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A logistic model of the effects of roadway, environmental, vehicle, crash and driver characteristics on hit-and-run crashes
Accident Analysis and Prevention 40 (2008) 1330–1336
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A logistic model of the effects of roadway, environmental, vehicle, crash and driver characteristics on hit-and-run crashes Richard Tay a,∗ , Shakil Mohammad Rifaat a,1 , Hoong Chor Chin b,2 a b
Department of Civil Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada Department of Civil Engineering, National University of Singapore, 10, Kent Ridge Crescent, Singapore 119260, Singapore
a r t i c l e
i n f o
Article history: Received 10 February 2007 Received in revised form 31 January 2008 Accepted 8 February 2008 Keywords: Hit-and-run Logistic model Singapore
a b s t r a c t Leaving the scene of a crash without reporting it is an offence in most countries and many studies have been devoted to improving ways to identify hit-and-run vehicles and the drivers involved. However, relatively few studies have been conducted on identifying factors that contribute to the decision to run after the crash. This study identifies the factors that are associated with the likelihood of hit-and-run crashes including driver characteristics, vehicle types, crash characteristics, roadway features and environmental characteristics. Using a logistic regression model to delineate hit-and-run crashes from nonhit-and-run crashes, this study found that drivers were more likely to run when crashes occurred at night, on a bridge and flyover, bend, straight road and near shop houses; involved two vehicles, two-wheel vehicles and vehicles from neighboring countries; and when the driver was a male, minority, and aged between 45 and 69. On the other hand, collisions involving right turn and U-turn maneuvers, and occurring on undivided roads were less likely to be hit-and-run crashes. © 2008 Elsevier Ltd. All rights reserved.
1. Introduction Road crashes are a leading cause of death and injuries in many countries and extract a high cost on society. In the city-state of Singapore, for example, there were 9896 injuries in 2006 resulting from motor vehicle collisions, of which 190 were fatal (Singapore Police Force, 2007a). Among the different types of crashes, hit-andrun crashes are of interest to researchers because they are not only unethical acts but punishable offences as well. Also, leaving the victims at the crash scene delay crash notification and may result in an increase in the severity of the crash. Moreover, Lewis (1936) argued that victims of hit-and-run crashes were the most hapless group because of the reduced chance of getting compensation. Hence, research aiming to improve the identification of hitand-run vehicles and the drivers involved has been an area of interest in diversified fields such as medical, forensic, legal, insurance and engineering. Many studies in the field of medical science, for example, examined the types of injury sustained by victims in hit-and-run crashes to identify the types of vehicles involved. For example, Teresinski and Madro (2001) evaluated knee joint
∗ Corresponding author. Tel.: +1 403 220 4725; fax: +1 403 282 7026. E-mail addresses: [email protected] (R. Tay), [email protected] (S.M. Rifaat), [email protected] (H.C. Chin). 1 Tel.: +1 403 220 5970; fax: +1 403 282 7026. 2 Tel.: +65 6874 2550; fax: +65 6779 1635. 0001-4575/$ – see front matter © 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2008.02.003
postmortem examinations of fatal pedestrian victims of traffic accidents to deduce the location as well as identify the type of vehicles involved. Karger et al. (2001) examined different types of fractures in the victims such as wedge-shaped bone fractures and fractures in the cervical and lumber spine to deduce the nature and the sequences of hit-and-run crashes. The identification of vehicles involved in hit-and-run crashes is also an important task faced by forensic science laboratories. Debris which include headlamps, sidelight fragments, wheel track marks and paint fragments recovered at the accident location contribute to identifying the make and model of the offending vehicle. Several methods have been proposed to measure the color of retrieved paint fragments from crash scenes or from the clothing of victims to identify the vehicles involved in hit-and-run crashes (Cousins et al., 1989; Taylor et al., 1989; Locke et al., 1982, 1987, 1988). Most of the studies discussed previously in various fields proposed different methods which would help to identify the fleeing vehicles in hit-and-run crashes. However, very few studies have explored the situations or circumstances under which hit-and-run crashes occurred. In one of the few studies that examined this issue, Solnick and Hemenway (1995) explored how victim’s characteristics, driver’s characteristics and circumstances of collision affected the hit-and-run choice in fatal pedestrian crashes. Their results showed that a driver was less likely to run when the victim was a child or an elderly pedestrian, if the crash occurred in the southern part of the America or in daylight, the driver was elderly, and the car driven was less than 5 years old. On the other hand, a driver
R. Tay et al. / Accident Analysis and Prevention 40 (2008) 1330–1336
was more likely to run when the victim was between 16 and 25 years old, the crash occurred in an urban area, on weekends, or in summer, and the driver was male or had no valid driver license, previous DWI convictions, positive or unknown BAC. In addition, Solnick and Hemenway (1994) found that 19% of all road crashes in the United States in 1989–1990 were hit-and-run cases caused by drunk driving and the hit-and-run motorists were disproportionately young and male. Although these studies gave some insight about the factors influencing drivers’ decision to leave the crash scenes, they were restricted to only pedestrian crashes. In this study, a logistic model will be estimated to examine the influence of road features, vehicle attributes, environmental factors, crash characteristics and driver particulars on hit-and-run crashes in Singapore. This study will contribute to the literature in several ways. First, it will extend the study by Solnick and Hemenway (1995) by examining all hit-and-run crashes instead of pedestrian involved crashes alone. Second, it will examine several additional influences including the types of vehicles involved in the crashes, the vehicles’ countries of origin, types of crashes, vehicle maneuvers, ethnicities of drivers, presence of surveillance cameras and other location information like residential housing, retail or central business district areas, which are expected to be very relevant. Last, it will examine hit-and-run crashes in an Asian country with a different social and cultural environment. Previous safety related studies in Singapore had focused mainly on examining intersection crashes (Chin and Quddus, 2003; Rifaat and Chin, 2007; Kumara and Chin, 2006; Mitra et al., 2002; Tay and Rifaat, 2007), motorcycle crashes (Quddus et al., 2002; Yuan, 2000), and red light running behaviors (Lum and Wong, 2002, 2003). No publicly available study has been found that deals with hit-and-run crashes in Singapore. Singapore is a city-state located on a small island (700 km2 ) at the southern tip of the Malayan Peninsula in Southeast Asia. It has a total population of about 4.48 million, with a resident population of 3.61 million (Statistics Singapore, 2007).3 The actual composition of the population is quite fluid due to high percentage of foreign workers. The resident population comprises of four major ethnic groups: Chinese (75.2%), Malay (13.6%), Indian (8.8%) and Others (2.4%).4 The vast majority of the population stays in high-rise apartments due to the shortage of land. Singapore has one of the highest population densities in the world and is almost 100% urbanized. Therefore, although it has a very good quality road system and fairly high gross domestic product per capita (S$46,832),5 very stringent vehicle ownership and use policies are implemented to mitigate traffic congestion (McCarthy and Tay, 1993; Tay, 1996). Nevertheless, passenger cars (private, rental and taxis) still comprise the majority (62.3%) of the 799,373 registered vehicles, followed by goods vehicles (18.1%), motorcycles and scooters (17.9%) and buses (1.8%). 2. Analytical framework The decision of a driver to stay and report the crash or to leave the scene without reporting the collision can be analyzed using the standard decision analysis framework based on the expected costs
3 Resident population refers to citizens and those who are granted permanent resident status. Non-residents comprise mainly of people on work permit, student visas or long-term social visit pass. 4 The majority of “Others” consists of Eurasian (Euro-Asian) which includes mostly migrants from Western Europe, Australia and New Zealand as well as their descendants. 5 Exchange rate: US$1 = S$1.59.
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and benefits of the choices. There are few uncertainties associated with the outcomes of reporting a crash. The costs of the reporting crash consist mainly of an increase in the insurance premium for reporting the crash if the driver is at fault and potential penalties associated with any illegal driving behaviors. Theoretically, the higher the expected cost of the crash, the less likely it is for the driver to report crash which implies that the likelihood of a hit-andrun crash is expected to increase with the severity of the crash, all else held constant. Also, factors that reduce the likelihood of blame or fault will also increase the likelihood of reporting crashes. Such mitigating factors include wet weather, sharp turn and blind corner as well as driver’s actions like stopping/slowing and making a turn. Since the expected costs and benefits associated with reporting a crash is relatively straightforward, the decision to report the crash or to simply run will largely depend on the expected cost of not reporting the crash or choosing the hit-and-run option. The expected costs of leaving the scene without reporting a crash consist of two possible outcomes which depend on whether the driver was subsequently apprehended. If the driver is not caught, then his or her cost is lower than the cost associated with reporting the crash. On the other hand, if the driver is caught subsequently, his or her cost is assumed to be higher than the cost associated with reporting the crash. In Singapore, the failure to stop after a traffic accident is a punishable offence which carries a maximum fine of S$1000 and/or a prison term of up to 3 months. Removing an accident vehicle without police authorization also carries a maximum fine of S$1000 and/or a prison term of 3 months. In addition, the failure to render assistance after a fatal accident carries a maximum fine of $3000 and/or a prison term of up to 12 months (Singapore Police Force, 2007b). These charges are likely to be added to any other charges of dangerous or illegal driving behaviors. The expected cost of choosing to run is assumed to depend significantly on the perceived likelihood of being caught subsequently and the risk-taking propensity of the driver. For the highly risk adverse, the uncertainties involved in hit-and-run is not likely to be an attractive option but it may be more attractive to the high risk taking drivers. For most drivers who are relatively risk neutral, however, the perceived likelihood of apprehension will play a significant part in influencing their choices. To this end, factors that may reduce the chances of apprehension will increase the likelihood of a hit-and-run crash. Examples of these factors include crashes during night time, crashes with fixed objects or parked vehicles and crashes involving a foreign vehicle. Similarly, the likelihood of a hit-and-run crash is expected to be lower when the crash involves a bus or occurs in a residential neighborhood due to likely presence of witnesses. It should be noted that drivers are assumed to act on their perceived likelihood of being apprehended and not on the actual probability of apprehension which is extremely high in Singapore. In our analysis, all cases included the relevant drivers’ information which meant that the drivers had been caught at a later stage.6 It is worth noting that very few hit-and-run drivers in Singapore escape detection. This extremely high detection rate is possible because Singapore is a small island state that is highly regulated and regimented, especially in the socio-political arenas, and its surveillance and vehicle monitoring system are very good. In addition, Singapore is almost 100% urbanized with a very high population density. The likelihood of having some witnesses to the accident is very high. The low crime rate in Singapore also enables the police to actively pursue any hit-and-run cases. Moreover, in a well-regulated vehicle repair industry, the ease of tracing a damaged vehicle among repair
6 Alternatively, it could be due to the way crashes are reported or recorded. Therefore, care should be exercised in interpreting the results.
