Evidence map›Paper›PMID 41826422›Full record

ArticleScientific reports2026

Improving elderly-oriented transportation in rural areas through a case study of Zhenglu Town.

Qianan Ai, Jun Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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

2 authors.

Qianan AiSchool of Public Utility, Jiangsu Urban and Rural Construction College, Changzhou, 213147, China.
Jun ZhangCollege of Civil Engineering and Transportation, Yangzhou University, Yangzhou, 225127, China. zhangjun93@yzu.edu.cn.

Funding

Natural Science Research Project of Colleges and Universities in Jiangsu Province 23KJB580015Science and Technology Plan Project of Changzhou CR20241030
6 · The paper itself

Abstract

Faced with the increasing contradiction between the elderly transportation and the traffic system in most rural areas, the road infrastructure enhancement, the bus service improvement, and the traffic safety management should be given full consideration. An investigation on current rural transportation infrastructures is first performed in the studied area, including the road network configuration, the traffic facility, the surface pavement, and the bus services. Meanwhile, to better grasp the trip behavior and characteristics, in-depth discussions on the elderly travel demands and experiences are performed based on field observation and public data, where the K-means clustering method is applied to identify different trip groups, and the natural language processing technology is adopted to extract specialized needs from public textual data. Based on the foregoing investigation and analysis, a hierarchical improvement framework for elderly-oriented rural traffic is then proposed, including network planning, transportation management, and facility configuration, where quantification models of evaluation indicators are established considering transit network topology and spatial demand distribution. Through a combined evaluation of qualitative analysis and quantitative analysis, the recommended strategies will greatly enhance the global network accessibility by upgrading the road network and reconstructing the bus network, and improve the trip safety and convenience by optimizing the maintenance works and bus services. Specially, under a three-layer rural bus network architecture, the enhancement rates of service coverage and average accessible distance are expected to be 26.2% and 54.6% respectively, at the expense of a 30.8% increase in the daily operation cost.

Identifiers

PMID41826422
PMCPMC13106767

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.