Severity analysis of head-on crashes on curves in mountainous terrain based on higher-order factor interactions: insights from association rule mining

{"OBJECTIVE:":[0],"The":[1,160,381,400,423],"study":[2,290],"addressed":[3],"the":[4,56,65,85,103,149,165,202,288,302,329,333],"multifaceted":[5],"nature":[6],"of":[7,68,93,164,186,264,287,304,328,403,436],"severe":[8,265,306],"curve-based":[9,61],"head-on":[10,62,266,351],"crashes":[11,63,267,352],"by":[12,196,212],"identifying":[13],"hidden":[14],"high-risk":[15,57,188,409,425],"scenarios/combinations,":[16],"stemming":[17],"from":[18,59],"higher-order":[19],"interaction":[20],"between":[21],"driver,":[22],"crash,":[23],"environmental,":[24],"traffic":[25],"and":[26,46,78,91,96,122,148,157,162,173,193,235,277,356,377,397],"roadway":[27],"characteristics":[28],"along":[29],"with":[30,118,124,129,254,342],"spatial":[31,404],"relationship":[32,405],"indicators.":[33],"METHOD:":[34],"Association":[35],"rule":[36,105,151],"mining":[37],"(ARM),":[38],"due":[39],"to":[40,54,136],"its":[41],"greater":[42],"flexibility":[43],"in":[44,64,297,323,407,420],"handling":[45],"quantifying":[47],"interactions":[48,73,80],"than":[49],"conventional":[50],"models,":[51],"was":[52,182],"used":[53],"extract":[55],"scenarios/combinations":[58],"533":[60],"mountainous":[66,421],"state":[67],"Himachal":[69],"Pradesh,":[70],"India.":[71],"Higher-order":[72],"were":[74,99,133,141,167,244,384],"manifested":[75],"through":[76,169],"3":[77],"4-factor":[79],"after":[81,144],"fixing":[82],"severity":[83,355],"as":[84,220,260,326],"consequent.":[86],"A":[87],"minimum":[88],"support,":[89,115],"confidence/severity-rate,":[90],"lift":[92,130],"2%,":[94],"50%,":[95],"1.3,":[97],"respectively,":[98],"established":[100],"for":[101,439],"extracting":[102],"initial":[104],"space.":[106],"From":[107],"this":[108],"space,":[109],"based":[110,431],"on":[111,205,224,249,308,387,432],"an":[112],"absolute":[113],"raw":[114,119],"\\"key":[116],"rules\\"":[117],"support":[120],">25":[121],"\\"exceptions\\"":[123],"that":[125,292],"under":[126,269,360],"25":[127],"but":[128],"≥":[131],"1.9,":[132],"further":[134],"extracted":[135],"prioritize":[137],"significant":[138,262],"associations.":[139],"These":[140],"then":[142],"integrated":[143],"eliminating":[145],"redundant":[146],"rules":[147,156,166],"final":[150],"set":[152],"constituted":[153],"20":[154],"key":[155,184],"4":[158],"exceptions.":[159],"stability":[161,177],"generalizability":[163],"validated":[168],"Fisher's":[170],"exact":[171],"test":[172],"stratified":[174],"bootstrap":[175],"sampling-based":[176],"analysis.":[178],"RESULTS:":[179],"Aggressive":[180],"driving":[181],"a":[183,221,261,293,305,309,414],"trigger":[185],"many":[187],"scenarios.":[189],"Ineffective/inadequate":[190],"risk":[191,303,337],"communication":[192],"visibility":[194,274,369],"restricted":[195],"narrow":[197,255],"mountainsides":[198],"(<1":[199],"m)":[200,253,276,300,322],"increased":[201,301,338,354],"collision":[203,334],"susceptibility":[204],"medium":[206],"speed":[207],"limit":[208],"curves":[209,251,316,396],"(30-50":[210],"kmph)":[211],"1.6":[213],"times.":[214],"Middle-aged":[215],"heavy":[216],"vehicle":[217],"operators":[218],"emerged":[219,259,325],"vulnerable":[222],"group":[223],"sections":[225],"characterized":[226],"opposing":[227,398],"sequences/reverse":[228],"curves,":[229,345],"pavement":[230,379],"width":[231],"<":[232,318],"7":[233],"m":[234],"insufficient":[236,278,371],"valley":[237,279,372],"side":[238,280,368,373],"clearance/buffer":[239],"(<2.5":[240,282],"m).":[241,283],"Severe":[242],"collisions":[243],"1.35":[245],"times":[246],"more":[247],"likely":[248],"longer":[250,343,394],"(>90":[252],"mountainsides.":[256],"Opposing":[257],"sequences":[258,390],"hotspot":[263],"especially":[268,419],"conditions":[270],"involving":[271,363],"inadequate":[272,378],"mountainside":[273],"(<1.5":[275],"buffer":[281],"An":[284],"important":[285],"finding":[286],"present":[289],"is":[291],"relatively":[294],"sharper":[295,392],"curve":[296,389,416],"proximity":[298],"(<240":[299],"crash":[307],"given":[310],"subject":[311],"curve.":[312],"Specifically,":[313],"very":[314],"sharp":[315],"(radius":[317],"40m,":[319],"length:":[320],"30-60":[321],"succession":[324],"one":[327],"riskiest":[330],"scenarios,":[331,410],"doubling":[332],"risk.":[335],"Collision":[336],"significantly":[339],"(1.986":[340],"times)":[341],"preceding":[344],"indicating":[346],"potential":[347],"hotspots.":[348],"CONCLUSION:":[349],"Curve-based":[350],"exhibited":[353],"produced":[357],"multiple":[358,408,441],"hotspots":[359],"specific":[361],"combinations/scenarios":[362],"aggressive":[364],"driver":[365],"behavior,":[366],"mountain":[367],"constraints,":[370],"buffer,":[374],"vehicle-specific":[375],"dynamics":[376],"width.":[380],"associated":[382],"risks":[383],"particularly":[385],"exacerbated":[386],"closely-spaced":[388],"featuring":[391],"or":[393],"approach":[395],"orientations.":[399],"consistent":[401],"involvement":[402],"indicators":[406],"advocates":[411],"shift":[412],"toward":[413],"system-based":[415],"safety":[417,429],"assessment,":[418],"areas.":[422],"identified":[424],"scenarios/locations/configurations":[426],"facilitate":[427],"comprehensive":[428],"profiling":[430],"factor":[433],"combinations":[434],"instead":[435],"individual":[437],"features,":[438],"designing":[440],"targeted":[442],"interventions.":[443]}