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workshops also makes police investigation into a hit-and-run case relatively successful. 3. Methodology In our study, the response variable, hit-and-run or non-hitand-run crash, is a binary or dichotomous variable. Therefore, the logistic regression is a suitable technique to use because it is developed to predict a binary dependent variable as a function of predictor variables. The logistic regression model is widely used in road safety studies where the dependent variable is binary (Valent et al., 2002; Jones and Whitfield, 1988; Lui et al., 1988; Shibata and Fukuda, 1994; Zhang et al., 2000; Simoncic, 2001). In this model, the logit is the natural logarithm of the odds or the likelihood ratio that the dependent variable is 1 (hit-and-run crash) as opposed to 0 (non-hit-and-run crash). The probability P of a hit-and-run is given by Y = logit(P) = ln
P 1−P
= ˇX
(1)
where ˇ is a vector of parameters to be estimated and X is a vector of independent variables. When an independent variable xi increases by one unit, with all other factors remaining constant, the odds increase by a factor exp(ˇi ) which is called the odds ratio (OR) and ranges from 0 to positive infinity. It indicates the relative amount by which the odds of the outcome (hit-and-run) increase (OR > 1) or decrease (OR < 1) when the value of the corresponding independent variable increases by one unit. In order to develop a logistic regression model that identifies the factors affecting hit-and-run, a crash data set is needed that would include the environmental factors, vehicle attributes, crash characteristics, roadway features and drivers’ particulars which are correlated with the collisions. Crash data for this study were obtained from the National Road Accident database maintained by the Traffic Police in Singapore. The information given in the collision record can be classified into three types: general collision information, vehicle and driver-related information and pedestrian information, each of which contains the description of different factors involved in the collisions. Traffic collision data from the year 1992 to 2002 were used to develop the model in our study. During the 11 years, 67,228 crashes occurred in Singapore. Among them, 1.83% was recorded as hit-and-run crashes. An important task in developing the model is the selection of appropriate factors from victim, vehicle, crash, road and environmental characteristics that could reasonably be expected to influence hit-and-run crashes. Two approaches were used to select these factors. The first approach used was to review similar research where these factors had been examined. The second option was to focus on local context to determine other variables that might have some influence on hit-and-run crashes. Following these considerations, 25 factors were selected for further investigation. Moreover, in most cases, several mutually exclusive categorical independent variables were formed from each of the factors in order to facilitate the estimation and interpretation of the odds ratios. Several factors were dropped after correlation tests were performed between these variables. For example, the type of road and speed limit were found to be strongly correlated. Since the type of road was a better indicator in predicting hit-and-run crashes than speed limit, it was kept in the model. Some other factors were also tested and excluded in the final model reported because they were found to be statistically insignificant. Nine factors were finally dropped from the model after the preliminary analyses. These variables included day of week, speed limit, central business district area, electronic road pricing hours if crash occurred in the central business district, the presence of surveillance camera, number of
Table 1 Descriptive statistics of variables Explanatory variables
Description of variables
Mean
S.D.
(1) Time trend
6 monthly increments (January 1992 = 1 to December 2002 = 22)
12.081
6.458
Day time = 1; otherwise = 0 Night time = 1; otherwise = 0
0.625 0.375
0.484 0.484
School = 1; otherwise = 0 Public housing estate = 1; otherwise = 0 Private residential area = 1; otherwise = 0 Factory = 1; otherwise = 0 Shopping complex = 1; otherwise = 0 Shop house = 1; otherwise = 0 MRT Station = 1; otherwise = 0 Occurred at other locations = 1; otherwise = 0
0.014 0.257
0.119 0.437
0.077
0.267
0.058 0.036
0.233 0.187
0.044 0.009 0.504
0.206 0.094 0.499
(4) Type of vehicle Car Bicycle Truck Bus Motorcycle Van/pickup Others (5) Foreign vehicle
Car = 1; otherwise = 0 Bicycle = 1; otherwise = 0 Truck = 1; otherwise = 0 Bus = 1; otherwise = 0 Motorcycle = 1; otherwise = 0 Van/pickup = 1; otherwise = 0 Other vehicle = 1; otherwise = 0 Foreign vehicle = 1; otherwise = 0
0.260 0.017 0.056 0.024 0.601 0.037 0.005 0.075
0.439 0.131 0.231 0.153 0.490 0.188 0.068 0.264
(6) Type of road One way street Undivided road Divided road Expressway
One way road = 1; otherwise = 0 Undivided road = 1; otherwise = 0 Divided road = 1; otherwise = 0 Expressway = 1; otherwise = 0
0.115 0.251 0.472 0.163
0.318 0.433 0.499 0.370
Straight road = 1; otherwise = 0 Curve/bend = 1; otherwise = 0 Slip road = 1; otherwise = 0 Intersection = 1; otherwise = 0 Bridge and/or flyover = 1; otherwise = 0 Other location = 1; otherwise = 0
0.483 0.049 0.036 0.381 0.016
0.499 0.217 0.187 0.486 0.126
0.034
0.181
Road surface is dry = 1; otherwise = 0 Wet surface = 1; otherwise = 0 Sandy/oily surface = 1; otherwise = 0
0.860
0.347
0.132 0.008
0.338 0.088
Normal roadway = 1; otherwise = 0 Merging lane = 1; otherwise = 0 Narrow lane = 1; otherwise = 0 Sharp turn = 1; otherwise = 0 Blind corner = 1; otherwise = 0
0.973
0.163
0.007 0.007 0.008 0.005
0.084 0.081 0.090 0.073
(10) Age of driver (years) <25 25–44 45–69 70 and above
Age < 25 = 1; otherwise = 0 25 ≤ Age ≤ 44 = 1; otherwise = 0 45 ≤ Age ≤ 69 = 1; otherwise = 0 70 ≤ Age = 1; otherwise = 0
0.205 0.534 0.254 0.007
0.404 0.499 0.435 0.085
(11) Offending (12) Driver gender
Offending party = 1; otherwise = 0 Male = 1; female = 0
0.438 0.930
0.496 0.255
(13) Driver race Chinese Malay Indian Eurasian Others
Chinese = 1; otherwise = 0 Malay = 1; otherwise = 0 Indian = 1; otherwise = 0 Eurasian = 1; otherwise = 0 Other race = 1; otherwise = 0
0.743 0.160 0.070 0.005 0.022
0.437 0.367 0.256 0.070 0.145
Single vehicle crash = 1; otherwise = 0 Crash between moving vehicles = 1; otherwise = 0
0.091
0.288
0.772
0.419
(2) Time of day Day time Night time 3. Area of Occurrence School Public housing estate Private residential area Factory Shopping complex Shop house MRT station Others
(7) Type of location Straight Curve/bend Slip road Intersection Bridge/flyover Others (8) Road surface Dry Wet Sandy/oily (9) Special road feature Normal roadway Merging Narrow Sharp turn Blind corner
(14) Type of collision Single vehicle Moving vehicles
R. Tay et al. / Accident Analysis and Prevention 40 (2008) 1330–1336 Table 1 (Continued ) Explanatory variables Vehicle and object Vehicle and pedestrian (15) Vehicle maneuver Driving ahead Turning right Stopping/slowing down Turning left Changing lane U-turn Others (16) Crash severity Minor Serious Fatal
Description of variables
Mean
S.D.
Vehicle and stationary object = 1; otherwise = 0 Vehicle and pedestrian = 1; otherwise = 0
0.052
0.222
0.084
0.278
Driving ahead = 1; otherwise = 0 Turning right = 1; otherwise = 0 Stopping/slowing down = 1; otherwise = 0 Turning left = 1; otherwise = 0 Changing lane = 1; otherwise = 0 U-turn = 1; otherwise = 0 Other maneuver = 1; otherwise = 0
0.658 0.136 0.087
0.475 0.343 0.282
0.030 0.028 0.017 0.045
0.170 0.164 0.128 0.207
Minor = 1; otherwise = 0 Serious = 1; otherwise = 0 Fatal = 1; otherwise = 0
0.919 0.050 0.031
0.273 0.218 0.172
involved vehicles, whether the head light was on during the crash, and the number of pedestrians involved in the crash. Sixty-two variables from 16 factors are retained in the final model and these are shown in Table 1 together with their definition, mean and standard deviation. Since most of the factors were characterized by a series of dichotomous variables that summed to unity for each factor, one of the variables must be used as a reference in the estimation. From the calibrated model, the effects of the identified factors on hit-andrun crashes were studied by examining the odds ratios against the reference case. For example, for the time of day effect, the reference case used was day time and the estimate for the night time variable therefore yielded the effect of a night time crash on hit-and-run relative to a day time crash.
