Spatial autocorrelation analysis of cargo trucks on highway crashes in Chile

Blázquez, Carola; Picarte, Barbara; Calderón, Juan Felipe; Losada, Fernando

doi:10.1016/j.aap.2018.08.022

Cited by 39 publications

(23 citation statements)

References 41 publications

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“…(C) The incidence per 100,000 people and number of influenza cases by each month and year from 2005 to 2015. (Blazquez et al, 2018). Local spatial autocorrelation at the provincial and autonomous level was reflected by the local indicators of spatial association (LISAs).…”

Section: Discussionmentioning

confidence: 99%

Epidemiological features and trends of influenza incidence in mainland China: A population-based surveillance study from 2005 to 2015

Sun

Cong

et al. 2019

International Journal of Infectious Diseases

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To investigate epidemiological features and trends of influenza incidence with 1,173,640 cases in mainland China from 2005 to 2015. Methods: Incidence and mortality data for influenza from 2005 to 2015 were provided by the data-center of China public health science and covered a population of about 1.3 billion people from 31 provinces and regions in mainland China. Joinpoint regression and exploratory spatial data analyses were used to examine the incidence trends from 2005 to 2015. Results: The first upsurge in influenza cases occurred in 2009, and the highest incidence of influenza occurred in 2014 (15.9045 cases/100,000 people). The average incidence per year from 2009 to 2015 was threefold higher than that from 2005 to 2008 (10.5308 vs 3.4589 cases/100,000 people; incidence rate ratio = 3.0446). The joinpoint regression results showed that there was an increasing influenza incidence trend from 2005 to 2015 (annual change in percentage = 13.6%, 95%CI 2.2-26.3, p = 0.0236). The seasonal pattern analysis showed that influenza typically occurred in winter and spring during each monitoring year, peaking from November to March the next year. Conclusions: This study will help governments to make valuable decisions in allocating scarce resources and providing strategies to limit the spread of influenza.

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Section: Discussionmentioning

confidence: 99%

Epidemiological features and trends of influenza incidence in mainland China: A population-based surveillance study from 2005 to 2015

Sun

Cong

et al. 2019

International Journal of Infectious Diseases

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“…Studies have also employed spatial autocorrelation indicators to detect hotspots of road crashes [9,10,11,12]. Additionally, researchers have conducted a spatio-temporal analysis of traffic crash hotspots [13,14,15]. For example, [14] conducted a spatio-temporal analysis to explore the traffic crash temporal evolution and to identify crash hot spots using Moran`s I and Getis-Ord Gi* spatial statistics.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Analysis of Public Transit Crashes in Viña del Mar and Valparaíso, Chile

Blázquez

Salazar

2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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The conurbation of Viña del Mar and Valparaíso presented the highest number of public transit crashes in Chile with a total of nearly 350 fatalities and severely injured victims between 2014 and 2018. These crashes are caused mainly due to the imprudence of the driver yielding collisions between two or more moving vehicles, impacts with stationary objects, and falls of passenger from buses. This study performed a spatio-temporal analysis of traffic crashes that involved microbuses and taxi-buses to identify emerging and disappearing hotspots and coldspots during the study period. The results revealed that most crash hotspots due to the imprudence of the driver persisted in time in Viña del Mar, and crash hotspots due to signage disobedience sporadically appeared in Valparaíso. Hotspots of collisions and falls of passengers are types of crashes that emerged solely in Viña del Mar, as well as new, consecutive, and sporadic hotspots of severely injured victims. While morning historical hotspots emerged in Viña del Mar, consecutive, persistent, and sporadic hotspots of crashes appeared in the downtown area in both cities. The results of this study will aid authorities, transportation professionals, and planners make informed decisions about traffic safety.

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“…Compared with sunny days, a CC happening on a rainy day had a 52.5% higher possibility to be severe, and that happening on a cloudy day had a 66.6% higher possibility to be severe. These two types of extreme weathers display similar associations with crash severity in other contexts and can both incur poor visibility and hinder drivers’ perceptions and reactions to potential hazards or a primary crash, and thus causes more severe CCs [ 19 , 46 , 52 , 53 ]. Besides, pavements of freeways tend to be more slippery on rainy days [ 19 , 20 , 46 ], probably leading to a longer braking distance, and consequently result in a severe CC.…”

Section: Discussionmentioning

confidence: 99%

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

Meng

Song

et al. 2020

IJERPH

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A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.

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Spatial autocorrelation analysis of cargo trucks on highway crashes in Chile

Cited by 39 publications

References 41 publications

Epidemiological features and trends of influenza incidence in mainland China: A population-based surveillance study from 2005 to 2015

Epidemiological features and trends of influenza incidence in mainland China: A population-based surveillance study from 2005 to 2015

Spatio-Temporal Analysis of Public Transit Crashes in Viña del Mar and Valparaíso, Chile

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

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