Identifying motorcycle high-risk traffic scenarios through interactive analysis of driver behavior and traffic characteristics

Transportation Research Part F 62 (2019) 844–854

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Transportation Research Part F journal homepage: www.elsevier.com/locate/trf

Identifying motorcycle high-risk traffic scenarios through interactive analysis of driver behavior and traffic characteristics Fangrong Chang a, Pengpeng Xu b, Hanchu Zhou a, Jaeyoung Lee c, Helai Huang a,⇑ a

School of Traffic &Transportation Engineering, Central South University, Changsha 410075, China Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, 999077, Hong Kong, China c Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, United States b

a r t i c l e

i n f o

Article history: Received 24 June 2018 Received in revised form 8 March 2019 Accepted 12 March 2019

Keywords: Illegal driving behavior Motorcycle crashes Injury outcomes Data mining technique

a b s t r a c t Although the importance of human factors to crash occurrence has been demonstrated previously, the roles played by human factors in motorcycle killed and severely injured (KSI) crashes have remained unclear. One aim of our study is therefore to empirically determine the relative contribution of illegal behavior to motorcycle KSI crashes, conditional on realworld collisions between motorcycles and motor vehicles. Given that a crash is typically the synthetical result of human, vehicle, roadway, and environmental factors, another aim is to identify high-risk scenarios where inappropriate behavior is more likely to result in severe injuries for motorcyclists through interactions with other related factors. Based on a comprehensive dataset of 4587 police-reported crashes involving motorcycles during 2015–2017 in Hunan province, China, a data mining technique namely classification and regression tree was elaborately employed. Our results demonstrated the illegal behavior of the striking motor-vehicle drivers as one of the most dominant factors contributory to motorcycle KSI crashes, with a normalized importance value of 36.9%. We also confirmed collision object (i.e., collision with heavy or light vehicles) and helmet use of motorcyclists as determinants influencing motorcycle rider injury severities. Two types of extreme highrisk traffic scenarios were identified accordingly. A motorcycle rider was hit at weekends by a heavy motor-vehicle driver who was driving without license, driving a substantial vehicle, speeding, changing lanes illegally or driving in the wrong direction, and a motorcyclist was hit on weekdays by a heavy motor-vehicle driver aged 18–34 or 45–54, who was driving without license, driving a substantial vehicle, speeding, changing lanes illegally or driving in the wrong direction. Our findings are expected to shed more light on a deeper understanding of the illegal driving behavior as causation of motorcycle KSI crashes. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Almost half of people killed from traffic crashes are vulnerable road users worldwide (World Health Organization, 2018a). As one of the most vulnerable road users, motorcycle riders are susceptible to being fatally injured if involved in a collision (Haworth, 2012). This situation is particularly true in southern China where motorcycles are prevalently used with fastmoving motor-vehicles. For example, according to the police record data in Hunan province, China, motorcycles share

⇑ Corresponding author. E-mail address: [email protected] (H. Huang). https://doi.org/10.1016/j.trf.2019.03.010 1369-8478/Ó 2019 Elsevier Ltd. All rights reserved.