4. Discussion of results The estimation results for the final model are shown in Table 2. Based on the p-values of the t-tests, 28 variables from 16 factors were found to be significant (p ≤ 0.05) or marginally significant (p ≤ 0.1). As suggested by Kockelman and Kweon (2002), variables with low statistical significance might also be retained in the model if they belonged to factors that had some significant effect on injury severity. Although this approach may reduce the efficiency of the estimates, it was adopted for ease of comparison and interpretation of the estimates. We adjusted for this potential decrease in efficiency by using a more liberal confidence level of 90% instead of the traditional 95%. Three general attributes were found to have a significant effect on hit-and-run crashes. First, the odds ratio for time trend was slightly greater than one (1.08) indicating that the likelihood of hit-and-run was increasing slightly over time. Second, hit-andrun crashes were more likely to occur at night relative to day time (OR = 2.31). Many previous studies had found that the time of crash, particularly night time, had a significant influence on crash occurrence as well as severity (Valent et al., 2002; Simoncic, 2001; Kockelman and Kweon, 2002; Chang and Mannering, 1999). Moreover, lighting condition is a crucial factor in determining whether the driver leaves the scene without reporting the crash or not. Inadequate lighting may encourage hit-and-run behavior because of the perceived lower probability of being identified. In contrast, when the crash occurs in daylight, drivers may decide to remain at the scene because they realize that the chance of escaping detection is low (Solnick and Hemenway (1995). Third, compared to public
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Table 2 Estimation results Variables
Odds ratio
p-Value
(1) Time trend (relative to first 6 months of 1992) Every 6 months after 1992 1.08
0.000
(2) Time of day (relative to day time) Night time
0.000
2.31
(3) Area of occurrence (relative to public housing estate) School 1.03 Private residential area 0.93 Factory 1.17 Shopping complex 1.06 Shop house 1.62 MRT station 1.53 Others 1.43
0.918 0.652 0.337 0.754 0.001 0.178 0.000
(4) Type of vehicle (relative to car) Bicycle Truck Bus Motorcycle Van/pickup Others
4.67 1.18 0.54 2.31 1.03 1.30
0.000 0.296 0.075 0.000 0.880 0.570
(5) Country of registration (relative to Singapore) Foreign 1.19
0.099
(6) Type of road (relative to one-way) Undivided road Divided road Expressway
0.57 0.85 1.09
0.000 0.101 0.482
(7) Type of location (relative to intersection) Curve/bend Slip road Straight Bridge/flyover Others
2.08 0.84 1.43 2.90 1.56
0.000 0.421 0.000 0.000 0.012
(8) Road surface (relative to dry) Wet Sandy/oily
0.86 1.96
0.110 0.040
(9) Special road feature (relative to normal roadway) Merging 1.01 Narrow 1.34 Sharp Turn 0.42 Blind Corner 0.17
0.983 0.521 0.138 0.080
(10) Age of driver (relative to age between 25 and 44) <25 1.06 45–69 1.26 70 and above 1.45
0.497 0.002 0.189
(11) Offending party (relative to non-offending) Offending driver 0.38
0.000
(12) Gender (relative to female) Male
1.30
0.090
(13) Race (relative to Chinese) Malay Indian Eurasian Others
1.16 1.63 2.32 1.33
0.065 0.000 0.007 0.123
(14) Type of collision (relative to single vehicle) Between moving vehicles 21.91 Vehicle and stationary object 4.84 Vehicle and pedestrian 10.91
0.000 0.000 0.000
(15) Maneuver (relative to driving ahead) Turning right Stopping/slowing Turning left Changing lane U-turn Others
0.69 0.50 1.10 0.90 0.31 0.96
0.006 0.000 0.661 0.559 0.019 0.768
(16) Crash severity (relative to minor) Serious Fatal
1.27 1.52
0.061 0.005
Number of observations = 115,323; square = 1307.54; p-value < 0.0001.
log
likelihood = −5549.3487;
chi-
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housing estates, crashes that took place near shop houses7 were 62% (OR = 1.62) more likely to be hit-and-run crashes. This result implies that drivers feel that they are less likely to be identified in crashes near shop houses where the population density is much lower than in public housing estates. Two vehicle characteristics, vehicle type and the country of registration, were found to affect hit-and-run crashes. Compared with car–car crashes, bicycle and motorcycle involved crashes increased the chance of drivers running after collisions (OR = 4.67 and OR = 2.31, respectively) whereas bus (OR = 0.54) involved crashes were 46% less likely to be hit-and-run crashes. Many studies found that crashes involving two-wheel-vehicles were often related to a higher risk of more severe injury (Branas and Knudson, 2001; Shankar and Mannering, 1996; Rutter and Quine, 1996; Mannering and Grodsky, 1995). Under this circumstance, the drivers may perceive that there is less chance of survival for the victims in twowheel-involved crashes. Instead of staying after the crash, drivers may be more likely to leave the scene to avoid being identified and face tough legal consequences. Drivers may also perceive the likelihood of being caught to be lower owing to the higher likelihood of fewer witnesses. On the other hand, the probability of running is lower in bus-involved crashes even though the bus-involved crashes often increase the likelihood of severe injury for the other road users involved in collision due to its greater vehicle mass. This result is expected because there are usually many witnesses in a bus-involved crash which reduces the chance of escaping detection. Our study also found that the likelihood of leaving the crash scene was 19% (OR = 1.19) higher when the vehicles were from neighboring countries compared to local vehicles. This outcome is expected since the local population is much more aware of local laws against leaving the scene after crashes than drivers from neighboring countries. Moreover, locals may feel that there is a lower chance to avoid identification and prosecution by fleeing from the crash location since Singapore is a small island with a good surveillance and monitoring system. In contrast, the drivers of the foreign vehicles may feel that if they could escape from the crash scene, there is a low probability to be caught later once they leave the country. Four roadway-related factors were found to be significant in determining hit-and-run crashes. Model results in Table 2 showed that crashes on undivided roads (OR = 0.54) decreased the probability of hit-and-run when compared to crashes on one-way roads. Also, relative to intersections, the likelihood of running after the crash was highest for crashes that occurred on bridges/flyovers (OR = 2.90), followed by curve (OR = 2.08) and straight roads (OR = 1.43). Regulatory measures, particularly traffic signals, often slow down vehicles at intersection approaches which may reduce the severity of crashes. Collisions were more likely to be hit-and-run crashes when the road surface was sandy or oily instead of dry (OR = 1.96). Also, relative to crashes on a normal roadway, crashes occurring at a blind corner of a road had a lower probability of hit-and-run. In an urban environment, blind corners are often located in highly built-up areas with street furniture or engineered as a traffic calming measure, which increase the likelihood of detection and reduce the crash severity. Four driver/rider attributes were investigated in this study: age, gender, race and whether the driver/rider was the offending party. All of these driver characteristics were found to be significant in the model. A number of studies had identified the effects of different age categories of the drivers on crash occurrences and severities (Dissanayake and Lu, 2002a,b; Abdel-Aty et al., 1998; Kim et al.,
7 Shop-houses are usually 2–3 story buildings with multiple units where the ground floor is used for retail or commercial purpose.
