioning

confidence: 99%

Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data

Cai

Zou

et al. 2022

Entropy

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To scientifically and effectively evaluate the service capacity of expressway service areas (ESAs) and improve the management level of ESAs, we propose a method for the recognition of vehicles entering ESAs (VeESAs) and estimation of vehicle dwell times using electronic toll collection (ETC) data. First, the ETC data and their advantages are described in detail, and then the cleaning rules are designed according to the characteristics of the ETC data. Second, we established feature engineering according to the characteristics of VeESA and proposed the XGBoost-based VeESA recognition (VR-XGBoost) model. Studied the driving rules in depth, we constructed a kinematics-based vehicle dwell time estimation (K-VDTE) model. The field validation in Part A/B of Yangli ESA using real ETC transaction data demonstrates that the effectiveness of our proposal outperforms the current state-of-the-art. Specifically, in Part A and Part B, the recognition accuracies of VR-XGBoost are 95.9% and 97.4%, respectively, the mean absolute errors (MAEs) of dwell time are 52 and 14 s, respectively, and the root mean square errors (RMSEs) are 69 and 22 s, respectively. In addition, the confidence level of controlling the MAE of dwell time within 2 min is more than 97%. This work can effectively recognize the VeESA and accurately estimate the dwell time, which can provide a reference idea and theoretical basis for the service capacity evaluation and layout optimization of the ESA.

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“…Expressway Section QD [ 39 ]: Each ETC gantry and the entrance/exit of an expressway toll station is collectively called a node

, and two adjacent nodes constitute an expressway section, referred to as QD :

where

and

are the start and end points of QD .…”

Section: Methodsmentioning

confidence: 99%

Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data

Cai

Zou

et al. 2022

Entropy

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“…Since the research on ETC transaction data mining is in its initial stage, there are not many researches. Currently, ETC transaction data mining mainly includes statistics and prediction of travel time [3,4], traffic flow [5], driving speed [6,7] etc. Zou et al had done a lot of researches based on ETC data, which contains ETC abnormal data detection [8], travel time prediction [3], expressway maximum speed limit recognition [9], expressway speed prediction [7].…”

Section: Etc Transaction Data Miningmentioning

confidence: 99%

“…Currently, ETC transaction data mining mainly includes statistics and prediction of travel time [3,4], traffic flow [5], driving speed [6,7] etc. Zou et al had done a lot of researches based on ETC data, which contains ETC abnormal data detection [8], travel time prediction [3], expressway maximum speed limit recognition [9], expressway speed prediction [7]. Wang et al [10] constructed a travel time dataset based on the time and place records of vehicles entering and leaving the expressway in ETC transaction data, and restored the historical trajectories of vehicles by data supplement algorithm, the characteristics of vehicle travel time in traffic accidents state and normal traffic state were used as training sets, and the K-nearest neighbour non-parametric regression was used for training and learning.…”

Section: Etc Transaction Data Miningmentioning

confidence: 99%

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Positioning method of expressway ETC gantry by multi‐source traffic data

Guo

Zou

Luo

et al. 2022

IET Intelligent Trans Sys

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With the rapid development of expressway Electronic Toll Collection (ETC) technology in China, the expressway management system is becoming digital and intelligent, which provides a solid foundation for expressway vehicle infrastructure cooperation and autonomous driving. The gantry position is the key part of the ETC system. However, there are still some problems (e.g. gantry position missing or false), which can seriously affect the intelligent development of expressways. To address these two issues, an ETC gantry positioning method is proposed. First, the ETC transaction data and the GPS data on expressways are preprocessed to remove abnormal data and retrieve missed data. Then, combined with Dead Reckoning (DR) and Median Center, the potential position of the gantry is calculated from ETC transaction data and GPS data. Finally, the switching strategy based on Kalman Filter (KF) is used to capture the final gantry position. By comparing the results of the proposal with the collected gantry position, it is found that the positioning error of the gantry position calculated by this proposal is about 37 m. The positioning accuracy is 98.78% with the threshold of 100 m. The experimental results show that the proposal can effectively locate the expressway gantry.

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“…ETC gantry data and toll data can record the driving conditions of each vehicle on the expressway. Therefore, using ETC gantry data and toll data, we can accurately estimate the location of ETC gantries [6], prediction of traffic flow [7], travel time [8], travel speed [9], and service area vehicle recognition and dwell time estimation [10]. Since the expressway traffic management department cannot fully grasp the changing trend of road status during holidays, travelers cannot timely understand the changes in road conditions, resulting in too concentrated vehicle travel and difficulty in effective traffic control and diversion, causing traffic congestion [11].…”

Section: Introductionmentioning

confidence: 99%

CSFPre: Expressway key sections based on CEEMDAN-STSGCN-FCM during the holidays for traffic flow prediction

Chen¹,

Ren

Zeng³

et al. 2023

PLoS ONE

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The implementation of the toll free during holidays makes a large number of traffic jams on the expressway. Real-time and accurate holiday traffic flow forecasts can assist the traffic management department to guide the diversion and reduce the expressway’s congestion. However, most of the current prediction methods focus on predicting traffic flow on ordinary working days or weekends. There are fewer studies for festivals and holidays traffic flow prediction, it is challenging to predict holiday traffic flow accurately because of its sudden and irregular characteristics. Therefore, we put forward a data-driven expressway traffic flow prediction model based on holidays. Firstly, Electronic Toll Collection (ETC) gantry data and toll data are preprocessed to realize data integrity and accuracy. Secondly, after Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) processing, the preprocessed traffic flow is sorted into trend terms and random terms, and the spatial-temporal correlation and heterogeneity of each component are captured simultaneously using the Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) model. Finally, the fluctuating traffic flow of holidays is predicted using Fluctuation Coefficient Method (FCM). Through experiments of real ETC gantry data and toll data in Fujian Province, this method is superior to all baseline methods and has achieved good results. It can provide reference for future public travel choices and further road network operation.

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Multi-View Travel Time Prediction Based on Electronic Toll Collection Data

Cited by 11 publications

References 37 publications

Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data

Recognition of Vehicles Entering Expressway Service Areas and Estimation of Dwell Time Using ETC Data

Positioning method of expressway ETC gantry by multi‐source traffic data

CSFPre: Expressway key sections based on CEEMDAN-STSGCN-FCM during the holidays for traffic flow prediction

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