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Characteristics of rear-end accidents at signalized

Accident Analysis and Prevention37(2005)

983–995

Characteristics of rear-end accidents at signalized intersections

using multiple logistic regression model

Xuedong Yan a,?,Essam Radwan b,Mohamed Abdel-Aty c

a Department of Civil and Environmental Engineering,College of Engineering,University of Central Florida,

12172Descartes Cr.Apt#1,Orlando,FL32826,USA

b Center for Advanced Transportation Systems Simulation,University of Central Florida,Orlando,FL32816-2450,USA

c Department of Civil an

d Environmental Engineering,University of Central Florida,Orlando,FL32816-2450,USA

Received9February2005;received in revised form4May2005;accepted4May2005

Abstract

Multi-vehicle rear-end accidents constitute a substantial portion of the accidents occurring at signalized intersections.To examine the accident characteristics,this study utilized the2001Florida traf?c accident data to investigate the accident propensity for different vehicle roles(striking or struck)that are involved in the accidents and identify the signi?cant risk factors related to the traf?c environment,the driver characteristics,and the vehicle types.The Quasi-induced exposure concept and the multiple logistic regression technique are used to perform this analysis.The results showed that seven road environment factors(number of lanes,divided/undivided highway,accident time,road surface condition,highway character,urban/rural,and speed limit),?ve factors related to striking role(vehicle type,driver age,alcohol/drug use, driver residence,and gender),and four factors related to struck role(vehicle type,driver age,driver residence,and gender)are signi?cantly associated with the risk of rear-end accidents.Furthermore,the logistic regression technique con?rmed several signi?cant interaction effects between those risk factors.

Keywords:Rear-end accidents;Signalized intersections;Quasi-induced exposure;Multiple logistic regression;Striking role;Struck role

1.Introduction

Rear-end accidents are one of the frequently occurring types of accidents,accounting for almost one-third of all reported accidents in the US(1.848million)and11.8%of multi-vehicle fatal accidents(1923)(National Transportation Safety Board,2001).Rear-end accidents are the most com-mon accident type at signalized intersections since the diver-sity of actions taken increases due to signal change.Speci?c causes of rear-end accidents include the following drivers’inattentive driving and following too closely.A proper space cushion is needed to provide a driver enough reaction time to recognize a hazardous situation and make a stop decision.?Corresponding author.Tel.:+14078235810;fax:+14078234676.

E-mail addresses:yxd22222@https://www.wendangku.net/doc/c75764794.html,(X.Yan),

aeradwan@https://www.wendangku.net/doc/c75764794.html,(E.Radwan),

mabdel@https://www.wendangku.net/doc/c75764794.html,(M.Abdel-Aty).Typically,driver,vehicle,and roadway/environment char-acteristics in?uence accident occurrence and injury severity. Moreover,since a rear-end accident is related to both driving behaviors and performances of the leading(struck)vehicle and the following(striking)vehicle,the accident risk is possibly associated with struck or striking role that a driver or vehicle would assume in a rear-end accident.

The driver age and gender were considered as main driver characteristics that might be associated with a rear-end acci-dent.There is general consensus among researchers that older drivers tend to process information and take a corresponding action more slowly than younger driver.Slower reaction times for older versus younger drivers contribute to a dispro-portionately heightened degree of risk especially when older drivers are faced with two or more choices of action(Staplin et al.,2001).However,the younger drivers,especially under 25years,are more likely to be involved in aggressive attitude and inattentive driving.A previous study on rear-end acci-

984X.Yan et al./Accident Analysis and Prevention37(2005)983–995

dents indicated that drivers younger than18years were most vulnerable to roadway accidents followed by18–24-year-old drivers;the propensity of drivers involved in accidents showed a decreasing trend with increasing age until the age of69,after which the drivers again showed a higher accident involvement propensity as compared to the drivers who were25–69year old(Santokh,2003).Additionally,among 18–24-year-old drivers,male drivers were found to be more prone to accident involvement as compared to their female counterparts.It was also found that in rear-end accidents, drivers up to the age of25years are more likely to be in the striking role than in the struck role.As drivers get older,they tend to be in the striking role less often than in the struck role.

For different type of vehicles,steering and braking perfor-mance are critical in the avoidance of accidents.Strandberg (1998)pointed out that except for the hazards due to unpre-dicted change in properties within one vehicle,differences between vehicles in braking performance are responsible for many rear-end accidents.Moreover,the size of the leading vehicle may in?uence the behavior of the following driver. Sayer et al.(2000)examined the effect that the leading vehi-cle size(speci?cally,height and width)has on a passenger car driver’s gap maintenance under near optimal driving condi-tions(e.g.daytime,dry weather,free-?owing traf?c).Results showed that passenger car drivers followed light trucks at shorter distances than they followed passenger cars,but at the same velocities.Speci?cally,it appears that when dimen-sions of lead vehicles permit following drivers to see through, over,or around them,drivers maintain signi?cantly longer (i.e.safer)distances.Abdel-Aty and Abdelwahab(2004) examined the relationship between vehicle type(car or LTV including light truck,van,and SUV)and the role(striking or struck)played by each vehicle in the https://www.wendangku.net/doc/c75764794.html,ing a nested logit structure model,they analyzed the probabil-ities of the four rear-end accident con?gurations(car–car, car–LTV,LTV–car,and LTV–LTV)as a function of driver’s age,gender,vehicle type,vehicle maneuver,light conditions, driver’s visibility and speed.Results showed that driver’s vis-ibility and inattention in the following(striker)vehicle have the largest effect on being involved in a rear-end collision of con?guration Car–truck(a regular passenger car striking an LTV).

The critical road environment conditions could play a signi?cant role in rear-end accidents and they may con-tain all kinds of non-driver related factors such as lighting conditions,the roadway surface conditions,highway char-acteristics,traf?c volume,the weather conditions,and so on. Braking performance of vehicle is substantially reduced in icy and snowy road surface condition and deceleration capacity may decrease by more than90%compared to dry condi-tion(Strandberg,1998).The heavy traf?c volume results in the smaller headway between gaps between leading and fol-lowing vehicles,which de?nitely increases the possibility of rear-end accidents.Khattak(2001)reported that a major-ity of the accidents(54.9%)occurred during the peak times 7:00–9:00a.m.and3:00–6:00p.m.A small portion(10.8%)of the accidents occurred at night on unlit streets and a smaller portion(4.9%)occurred at night on lighted streets.