1995; Richardson et al., 1996). When considering the effect of age on hit-and-run crashes, an interesting finding was observed in this study. Holding other factors constant, drivers aged 45–69 were 1.26 times more likely to leave the crash scene (OR = 1.26) when compared to drivers in the 25–44 age group. One possible reason for this result may be the positive influence on discipline and behavior instilled by the compulsory military training and military reserve services which ends at the age of 40–45 for all male citizens. Our results showed that the driver’s gender had a significant impact on the probability of hit-and-run crashes. Male drivers were 30% more likely than female drivers to run after the crash. This result is consistent with the finding of Solnick and Hemenway (1995) that men are 60% more likely than females to run. They also found that males are more likely to commit other driving violations such as speeding and driving after drinking. Hence, it is not astonishing that male drivers are more likely to leave the scene compared with their female counterparts. Surprisingly, our results also showed that offending drivers were 62% (OR = 0.38) less likely to run when compared to non-offending drivers. Perhaps drivers may perceive that the likelihood of the police pursuing further investigation, and thus the likelihood of detection and apprehension, is higher when they are at fault. Also, since it is a punishable offence, the offending drivers may not wish to compound their punishment by leaving the scene without reporting the crash. In any case, care should be exercised in interpreting this result because the actual assignment of fault by the police may be different from the perception of the driver at the time of the crash, especially when the crash involved cyclists or pedestrians as well as intoxicated road users (drivers, cyclists or pedestrians). Our results also showed that ethnic minorities were more likely to be involved in hit-and-run crashes. Eurasian drivers were the most likely race to be associated with hit-and-run crashes. The likelihood that Eurasian drivers would leave the crash sites was 2.32 times higher than Chinese drivers. Indian and Malay drivers also had an increased likelihood of hit-and-run crashes (OR = 1.63 and OR = 1.16). The differences among races in the likelihood of running after crashes could be due to racial differences and the socio-cultural environment that may influence their risk taking behavior. Three crash characteristics (type of collision, preceding maneuver and crash severity) were found to affect hit-and-run crashes. First, relative to single vehicle crashes, crashes between moving vehicles increased the likelihood of hit-and-run crashes the most (OR = 21.91), followed by vehicle and pedestrian crashes (OR = 10.91), and vehicle and stationary object crashes (OR = 4.84). Drivers were less likely to leave the scene when the collision was with a pedestrian rather than another vehicle. It may be that drivers feel more guilt and more responsibility when they injure pedestrians who do not enjoy any protective measures (i.e., air-bag, seat belts, helmets, etc.). In addition, the drivers may experience more remorse and give themselves up voluntarily or they may feel that the police will be more vigorous in pursuing the hit-and-run drivers in crashes involving pedestrians. Compared with crashes between vehicles and crashes with pedestrians, drivers were less likely to run after crashes with stationary objects because this type of crash was considered to be a minor offence and the legal consequences might not be as serious. Also, since there is no third party involved, the likelihood of detection is lower. In comparison with vehicles which had been driven on a straight road prior to collision, vehicles that were slowing down had a decreased probability of leaving the crash scene (OR = 0.50). Moreover, drivers in crashes involving vehicles doing right turns (OR = 0.69) and U-turns (OR = 0.31) were found to be less likely to run. Generally, when a vehicle is being driven at a high speed
R. Tay et al. / Accident Analysis and Prevention 40 (2008) 1330–1336
prior to the crash, there is a higher chance of the driver leaving the crash scene. Relative to driving straight, right turns and U-turns maneuvers are often undertaken at lower speeds which reduce the severity of the crash and may influence the drivers not to run after the crash. Also, it is likely that most of the right turns and Uturns crashes occur at intersections with a higher likelihood of the presence of witnesses and thus a higher likelihood of apprehension. As expected, our results showed that collision severity had a significant impact on the likelihood of hit-and-run crashes. The chances of leaving the crash location in a fatal crash and a serious crash were 1.52 times (OR = 1.52) and 1.27 times (OR = 1.27) higher, respectively than that of a minor crash.
5. Conclusion Although various measures such as improvements in vehicle safety and road designs as well as developments of telecommunication systems and increasing application of information technology in transportation systems have brought about better traffic safety standards, they have little impact in reducing hit-and-run crashes. Running after a crash is more likely to depend on the situational factors surrounding the crash. Although in-depth field investigations are preferred and should be considered in future research, the legal and logistical problems involved preclude them from this research. In this study, the logistic regression model was applied to a large and highly disaggregate set of traffic collision data obtained from the traffic police to identify the factors that might affect the probability of hit-and-run crashes relative to other non-hit-and-run crashes. Some of the critical factors that contributed significantly to hit-and-run crashes included the type of vehicle, the type of road, the type of collision, drivers’ characteristics and environmental characteristics. This study recognizes that there is a multiplicity of factors that affect hit-and-run crashes. However, on examining these factors together, it is clear that there are some common features that are worth noting. The first major influence is whether the driver feels he can escape detection. This hypothesis is supported by findings that drivers are more likely to run at night when the visibility and vehicle traffic are lower. Drivers are also more likely to run from crashes on bridges and flyovers, bends, straight roads and shop houses. These locations may be easier for the drivers to leave the crash sites prior to detection. Drivers of vehicles from neighboring countries are also more likely to run from the crash sites than drivers of local vehicles because of a lower perceived chance of detection. Deployment of more surveillance cameras on hit-and-run crash prone areas may be one way to address this problem. Another common condition that may influence drivers’ decision to leave the crash scene is their perception of subsequent legal consequences of the crash. Assuming that the crash is serious and the offender is less likely to avoid conviction, crashes involving twowheel-vehicles would result in higher likelihood of running after the crash. Crashes between moving vehicles, crashes with pedestrians and stationary objects tend to influence drivers to run as well. This study also shows that human factors play an important role in influencing running after a crash. Interestingly, drivers in the mature age group (45–69) are more likely to leave the crash scene. As expected, men are more likely to run than females, which may be related to their higher risk taking and higher perceived legal consequences of the crash. However, offending drivers are less likely to run after a crash. Socio-cultural differences also play an important part in the decision to run after a crash. Ethnic minorities like Eurasians, Indians and Malays are more likely to run in comparison with Chinese drivers. In dealing with these problems, targeted
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campaigns to educate specific road user types may be more effective. It should be noted that whereas the theoretical framework can be generalized and applied to other jurisdictions, the empirical results obtained may be unique to Singapore due to its unique political and social environment. Therefore, until more analyses are conducted using data from other jurisdictions to ascertain whether these factors are salient to the hit-and-run problem, caution should be exercised in generalizing them to other settings. Acknowledgements Support from the Natural Sciences and Engineering Research Council of Canada, the Alberta Motor Association and the Centre for Transportation Engineering and Planning are gratefully acknowledged. The authors also thank the Singapore Traffic Police for providing the data. References Abdel-Aty, M., Chen, C., Schott, J., 1998. An assessment of the effect of driver age on traffic accident involvement using log–linear models. Accid. Anal. Prev. 30 (6), 851–861. Branas, C., Knudson, M., 2001. Helmet laws and motorcycle rider death rates. Accid. Anal. Prev. 33 (6), 641–648. Chang, L., Mannering, F., 1999. Analysis of vehicle occupancy and the severity of truck- and non-truck-involved accidents. Accid. Anal. Prev. 31 (5), 579–592. Chin, H., Quddus, M., 2003. Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections. Accid. Anal. Prev. 35 (2), 253–259. Cousins, R., Holding, R., Locke, J., Wilkinson, J., 1989. A data collection of vehicle topcoat colours. 4. A trial to assess the effectiveness of colour identification. Forensic Sci. Int. 43, 183–197. Dissanayake, S., Lu, J., 2002a. Analysis of severity of young driver crashes—sequential binary logistic regression modeling. Transportation Res. Rec. 1784, 108–114. Dissanayake, S., Lu, J., 2002b. Factors influential in making an injury severity difference to older drivers involved in fixed object-passenger car crashes. Accid. Anal. Prev. 34 (5), 609–618. Jones, I., Whitfield, R., 1988. Predicting injury risk and risk-taking behavior among young drivers. Accid. Anal. Prev. 20 (6), 411–419. Karger, B., Teige, K., Fuchs, M., Brinkmann, M., 2001. Was the pedestrian hit in an erect position before being run over? Forensic Sci. Int. 119, 217–220. Kim, K., Nitz, L., Richardson, J., Li, L., 1995. Personal and behavioral predictors of automobile crash and injury severity. Accid. Anal. Prev. 2 (4), 469–481. Kockelman, K., Kweon, Y., 2002. Driver injury severity: an application of ordered probit models. Accid. Anal. Prev. 34 (3), 313–321. Kumara, S., Chin, H., 2006. Disaggregate models to examine signalized intersection crash frequencies. In: Proceedings of the TRB 2006 Annual Meeting, (CD-ROM). Lewis, S., 1936. The merits of the automobile accident compensation plan. Law Contemp. Plan 3 (4), 583–597. Locke, J., Sanger, D., Roopnarine, G., 1982. The identification of toughened glass by annealing. Forensic Sci. Int. 20, 295–301. Locke, J., Wilkinson, J., Hanford, T., 1988. A data collection of vehicle topcoat colours. 2. The measurement of colour samples used in the vehicle refinishing industry. Forensic Sci. Int. 37, 177–187. Locke, J., Cousins, D., Russell, L., Jenkins, C., Wilkinson, J., 1987. A data collection of vehicle topcoat colours. 1. Instrumentation for colour measurements. Forensic Sci. Int. 34, 131–142. Lui, K., McGee, D., Rhodes, P., Pollock, D., 1988. An application of a conditional logistic regression to study the effects of safety belts, principal impact points, and car weights on drivers’ fatalities. J. Saf. Res. 19, 197–203. Lum, K., Wong, Y., 2002. A study of stopping propensity at matured light camera T-intersections. J. Saf. Res. 33, 355–369. Lum, K., Wong, Y., 2003. A before-and-after study of driver stopping propensity at red light camera intersections. Accid. Anal. Prev. 35, 111–120. Mannering, F., Grodsky, L., 1995. Statistical analysis of motorcyclists’ perceived accident risk. Accid. Anal. Prev. 27 (1), 21–31. McCarthy, P., Tay, R., 1993. Economic efficiency vs. traffic restraint: a note on the Singapore’s Area Licensing Scheme. J. Urban Econ. 34 (1), 96–100. Mitra, S., Chin, H., Quddus, M., 2002. Study of intersection accidents by maneuver type. Transportation 1784, 45–50. Quddus, M., Noland, R., Chin, H., 2002. An analysis of motorcycle injury and vehicle damage severity using ordered probit models. J. Saf. Res. 33 (4), 445–462. Richardson, J., Kim, K., Li, L., Nitz, L., 1996. Patterns of motor vehicle crash involvement by driver age and sex in Hawaii. J. Saf. Res. 27 (2), 117–125. Rifaat, S., Chin, H., 2007. Accident severity analysis using ordered probit model. J. Adv. Transportation 41 (1), 91–114. Rutter, D., Quine, L., 1996. Age and experience in motorcycling safety. Accid. Anal. Prev. 28 (1), 15–21.