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44% of the overall motorized fleets and 33% of the total death in road traffic crashes, compared to 17% for other motorvehicles which represent 51% of motorized. Furthermore, the fatality rate for motorcyclists once being involved in a crash is nearly six times the figure for other drivers (Hunan Provincial Bureau of Statistics, 2017). In order to improve motorcycle road traffic safety, understanding the causes and taking prevention countermeasures accordingly continues to be the critical matter. Although there are several potential causes of traffic crashes, human factors, especially driving behavior have long been demonstrated to make the dominant contribution (Aworemi, Abdul-Azeez, & Olabode, 2010; Treat et al., 1979). Xu and Huang (2016) also highlighted that road users’ improper behavior is the most fundamental premise for the occurrence of most dangerous situations leading to traffic crashes. The finding triggered much research about the relationship between motorcycle riding behavior and crash risk (Cheng & Ng, 2010; Elliott, Baughan, & Sexton, 2007; Papadimitriou, Theofilatos, Yannis, Cestac, & Kraïem, 2014). Concerning another direction of traffic safety research – crash severity, most studies about motorcycle crash severity only focus on driver characteristics, road types, environment factors, crash types, collision objects (Quddus, Noland, & Chin, 2002; De Lapparent, 2006; Pai and Saleh, 2007, 2008), riders’ illegal behavior (Chung, Song, & Yoon, 2014; Rifaat, Tay, & Barros, 2012), other motorists’ specific behavior such as alcohol, drug or medication use (Shaheed, Gkritza, Zhang, & Hans, 2013), failing to yield (Savolainen & Mannering, 2007). Although Turner, McClure, and Pirozzo (2004) provided empirical evidence supporting the association between risk-taking behavior and crash-related injuries, few studies tried to comprehensively assess the contribution of all drivers’ behavior prior to the crash to crash severity. The ignoration of riders’ or other drivers’ illegal behavior during crash injury severity analysis could lead to ignoration of critical factors influencing injury levels, biased estimations, and ineffective countermeasures. One objective of this study is therefore to reveal the contribution of both motorcyclists’ and counterpart drivers’ behavior to motorcycle KSI crashes. Given the fact that a crash is the aggregative consequence of multiple interactive factors and it is impossible for a single factor to provide a complete explanation for crash injury, another objective of this study is to identify high-risk traffic scenarios, in which drivers’ inappropriate behavior is more prone to result in severe injuries for motorcyclists through the interactions with other factors. To identify the high-risk traffic scenarios, traditional regression approaches extensively applied to analyze injury severity, including binary logit models (Pai, 2009), ordered logit/probit models (Pai & Saleh, 2007), multinomial logit models (Savolainen & Mannering, 2007), nested logit models (Savolainen & Mannering, 2007), latent class multinomial logit models (Shaheed & Gkritza, 2014), and random parameters logit models (Chang et al., 2016; Shaheed et al., 2013) were replaced by the machine learning method – Classification and Regression Trees method (CART), due to the incapability to capture the high-order interactions between variables. Only linear additive effects are considered in these statistical models, making it hard to achieve the intention of study. Another limitation imposed on regression models is their dependence on heavy assumptions about data and its pre-specified distributions. If the parameters are assigned a distribution improperly, biased results will be produced and incorrect inference can be drawn (Chang & Chien, 2013; Chang & Wang, 2006). Classification and Regression Trees method (CART) successfully applied to reveal the sophisticated relationship between contributing factors and crash injury severity was therefore selected to identify the high-risk traffic scenarios in this study due to its advantage of relying on less pre-assumptions and being able to deal with high-order interactions between explanatory variables (Huang, Peng, Wang, Luo, & Li, 2018; Jung, Qin, & Oh, 2016). In terms of drivers’ illegal behavior, one most popular research topic is how psychological traits influence risky driving behavior (Antoniazzi & Klein, 2019; Chen & Chen, 2011; Manan, Ho, Arif, Ghani, & Várhelyi, 2017). These studies could help to provide a deeper understanding of why drivers conduct these behaviors. However, it is hard to propose enforceable interventions for the intrinsic motivations of risky riding behaviors, for the psychological traits and personality involved usually indirectly influence driving behaviors via other factors (Wong, Chung, & Huang, 2010). In addition, the data from questionnaires, on-scene investigations or observations are always based on a convenience sampling technique which could lead to biased sample. More importantly, data collected using the aforementioned methods just focus on risky driving behavior without relating to crash. However, not all risky behavior necessarily to cause traffic crash not to mention severe injuries but illegal behavior is indeed likely to be highly risky in specific cases where a high probability of causing severe injuries in crashes is expected through the interactions with other factors. Studies based on data collected through the aforementioned methods may lead to the overemphasis of the importance of drivers’ illegal actions and ignoration of vehicle, road and environmental characteristics. Thus, the data based on real-world traffic crash reported by traffic police, covering the most detailed crash records in China is used in this study. The illegal behavior of relevant drivers prior to the crash and other relevant factors are included in the police crash reports after the investigation of crash scenes, making it possible to study the influences of illegal maneuvers on drivers’ injury severity while controlling other factors. Identifying the external factors of the risk-taking behaviors (high-risk traffic scenes) could be more beneficial to suggest enforceable and effective interventions to improve motorcycle traffic safety, as well as a supplement of previous papers about intrinsic factors exploration. Based on 4587 motorcycle-motor vehicle crashes during 2015–2017 in Hunan province, China, this study intends to illustrate the role of human factors in motorcycle KSI crashes and to identify the high-risk traffic scenarios through the interactive analysis of drivers’ behaviors and other related characteristics by virtue of CART. The findings are expected to stimulate the formulation of evidence-based safety strategies to better protect motorcycle riders.