However,of those previous research?ndings,relatively few studies used the accident database and related statisti-cal model to explore the propensity of rear-end accidents that occurred at signalized intersections.This paper presents the results of a thorough investigation into the relationship between the rear-end accidents and a series of potential risk factors classi?ed by driver characteristics,road environ-ments,and vehicle type.The quasi-induced exposure con-cept is used to compare the relative accident involvements between different risk conditions based on the2001Florida accident database.For striking role and struck role in the rear-end accidents respectively,multiple logistic regression models are used for hypothesis testing to identify the signif-icant factors that contribute to the rear-end accidents.

2.Methodology

2.1.Accident database and rear-end accident

identi?cation

The2001accident database,obtained from the Florida Department of Highway Safety and Motor Vehicles (DHSMV),was used in this study.The DHSMV data con-stitute a relational database that includes seven?les.Each ?le deals with a speci?c feature of a traf?c accident.Files may be linked as needed to combine the information con-tained in each other.Three?les used in the analysis presented here were the event(containing the characteristics and envi-ronment of the accident),drivers(containing the drivers’characteristics),and vehicles(information about the vehicles’characteristics and vehicles actions in the traf?c accident)?les.

In Florida,when an accident occurs and the local police department is noti?ed,the responding of?cer will determine whether to?ll out a long-or short-form accident report.If an accident involves an injury or a felony(e.g.hit and run),the accident must be?led on a long-form.If an accident involved only property damage(a minor accident),usually it is up to the of?cer to report it on a long or a short-form.Accident forms are then forwarded to the respective counties,which choose whether or not to?le accidents reported on short-forms.From here,only the accidents reported on long-forms are forwarded onto the DHSMV,which maintain records based on only accidents reported on long-forms.Since most accidents that occur involve only property damage and not a serious injury or a felony,it can be argued that the FDOT and DHSMV accident databases under-represent the property-damage-only accidents.Abdel-Aty et al.(2005)investigated the signi?cant differences in the important accident-related factors between models based solely on accidents reported on long-forms and models based on accidents reported on both long-and short-forms(i.e.models based on restricted and complete datasets).They found that for rear-end,right-turn

X.Yan et al./Accident Analysis and Prevention37(2005)983–995

985

Fig.1.Code for collision positions of vehicles in DHSMV.

and sideswipe accidents,the important factors are fairly con-sistent between the models created by complete and restricted datasets.The results show that factors in accidents causing mostly injuries for rear-end,right-turn and sideswipe acci-dents are generally the same as factors causing non-injury or minor accidents.This indicates that models based on com-plete and restricted datasets for these types of accidents are roughly equivalent.Therefore,this rear-end accident analy-ses based on the2001accident database from the DHSMV long-form data should present similar results if based on a complete accident database.

Rear-end accident scenarios may involve three or more vehicles.To simplify the assignment of driver culpability and easily identify accident roles of vehicle(leading or follow-ing)in the accident,the analysis was restricted to two-vehicle collisions,in which both vehicles proceeded straight at sig-nalized intersections.The collision positions of vehicles are coded and recorded in the DHSMV,as shown in Fig.1.In a two-vehicle rear-end accident,the code7,8,or9can identify the struck vehicle and the vehicle with the code1,2,or14 should be the striking one.In most rear-end cases,the leading vehicles did not take a major responsibility for the accidents but they could have performed an unexpected stop;on the other hand,the main contributing causes to the accident for the striking vehicle are careless driving and following too closely.

2.2.Quasi-induced exposure technique

To test accident propensity and explore the traf?c acci-dent database,quasi-induced exposure technique(Carr,1970; Haight,1973;Stamatiadis and Deacon,1995,1997)is becoming widely used in traf?c safety research.Stamatiadis and Deacon(1995)developed the term relative accident involvement ratio(RAIR),as the measure of accident caus-ing propensity used in the quasi-induced exposure analysis. It is equal to the ratio of the percentage of a speci?c subgroup in at-fault drivers to the percentage of the same subgroup in not-at-fault drivers.The at-fault drivers are those who were mostly responsible for the accident occurrence and the not-at-fault drivers are those less responsible for the accidents.The key assumption is that the distribution of not-at-fault drivers closely represents the distribution of all drivers exposed to accident hazards.Previous studies had successfully applied the quasi-induced exposure method to analyze traf?c accident risks of drivers and vehicles under a given set of road environ-ment conditions(Stamatiadis and Deacon,1995;Aldridge et al.,1999;Hing et al.,2003).However,few of them focused on the investigation of non-driver-related(road environment) factors as exclusive main effects on the traf?c safety.

To introduce the road environment factors into statis-tical model and test their exclusive main effects on acci-dents,this research extended the application of the quasi-induced exposure.In this study,?rst,two-vehicle accidents occurring at signalized intersections are identi?ed,which are categorized into two groups:rear-end accidents and non-rear-end accidents(exposure).Those rear-end accidents include information of struck drivers/vehicles and striking drivers/vehicles,as well as the corresponding road envi-ronment conditions.Those non-rear-end accidents include information of drivers/vehicles that had no improper driv-ing action but were involved in the accidents,as well as their corresponding road environment conditions.Thus,the comparisons between drivers/vehicles/environments distri-bution in the rear-end group and the non-rear-end group can be used to investigate the accident propensities.To increase the comparability between rear-end drivers and not-at-fault drivers,the driving behaviors for both groups are limited in ‘going-through’the signalized intersections when the acci-dents were happening.For better understanding the principle of the data classi?cation,Fig.2illustrated the relationship between rear-end and non-rear-end groups.According to the accident de?nition,the dataset identi?ed7666two-vehicle rear-end accidents that happened at signalized intersections and15,734non-rear-end accidents involved by not-at-fault drivers as exposure.