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Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
A logistic model of the effects of roadway, environmental, vehicle, crash and driver characteristics on hit-and-run crashes Richard Tay a,∗ , Shakil Mohammad Rifaat a,1 , Hoong Chor Chin b,2 a b
Department of Civil Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada Department of Civil Engineering, National University of Singapore, 10, Kent Ridge Crescent, Singapore 119260, Singapore
a r t i c l e
i n f o
Article history: Received 10 February 2007 Received in revised form 31 January 2008 Accepted 8 February 2008 Keywords: Hit-and-run Logistic model Singapore
a b s t r a c t Leaving the scene of a crash without reporting it is an offence in most countries and many studies have been devoted to improving ways to identify hit-and-run vehicles and the drivers involved. However, relatively few studies have been conducted on identifying factors that contribute to the decision to run after the crash. This study identifies the factors that are associated with the likelihood of hit-and-run crashes including driver characteristics, vehicle types, crash characteristics, roadway features and environmental characteristics. Using a logistic regression model to delineate hit-and-run crashes from nonhit-and-run crashes, this study found that drivers were more likely to run when crashes occurred at night, on a bridge and flyover, bend, straight road and near shop houses; involved two vehicles, two-wheel vehicles and vehicles from neighboring countries; and when the driver was a male, minority, and aged between 45 and 69. On the other hand, collisions involving right turn and U-turn maneuvers, and occurring on undivided roads were less likely to be hit-and-run crashes. © 2008 Elsevier Ltd. All rights reserved.
1. Introduction Road crashes are a leading cause of death and injuries in many countries and extract a high cost on society. In the city-state of Singapore, for example, there were 9896 injuries in 2006 resulting from motor vehicle collisions, of which 190 were fatal (Singapore Police Force, 2007a). Among the different types of crashes, hit-andrun crashes are of interest to researchers because they are not only unethical acts but punishable offences as well. Also, leaving the victims at the crash scene delay crash notification and may result in an increase in the severity of the crash. Moreover, Lewis (1936) argued that victims of hit-and-run crashes were the most hapless group because of the reduced chance of getting compensation. Hence, research aiming to improve the identification of hitand-run vehicles and the drivers involved has been an area of interest in diversified fields such as medical, forensic, legal, insurance and engineering. Many studies in the field of medical science, for example, examined the types of injury sustained by victims in hit-and-run crashes to identify the types of vehicles involved. For example, Teresinski and Madro (2001) evaluated knee joint
∗ Corresponding author. Tel.: +1 403 220 4725; fax: +1 403 282 7026. E-mail addresses: [email protected] (R. Tay), [email protected] (S.M. Rifaat), [email protected] (H.C. Chin). 1 Tel.: +1 403 220 5970; fax: +1 403 282 7026. 2 Tel.: +65 6874 2550; fax: +65 6779 1635. 0001-4575/$ – see front matter © 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2008.02.003
postmortem examinations of fatal pedestrian victims of traffic accidents to deduce the location as well as identify the type of vehicles involved. Karger et al. (2001) examined different types of fractures in the victims such as wedge-shaped bone fractures and fractures in the cervical and lumber spine to deduce the nature and the sequences of hit-and-run crashes. The identification of vehicles involved in hit-and-run crashes is also an important task faced by forensic science laboratories. Debris which include headlamps, sidelight fragments, wheel track marks and paint fragments recovered at the accident location contribute to identifying the make and model of the offending vehicle. Several methods have been proposed to measure the color of retrieved paint fragments from crash scenes or from the clothing of victims to identify the vehicles involved in hit-and-run crashes (Cousins et al., 1989; Taylor et al., 1989; Locke et al., 1982, 1987, 1988). Most of the studies discussed previously in various fields proposed different methods which would help to identify the fleeing vehicles in hit-and-run crashes. However, very few studies have explored the situations or circumstances under which hit-and-run crashes occurred. In one of the few studies that examined this issue, Solnick and Hemenway (1995) explored how victim’s characteristics, driver’s characteristics and circumstances of collision affected the hit-and-run choice in fatal pedestrian crashes. Their results showed that a driver was less likely to run when the victim was a child or an elderly pedestrian, if the crash occurred in the southern part of the America or in daylight, the driver was elderly, and the car driven was less than 5 years old. On the other hand, a driver
R. Tay et al. / Accident Analysis and Prevention 40 (2008) 1330–1336
was more likely to run when the victim was between 16 and 25 years old, the crash occurred in an urban area, on weekends, or in summer, and the driver was male or had no valid driver license, previous DWI convictions, positive or unknown BAC. In addition, Solnick and Hemenway (1994) found that 19% of all road crashes in the United States in 1989–1990 were hit-and-run cases caused by drunk driving and the hit-and-run motorists were disproportionately young and male. Although these studies gave some insight about the factors influencing drivers’ decision to leave the crash scenes, they were restricted to only pedestrian crashes. In this study, a logistic model will be estimated to examine the influence of road features, vehicle attributes, environmental factors, crash characteristics and driver particulars on hit-and-run crashes in Singapore. This study will contribute to the literature in several ways. First, it will extend the study by Solnick and Hemenway (1995) by examining all hit-and-run crashes instead of pedestrian involved crashes alone. Second, it will examine several additional influences including the types of vehicles involved in the crashes, the vehicles’ countries of origin, types of crashes, vehicle maneuvers, ethnicities of drivers, presence of surveillance cameras and other location information like residential housing, retail or central business district areas, which are expected to be very relevant. Last, it will examine hit-and-run crashes in an Asian country with a different social and cultural environment. Previous safety related studies in Singapore had focused mainly on examining intersection crashes (Chin and Quddus, 2003; Rifaat and Chin, 2007; Kumara and Chin, 2006; Mitra et al., 2002; Tay and Rifaat, 2007), motorcycle crashes (Quddus et al., 2002; Yuan, 2000), and red light running behaviors (Lum and Wong, 2002, 2003). No publicly available study has been found that deals with hit-and-run crashes in Singapore. Singapore is a city-state located on a small island (700 km2 ) at the southern tip of the Malayan Peninsula in Southeast Asia. It has a total population of about 4.48 million, with a resident population of 3.61 million (Statistics Singapore, 2007).3 The actual composition of the population is quite fluid due to high percentage of foreign workers. The resident population comprises of four major ethnic groups: Chinese (75.2%), Malay (13.6%), Indian (8.8%) and Others (2.4%).4 The vast majority of the population stays in high-rise apartments due to the shortage of land. Singapore has one of the highest population densities in the world and is almost 100% urbanized. Therefore, although it has a very good quality road system and fairly high gross domestic product per capita (S$46,832),5 very stringent vehicle ownership and use policies are implemented to mitigate traffic congestion (McCarthy and Tay, 1993; Tay, 1996). Nevertheless, passenger cars (private, rental and taxis) still comprise the majority (62.3%) of the 799,373 registered vehicles, followed by goods vehicles (18.1%), motorcycles and scooters (17.9%) and buses (1.8%). 2. Analytical framework The decision of a driver to stay and report the crash or to leave the scene without reporting the collision can be analyzed using the standard decision analysis framework based on the expected costs
3 Resident population refers to citizens and those who are granted permanent resident status. Non-residents comprise mainly of people on work permit, student visas or long-term social visit pass. 4 The majority of “Others” consists of Eurasian (Euro-Asian) which includes mostly migrants from Western Europe, Australia and New Zealand as well as their descendants. 5 Exchange rate: US$1 = S$1.59.
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and benefits of the choices. There are few uncertainties associated with the outcomes of reporting a crash. The costs of the reporting crash consist mainly of an increase in the insurance premium for reporting the crash if the driver is at fault and potential penalties associated with any illegal driving behaviors. Theoretically, the higher the expected cost of the crash, the less likely it is for the driver to report crash which implies that the likelihood of a hit-andrun crash is expected to increase with the severity of the crash, all else held constant. Also, factors that reduce the likelihood of blame or fault will also increase the likelihood of reporting crashes. Such mitigating factors include wet weather, sharp turn and blind corner as well as driver’s actions like stopping/slowing and making a turn. Since the expected costs and benefits associated with reporting a crash is relatively straightforward, the decision to report the crash or to simply run will largely depend on the expected cost of not reporting the crash or choosing the hit-and-run option. The expected costs of leaving the scene without reporting a crash consist of two possible outcomes which depend on whether the driver was subsequently apprehended. If the driver is not caught, then his or her cost is lower than the cost associated with reporting the crash. On the other hand, if the driver is caught subsequently, his or her cost is assumed to be higher than the cost associated with reporting the crash. In Singapore, the failure to stop after a traffic accident is a punishable offence which carries a maximum fine of S$1000 and/or a prison term of up to 3 months. Removing an accident vehicle without police authorization also carries a maximum fine of S$1000 and/or a prison term of 3 months. In addition, the failure to render assistance after a fatal accident carries a maximum fine of $3000 and/or a prison term of up to 12 months (Singapore Police Force, 2007b). These charges are likely to be added to any other charges of dangerous or illegal driving behaviors. The expected cost of choosing to run is assumed to depend significantly on the perceived likelihood of being caught subsequently and the risk-taking propensity of the driver. For the highly risk adverse, the uncertainties involved in hit-and-run is not likely to be an attractive option but it may be more attractive to the high risk taking drivers. For most drivers who are relatively risk neutral, however, the perceived likelihood of apprehension will play a significant part in influencing their choices. To this end, factors that may reduce the chances of apprehension will increase the likelihood of a hit-and-run crash. Examples of these factors include crashes during night time, crashes with fixed objects or parked vehicles and crashes involving a foreign vehicle. Similarly, the likelihood of a hit-and-run crash is expected to be lower when the crash involves a bus or occurs in a residential neighborhood due to likely presence of witnesses. It should be noted that drivers are assumed to act on their perceived likelihood of being apprehended and not on the actual probability of apprehension which is extremely high in Singapore. In our analysis, all cases included the relevant drivers’ information which meant that the drivers had been caught at a later stage.6 It is worth noting that very few hit-and-run drivers in Singapore escape detection. This extremely high detection rate is possible because Singapore is a small island state that is highly regulated and regimented, especially in the socio-political arenas, and its surveillance and vehicle monitoring system are very good. In addition, Singapore is almost 100% urbanized with a very high population density. The likelihood of having some witnesses to the accident is very high. The low crime rate in Singapore also enables the police to actively pursue any hit-and-run cases. Moreover, in a well-regulated vehicle repair industry, the ease of tracing a damaged vehicle among repair
6 Alternatively, it could be due to the way crashes are reported or recorded. Therefore, care should be exercised in interpreting the results.
