Evidence map›Paper›PMID 37571665›Full record

ArticleSensors (Basel, Switzerland)2023

Optimization of Signal Timing for Urban Expressway Exit Ramp Connecting Intersection.

Mingjun Deng, Fei Chen, Yongtai Gong, Xiang Li, Shuhang Li

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Mingjun DengSchool of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China.ORCID 0000-0003-3411-4404
Fei ChenSchool of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China.
Yongtai GongSchool of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China.
Xiang LiSchool of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China.
Shuhang LiSchool of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China.

Funding

National Natural Science Foundation of China 51965021Natural Science Foundation of Jiangxi Province 20142BAB201015
6 · The paper itself

Abstract

To alleviate the traffic problems of congestion and queue overflow on a mainline at the intersection of an urban expressway exit ramp articulation during peak hours, a bi-level programming optimization model of signal timing is proposed. The lower-level optimization objective is to maximize the capacity of the expressway exit ramp that articulates with the entrance road, while the upper-level optimization objective is to minimize the average vehicle delay and the number of stops per vehicle, taking into account the queue length in the direction of the ramp and other directions. The particle swarm optimization algorithm is selected to solve the proposed model, applied to a real case, and is validated using MATLAB and VISSIM simulation platforms. The simulation results show that the average vehicle delay and the number of stops per vehicle in the exit ramp on the expressway are reduced by 22.09% and 18.60%, while those in the intersection area are reduced by 20.96% and 17.19%, respectively. The conclusion indicates that the signal timing scheme obtained by this method can effectively improve the traffic efficiency at the intersection of the exit ramp on the expressway and alleviate the problem of congestion and the overflow of the exit ramp back to the mainline.

Indexed as

bi-level programmingparticle swarm optimizationsignal timing optimizationtraffic simulationurban traffic

Identifiers

PMID37571665
PMCPMC10422580

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.