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      • Record: found
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      Impact of law enforcement and increased traffic fines policy on road traffic fatality, injuries and offenses in Iran: Interrupted time series analysis

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          Abstract

          Background

          Road traffic law enforcement was implemented on 1 st April 2011 (the first intervention) and traffic ticket fines have been increased on 1 st March 2016 (the second intervention) in Iran. The aim of the current study was to evaluate the effects of the law enforcement on reduction in the incidence rate of road traffic fatality (IRRTF), the incidence rate of road traffic injuries (IRRTI) and the incidence rate of rural road traffic offenses (IRRRTO) in Iran.

          Methods

          Interrupted time series analysis was conducted to evaluate the impact of law enforcement and increased traffic tickets fines. Monthly data of fatality on urban, rural and local rural roads, injuries with respect to gender and traffic offenses namely speeding, illegal overtaking and tailgating were investigated separately for the period 2009–2016.

          Results

          Results showed a reduction in the incidence rate of total road traffic fatality (IRTRTF), the incidence rate of rural road traffic fatality (IRRRTF) and the incidence rate of urban road traffic fatality (IRURTF) by –21.44% (–39.3 to –3.59, 95% CI), –21.25% (–31.32 to –11.88, 95% CI) and –26.75% (–37.49 to –16, 95% CI) through the first intervention which resulted in 0.383, 0.255 and 0.222 decline in casualties per 100 000 population, respectively. Conversely, no reduction was found in the incidence rate of local rural road traffic fatality (IRLRRTF) and the IRRTI. Second intervention was found to only affect the IRURTF with –26.75% (–37.49 to –16, 95% CI) which led to 0.222 casualties per 100 000 population. In addition, a reduction effect was observed on the incidence rate of illegal overtaking (IRIO) and the incidence rate of speeding (IRS) with –42.8% (–57.39 to –28.22, 95% CI) and –10.54% (–21.05 to –0.03, 95% CI which implied a decrease of 415.85 and 1003.8 in monthly traffic offenses per 100 000 vehicles), respectively.

          Conclusion

          Time series analysis suggests a decline in IRTRTF, IRRRTF, and IRURTF caused by the first intervention. However, the second intervention found to be only effective in IRURTF, IRIO, and IRS with the implication that future initiatives should be focused on modifying the implementation of the traffic interventions.

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          Most cited references 34

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          Intervention Analysis with Applications to Economic and Environmental Problems

           G. BOX,  G. Tiao (1975)
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            Injury severities of truck drivers in single- and multi-vehicle accidents on rural highways.

             Feng Chen,  Suren Chen (2011)
            In adverse driving conditions, such as inclement weather and/or complex terrain, trucks are often involved in single-vehicle (SV) accidents in addition to multi-vehicle (MV) accidents. Ten-year accident data involving trucks on rural highway from the Highway Safety Information System (HSIS) is studied to investigate the difference in driver-injury severity between SV and MV accidents by using mixed logit models. Injury severity from SV and MV accidents involving trucks on rural highways is modeled separately and their respective critical risk factors such as driver, vehicle, temporal, roadway, environmental and accident characteristics are evaluated. It is found that there exists substantial difference between the impacts from a variety of variables on the driver-injury severity in MV and SV accidents. By conducting the injury severity study for MV and SV accidents involving trucks separately, some new or more comprehensive observations, which have not been covered in the existing studies can be made. Estimation findings indicate that the snow road surface and light traffic indicators will be better modeled as random parameters in SV and MV models respectively. As a result, the complex interactions of various variables and the nature of truck-driver injury are able to be disclosed in a better way. Based on the improved understanding on the injury severity of truck drivers from truck-involved accidents, it is expected that more rational and effective injury prevention strategy may be developed for truck drivers under different driving conditions in the future.
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              On a Measure of Lack of Fit in Time Series Models

               G M Ljung,  G. BOX (1978)
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: SoftwareRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Software
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                17 April 2020
                2020
                : 15
                : 4
                Affiliations
                [1 ] Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Razavi Khorasan, Iran
                [2 ] Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Razavi Khorasan, Iran
                Tongii University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-19-32920
                10.1371/journal.pone.0231182
                7164613
                32302374
                © 2020 Delavary Foroutaghe et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 3, Tables: 4, Pages: 13
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100003121, Ferdowsi University of Mashhad;
                Award ID: 3/45430
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003121, Ferdowsi University of Mashhad;
                Award ID: 3/45430
                Award Recipient :
                Milad Delavary Foroutaghe, Abolfazl Mohammadzadeh Moghaddam. This work was supported by Ferdowsi University of Mashhad [grant number: 3/45430]. Ferdowsi University of Mashhad - http://um.ac.ir/. This work was supported about 250$ by Ferdowsi University of Mashhad for data collection and specific study [grant number: 3/45430]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Engineering and Technology
                Civil Engineering
                Transportation Infrastructure
                Roads
                Engineering and Technology
                Transportation
                Transportation Infrastructure
                Roads
                Social Sciences
                Law and Legal Sciences
                Criminal Justice System
                Law Enforcement
                Medicine and Health Sciences
                Public and Occupational Health
                Safety
                Traffic Safety
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Time Series Analysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Time Series Analysis
                Engineering and Technology
                Signal Processing
                Noise Reduction
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Traumatic Injury Risk Factors
                Road Traffic Collisions
                Medicine and Health Sciences
                Public and Occupational Health
                Traumatic Injury Risk Factors
                Road Traffic Collisions
                Biology and Life Sciences
                Nutrition
                Diet
                Alcohol Consumption
                Medicine and Health Sciences
                Nutrition
                Diet
                Alcohol Consumption
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Death Rates
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized

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