ArticlePloS one2020
A multi-stage emergency supplies pre-allocation approach for freeway black spots: A Chinese case study.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a multi-stage random regret minimization (RRM) model as an emergency rescue decision support system to determine the emergency resource pre-allocation schedule for the freeway network. The proposed methodology consists of three steps: (1) improved accident frequency approach to identify the black spots on the freeway network, (2) stochastic programming (SP) model to determine the initial allocation plan sets, and (3) regret-based model in the logarithmical specification to select the most minimal regret one considering the factors of the response time, total cost and demand. The model is applied to the case study of 2014-2016 freeway network in Shandong, China. The results show that the random regret minimization (RRM) model can improve the full-compensation of SP model to a certain degree. RRM in logarithmical specification performs lightly better than random utility maximization (RUM) and RRM in the linear-additive specification in this case. This approach emerges as a valuable tool to help decision makers to allocate resources before traffic accident occurs, with the aim of minimizing the total regret of their decisions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.