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workshops also makes police investigation into a hit-and-run case relatively successful. 3. Methodology In our study, the response variable, hit-and-run or non-hitand-run crash, is a binary or dichotomous variable. Therefore, the logistic regression is a suitable technique to use because it is developed to predict a binary dependent variable as a function of predictor variables. The logistic regression model is widely used in road safety studies where the dependent variable is binary (Valent et al., 2002; Jones and Whitfield, 1988; Lui et al., 1988; Shibata and Fukuda, 1994; Zhang et al., 2000; Simoncic, 2001). In this model, the logit is the natural logarithm of the odds or the likelihood ratio that the dependent variable is 1 (hit-and-run crash) as opposed to 0 (non-hit-and-run crash). The probability P of a hit-and-run is given by Y = logit(P) = ln
P 1−P
= ˇX
(1)
where ˇ is a vector of parameters to be estimated and X is a vector of independent variables. When an independent variable xi increases by one unit, with all other factors remaining constant, the odds increase by a factor exp(ˇi ) which is called the odds ratio (OR) and ranges from 0 to positive infinity. It indicates the relative amount by which the odds of the outcome (hit-and-run) increase (OR > 1) or decrease (OR < 1) when the value of the corresponding independent variable increases by one unit. In order to develop a logistic regression model that identifies the factors affecting hit-and-run, a crash data set is needed that would include the environmental factors, vehicle attributes, crash characteristics, roadway features and drivers’ particulars which are correlated with the collisions. Crash data for this study were obtained from the National Road Accident database maintained by the Traffic Police in Singapore. The information given in the collision record can be classified into three types: general collision information, vehicle and driver-related information and pedestrian information, each of which contains the description of different factors involved in the collisions. Traffic collision data from the year 1992 to 2002 were used to develop the model in our study. During the 11 years, 67,228 crashes occurred in Singapore. Among them, 1.83% was recorded as hit-and-run crashes. An important task in developing the model is the selection of appropriate factors from victim, vehicle, crash, road and environmental characteristics that could reasonably be expected to influence hit-and-run crashes. Two approaches were used to select these factors. The first approach used was to review similar research where these factors had been examined. The second option was to focus on local context to determine other variables that might have some influence on hit-and-run crashes. Following these considerations, 25 factors were selected for further investigation. Moreover, in most cases, several mutually exclusive categorical independent variables were formed from each of the factors in order to facilitate the estimation and interpretation of the odds ratios. Several factors were dropped after correlation tests were performed between these variables. For example, the type of road and speed limit were found to be strongly correlated. Since the type of road was a better indicator in predicting hit-and-run crashes than speed limit, it was kept in the model. Some other factors were also tested and excluded in the final model reported because they were found to be statistically insignificant. Nine factors were finally dropped from the model after the preliminary analyses. These variables included day of week, speed limit, central business district area, electronic road pricing hours if crash occurred in the central business district, the presence of surveillance camera, number of
Table 1 Descriptive statistics of variables Explanatory variables
Description of variables
Mean
S.D.
(1) Time trend
6 monthly increments (January 1992 = 1 to December 2002 = 22)
12.081
6.458
Day time = 1; otherwise = 0 Night time = 1; otherwise = 0
0.625 0.375
0.484 0.484
School = 1; otherwise = 0 Public housing estate = 1; otherwise = 0 Private residential area = 1; otherwise = 0 Factory = 1; otherwise = 0 Shopping complex = 1; otherwise = 0 Shop house = 1; otherwise = 0 MRT Station = 1; otherwise = 0 Occurred at other locations = 1; otherwise = 0
0.014 0.257
0.119 0.437
0.077
0.267
0.058 0.036
0.233 0.187
0.044 0.009 0.504
0.206 0.094 0.499
(4) Type of vehicle Car Bicycle Truck Bus Motorcycle Van/pickup Others (5) Foreign vehicle
Car = 1; otherwise = 0 Bicycle = 1; otherwise = 0 Truck = 1; otherwise = 0 Bus = 1; otherwise = 0 Motorcycle = 1; otherwise = 0 Van/pickup = 1; otherwise = 0 Other vehicle = 1; otherwise = 0 Foreign vehicle = 1; otherwise = 0
0.260 0.017 0.056 0.024 0.601 0.037 0.005 0.075
0.439 0.131 0.231 0.153 0.490 0.188 0.068 0.264
(6) Type of road One way street Undivided road Divided road Expressway
One way road = 1; otherwise = 0 Undivided road = 1; otherwise = 0 Divided road = 1; otherwise = 0 Expressway = 1; otherwise = 0
0.115 0.251 0.472 0.163
0.318 0.433 0.499 0.370
Straight road = 1; otherwise = 0 Curve/bend = 1; otherwise = 0 Slip road = 1; otherwise = 0 Intersection = 1; otherwise = 0 Bridge and/or flyover = 1; otherwise = 0 Other location = 1; otherwise = 0
0.483 0.049 0.036 0.381 0.016
0.499 0.217 0.187 0.486 0.126
0.034
0.181
Road surface is dry = 1; otherwise = 0 Wet surface = 1; otherwise = 0 Sandy/oily surface = 1; otherwise = 0
0.860
0.347
0.132 0.008
0.338 0.088
Normal roadway = 1; otherwise = 0 Merging lane = 1; otherwise = 0 Narrow lane = 1; otherwise = 0 Sharp turn = 1; otherwise = 0 Blind corner = 1; otherwise = 0
0.973
0.163
0.007 0.007 0.008 0.005
0.084 0.081 0.090 0.073
(10) Age of driver (years) <25 25–44 45–69 70 and above
Age < 25 = 1; otherwise = 0 25 ≤ Age ≤ 44 = 1; otherwise = 0 45 ≤ Age ≤ 69 = 1; otherwise = 0 70 ≤ Age = 1; otherwise = 0
0.205 0.534 0.254 0.007
0.404 0.499 0.435 0.085
(11) Offending (12) Driver gender
Offending party = 1; otherwise = 0 Male = 1; female = 0
0.438 0.930
0.496 0.255
(13) Driver race Chinese Malay Indian Eurasian Others
Chinese = 1; otherwise = 0 Malay = 1; otherwise = 0 Indian = 1; otherwise = 0 Eurasian = 1; otherwise = 0 Other race = 1; otherwise = 0
0.743 0.160 0.070 0.005 0.022
0.437 0.367 0.256 0.070 0.145
Single vehicle crash = 1; otherwise = 0 Crash between moving vehicles = 1; otherwise = 0
0.091
0.288
0.772
0.419
(2) Time of day Day time Night time 3. Area of Occurrence School Public housing estate Private residential area Factory Shopping complex Shop house MRT station Others
(7) Type of location Straight Curve/bend Slip road Intersection Bridge/flyover Others (8) Road surface Dry Wet Sandy/oily (9) Special road feature Normal roadway Merging Narrow Sharp turn Blind corner
(14) Type of collision Single vehicle Moving vehicles
R. Tay et al. / Accident Analysis and Prevention 40 (2008) 1330–1336 Table 1 (Continued ) Explanatory variables Vehicle and object Vehicle and pedestrian (15) Vehicle maneuver Driving ahead Turning right Stopping/slowing down Turning left Changing lane U-turn Others (16) Crash severity Minor Serious Fatal
Description of variables
Mean
S.D.
Vehicle and stationary object = 1; otherwise = 0 Vehicle and pedestrian = 1; otherwise = 0
0.052
0.222
0.084
0.278
Driving ahead = 1; otherwise = 0 Turning right = 1; otherwise = 0 Stopping/slowing down = 1; otherwise = 0 Turning left = 1; otherwise = 0 Changing lane = 1; otherwise = 0 U-turn = 1; otherwise = 0 Other maneuver = 1; otherwise = 0
0.658 0.136 0.087
0.475 0.343 0.282
0.030 0.028 0.017 0.045
0.170 0.164 0.128 0.207
Minor = 1; otherwise = 0 Serious = 1; otherwise = 0 Fatal = 1; otherwise = 0
0.919 0.050 0.031
0.273 0.218 0.172
involved vehicles, whether the head light was on during the crash, and the number of pedestrians involved in the crash. Sixty-two variables from 16 factors are retained in the final model and these are shown in Table 1 together with their definition, mean and standard deviation. Since most of the factors were characterized by a series of dichotomous variables that summed to unity for each factor, one of the variables must be used as a reference in the estimation. From the calibrated model, the effects of the identified factors on hit-andrun crashes were studied by examining the odds ratios against the reference case. For example, for the time of day effect, the reference case used was day time and the estimate for the night time variable therefore yielded the effect of a night time crash on hit-and-run relative to a day time crash.
