Modelling Severity Level in Multi-vehicle Collision on Indian Highways

Bandyopadhyaya, Ranja; Mitra, Sophie

doi:10.1016/j.sbspro.2013.11.196

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…Perhaps the presence of more vulnerable users on the streets in residential areas, and the fact that the urban form of Medellín does not always separate high-hierarchy roads from residential areas, might promote undesired interactions between fast vehicles and non-motorized users, with unfortunate consequences. Other studies found that crashes in residential areas increase the probability of injuries ( 12 ).…”

Section: Estimation Resultsmentioning

confidence: 99%

“…Ordered outcome models are popular because CIS is an ordinal variable where fatal crashes are more severe than crashes with injuries, which are more severe than property damage only (PDO) crashes. Multiple works follow this approach (e.g., [4][5][6][7][8][9][10][11][12]). However, in many cases unordered discrete-choice models are more suitable because CIS does not strictly increase/decrease monotonically with respect to all explanatory variables, for example, airbag deployment increases the probability of injuries (intermediate outcome) and reduces the probability of PDO and fatalities (see Section 14.3 in [13] for more details).…”

Section: Key Econometric Approaches To Modeling Cismentioning

confidence: 99%

“…Previous works focus their attention on very specific types of crashes. Thus, several authors focus on single-vehicle crashes ( 9, 17, 19–21, 26 ), two-vehicle crashes ( 5, 7, 23 ), pedestrian crashes ( 22, 27, 28 ), specific types of vehicles like motorcycles ( 20, 23 ) or heavy-vehicles ( 12, 19, 29 ), and contributing/aggravating attributes from transportation infrastructure and driver behavior ( 18 ). However, few papers combine multiple types of crashes to achieve holistic conclusions and find the common explanatory power of variables across crash types.…”

Section: Main Cis Covariatesmentioning

confidence: 99%

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Influence of Socioeconomic Conditions on Crash Injury Severity for an Urban Area in a Developing Country

Mesa-Arango

Alaix

Pineda-Mendez

et al. 2018

Transportation Research Record

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This paper includes macroeconomic conditions in an econometric framework to understand urban crash injury severity (CIS) in a developing country, and identify its distinctive socioeconomic conditions. The work combines classic variables from a unique data set of crashes in Medellín, Colombia, with macroeconomic indicators. A multinomial logit (MNL) model with random parameters mines valuable information from the data. Numerical results support the following CIS mitigation policies: upgrading intersections with traffic signals; incorporating forgiving roadway designs; providing better conditions for motorcyclists and non-motorized users; prioritizing education, outreach, and enforcement campaigns during periods of good macroeconomic conditions (for some segments of the population), high motorization rates, and regarding specific periods, that is, times within the day, the week, and the year.

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Section: Estimation Resultsmentioning

confidence: 99%

Section: Key Econometric Approaches To Modeling Cismentioning

confidence: 99%

Section: Main Cis Covariatesmentioning

confidence: 99%

See 1 more Smart Citation

Influence of Socioeconomic Conditions on Crash Injury Severity for an Urban Area in a Developing Country

Mesa-Arango

Alaix

Pineda-Mendez

et al. 2018

Transportation Research Record

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“…[ 20 - 23 ] Similarly, the timings of high probability for accidents on the highways were well reported by previous researchers. [ 24 - 26 ] Generally, the probability of accidents on the highways is higher in the early morning. Bigdeli et al .…”

Section: Discussionmentioning

confidence: 99%

Road accidents on Indian National highways, ambulance reachability and transportation of injured to trauma facility: Survey-based introspection of golden hour

Giribabu,

Ghosh,

Hari

et al. 2024

Journal of Family Medicine and Primary Care

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Background: The transportation system plays a crucial role in the context of socioeconomic development, whereas the highway infrastructure acts as a base for the transportation system. In recent years, a rich impetus has been given to the development of road infrastructure by Indian governance. There is a need to introspect how well the prevailing highway infrastructure is equipped with emergency rescue management during road accidents. Lack of ambulance service and trauma facilities along the highways results in a steady loss of lives and injuries and increases people’s exposure to risks. Objective: This study aims to determine the response time of ambulance reachability to the accident spot on Indian national highways associated with heavy commercial transportation. Also, determining the time to transport the injured to the nearest trauma facility is another factor included as an objective in this investigation. Methods: The study adopted survey-based research, whereby the variables in the questionnaire were designed to record and assess the time for an ambulance to reach the accident spot and, from there, to transport the injured to the trauma management facility on Indian highways. Two hundred twenty-five participants who were either victims/relatives of victims or those involved in the rescue of the injured have participated in the survey. The dates of the accident events were 2017 and 2022. Results: The survey resulted in the identification of two categories of highway accidents. The first category of accidents happened on the highways near city limits/dense settlements, and the second category occurred on the core highways. The percentage of accidents caused on the highways either adjacent to or passing through the city limits/dense settlements was reported to be higher than the accidents on the core highways. Ninety percent of the participants reported successful contact with the ambulance call/service centre, but only ~75% success rate exists for ambulances to reach the accident scene. On the core highways, the time taken for the ambulance to arrive at the accident scene is 25–35 minutes. The results from the survey ascertained that the patients were prioritised for treatment in the nearest hospitals (irrespective of having a trauma facility) at a distance of ~12–20 km, for which the time taken is ~15–25 minutes. Importantly, from the interviews, it is understood that in many cases, these hospitals have further referred to specialty hospitals located in nearby cities or trauma centres with greater facilities. Occasions exist where the injured were taken directly to hospitals 30–40 km from the accident spot, for which the time was more than 40 minutes. Conclusions: The results provide evidence that in either of the accident cases on the highways that are adjacent to/passing through the city limits or on the core highways, the total time for emergency care accessibility is nearly 60 minutes or greater; this implies that in the majority of cases, there is very meagre time left to provide emergency medical care to the needy and injured on the Indian highways to abide by the concept of golden hour. Plausible reforms backed by technology for enabling highways into ‘emergency rescuable highways’ are highly needed to guarantee a safer and more sustainable transportation system in India.

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“…road accident data analysis used traditional statistical techniques [2,[5][6][7][8][9] and data mining techniques [3,[10][11][12][13][14]18].…”

mentioning

confidence: 99%

A novel framework to analyze road accident time series data

Kumar

Toshniwal

2016

J Big Data

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Modelling Severity Level in Multi-vehicle Collision on Indian Highways

Cited by 10 publications

References 12 publications

Influence of Socioeconomic Conditions on Crash Injury Severity for an Urban Area in a Developing Country

Influence of Socioeconomic Conditions on Crash Injury Severity for an Urban Area in a Developing Country

Road accidents on Indian National highways, ambulance reachability and transportation of injured to trauma facility: Survey-based introspection of golden hour

A novel framework to analyze road accident time series data

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