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2. Data Motorcycle-vehicle crash data in Hunan province of China were collected from the Traffic Management Sector-Specific Incident Case Data Report 2015–2017 maintained by the Traffic Administration Bureau of Hunan Provincial Public Security Ministry. Located in southern China, Hunan is a typical province with a population of 67.4 million and an area of 211,800 km2, composed of 28 prefecture-level and county-level cities, and covered by a complex geographical environment including plains, hills and mountainous regions. A total of 23,343 motorcycle crashes were reported during the period of 2015–2017, of which approximately 36% were two-vehicle crashes between motorcycle and motor-vehicle, 30% for multivehicle crashes, 2% for single motorcycle crashes, and the rest for crashes involving motorcycle and nonmotorized vehicle or pedestrian. Given that motorcycle riders are more likely to be severely injured if colliding with motor-vehicles and it is viable to determine the relative role of struck motorcyclists and the striking drivers in two-vehicle crashes, 4587 motorcycle-motor vehicle crashes were finally retrieved for further investigation after excluding the records with missing information and property damage only crashes which are often largely underreported (Elvik & Mysen, 1999). The data used in the study were dichotomized as slight injury (non-disability injury) and KSI which includes serious injury (disability injury) and fatal injury (immediate or subsequent death from injuries within 7 days after a crash). By aggregating the crash and casualty injury profiles, individual-level variables reflecting the demographic characteristics of the struck motorcyclists and the striking drivers (age and gender), illegal behavior of both sides contributory to the collision, vehicle movements before collision, crash characteristics (collision object and collision type), geometric design features (collision location, road type and traffic control measure), temporal characteristics (collision season, day of week, and time of day) together with the environmental factors (road surface, weather, visibility and lighting condition) were collectively extracted. It is noteworthy that the focus of our analysis is the injury severity sustained by motorcycle riders only. Table 1 illustrates the variables available for analysis in our study. Violations which account for less than 1% of the whole sample were combined as other violations. Based on the transport task, function and traffic volume, roads are divided into three types in China including expressway, ordinary highway, and urban highway. Specifically, the ordinary highway is classified as first-class, second-class, third-class and fourth-class. Urban highway includes urban expressway and general city road. Substandard road refers to the road which fails to meet the requirements of the National Highway Engineering Technical Standards. As for motor-vehicle type, the heavy motor-vehicle includes large and medium-sized coaches and trucks in our study. 3. Methodology To achieve our goals, classification and regression trees (CART), one of the most commonly applied data mining techniques introduced by Breiman, Friedman, Olshen, and Stone (1984), was used in the study. As a non-parametric decision tree learning approach, CART is a white box model, which can display the results graphically in a way that is easy to interpret. CART is also able to capture non-addictive behaviors, allowing to highlight sophisticated relationships that are difficult to reveal otherwise (Chambers and Hastie, 1992; Washington, 2000). In addition, explanatory variable correlations and outliers are not problematic in the CART approach. Interactions between explanatory variables and relationships between risk factors and KSI crashes can be identified by splitting the whole data into more homogeneous subsets to reveal KSI crash patterns. Decision trees are created by a collection of rules based on the variable importance in the modelling data set. The classification tree starts from the root node, where statistical tests are run against every attribute in the data. After selecting a rule for splitting data based on variables’ values and splitting a node into two, the same process is applied to other nodes. A rule for stopping the further splitting is also needed to obtain the classification tree. Each branch of the tree represents a test outcome and each terminal node containing a class label is considered to be homogeneous or ‘‘pure”. A greater distance between a node and the root node represents a higher order of interaction (Washington, 2000). Gini index is selected as the split criterion in the CART method, which represents the diversity of a factor, and is calculated using the formula:

GiniðtÞ ¼ 1 

n X

p2ti

ð1Þ

i¼1

While p2ti is the percentage of KSI or slight injury in node t. A smaller Gini index means a greater pure. Specifically, if all samples in node t are in one class, the Gini index of node t is zero which is the greatest purity. A simple split-sample validation method was used for its computational ease, its moderate sample size, and its proven validity (Chang & Chien, 2013). To assess the accuracy of the model, the dataset was divided into two subsets: 80% of the data was randomly extracted for training the model while the remaining 20% was used for validation. The learning sample selection was randomly operated by computer, which was repeated for many times. It aims to find a less-complex tree with a predictive error comparable to that of the most accurate one. The accuracy can be calculated as follows:

Pn TP i Percentage correct ¼ Pn i¼1 TP þ FNi i i¼1

ð2Þ

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F. Chang et al. / Transportation Research Part F 62 (2019) 844–854 Table 1 Descriptive statistics of variables included in the models. Variables

Number

Killed/severe injury

Slight injury

Motorcycle rider injury

4587 (100%)

1138 (24.81%)

3449 (75.19%)

Rider age Under 18 18–24 25–34 35–44 45–54 55–64 Over 64

202 (4.40%) 298 (6.50%) 653 (14.24%) 743 (16.20%) 1589 (34.64%) 845 (18.42%) 257 (5.60%)

40 (3.51%) 61 (5.36%) 135 (11.86%) 170 (14.94%) 401 (35.24%) 244 (21.44%) 87 (7.65%)

162 (4.70%) 237 (6.87%) 518 (15.02%) 573 (16.61%) 1188 (34.44%) 601 (17.43%) 170 (4.93%)

Rider gender Male Female

3742 (81.58%) 845 (18.42%)

940 (82.60%) 198 (17.40%)

2802 (81.24%) 647 (18.76%)

Illegal behavior of motorcyclist No violations Approaching illegally Drunk riding Failing to give way Overtaking illegally Riding in the wrong direction Failing to keep safe distance Traffic-control facilities violation Riding without license Other violations

3320 (72.38%) 182 (3.97%) 65 (1.41%) 298 (6.50%) 88 (1.92%) 107 (2.33%) 77 (1.68%) 152 (3.31%) 88 (1.92%) 210 (4.58%)

799 (70.21%) 46 (4.04%) 21 (1.85%) 67 (5.89%) 17 (1.49%) 35 (3.08%) 20 (1.76%) 50 (4.39%) 23 (2.02%) 60 (5.27%)

2521 (73.09%) 136 (3.94%) 44 (1.28%) 231 (6.70%) 71 (2.06%) 72 (2.09%) 57 (1.65%) 102 (2.96%) 65 (1.88%) 150 (4.35%)

Helmet use With helmet Without helmet

1324 (28.86%) 3263 (71.14%)

236 (20.74%) 902 (79.26%)

1088 (31.55%) 2361 (68.5%)

Motorcycle movement prior to collision Changing lane Turing left Turning Right Going Straight

93 (2.03%) 640 (13.95%) 168 (3.66%) 3686 (80.36%)

23 (2.02%) 184 (16.17%) 45 (3.95%) 886 (77.86%)

70 (2.03%) 456 (13.22%) 123 (3.57%) 2800 (81.18%)

Age of the counterpart driver Under 18 18–24 25–34 35–44 45–54 55–64 Over 64

27 (0.59%) 318 (6.93%) 1509 (32.90%) 1405 (30.63%) 1071 (23.35%) 228 (4.97%) 29 (0.63%)

7 (0.62%) 60 (5.27%) 376 (33.04%) 367 (32.25%) 285 (25.04%) 41 (3.60%) 2 (0.18%)

20 (0.58%) 258 (7.48%) 1133 (32.85%) 1038 (30.10%) 786 (22.79%) 187 (5.42%) 27 (0.78%)

Gender of the counterpart driver Male Female

4165 (90.80%) 422 (9.20%)

1072 (94.20%) 66 (5.80%)

3093 (89.68%) 356 (10.32%)

Illegal behavior of counterpart driver No violations (No) Approaching illegally (AI) Changing lane illegally (CLI) Driving in wrong direction (DIWD) Driving substandard vehicle (DSV) Driving without license (DWL) Failing to give way (FGW) Making U-turn illegally (MUTI) Overtaking violation (OV) Safety distance violation (SDV) Speeding (SP) Traffic-control facilities violation (TCFV) Other violations (Other)

1267 (27.62%) 443 (9.66%) 105 (2.29%) 114 (2.49%) 130 (2.83%) 227 (4.95%) 594 (12.95%) 92 (2.00%) 243 (5.30%) 162 (3.53%) 68 (1.48%) 264 (5.76%) 878 (19.14%)

339 (29.79%) 101 (8.88%) 23 (2.02%) 38 (3.34%) 49 (4.30%) 51 (4.48%) 102 (8.96%) 6 (0.53%) 52 (4.57%) 37 (3.25%) 32 (2.81%) 58 (5.10%) 250 (21.97%)