Based on the above categorization,three types of relative accident involvement ratios are calculated to test the main effects of driver,vehicle,and environment factors related to rear-end accidents.The extended assumption here is that the distributions of road environment factors in non-rear-end accidents may represent the distributions of road environ-ment factors confronted by all vehicles/https://www.wendangku.net/doc/c75764794.html,paring the variable distributions in non-rear-end accidents to those in all two-vehicle accidents at intersections showed similar https://www.wendangku.net/doc/c75764794.html,ing the RAIR formula developed by Stamatiadis and Deacon(1995),the relative accident involvement ratio is de?ned as Eq.(1):

RAIR i=

D1i

D2i

or RAIR i=

V1i

V2i

RAIR i=

E1i

E2i

(1)

where RAIR i is the relative accident involvement ratio for type i drivers/vehicles/environments,D1i the number of strik-ing drivers of type i in rear-end accidents,D2i the number of not-at-fault drivers of type i in non-rear-end accidents,V1i the

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983–995

Fig.2.Rear-end and not-rear-end accident classi?cation used for quasi-induced exposure analysis.

number of striking vehicles of type i in rear-end accidents,V 2i the number of not-at-fault vehicles of type i in non-rear-end accidents,E 1i the number of rear-end accidents involving environment type i ,and E 2i is the number of non-rear-end accidents involving environment type i .

Furthermore,to test the interaction between type i drivers/vehicles/environments and type j drivers/vehicles/environ-ments,the RAIR can be de?ned as Eq.(2):

RAIR i,j =

N 1i,j

N 1i,j N 2i,j

N 2i,j

(2)

where RAIR i ,j is the relative accident involvement ratio types

i and j drivers/vehicles/environments,N 1i ,j the number of rear-end drivers,vehicles,or the related environments of types i and j in rear-end accidents,and N 2i ,j is the number of not-at-fault drivers,vehicles,or the related environments of types i and j in non-rear-end accidents.2.3.Statistical modeling

Previous studies had appropriately applied logistic regres-sion analysis to test the signi?cance of traf?c accident risk factors based on the technique of induced exposure (Stamatiadis and Deacon,1995;Hing et al.,2003).Logis-tic regression belongs to the group of regression methods for describing the relationship between explanatory variables and a discrete response variable.A binary logistic regres-sion is proper to use when the dependent is a dichotomy (an event happened or not)and can be applied to test association between a dependent variable and the related potential fac-tors,to rank the relative importance of independents,and to assess interaction effects.Binary logistic regression is used in this study since the dependent variable Y (accident clas-si?cation)can only take on two values:Y =1for rear-end accidents,and Y =0for non-rear-end accidents.The proba-bility that a rear-end accident will occur or not is modeled as logistic distribution in Eq.(3):π(x )=

e g (x )

1+e g (x )

(3)

The logit of the multiple logistic regression model (Link

Function)is given by Eq.(4):

g (x )=ln

π(x )

1?π(x )

=β0+β1x 1+β2x 2+β3x 3+···+βn x n

(4)

where π(x )is conditional probability of a rear-end accident,which is equal to the number of rear-end accidents divided by the total number of accidents.x n are independent variables (driver/vehicle/environment factors).The independent vari-ables can be either categorical or continuous,or a mixture of both.Both main effects and interactions can generally be accommodated.βn is model coef?cient,which directly deter-mines odds ratio involved in the rear-end accident.The odds of an event are de?ned as the probability of the outcome event occurring divided by the probability of the event not occurring.The odds ratio that is equal to exp(βn )tells the rel-ative amount by which the odds of the outcome increase (OR greater than 1.0)or decrease (OR less than 1.0)when the value of the predictor value is increased by 1.0units (David and Lemeshow,1989).Especially for categorical independent variables,the odds ratios represent the accident risk compari-son among different levels of drivers/vehicles/environments.The previous studies (Stamatiadis and Deacon,1995;Hing et al.,2003)clearly expressed the relationship between logistic regression and RAIR in the quasi-induced expo-sure analysis.In fact,for a speci?c type of drivers/vehicles/environments,the odds generated from the logistic regression model are analogous to the corresponding RAIRs,and the odds ratios from the model are equivalent to the comparisons among those RAIRs.Furthermore,a signi?cant P -value (e.g.P ≤0.01)for the Wald χ2statistic is evidence that a regression coef?cient in the model is nonzero,which also indicates the statistical importance of those RAIR comparisons between different types of drivers/vehicles/environments.

In addition,since either striking or struck drivers/vehicles might be associated with the rear-end accidents,two separate models were developed for different roles in the accidents (striking or struck),to investigate the relationship between rear-end accidents and a series of potential risk factors.For

X.Yan et al./Accident Analysis and Prevention37(2005)983–995987

simplicity and ease of interpretation of the results,all those factors are chosen as categorical variables to?t models.The SAS program procedure,LOGISTIC,was used for model development and the hypothesis testing was based on0.01 signi?cance level because of the large sample size.

3.Modeling results for environments and striking drivers/vehicles

3.1.Main effect estimation

Based on the main effect model,the multivariate logistic regression analysis identi?ed signi?cant factors directly asso-ciated with rear-end accidents happening at signalized inter-sections.Table1lists the model estimation and odds ratios properly adjusting other factors for signi?cant independent variables.The following sections document the interpretation of the regression results for those variables classi?ed by road environment factors and striking driver/vehicle characteris-tics.

3.1.1.Road environment factors

Seven road environment factors including number of lanes,divided/undivided highway,accident time,road sur-face condition,highway character,urban/rural,and speed limit shows signi?cant association with the risk of rear-end accidents.Fig.3illustrates univariate comparisons of relative accident involvement ratios between difference conditions for each road environment factor.

The number of lanes at the accident site as originally recorded by the reporting of?cer includes both sides of the median where applicable.This analysis focuses on intersec-tions located on the most typical highways with2,4,and6 lanes.As shown in Fig.3,relative accident involvement ratio (RAIR)for6-lane highway is larger than2-lane highway and (RAIR)for4-lane highway is smaller than2-lane highway. However,adjusting other variables in the main effect model, Table1shows that there is no signi?cant difference between 6-and2-lane highways(P=0.216),but the risk of rear-end accidents happening at4-lane highways could be27%lower than the2-lane ones with a0.01level of signi?https://www.wendangku.net/doc/c75764794.html,-paring RAIRs,rear-end accidents are more likely to happen at divided highways than undivided highways.Adjusting for other variables,the risk for divided highways could be16% higher than the risk for undivided highways with a0.01level of signi?cance.However,the crash trend for number of lanes and divided/undivided highway may be confounded by the traf?c volume effect because the highways with high heavy traf?c tend to have more lanes and be separated by medians.