4. Discussion of results The estimation results for the final model are shown in Table 2. Based on the p-values of the t-tests, 28 variables from 16 factors were found to be significant (p ≤ 0.05) or marginally significant (p ≤ 0.1). As suggested by Kockelman and Kweon (2002), variables with low statistical significance might also be retained in the model if they belonged to factors that had some significant effect on injury severity. Although this approach may reduce the efficiency of the estimates, it was adopted for ease of comparison and interpretation of the estimates. We adjusted for this potential decrease in efficiency by using a more liberal confidence level of 90% instead of the traditional 95%. Three general attributes were found to have a significant effect on hit-and-run crashes. First, the odds ratio for time trend was slightly greater than one (1.08) indicating that the likelihood of hit-and-run was increasing slightly over time. Second, hit-andrun crashes were more likely to occur at night relative to day time (OR = 2.31). Many previous studies had found that the time of crash, particularly night time, had a significant influence on crash occurrence as well as severity (Valent et al., 2002; Simoncic, 2001; Kockelman and Kweon, 2002; Chang and Mannering, 1999). Moreover, lighting condition is a crucial factor in determining whether the driver leaves the scene without reporting the crash or not. Inadequate lighting may encourage hit-and-run behavior because of the perceived lower probability of being identified. In contrast, when the crash occurs in daylight, drivers may decide to remain at the scene because they realize that the chance of escaping detection is low (Solnick and Hemenway (1995). Third, compared to public
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Table 2 Estimation results Variables
Odds ratio
p-Value
(1) Time trend (relative to first 6 months of 1992) Every 6 months after 1992 1.08
0.000
(2) Time of day (relative to day time) Night time
0.000
2.31
(3) Area of occurrence (relative to public housing estate) School 1.03 Private residential area 0.93 Factory 1.17 Shopping complex 1.06 Shop house 1.62 MRT station 1.53 Others 1.43
0.918 0.652 0.337 0.754 0.001 0.178 0.000
(4) Type of vehicle (relative to car) Bicycle Truck Bus Motorcycle Van/pickup Others
4.67 1.18 0.54 2.31 1.03 1.30
0.000 0.296 0.075 0.000 0.880 0.570
(5) Country of registration (relative to Singapore) Foreign 1.19
0.099
(6) Type of road (relative to one-way) Undivided road Divided road Expressway
0.57 0.85 1.09
0.000 0.101 0.482
(7) Type of location (relative to intersection) Curve/bend Slip road Straight Bridge/flyover Others
2.08 0.84 1.43 2.90 1.56
0.000 0.421 0.000 0.000 0.012
(8) Road surface (relative to dry) Wet Sandy/oily
0.86 1.96
0.110 0.040
(9) Special road feature (relative to normal roadway) Merging 1.01 Narrow 1.34 Sharp Turn 0.42 Blind Corner 0.17
0.983 0.521 0.138 0.080
(10) Age of driver (relative to age between 25 and 44) <25 1.06 45–69 1.26 70 and above 1.45
0.497 0.002 0.189
(11) Offending party (relative to non-offending) Offending driver 0.38
0.000
(12) Gender (relative to female) Male
1.30
0.090
(13) Race (relative to Chinese) Malay Indian Eurasian Others
1.16 1.63 2.32 1.33
0.065 0.000 0.007 0.123
(14) Type of collision (relative to single vehicle) Between moving vehicles 21.91 Vehicle and stationary object 4.84 Vehicle and pedestrian 10.91
0.000 0.000 0.000
(15) Maneuver (relative to driving ahead) Turning right Stopping/slowing Turning left Changing lane U-turn Others
0.69 0.50 1.10 0.90 0.31 0.96
0.006 0.000 0.661 0.559 0.019 0.768
(16) Crash severity (relative to minor) Serious Fatal
1.27 1.52
0.061 0.005
Number of observations = 115,323; square = 1307.54; p-value < 0.0001.
log
likelihood = −5549.3487;
chi-
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housing estates, crashes that took place near shop houses7 were 62% (OR = 1.62) more likely to be hit-and-run crashes. This result implies that drivers feel that they are less likely to be identified in crashes near shop houses where the population density is much lower than in public housing estates. Two vehicle characteristics, vehicle type and the country of registration, were found to affect hit-and-run crashes. Compared with car–car crashes, bicycle and motorcycle involved crashes increased the chance of drivers running after collisions (OR = 4.67 and OR = 2.31, respectively) whereas bus (OR = 0.54) involved crashes were 46% less likely to be hit-and-run crashes. Many studies found that crashes involving two-wheel-vehicles were often related to a higher risk of more severe injury (Branas and Knudson, 2001; Shankar and Mannering, 1996; Rutter and Quine, 1996; Mannering and Grodsky, 1995). Under this circumstance, the drivers may perceive that there is less chance of survival for the victims in twowheel-involved crashes. Instead of staying after the crash, drivers may be more likely to leave the scene to avoid being identified and face tough legal consequences. Drivers may also perceive the likelihood of being caught to be lower owing to the higher likelihood of fewer witnesses. On the other hand, the probability of running is lower in bus-involved crashes even though the bus-involved crashes often increase the likelihood of severe injury for the other road users involved in collision due to its greater vehicle mass. This result is expected because there are usually many witnesses in a bus-involved crash which reduces the chance of escaping detection. Our study also found that the likelihood of leaving the crash scene was 19% (OR = 1.19) higher when the vehicles were from neighboring countries compared to local vehicles. This outcome is expected since the local population is much more aware of local laws against leaving the scene after crashes than drivers from neighboring countries. Moreover, locals may feel that there is a lower chance to avoid identification and prosecution by fleeing from the crash location since Singapore is a small island with a good surveillance and monitoring system. In contrast, the drivers of the foreign vehicles may feel that if they could escape from the crash scene, there is a low probability to be caught later once they leave the country. Four roadway-related factors were found to be significant in determining hit-and-run crashes. Model results in Table 2 showed that crashes on undivided roads (OR = 0.54) decreased the probability of hit-and-run when compared to crashes on one-way roads. Also, relative to intersections, the likelihood of running after the crash was highest for crashes that occurred on bridges/flyovers (OR = 2.90), followed by curve (OR = 2.08) and straight roads (OR = 1.43). Regulatory measures, particularly traffic signals, often slow down vehicles at intersection approaches which may reduce the severity of crashes. Collisions were more likely to be hit-and-run crashes when the road surface was sandy or oily instead of dry (OR = 1.96). Also, relative to crashes on a normal roadway, crashes occurring at a blind corner of a road had a lower probability of hit-and-run. In an urban environment, blind corners are often located in highly built-up areas with street furniture or engineered as a traffic calming measure, which increase the likelihood of detection and reduce the crash severity. Four driver/rider attributes were investigated in this study: age, gender, race and whether the driver/rider was the offending party. All of these driver characteristics were found to be significant in the model. A number of studies had identified the effects of different age categories of the drivers on crash occurrences and severities (Dissanayake and Lu, 2002a,b; Abdel-Aty et al., 1998; Kim et al.,
7 Shop-houses are usually 2–3 story buildings with multiple units where the ground floor is used for retail or commercial purpose.