928 (26.91%) 342 (9.92%) 82 (2.38%) 76 (2.20%) 81 (2.35%) 176 (5.10%) 492 (14.27%) 86 (2.49%) 191 (5.54%) 125 (3.62%) 36 (1.04%) 206 (5.97%) 628 (18.21%)

Motor vehicle movement prior to collision Changing lane Going straight Turning left Turning right

120 (2.61%) 3458 (75.39%) 699 (15.24%) 310 (6.76%)

22 (1.94%) 920 (80.84%) 144 (12.65%) 52 (4.57%)

98 (2.84%) 2538 (73.59%) 555 (16.09%) 258 (7.48%)

Collision object Heavy motor vehicle Light motor vehicle

780 (17.00%) 3807 (83.00%)

362 (31.81%) 776 (68.19%)

418 (12.12%) 3031 (87.88%) (continued on next page)

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Table 1 (continued) Variables

Number

Killed/severe injury

Slight injury

Collision type Rear-end collision Head-on collision Side-impact collision Sideswipe

474 (10.33%) 858 (18.71%) 3109 (67.78%) 146 (3.18%)

152 (13.36%) 255 (22.41%) 699 (61.42%) 32 (2.81%)

322 (9.34%) 603 (17.48%) 2410 (69.88%) 114 (3.31%)

Collision location Road segments Three-legged junctions Four-legged junctions

3464 (75.52%) 618 (13.47%) 505 (11.01%)

885 (77.77%) 126 (11.07%) 127 (11.16%)

2579 (74.77%) 492 (14.27%) 378 (10.96%)

Road type First-class highway Second-class highway Third-class highway Fourth-class highway Urban expressway General city road Substandard road

172 (3.75%) 728 (15.87%) 915 (19.95%) 851 (18.55%) 149 (3.25%) 1187 (25.88%) 585 (12.75%)

63 (5.54%) 225 (19.77%) 254 (22.32%) 168 (14.76%) 49 (4.31%) 251 (22.05%) 128 (11.25%)

109 503 661 683 100 936 457

Traffic control measure Uncontrolled Traffic signal Traffic marking and/or sign

2282 (49.75%) 250 (5.45%) 2055 (44.80%)

505 (44.37%) 78 (6.86%) 555 (48.77%)

1777 (51.52%) 172 (4.99%) 1500 (43.49%)

Road surface Dry Wet

3760 (81.97%) 827 (18.03%)

904 (79.44%) 234 (20.56%)

2856 (82.81%) 593 (17.19%)

Weather Clear Cloudy Rainy/foggy/snowy

3015 (65.73%) 918 (20.01%) 654 (14.26%)

729 (64.06%) 229 (20.12%) 180 (15.82%)

2286 (66.28%) 689 (19.98%) 474 (13.74%)

Visibility Lower than 50 m 50–100 m 100–200 m Over 200 m

481 (10.49%) 1314 (28.65%) 1105 (24.08%) 1687 (36.78%)

146 322 251 419

(12.82%) (28.30%) (22.06%) (36.82%)

335 (9.72%) 992 (28.76%) 854 (24.76%) 1268 (36.76%)

Lighting condition Daylight Twilight Street lighting at night Complete darkness

3414 (74.42%) 264 (5.76%) 525 (11.45%) 384 (8.37%)

780 (68.54%) 72 (6.33%) 144 (12.65%) 142 (12.48%)

2634 (76.37%) 192 (5.57%) 381 (11.05%) 242 (7.01%)

Season Spring Summer Autumn Winter

1099 1363 1071 1054

278 334 284 242

(24.42%) (29.35%) (24.96%) (21.27%)

821 (23.81%) 1029 (29.83%) 787 (22.82%) 812 (23.54%)

Day of week Weekday Weekend

3279 (71.48%) 1308 (28.52%)

794 (69.77%) 344 (30.23%)

2485 (72.05%) 964 (27.95%)

Time of day Nighttime after midnight (00:00–06:59) Morning peak hours (07:00–08:59) Morning non-peak hours (09:00–11:59) Afternoon non-peak hours (12:00–16:59) Afternoon peak hours (17:00–19:59) Nighttime before midnight (20:00–23:59)

335 (7.31%) 517 (11.27%) 824 (17.96%) 1498 (32.66%) 968 (21.10%) 445 (9.70%)