The results show that most rear-end accidents occurred at daytime(71.7%),versus28.3%ones at night.An interesting ?nding is that the RAIR(1.06)for daytime is also higher than that(0.88)for night,as shown in Fig.3.Based on the model, the accident risk for the night condition could be50%lower than that for the daytime condition at a0.0001level of sig-ni?cance.The presumable reasons are that the traf?c volume at daytime is higher than that at night;the morning peak and afternoon peak may affect driving attitude and contribute to the accidents.

Fig.3shows that there is a clear trend between the RAIR and the road surface condition.The plot shows that as RAIR increases the road surface conditions become more and more https://www.wendangku.net/doc/c75764794.html,pared to a dry road surface,the accident risk on the wet surface could be1.788times higher and that on the slippery surface could be3.302times higher.The results testi?ed that reduction of braking capacity will de?nitely con-tribute to rear-end accidents.

The highway characters are classi?ed as four levels in the database,including straight-level(S-L),straight-up/downgrade(S-U/D),curve-level(C-L),and curve-upgrade/downgrade(C-U/D).Fig.3illustrates that as the highway character becomes more complex,the rear-end acci-dent is more likely to occur.At signalized intersections,the risks of curve and grade approaches could be79%and28% higher than straight approaches.If both curve and grade are present at the same time,the rear-end risk could be twice as that for normal straight highways.Generally,the grade of an intersection approach may signi?cantly affect the time and the distance needed for a motorist to stop a vehicle at an inter-section.If approaching the intersection on a downhill grade, motorists may not account for vehicle mass and momentum which will require a longer stopping time.Moreover,if the intersection is located on a horizontal curve,the potential sight obstruction on the inside of curves,such as cut slopes, walls,buildings,bridge piers,and longitudinal barriers,could restrict stop sight distance and result in rear-end accidents.

The analysis also shows that rear-end is particularly rele-vant to urban accidents(64%)and the accident risk in urban area could be20%higher than rural area,presumably because urban areas are more dominated by signalized intersections and have higher traf?c volume than rural area.As shown in Table1and Fig.3,there is a clear trend between RAIR and speed limit.As the speed limit increases,the risk of the rear-end accidents increases.For55mph speed limit,the odds of being involved in an accident could be4.6times higher than25mph speed limit.At signalized intersections, with the higher speed limits,generally,drivers are more likely to fall into the dilemma zone where they possibly can neither execute the intersection crossing nor execute the stopping maneuver safely and comfortably at the onset of yellow.Such a negative situation may result in rear-end acci-dents due to relatively higher operation speeds.On the other hand,at the lower operation speed,the following driver can more easily change lane or brake to avoid striking the lead vehicle.

3.1.2.Striking driver/vehicle characteristics

Vehicle type and four factors related to driver characteris-tics including driver age,alcohol/drug use,driver residence, and gender shows signi?cant association with the risk of rear-end accidents.Fig.4illustrates comparisons of relative

988X.Yan et al./Accident Analysis and Prevention37(2005)983–995

Table1

Model estimation and odds ratios for signi?cant independent variables for striking role

Parameter Coef?cient estimate Odds ratio95%Wald con?dence limits Waldχ2P-value Intercept?1.22182.429<0.0001

Number of lanes66.122<0.0001 Others vs.2-lane?0.1040.9010.8031.0123.0720.080 6-lane vs.2-lane?0.0710.9310.8321.0421.5320.216 4-lane vs.2-lane?0.3190.7270.6570.80438.556<0.0001

Divided/undivided highway15.948<0.0001 Undivided vs.divided?0.1520.8590.7980.92615.948<0.0001

Accident time341.878<0.0001 Night vs.daytime?0.7010.4960.4610.534341.878<0.0001

Road surface condition209.789<0.0001 Wet vs.dry1.1953.3022.2554.83537.713<0.0001 Slippery vs.dry0.5811.7881.6421.947177.863<0.0001

Highway character38.376<0.0001 C-U/D vs.S-L0.7232.061.343.16910.8330.001 C-L vs.S-L0.5811.7881.3532.36416.665<0.0001 S-U/D vs.S-L0.2431.2751.1131.4612.2850.001

Urban/rural26.718<0.0001 Urban vs.rural0.1781.1951.1171.27826.718<0.0001

Posted speed532.690<0.0001 55mph vs.25mph1.5284.6073.4336.183103.551<0.0001 50mph vs.25mph1.3623.9032.9375.18688.195<0.0001 45mph vs.25mph1.0222.7792.1773.54867.249<0.0001 40mph vs.25mph0.5781.7831.392.28620.744<0.0001 35mph vs.25mph0.4831.621.2672.07314.7600.000 30mph vs.25mph?0.0290.9720.7531.2530.0490.825

Driver age152.224<0.0001 >75vs.<260.1161.1230.9461.3341.7640.184 66–75vs.<26?0.3920.6760.5780.78924.373<0.0001 56–65vs.<26?0.6180.5390.4720.61683.104<0.0001 46–55vs.<26?0.4200.6570.5920.72962.062<0.0001 36–45vs.<26?0.3430.710.6490.77755.663<0.0001 26–35vs.<26?0.2900.7480.6860.81742.105<0.0001 Alcohol/drug use938.944<0.0001 6vs.12.40811.1166.42219.24274.012<0.0001 5vs.12.2609.5847.51312.226331.021<0.0001 4vs.13.81145.20616.181126.29352.864<0.0001 3vs.14.60499.8324.337409.49940.863<0.0001 2vs.15.008149.56395.419234.429476.914<0.0001 Residence code51.372<0.0001 4vs.10.5821.7891.1142.8755.7810.016 3vs.10.3691.4461.1961.74814.5330.000 2vs.10.2881.3341.2121.46834.879<0.0001 Gender9.9210.002 Female vs.male?0.1050.90.8430.9619.9210.002

Type of vehicle25.429<0.0001 Large vehicle vs.car0.2241.2511.0681.4647.7340.005 Light truck vs.car0.1891.2081.1081.31718.501<0.0001 Van vs.car0.1381.1481.0241.2875.5730.018

accident involvement ratios between different levels of those factors.