Authors

Institutions

Publication Details

Journal
Traffic Injury Prevention
Published
2026-09-01
DOI
https://doi.org/10.1080/15389588.2026.2722338
Primary Topic
Traffic and Road Safety
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Severity analysis of head-on crashes on curves in mountainous terrain based on higher-order factor interactions: insights from association rule mining

Raman Parti, Deepak Awasthi
Traffic Injury Prevention
Traffic and Road Safety
article

Severity analysis of head-on crashes on curves in mountainous terrain based on higher-order factor interactions: insights from association rule mining

Raman Parti, Deepak Awasthi
article en

Abstract

OBJECTIVE: The study addressed the multifaceted nature of severe curve-based head-on crashes by identifying hidden high-risk scenarios/combinations, stemming from higher-order interaction between driver, crash, environmental, traffic and roadway characteristics along with spatial relationship indicators. METHOD: Association rule mining (ARM), due to its greater flexibility in handling and quantifying interactions than conventional models, was used to extract the high-risk scenarios/combinations from 533 curve-based head-on crashes in the mountainous state of Himachal Pradesh, India. Higher-order interactions were manifested through 3 and 4-factor interactions after fixing severity as the consequent. A minimum support, confidence/severity-rate, and lift of 2%, 50%, and 1.3, respectively, were established for extracting the initial rule space. From this space, based on an absolute raw support, "key rules" with raw support >25 and "exceptions" with that under 25 but with lift ≥ 1.9, were further extracted to prioritize significant associations. These were then integrated after eliminating redundant rules and the final rule set constituted 20 key rules and 4 exceptions. The stability and generalizability of the rules were validated through Fisher's exact test and stratified bootstrap sampling-based stability analysis. RESULTS: Aggressive driving was a key trigger of many high-risk scenarios. Ineffective/inadequate risk communication and visibility restricted by narrow mountainsides (<1 m) increased the collision susceptibility on medium speed limit curves (30-50 kmph) by 1.6 times. Middle-aged heavy vehicle operators emerged as a vulnerable group on sections characterized opposing sequences/reverse curves, pavement width < 7 m and insufficient valley side clearance/buffer (<2.5 m). Severe collisions were 1.35 times more likely on longer curves (>90 m) with narrow mountainsides. Opposing sequences emerged as a significant hotspot of severe head-on crashes especially under conditions involving inadequate mountainside visibility (<1.5 m) and insufficient valley side buffer (<2.5 m). An important finding of the present study is that a relatively sharper curve in proximity (<240 m) increased the risk of a severe crash on a given subject curve. Specifically, very sharp curves (radius < 40m, length: 30-60 m) in succession emerged as one of the riskiest scenarios, doubling the collision risk. Collision risk increased significantly (1.986 times) with longer preceding curves, indicating potential hotspots. CONCLUSION: Curve-based head-on crashes exhibited increased severity and produced multiple hotspots under specific combinations/scenarios involving aggressive driver behavior, mountain side visibility constraints, insufficient valley side buffer, vehicle-specific dynamics and inadequate pavement width. The associated risks were particularly exacerbated on closely-spaced curve sequences featuring sharper or longer approach curves and opposing orientations. The consistent involvement of spatial relationship indicators in multiple high-risk scenarios, advocates shift toward a system-based curve safety assessment, especially in mountainous areas. The identified high-risk scenarios/locations/configurations facilitate comprehensive safety profiling based on factor combinations instead of individual features, for designing multiple targeted interventions.

Traffic Injury Prevention
National Institute of Technology (JP)
Openalex Percentile: Top 11%
Traffic and Road Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.