1995; Richardson et al., 1996). When considering the effect of age on hit-and-run crashes, an interesting finding was observed in this study. Holding other factors constant, drivers aged 45–69 were 1.26 times more likely to leave the crash scene (OR = 1.26) when compared to drivers in the 25–44 age group. One possible reason for this result may be the positive influence on discipline and behavior instilled by the compulsory military training and military reserve services which ends at the age of 40–45 for all male citizens. Our results showed that the driver’s gender had a significant impact on the probability of hit-and-run crashes. Male drivers were 30% more likely than female drivers to run after the crash. This result is consistent with the finding of Solnick and Hemenway (1995) that men are 60% more likely than females to run. They also found that males are more likely to commit other driving violations such as speeding and driving after drinking. Hence, it is not astonishing that male drivers are more likely to leave the scene compared with their female counterparts. Surprisingly, our results also showed that offending drivers were 62% (OR = 0.38) less likely to run when compared to non-offending drivers. Perhaps drivers may perceive that the likelihood of the police pursuing further investigation, and thus the likelihood of detection and apprehension, is higher when they are at fault. Also, since it is a punishable offence, the offending drivers may not wish to compound their punishment by leaving the scene without reporting the crash. In any case, care should be exercised in interpreting this result because the actual assignment of fault by the police may be different from the perception of the driver at the time of the crash, especially when the crash involved cyclists or pedestrians as well as intoxicated road users (drivers, cyclists or pedestrians). Our results also showed that ethnic minorities were more likely to be involved in hit-and-run crashes. Eurasian drivers were the most likely race to be associated with hit-and-run crashes. The likelihood that Eurasian drivers would leave the crash sites was 2.32 times higher than Chinese drivers. Indian and Malay drivers also had an increased likelihood of hit-and-run crashes (OR = 1.63 and OR = 1.16). The differences among races in the likelihood of running after crashes could be due to racial differences and the socio-cultural environment that may influence their risk taking behavior. Three crash characteristics (type of collision, preceding maneuver and crash severity) were found to affect hit-and-run crashes. First, relative to single vehicle crashes, crashes between moving vehicles increased the likelihood of hit-and-run crashes the most (OR = 21.91), followed by vehicle and pedestrian crashes (OR = 10.91), and vehicle and stationary object crashes (OR = 4.84). Drivers were less likely to leave the scene when the collision was with a pedestrian rather than another vehicle. It may be that drivers feel more guilt and more responsibility when they injure pedestrians who do not enjoy any protective measures (i.e., air-bag, seat belts, helmets, etc.). In addition, the drivers may experience more remorse and give themselves up voluntarily or they may feel that the police will be more vigorous in pursuing the hit-and-run drivers in crashes involving pedestrians. Compared with crashes between vehicles and crashes with pedestrians, drivers were less likely to run after crashes with stationary objects because this type of crash was considered to be a minor offence and the legal consequences might not be as serious. Also, since there is no third party involved, the likelihood of detection is lower. In comparison with vehicles which had been driven on a straight road prior to collision, vehicles that were slowing down had a decreased probability of leaving the crash scene (OR = 0.50). Moreover, drivers in crashes involving vehicles doing right turns (OR = 0.69) and U-turns (OR = 0.31) were found to be less likely to run. Generally, when a vehicle is being driven at a high speed
R. Tay et al. / Accident Analysis and Prevention 40 (2008) 1330–1336
prior to the crash, there is a higher chance of the driver leaving the crash scene. Relative to driving straight, right turns and U-turns maneuvers are often undertaken at lower speeds which reduce the severity of the crash and may influence the drivers not to run after the crash. Also, it is likely that most of the right turns and Uturns crashes occur at intersections with a higher likelihood of the presence of witnesses and thus a higher likelihood of apprehension. As expected, our results showed that collision severity had a significant impact on the likelihood of hit-and-run crashes. The chances of leaving the crash location in a fatal crash and a serious crash were 1.52 times (OR = 1.52) and 1.27 times (OR = 1.27) higher, respectively than that of a minor crash.
5. Conclusion Although various measures such as improvements in vehicle safety and road designs as well as developments of telecommunication systems and increasing application of information technology in transportation systems have brought about better traffic safety standards, they have little impact in reducing hit-and-run crashes. Running after a crash is more likely to depend on the situational factors surrounding the crash. Although in-depth field investigations are preferred and should be considered in future research, the legal and logistical problems involved preclude them from this research. In this study, the logistic regression model was applied to a large and highly disaggregate set of traffic collision data obtained from the traffic police to identify the factors that might affect the probability of hit-and-run crashes relative to other non-hit-and-run crashes. Some of the critical factors that contributed significantly to hit-and-run crashes included the type of vehicle, the type of road, the type of collision, drivers’ characteristics and environmental characteristics. This study recognizes that there is a multiplicity of factors that affect hit-and-run crashes. However, on examining these factors together, it is clear that there are some common features that are worth noting. The first major influence is whether the driver feels he can escape detection. This hypothesis is supported by findings that drivers are more likely to run at night when the visibility and vehicle traffic are lower. Drivers are also more likely to run from crashes on bridges and flyovers, bends, straight roads and shop houses. These locations may be easier for the drivers to leave the crash sites prior to detection. Drivers of vehicles from neighboring countries are also more likely to run from the crash sites than drivers of local vehicles because of a lower perceived chance of detection. Deployment of more surveillance cameras on hit-and-run crash prone areas may be one way to address this problem. Another common condition that may influence drivers’ decision to leave the crash scene is their perception of subsequent legal consequences of the crash. Assuming that the crash is serious and the offender is less likely to avoid conviction, crashes involving twowheel-vehicles would result in higher likelihood of running after the crash. Crashes between moving vehicles, crashes with pedestrians and stationary objects tend to influence drivers to run as well. This study also shows that human factors play an important role in influencing running after a crash. Interestingly, drivers in the mature age group (45–69) are more likely to leave the crash scene. As expected, men are more likely to run than females, which may be related to their higher risk taking and higher perceived legal consequences of the crash. However, offending drivers are less likely to run after a crash. Socio-cultural differences also play an important part in the decision to run after a crash. Ethnic minorities like Eurasians, Indians and Malays are more likely to run in comparison with Chinese drivers. In dealing with these problems, targeted
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campaigns to educate specific road user types may be more effective. It should be noted that whereas the theoretical framework can be generalized and applied to other jurisdictions, the empirical results obtained may be unique to Singapore due to its unique political and social environment. Therefore, until more analyses are conducted using data from other jurisdictions to ascertain whether these factors are salient to the hit-and-run problem, caution should be exercised in generalizing them to other settings. Acknowledgements Support from the Natural Sciences and Engineering Research Council of Canada, the Alberta Motor Association and the Centre for Transportation Engineering and Planning are gratefully acknowledged. The authors also thank the Singapore Traffic Police for providing the data. References Abdel-Aty, M., Chen, C., Schott, J., 1998. An assessment of the effect of driver age on traffic accident involvement using log–linear models. Accid. Anal. Prev. 30 (6), 851–861. Branas, C., Knudson, M., 2001. Helmet laws and motorcycle rider death rates. Accid. Anal. Prev. 33 (6), 641–648. Chang, L., Mannering, F., 1999. Analysis of vehicle occupancy and the severity of truck- and non-truck-involved accidents. Accid. Anal. Prev. 31 (5), 579–592. Chin, H., Quddus, M., 2003. Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections. Accid. Anal. Prev. 35 (2), 253–259. Cousins, R., Holding, R., Locke, J., Wilkinson, J., 1989. A data collection of vehicle topcoat colours. 4. A trial to assess the effectiveness of colour identification. Forensic Sci. Int. 43, 183–197. Dissanayake, S., Lu, J., 2002a. Analysis of severity of young driver crashes—sequential binary logistic regression modeling. Transportation Res. Rec. 1784, 108–114. Dissanayake, S., Lu, J., 2002b. Factors influential in making an injury severity difference to older drivers involved in fixed object-passenger car crashes. Accid. Anal. Prev. 34 (5), 609–618. Jones, I., Whitfield, R., 1988. Predicting injury risk and risk-taking behavior among young drivers. Accid. Anal. Prev. 20 (6), 411–419. Karger, B., Teige, K., Fuchs, M., Brinkmann, M., 2001. Was the pedestrian hit in an erect position before being run over? Forensic Sci. Int. 119, 217–220. Kim, K., Nitz, L., Richardson, J., Li, L., 1995. Personal and behavioral predictors of automobile crash and injury severity. Accid. Anal. Prev. 2 (4), 469–481. Kockelman, K., Kweon, Y., 2002. Driver injury severity: an application of ordered probit models. Accid. Anal. Prev. 34 (3), 313–321. Kumara, S., Chin, H., 2006. Disaggregate models to examine signalized intersection crash frequencies. In: Proceedings of the TRB 2006 Annual Meeting, (CD-ROM). Lewis, S., 1936. The merits of the automobile accident compensation plan. Law Contemp. Plan 3 (4), 583–597. Locke, J., Sanger, D., Roopnarine, G., 1982. The identification of toughened glass by annealing. Forensic Sci. Int. 20, 295–301. Locke, J., Wilkinson, J., Hanford, T., 1988. A data collection of vehicle topcoat colours. 2. The measurement of colour samples used in the vehicle refinishing industry. Forensic Sci. Int. 37, 177–187. Locke, J., Cousins, D., Russell, L., Jenkins, C., Wilkinson, J., 1987. A data collection of vehicle topcoat colours. 1. Instrumentation for colour measurements. Forensic Sci. Int. 34, 131–142. Lui, K., McGee, D., Rhodes, P., Pollock, D., 1988. An application of a conditional logistic regression to study the effects of safety belts, principal impact points, and car weights on drivers’ fatalities. J. Saf. Res. 19, 197–203. Lum, K., Wong, Y., 2002. A study of stopping propensity at matured light camera T-intersections. J. Saf. Res. 33, 355–369. Lum, K., Wong, Y., 2003. A before-and-after study of driver stopping propensity at red light camera intersections. Accid. Anal. Prev. 35, 111–120. Mannering, F., Grodsky, L., 1995. Statistical analysis of motorcyclists’ perceived accident risk. Accid. Anal. Prev. 27 (1), 21–31. McCarthy, P., Tay, R., 1993. Economic efficiency vs. traffic restraint: a note on the Singapore’s Area Licensing Scheme. J. Urban Econ. 34 (1), 96–100. Mitra, S., Chin, H., Quddus, M., 2002. Study of intersection accidents by maneuver type. Transportation 1784, 45–50. Quddus, M., Noland, R., Chin, H., 2002. An analysis of motorcycle injury and vehicle damage severity using ordered probit models. J. Saf. Res. 33 (4), 445–462. Richardson, J., Kim, K., Li, L., Nitz, L., 1996. Patterns of motor vehicle crash involvement by driver age and sex in Hawaii. J. Saf. Res. 27 (2), 117–125. Rifaat, S., Chin, H., 2007. Accident severity analysis using ordered probit model. J. Adv. Transportation 41 (1), 91–114. Rutter, D., Quine, L., 1996. Age and experience in motorcycling safety. Accid. Anal. Prev. 28 (1), 15–21.
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