The10-year interval was chosen to group drivers by age. The seven driver age groups included younger than26,26–35, 36–45,46–55,56–65,66–75,and older https://www.wendangku.net/doc/c75764794.html,-paring relative accident involvement ratios,the graph for driver age illustrated in Fig.4doesn’t show a smooth U-shape pattern.The result is consistent with the previous analysis (Santokh,2003):the propensity of drivers involved in acci-dents showed a decreasing trend with increasing age until the age of56–65,after which the drivers increase to a higher accident involvement propensity for the age group older than

X.Yan et al./Accident Analysis and Prevention37(2005)983–995

989

Fig.3.Relative accident involvement ratios by road environment factors.

75,as compared to the drivers who were younger than26 year old.However,adjusting for other factors in the model, there is no signi?cant difference between the oldest group and youngest group(P-value=0.184).The56–65years drivers who have lowest risk on rear-end accidents may bene?t from lower following speed and conservative driving attitude.The oldest group(>75years)presented the highest risk,presum-ably because of age-related deterioration of their physical and cognitive abilities.The younger group has a larger accident propensity,presumably because of risk-taking and attitudinal factors.

Alcohol reduces alertness,interferes with judgment and impairs vision.Most drugs that affect the central nervous sys-tem may have the potential to impair driving ability.There are six levels of potential alcohol/drug use recorded in the Florida accident database:(1)No,(2)Alc-under in?uence,(3)Drug-under in?uence,(4)Alc&Drug-under in?uence,(5)Had been drinking,and(6)Pending BAC test results.As shown in

990X.Yan et al./Accident Analysis and Prevention 37(2005)

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Fig.4.Relative accident involvement ratios by striking driver characteristics and vehicle types.

Table 1and Fig.4,the drivers under in?uence of alcohol,drug,or alcohol and drug are substantially over-involved in accidents and their accidents risk can be 149.6,99.8,and 45.2times higher than those of normal drivers,adjust-ing for other factors.The results showed that both alcohol and drug have extremely negative effect on drivers involv-ing rear-end accidents.Even drivers who had been drinking also show much higher involvement risk than non-drinking drivers (OR =9.58).However,as not-at-fault drivers,those with alcohol/drug use performed worse to avoid accidents than normal ones.There is a higher chance to be underes-timating their risk involving accidents based on the induced exposure technique.In addition,the data sample sizes in some levels of alcohol/drug use are very small,so that the point esti-mates of their odds ratios are less signi?cant due to the large standard errors.

The accident database also provided driver classi?cation by residence.The level 1,23,or 4stand for the residence of drivers living in the local county,elsewhere in the state of Florida,other state,or other country.Normally,local drivers can bene?t from their driving experiences to the familiar traf-?c environments so as to avoid the adverse traf?c conditions.As shown in Fig.4,it appears there is a clear trend which indi-cates that as the degree of drivers’familiarity with the driving environment decreases,they are more likely to be involved in rear-end accidents.Especially for the foreign drivers,their risk rate is around 79%higher than the county residents.The results also showed signi?cant gender difference in rear-end accidents (P -value =0.002).According to the model,the male risk of involvement in accidents could be 19%higher than female drivers.The analysis is consistent with previous studies:gender is signi?cant in predicting involve-ment in accidents (Ulleberg,2001).Storie (1977)found that whereas men were involved more often than women in acci-dents caused by speeding and driving under the in?uence of alcohol,women were more frequently involved in accidents caused by judgment errors.

In addition to driver characteristics,vehicle type was also found to signi?cantly affect accident propensity at a 0.0001level of signi?cance.There are total 13types of vehicles classi?ed by accident vehicles in the database.Four types of vehicles are focused on in the study including (1)passenger car,(2)passenger van,(3)pickup/light truck,and (4)large size https://www.wendangku.net/doc/c75764794.html,rge size vehicle is combined with medium

X.Yan et al./Accident Analysis and Prevention 37(2005)983–995991

truck,heavy truck,truck-tractor,motor home,and bus,since the sample size of these types of vehicles is relatively small.The other vehicles such as motorcycle,moped,terrain vehi-cle,and train were excluded from further analysis.Results show that there are 69.38%automobiles,8.06%passenger vans,18.5%pickup/light trucks,and 4.06%large size vehi-cles involved in rear-end accidents as striking vehicle.As shown in Fig.4,the RAIRs for passenger van,pickup/light truck,and large size vehicle are higher than passenger car.In the logistic regression model,adjusting other factors,it shows a signi?cant trend that the accident risk is increasing with increment of vehicle size (see Table 1).The odds of large size vehicles could be 25%higher than automobiles.This result is consistent with the conclusion drawn by the Fed-eral Motor Carrier Safety Administration (FMCSA,2004)that trucks strike other vehicles in the rear much more often than they are struck by other https://www.wendangku.net/doc/c75764794.html,rge size vehicles are heavier than automobiles in the traf?c stream.Relatively,they are less maneuverable and take longer to stop.Bus or truck drivers especially sit up much higher than passenger vehicle drivers and can see much further down the road,but they may have dif?cultly responding to brake light of the leading car with a small headway.3.2.Interaction effects

After con?rming the main effect model,the next regres-sion analysis is to explore the possible signi?cant interactions between these factors.It is found that there are three inter-

action factors associated with rear-end accidents including:driver age and number of lanes (P -value =0.008),driver age and accident time (P -value =0.002),and speed limit and num-ber of lanes (P -value =0.000).Based on 0.01signi?cance level,driver age and gender is not statistically signi?cant,but its P -value is very small (0.012).Since they are always important to the traf?c safety research,they are also included in the discussion of this study.

Fig.5illustrates the effects of these interaction factors by relative accident involvement ratios.The results show that the driver gender difference is mainly present in the younger and middle groups,and after 55years of age,the gender dif-ference is not apparent.Beside the oldest group,male drivers younger than 55years have the larger rear-end propensity compared to female drivers.The driver age effect is also in?uenced by driving environments.The ?gure shows that groups older than 55years are more sensitive to the larger number of lanes than younger groups and they show higher accident involvement risk on 4or 6-lane highways than 2-lane ones.Moreover,the results con?rm that accident time effects are difference in age groups.For youngest and old-est groups,rear-end accidents more likely to happen during daytime than night;for middle age groups,the difference is not very apparent.Generally,older road users react more slowly to events that are not expected and take signi?cantly longer to make decisions than younger road users.This dif-ference becomes more exaggerated in complex situations at intersections with larger number of lanes and higher traf?c

volume.