110 115 169 358 235 151

225 (6.53%) 402 (11.66%) 655 (18.99%) 1140 (33.05%) 733 (21.25%) 294 (8.52%)

(23.96%) (29.71%) (23.35%) (22.98%)

(9.67%) (10.10%) (14.85%) (31.46%) (20.65%) (13.27%)

(3.16%) (14.58%) (19.16%) (19.80%) (2.90%) (27.15%) (13.25%)

where TPi is true positive and FNi is false negative. The variable importance measure (VIM) is one of the most important outputs of CART. It was proposed and specified by Breiman et al. (1984) in Eq. (3). T   X Nt VIM xj ¼ DGiniðSxj ; tÞ N t¼1

ð3Þ

  NtR NtL DGini Sxj ; t ¼ Giniðt Þ  Giniðt R Þ  Giniðt L Þ Nt Nt

ð4Þ

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where T is the total number of nodes; N t refers to the number of samples that belongs to node t; N is the total number of observations; DGiniðSxj ; tÞ represents the reduction in the Gini index at node t; N tR and N tL are the number of observations at the child node t R and tL from node t, respectively; The VIM was calculated for the variable that reduces Gini index value, for each variable with a weight of the number of observations at node t over the tree. CART ranks the explanatory variables according to their importance to the model. 4. Results and discussions The ranking of the variables through variable importance measure indicated the dominant role of human factors in motorcycle KSI crashes. Although human factors are considered to be the most important in the occurrence of traffic crashes, a crash is the synthetical result of human factors, vehicle, roadway and environment characteristics. Thus, this study also aimed to identify the interactions between road users’ behavior and traffic environment in motorcycle KSI crashes. Motorcycle high-risk traffic scenes can be identified by revealing the highly interactive effects between drivers’ behavior and other variables through the CART tree graph. In our study, the CART provided a high overall prediction accuracy both for training and testing sample, which was 76.9% and 72.9%, respectively. During the development of decision trees, the prediction accuracies varied from 75% to 78%, which is a quite small interval. Although CART may be unstable, the variations in the resultant tree are negligible given prediction accuracy and variable importance measures. 4.1. Human factors in motorcycle KSI crashes According to Table 2, collision object, illegal behavior of the counterpart driver, helmet use, rider age, road type, motor vehicle movement prior to collision, time of day, and day of week had a relatively higher VIM value. Among these important variables, 4 out of 8 involved driver characteristics and behavior, confirming the substantial contribution of driver-related factors to motorcycle KSI crashes. Specifically, the counterpart drivers’ irregular behavior ranked as the second highest with a VIM value much higher than other variables. This variable was also the second splitter and appeared repeatedly during the tree growth, implying that the violation of the other party driver played an indispensable role to determine the motorcyclist injury severities in motorcycle-motor vehicle crashes. 4.2. Identification of high-risk traffic scenes Fig. 1 showed the classification tree obtained based on the training sample. The classification tree generated eight splitters and 14 terminal nodes, revealing the interactive effects of risk factors (splitters) on rider injury severity with a brief graphic display. The high-risk traffic crash circumstances identified in the decision tree could be further confirmed by the result of variable importance measure. In addition, the structure of the classification tree reveals a consistent ranking of variables as VIM. The first parent node of the decision tree is collision object, which suggests that collision vehicle type is the best predictor to classify and model riders’ injury severity. The collision object was then spilt into two child nodes including heavy vehicle (node 1) and light vehicle (node 2), which showed motorcycle riders colliding with a heavy vehicle have a higher probability of getting killed or severely injured (46%) compared with the collision with a light vehicle (20%). To the left, as the second splitter, the illegal behavior of the counterpart driver split node 1 into two child nodes, including failing to give way, traffic control facility violation, safety distance violation, making U-turn illegally and overtaking illegally (node 3), and driving a substandard vehicle, speeding, travelling in the wrong direction, changing lane illegally, driving without license, other violations or and no violations (node 4). It was found that the counterpart driver who has the behavior at node 4 tend to be

Table 2 Independent variable importance. Independent variable

Importance

Normalized importance

Collision object Illegal behavior of the counterpart driver Helmet use Rider age Road type Motor vehicle movement prior to collision Time of day Day of week Age of the counterpart driver Lighting condition Illegal behavior of motorcyclist Gender of the counterpart driver Crash location Rider gender

0.019 0.007 0.003 0.003 0.003 0.002 0.002 0.002 0.001 0.001 0.000 0.000 0.000 0.000

