Evidence map›Paper›PMID 35996651›Full record

ArticleComputational intelligence and neuroscience2022

Deconstruction of the Optimal Design of Urban Road Interchange Based on the Integration of Smart Transportation and Big Data.

Jiyong Zhang, Yukui Yang, Lijing Chu, Shiliang Chen, Wenchao Bian, Meining Ling, Shengfu Li

RetractedAbstract readRetracted Publication
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Jiyong ZhangGuangzhou Urban Planning & Design Survey Research Institute, Guangzhou, Guangdong 510060, China.
Yukui YangGuangzhou Urban Planning & Design Survey Research Institute, Guangzhou, Guangdong 510060, China.
Lijing ChuGuangzhou Urban Planning & Design Survey Research Institute, Guangzhou, Guangdong 510060, China.ORCID https://orcid.org/0000-0003-2381-6996
Shiliang ChenProduction & Technology Department, Guangzhou Municipal Group Co., Ltd, Guangzhou, Guangdong 410060, China.
Wenchao BianGuicheng Transportation Service Institute of Nanhai District Transportation Bureau, Foshan, Guangdong 528253, China.
Meining LingGuangzhou Urban Planning & Design Survey Research Institute, Guangzhou, Guangdong 510060, China.
Shengfu LiGuangzhou Municipal Engineering Design & Research Institute Co., Ltd, Guangzhou, Guangdong 510060, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban interchange is the core hub connecting various regions, and it is of great significance for alleviating the problem of traffic congestion. In the process of urban interchange design, it is impossible to strictly control the traffic volume, interchange types, and standards by relying on traditional technologies. Smart transportation and big data are emerging technologies based on data, which can provide technical support for design and decision making. Based on this, this paper first uses smart transportation and big data technology to predict the traffic volume of Nancheng New District, so as to calculate the future development trend of the target area. Then, on the basis of traffic volume, the article uses smart transportation and big data technology to optimize the original urban interchange design scheme from the aspects of traffic capacity, safety, economic benefits, and environmental benefits. Finally, the article evaluates the optimized urban interchange scheme by means of comprehensive quantitative indicators and evaluation methods. Experiments show that the traffic capacity of the interchange on the outer ring road optimized by smart transportation and big data has increased to 72.6%, and the environmental coordination has increased from 45.2% to 55.2%. Moreover, the design aesthetics of the urban interchange after optimized design based on smart transportation and big data has increased to 65.9%. In addition, the comprehensive evaluation value of the urban interchange after optimization of smart transportation and big data reached 82.6. This fully shows that the optimal design of urban interchange based on the integration of smart transportation and big data can greatly improve the traffic capacity of urban roads.

Indexed as

Big DataTransportation

Identifiers

PMID35996651
PMCPMC9392620

What OpenQuestion holds

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Registered trials

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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.