Fig.5.Effects of interaction factors by relative accident involvement ratios.

992X.Yan et al./Accident Analysis and Prevention37(2005)983–995

Additionally,the results show interaction effect between speed limit and number of lanes.For lower speed limits (25–35mph),the rear-end risk of2-lane highway is larger than those with6lanes;however,for higher speed limits (45–55mph),the rear-end risk of2-lane highway is lower than those with6lanes,presumably because the intersec-tions located on2-lane highways with lower speed limit are more likely to be located in urban areas with higher traf?c volume and those with higher speed limit are more likely to be in rural areas with lower traf?c volume compared to6-lane highways.

4.Modeling results for struck drivers/vehicles

In most two-vehicle rear-end cases,the leading vehicles were stopping or staying at signalized intersections and had no improper action.However,their sudden stops due to signal change or emergent situation with unexpectedly higher decel-eration rates may contribute to rear-end accidents.Therefore, it is possible to?nd some signi?cant patterns for the struck vehicle/driver associated with the accident risk.The term relative accident involvement ratio and logistic regression method can still be applied to perform this analysis.Then in Eq.(1),the D1i is the number of struck drivers of type i in rear-end accidents;V1i is the number of struck vehicles of type i in rear-end accidents;but the de?nitions for D2i and V2i keep unchanged.

Based on the results of logistic regression,there are four factors found at a0.0001level of signi?cance,including vehi-cle type,driver age,driver residence,and gender.Table2lists the model estimation and odds ratios properly adjusting other factors and Fig.6shows the relative accident involvement ratios by struck driver characteristics and vehicle types.

For the leading driver age,the?gure shows an opposite-U-shape trend:the older group and younger group have similar and smaller accident propensity,but middle age group has larger propensity.Drivers of46–55years of age are most likely to be involved in rear-end accidents as struck drivers, whose accident risk could be74%higher than the younger drivers less than26years.The presumable reason is that for middle age drivers,better driving experience and physical performance may be helpful to detect a potential con?ict ear-lier than younger and older drivers,but their emergent stop could contribute to their struck role in a rear-end accident. Furthermore,it is necessary to point out that for the quasi-induced exposure,the native weakness of the technique to obtain exposure from the database is that it does not account for the fact that skilled driving can be used to reduce not-at-fault accident involvements(Preusser et al.,1998).For the middle age drivers,they may be under-represented in the exposure group(victims in non-rear-end accidents)due to their better driving performance,but as struck drivers in rear-end accidents,they have no advantage to avoid the accidents compared to other groups.Therefore,their rear-end involve-ment ratio as struck drivers is possibly overestimated.The results also show that the female divers are more likely to be in the struck role as compared to the male drivers(P-value<0.0001),presumably because the female drivers are more likely to brake when faced with critical traf?c events (for example,yellow signal)than the male drivers.

As similar as striking drivers,if drivers are unfamiliar with the driving environment,they are more likely to be involved in the accidents as struck drivers.As shown in Table2,the struck risk of the non-local drivers who lived in Florida area, other state,or other country could be52%,99%,and126% higher than the local drivers.The non-local drivers have more dif?culties to?nd their destination and possibly have abnor-

Table2

Model estimation and odds ratios for signi?cant independent variables for struck role

Parameter Coef?cient estimate Odds ratio95%Wald con?dence limits Waldχ2P-value Intercept?1.2541250.918<0.0001

Driver age184.1119<0.0001 >75vs.<260.148 1.1590.963 1.3952.4250.119 66–75vs.<260.416 1.516 1.323 1.73735.851<0.0001 56–65vs.<260.497 1.643 1.475 1.83280.674<0.0001 46–55vs.<260.556 1.744 1.592 1.912142.112<0.0001 36–45vs.<260.422 1.525 1.403 1.65997.216<0.0001 26–35vs.<260.312 1.365 1.255 1.48552.811<0.0001

Residence code166.8871<0.0001 4vs.10.813 2.256 1.522 3.34316.410<0.0001 3vs.10.687 1.988 1.698 2.32773.091<0.0001 2vs.10.418 1.518 1.394 1.65491.102<0.0001 Gender91.6433<0.0001 Female/male0.280 1.323 1.249 1.40191.643<0.0001

Type of vehicle36.6844<0.0001 Large vehicle vs.car?0.3230.7240.6150.85314.9630.000 Light truck vs.car0.163 1.177 1.09 1.27217.052<0.0001 Van vs.car?0.0060.9940.897 1.1010.0150.902

X.Yan et al./Accident Analysis and Prevention37(2005)983–995

993

Fig.6.Relative accident involvement ratios by struck driver characteristics and vehicle types.

mal behaviors at intersections,such as improper changing lane or sudden stop for right/left-turn,which may contribute to rear-end accidents.

The effect of struck vehicle type supports earlier?ndings. The light trucks are18%more likely to be in the struck role as compared to the passenger car(P-value<0.0001).Abdel-Aty and Abdelwahab(2004)explained that light truck vehicles obscure drivers’visibility of other passenger cars.They may prevent drivers in cars behind them from being aware of traf?c situation ahead,therefore more susceptible to collide with the light trucks in case of sudden application of breaks.More-over,the results indicate that the risk of large size vehicle could be28%lower than passenger cars.Normally,large size vehicles are slowly and noticeable in the traf?c stream, their deceleration rate is lower than automobiles,and those aggressive drivers dislike following a truck or bus.So,the large size vehicle(trucks or buses)are less likely to be in the struck role.

5.Discussions on the effect of traf?c volume on

rear-end accidents

Since the DHSMV database only recorded the average daily traf?c data for those state highway intersections,the full traf?c volume data is not available in the database.To check the effect of traf?c volume on the model results,the model was recalibrated based on the available data from Dade County(in Florida)which include1248observations that recorded the average daily traf?c(ADT)on the major road at the intersections where the accidents occurred.

