Evidence map›Paper›PMID 36141094›Full record

ArticleEntropy (Basel, Switzerland)2022

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

Qiqin Cai, Dingrong Yi, Fumin Zou, Zhaoyi Zhou, Nan Li, Feng Guo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Qiqin CaiSchool of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China.ORCID 0000-0003-2673-6633
Dingrong YiSchool of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China.
Fumin ZouFujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China.
Zhaoyi ZhouDigital Fujian Traffic Big Data Research Institute, Fujian University of Technology, Fuzhou 350118, China.
Nan LiDigital Fujian Traffic Big Data Research Institute, Fujian University of Technology, Fuzhou 350118, China.
Feng GuoCollege of Computer and Data Science, Fuzhou University, Fuzhou 350108, China.

Funding

2020 Fujian Province "the Belt and Road" Technology Innovation Platform 2020D002Fujian Expressway Science and Technology Project KJ202001National Natural Science Foundation of China 41971340Special Funds for the Central Government to Guide Local Scientific and Technological Development 2020L3014
6 · The paper itself

Abstract

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 (

Indexed as

data miningESAsETC dataK-VDTEVR-XGBoost

Identifiers

PMID36141094
PMCPMC9497720

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.