Evidence map›Paper›PMID 42265297›Full record

ArticleScientific reports2026

Transportation 4.0 planning in emergency medical services considering real-time ambulance two-phase assignment-routing and mission change.

Saba Azarnoush, Arash Nemati

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
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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

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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.

Saba AzarnoushDepartment of Industrial Engineering, Babol Noshirvani University of Technology, Babol City, 47148- 71167, Mazandaran province, Iran.
Arash NematiDepartment of Industrial Engineering, Babol Noshirvani University of Technology, Babol City, 47148- 71167, Mazandaran province, Iran. r.nemati@nit.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transportation 4.0 enables smart planning in urban and interurban transportation networks by leveraging intelligent technologies, such as Internet of Things (IoT), cloud computing, information system integration, and artificial intelligence, to facilitate real-time vehicle assignment and routing. Nevertheless, Transportation 4.0 planning involving vehicle smart assignment and routing, particularly in healthcare logistics, has been less addressed in the literature. However, this study takes an initial step toward intelligent planning of emergency medical services by proposing an Intelligent Emergency Information System (IEIS) for real-time ambulance assignment and routing. As part of data analytics in the designed IEIS, an integer linear mathematical model is proposed to optimize total travel times in the ambulance assignment and routing problem based on deterministic real-time input data in two phases: reaching the patients and transporting them to hospitals. The proposed mathematical model is solved in a case study of Amol city, Iran, incorporating 25 street nodes, nine ambulance nodes, six call nodes, and five hospital nodes, using the CPLEX solver. Although designing and implementing the suggested conceptual model of IEIS was not applicable in the case study, to evaluate the impact of real-time parameter updates, two scenarios involving real-time inclusion of a new call node and an ambulance breakdown are examined. Accordingly, the model was re-solved, producing updated ambulance assignments and routing in each scenario, even by rearranging the ambulances' missions. Results validated the flexibility of the proposed mathematical model in optimizing two-phase ambulance assignment and routing under real-time input updates.

Indexed as

AmbulancesEmergency Medical ServicesAlgorithmsHumansModels, TheoreticalAmbulance mission changeHealthcare logistics smart planningIntelligent emergency medical servicesReal-time vehicle assignment-routingTransportation 4.0Two-phase ambulance assignment-routing

Identifiers

PMID42265297
PMCPMC13493795

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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.