Comparing the rebuilt model results of multiple logistic regression without ADT to that with ADT,the coef?cients of most independent variables did not show signi?cant changes. However,it was found that the number of lanes is apparently confounded with the ADT.Normally,only the roadways with larger daily traf?c have more lanes to handle heavy traf?c,so the number of lanes and ADT are positively correlated.This analysis indicated that either of them can be used to?t the model but they may not appear in the same model together.

According to univariate logistic regression analyses,the ADT,treated as continuous variable,is signi?cantly associ-ated with the rear-end risk(P=0.0018).Generally,higher traf?c volumes result in smaller gaps between vehicles and consequently higher rear-end probability.The odds ratio esti-mator for ADT is1.12and its con?dence interval at the 0.95signi?cance level is[1.043,1.203];without considering other factors,drivers on the highway with the higher

average Fig.7.Relative accident involvement ratios by major road ADT.

994X.Yan et al./Accident Analysis and Prevention37(2005)983–995

daily traf?c?ow might be12%more likely to be involved in rear-end accidents compared to those with the average daily traf?c?ow that is10000vehicle/day lower.Segmenting ADT by20000-vehicle/day interval,as a categorical variable,?ve ADT levels include ADT less than20001,20001–40000, 40001–60000,60001–80000,and more https://www.wendangku.net/doc/c75764794.html,paring relative accident involvement ratios,the graph for ADT illustrated in Fig.7shows a clear trend that the rear-end accident risk increases as the traf?c volume increases.

6.Conclusions and suggestions

Because signalized intersections are accident-prone areas for rear-end accidents,in order to develop effective rear-end accident countermeasures,it is important to understand the driver characteristics,vehicle types,and road environment features of rear-end https://www.wendangku.net/doc/c75764794.html,ing the2001Florida traf?c accident database,this study investigated the overall char-acteristics of rear-end accidents at signalized intersections based on the quasi-induced exposure concept and logistic regression method.

The analysis showed that the risk of rear-end accidents for 6-lane highways is higher than2-lane and4-lane highways; the rear-end accidents are more likely to happen at divided highways than undivided highways.However,the crash trend for higher number of lanes and divided highways may be con-founded by the traf?c volume effect because the highways with high heavy traf?c tend to have more lanes and be sepa-rated by medians;the results con?rmed that the higher traf?c volume contributes to the higher rear-end risk.It was found that the relative accident involvement ratio for night is appar-ently lower than daytime;compared to a dry road surface, the wet and slippery road surface could greatly contribute to rear-end accidents;furthermore,as the highway charac-ter is more complex,the rear-end accident is more likely to occur.When horizontal curve and grade are present at the same time,the rear-end risk could be twice as that for nor-mal straight highways.Moreover,the analysis showed a clear trend that as the speed limit increases,the risk of the rear-end accidents increases,especially when the speed limit is higher than40mph.

Corresponding to the adverse road environment factors, appropriate engineering countermeasures need to be consid-ered to reduce the rear-end crash rate.From the perspective of the intersection design and operation,improvement of con?g-uration conditions(geometrics)may contribute to reducing reaction and stopping times,eliminating motorist confusion, and improving visibility of traf?c control devices.It is sug-gested to avoid designing intersections located on a horizontal curve and vertical curve if possible.For an existing intersec-tion with a curve or up/down grade,suf?cient sight distance not only to the signal head but also to the other approaches should be satis?ed,in order that the drivers going-through the intersection can detect potential con?icting vehicles in time.In addition,motorist information countermeasures may provide advance information to the driver about the signal ahead,such as advanced warning signs installed upstream at the intersection.That may reduce sudden-stop behaviors because of insuf?cient reaction time due to a signal change (dilemma zone).In the area where drivers frequently drive in the rainy weather condition,the drainage design should ensure that the rainwater is removed from the intersection surface in time.If intersections with the50or55mph speed limit have been identi?ed to have higher rear-end crash rates, reducing the speed limit to40or45mph may ef?ciently con-tribute to the lower accident rate.

The modeling results indicated that the driver factors as the striking role have different effects on the rear-end risk compared to the struck role.The struck drivers are more likely to be middle-age and female drivers,while the strik-ing drivers with relatively larger accident propensity tend to be male,younger(<26),or older drivers(>75).For strik-ing drivers,the accident propensity appeared a decreasing trend with increasing age until the age of56–65,and then increase to a higher accident involvement for the age group older than75.In general,the middle age drivers are usually under-involved in accidents due to better physical conditions and more driver experiences compared to the younger and older drivers.However,as the struck role in rear-end acci-dents,the middle age drivers have no advantage to void the accidents and even their quick reaction to the hazard in front of them might contribute to the struck role in a rear-end acci-dent.Based on vehicle type,the rear-end accident risk for the striking role is increasing with increment of vehicle size, but compared to the passenger car,the large trucks or buses are less likely to be in the struck role and light trucks are more likely to be struck.For the driver residence,the non-local drivers always have higher accident risk compared to the local drivers,whatever as the striking role or struck role in a rear-end accident.

Corresponding to the higher risk driver populations,gen-erally,younger drivers have less driving experience and tend to drive in situations conditions that increase their risk.An education program to emphasize the rear-end risk at signal-ized intersections is strongly suggested for the younger group. According to a precious study(Eby and Molnar,1998),some of these young drivers may engage in risky driving behaviors because they are risk taking(i.e.they perceive the risk and do it anyway)and others engage in this behavior because they are risk ignorant(i.e.they do not perceive the risk in their behavior).Those who are risk ignorant may bene?t from the education countermeasure and become safer drivers by hav-ing a better understanding of the risk inherent in their driving behaviors.For the older drivers,their higher rear-end risk may result from deteriorating physical conditions,decreasing judgment ability,and vision problem.It is necessary to make a further analysis of the criteria of driving license issuance for the older drivers related to driver age and health condi-tion.In the other hand,properly designed and implemented, the avoidance warning systems(CAWS)would notify drivers

X.Yan et al./Accident Analysis and Prevention37(2005)983–995995

about potential dangers from roadway departures and other automobiles,particularly in rear-end collisions(James and Sarah,2003).As a sort of in-vehicle technologies,CAWS may help reduce the crash risk for those drivers with weak driving abilities;CAWS may also effectively help bus and truck drivers to perceive the brake behavior of the leading vehicle since they may have dif?cultly responding to brake light of the leading car with a small headway because of a higher eyesight position.Moreover,the analysis con?rmed the substantial affect of alcohol/drug use on driver’s safety. Even drivers who had been drinking under legal alcohol use level could be9.58times more likely involved in a rear-end accident than non-drinking drivers,which strongly supports that the threshold of legal alcohol use level on the road should be reduced.If the data of illegal blood alcohol concentration (BAC)are available in traf?c accident database,the fur-ther quantitative analysis of relationship between BAC and accident risk(not limited in rear-end accident)is strongly suggested.

