ArticleHeliyon2023
Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 6 papers.
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
6 citing papers in PubMed.
- Epidemiological trends and comparative forecasting models of human brucellosis in inner mongolia autonomous region, mainland China, 2004-2024.PLoS neglected tropical diseases · 2026Article
- Leveraging machine learning for accurate forecasting of pulmonary tuberculosis epidemics in a coastal city in China.Tropical medicine and health · 2026Article
- Forecasting Road Traffic Injuries in North-Eastern Iran: The Effects of COVID-19 and Time Series Analysis (2009-2023).Open access emergency medicine : OAEM · 2026Article
- Epidemiology and SARIMA model of deaths in a tertiary comprehensive hospital in Hangzhou from 2015 to 2022.BMC public health · 2024Article
- How Effective Are Forecasting Models in Predicting Effects of Exoskeletons on Fatigue Progression?Sensors (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
- Retraction · 2025-12-09Compromised Peer Review · Concerns/Issues about Referencing/Attributions · Investigation by Journal/Publisher · Objections by Author(s) · · See also: https://pubpeer.com/publications/09531AC83B301FD42497D9FC82F031
- Retracted
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Road traffic accident (RTA) is a critical global public health concern, particularly in developing countries. Analyzing past fatalities and predicting future trends is vital for the development of road safety policies and regulations. The main objective of this study is to assess the effectiveness of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) and Facebook (FB) Prophet models, with potential change points, in handling time-series road accident data involving seasonal patterns in contrast to other statistical methods employed by key governmental agencies such as Ghana's Motor Transport and Traffic Unit (MTTU). The aforementioned models underwent training with monthly RTA data spanning from 2013 to 2018. Their predictive accuracies were then evaluated using the test set, comprising monthly RTA data from 2019. The study employed the Box-Jenkins method on the training set, yielding the development of various tentative time series models to effectively capture the patterns in the monthly RTA data.
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.