Evidence map›Paper›PMID 42590513›Full record

ReviewSensors (Basel, Switzerland)2026

Forecasting Congestion and Enabling Proactive Management on Expressways During Public Holidays: A Survey of Methods, Open Issues, and Research Directions.

Zheng Yang, Yizhe Wang, Yan Zhu, Qing Peng

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 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

4 authors.

Zheng YangSchool of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0002-4444-8638
Yizhe WangThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China.ORCID 0000-0003-3494-378X
Yan ZhuJiangsu Runyang Bridge Development Co., Ltd., Bridge Park Shiyezhou, Zhenjiang 212115, China.
Qing PengThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China.

Funding

China Postdoctoral Science Foundation 2022M712410the Science and Technology Research Project of Jiangsu Communications Holding Co., Ltd. 2024JKY39, Sub-project 5
6 · The paper itself

Abstract

Expressway travel demand climbs steeply during public holidays, and the severe congestion that follows has become a principal constraint on both network operating efficiency and the quality of travel services. Dependable forecasting of holiday congestion, coupled with the practical realization of proactive management, accordingly carries considerable theoretical and practical weight for improving how expressways are operated and administered and for safeguarding efficient and safe public travel. Organized around the central theme of congestion prediction and proactive management for expressways over holiday periods, this paper reviews the progress of research across five interrelated areas: traffic speed and flow prediction methods; congestion state identification and forecasting; holiday travel characteristic analysis together with demand prediction; multi-source data fusion and congestion propagation mechanisms; and expressway traffic control together with traveler behavior guidance. Having surveyed the theoretical underpinnings, core technologies, and representative methods of each area, the paper concentrates in particular on the shortcomings and difficulties that existing studies encounter in coping with the hallmark features of holiday traffic, namely demand surges of short duration combined with pronounced spatiotemporal heterogeneity, and proceeds to outline promising avenues for subsequent research. Overall, the review seeks to provide wide-ranging literature support and a theoretical reference for developing technologies that predict and proactively manage congestion on expressways during public holidays.

Indexed as

expressway operations managementheterogeneous data fusionholiday traffic congestionproactive traffic controltraffic state prediction

Identifiers

PMID42590513
PMCPMC13469459

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.