Lastly,it is worth mentioning that the analyses in this paper pertain to identifying the signi?cant factors and comparing relative accident involvements between different traf?c con-ditions but not to predicting rear-end occurrence rate.The accident propensity analyses in this paper may provide a bet-ter understanding of the rear-end risks and more information to seek effective accident countermeasures.

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如何写先进个人事迹

如何写先进个人事迹篇一：如何写先进事迹材料如何写先进事迹材料一般有两种情况：一是先进个人，如先进工作者、优秀党员、劳动模范等；一是先进集体或先进单位，如先进党支部、先进车间或科室，抗洪抢险先进集体等。无论是先进个人还是先进集体，他们的先进事迹，内容各不相同，因此要整理材料，不可能固定一个模式。一般来说，可大体从以下方面进行整理。 (1)要拟定恰当的标题。先进事迹材料的标题，有两部分内容必不可少，一是要写明先进个人姓名和先进集体的名称，使人一眼便看出是哪个人或哪个集体、哪个单位的先进事迹。二是要概括标明先进事迹的主要内容或材料的用途。例如《王鬃同志端正党风的先进事迹》、《关于评选张鬃同志为全国新长征突击手的材料》、《关于评选鬃处党支部为省直机关先进党支部的材料》等。 (2)正文。正文的开头，要写明先进个人的简要情况，包括：姓名、性别、年龄、工作单位、职务、是否党团员等。此外，还要写明有关单位准备授予他(她)什么荣誉称号，或给予哪种形式的奖励。对先进集体、先进单位，要根据其先进事迹的主要内容，寥寥数语即应写明，不须用更多的文字。然后，要写先进人物或先进集体的主要事迹。这部分内容是全篇材料

的主体，要下功夫写好，关键是要写得既具体，又不繁琐；既概括，又不抽象；既生动形象，又很实在。总之，就是要写得很有说服力，让人一看便可得出够得上先进的结论。比如，写一位端正党风先进人物的事迹材料，就应当着重写这位同志在发扬党的优良传统和作风方面都有哪些突出的先进事迹，在同不正之风作斗争中有哪些突出的表现。又如，写一位搞改革的先进人物的事迹材料，就应当着力写这位同志是从哪些方面进行改革的，已经取得了哪些突出的成果，特别是改革前后的．经济效益或社会效益都有了哪些明显的变化。在写这些先进事迹时，无论是先进个人还是先进集体的，都应选取那些具有代表性的具体事实来说明。必要时还可运用一些数字，以增强先进事迹材料的说服力。为了使先进事迹的内容眉目清晰、更加条理化，在文字表述上还可分成若干自然段来写，特别是对那些涉及较多方面的先进事迹材料，采取这种写法尤为必要。如果将各方面内容材料都混在一起，是不易写明的。在分段写时，最好在每段之前根据内容标出小标题，或以明确的观点加以概括，使标题或观点与内容浑然一体。最后，是先进事迹材料的署名。一般说，整理先进个人和先进集体的材料，都是以本级组织或上级组织的名义；是代表组织意见的。因此，材料整理完后，应经有关领导同志审定，以相应一级组织正式署名上报。这类材料不宜以个人名义署名。写作典型经验材料-般包括以下几部分： (1)标题。有多种写法，通常是把典型经验高度集中地概括出来，一

关于时间管理的英语作文 manage time

How to manage time Time treats everyone fairly that we all have 24 hours per day. Some of us are capable to make good use of time while some find it hard to do so. Knowing how to manage them is essential in our life. Take myself as an example. When I was still a senior high student, I was fully occupied with my studies. Therefore, I hardly had spare time to have fun or develop my hobbies. But things were changed after I entered university. I got more free time than ever before. But ironically, I found it difficult to adjust this kind of brand-new school life and there was no such thing called time management on my mind. It was not until the second year that I realized I had wasted my whole year doing nothing. I could have taken up a Spanish course. I could have read ten books about the stories of successful people. I could have applied for a part-time job to earn some working experiences. B ut I didn’t spend my time on any of them. I felt guilty whenever I looked back to the moments that I just sat around doing nothing. It’s said that better late than never. At least I had the consciousness that I should stop wasting my time. Making up my mind is the first step for me to learn to manage my time. Next, I wrote a timetable, setting some targets that I had to finish each day. For instance, on Monday, I must read two pieces of news and review all the lessons that I have learnt on that day. By the way, the daily plan that I made was flexible. If there’s something unexpected that I had to finish first, I would reduce the time for resting or delay my target to the next day. Also, I would try to achieve those targets ahead of time that I planed so that I could reserve some more time to relax or do something out of my plan. At the beginning, it’s kind of difficult to s tick to the plan. But as time went by, having a plan for time in advance became a part of my life. At the same time, I gradually became a well-organized person. Now I’ve grasped the time management skill and I’m able to use my time efficiently.

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三、生活朴素，乐于助人平时重视与同事间的关系，主动与同事打成一片，善于发现他人的难处，及时妥善地给予帮助。在其它同志遇到困难时，积极主动伸出援助之手，尽自己最大努力帮助有需要的人。养成了批评与自我批评的优良作风，时常反省自己的工作，学习和生活。不但能够真诚的指出同事的缺点，也能够正确的对待他人的批评和意见。面对误解，总是一笑而过，不会因为误解和批评而耿耿于怀，而是诚恳的接受，从而不断的提高自己。在生活上勤俭节朴，不铺张浪费。身为一名老党员，我感到责任重大，应该做出表率，挤出更多的时间来投入到**党总支的工作中，不找借口，不讲条件，不畏困难，将总支建设摆在更重要的位置，解开工作中的思想疙瘩，为攻坚克难铺平道路，以支部为纽带，像战友一样团结，像家庭一样维系，像亲人一样关怀，践行入党誓言。把握机遇，迎接挑战，不负初心。

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