100.0% 36.9% 15.7% 14.1% 13.2% 12.0% 10.9% 8.5% 6.9% 3.3% 2.1% 1.8% 1.4% 1.3%

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Fig. 1. Classification tree for motorcycle rider injury severity.

fatally or severely injured in the crash with the probability of 51.8%. Further down to the tree, node 4 was split based on the day of week into a terminal node 7 and child node 8, indicating traffic crashes on weekends have the highest probability of causing riders fatal or severe injury (64.8%). Node 8 was further divided into two terminal nodes (node 13 and node 14), from which we can find motorcycle riders who collide with drivers aged 18–34 and 45–54 have a higher probability of getting killed or severely injured, compared with the ones interacting with drivers in other age groups (52.7% vs 38.2%). Turning to the right branch, the helmet use was the second splitter dividing node 2 into two child nodes (node 5 and node 6), which means that helmet use is also an important predictor to classify and model riders’ injury severity. The statistics comparison between node 5 and 6 suggests that riders without helmets are more likely to involve in fatal or serious crashes. Node 5 was then split into terminal node 10 and child node 9 based on the irregular behavior of the counterpart driver, which indicates that drivers’ behavior of operating a substandard vehicle, speeding, failing to keep a safe distance, overtaking irregularly, other irregular operations, and regular driving are more likely to be associated with a fatally or severely injured rider in the crash, compared with other violation types (26.3% vs 16.7%). Further down to the decision tree, node 9 generated node 15 and node 16 based on rider age. Node 15 was split into terminal node 21 and 22 based on the irregular behavior of the counterpart driver, suggesting that the other party driver who operates a substandard vehicle, overspeed drives, or travels in the opposite direction tends to cause more severe injuries for motorcycle riders than drivers with other violation types (36.5% vs 17.9%). Road type divided node 16 into 2 terminal nodes. Compared with other road types (terminal node 23), urban expressway and second-class highway (terminal node 24) were discovered to be associated with riders’ more severe injuries in traffic crashes. In terms of node 6, it generated two child nodes (node 11 and node 12) based on the road type. The proportion of KSI crashes showed that first-class, second-class highway and urban expressway pose a greater threat to riders. Node 12 produced terminal node 19 and 20 according to the variable-time of day. Non-peak time at afternoon and nighttime before midnight are more related with severely injured motorcycle riders, relative to other period of time. As for node 11, it was split based on the irregular behavior of the counterpart driver, forming child node 17 and terminal node 18. Further down to the classification tree, node 17 was divided into terminal node 25 and 26 without a further split. Examining all terminal nodes thoroughly, terminal nodes 7 and 14 have a higher potential of causing rider fatalities and serious injuries. Node 7 showed a deadly traffic scene where a heavy vehicle driver who operates a substandard vehicle, drives exceeding the speed limit, travels in the wrong direction, changes lanes illegally, drives without license at the weekend. While node 14 suggests that an 18–34 or 45–54 heavy driver who has those behavior at weekday is also a deadly threat to motorcycle riders. The findings from the left branch of tree suggest that the collision with a heavy motor-vehicle whose driver commit specific violations, weekend, specific age drivers at weekday increases the likelihood of fatality and serious injuries in riders. Because of their great mass, heavy motor-vehicles are always over-represented in the KSI crashes and most of the deaths are not heavy vehicle occupants but rather the other road users in crashes involving heavy vehicles (Kim, Kim, Ulfarsson, & Porrello, 2007). In terms of aforementioned driving behavior, they are all risky in nature as explained in previous

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studies. For example, speeding was found to be associated with an elevated injury level as a result of high impact force beyond human tolerance (Abegaz, Berhane, Worku, Assrat, & Assefa, 2014); driving against the traffic flow always lead to head-on crashes which were considered as one of the most severe crash types (Hosseinpour, Yahaya, & Sadullah, 2014). The aggregating effects of heavy motor-vehicles and risky behaviors are more likely to pose a great threat to riders at weekends when people are often relaxed by weekend activities and are less alert to surrounding traffic environment or at weekdays involving an 18–34 or 45–54 heavy vehicle driver. 5. Safety implication In order to better present the importance of related factors, the factors related to two high-risk traffic scenarios and another three traffic scenarios (node 24, node 19 and node 22) where KSI crashes account for more than 30% were displayed in the Fig. 2. As we can see, collision object, at-fault driver violation, and helmet usage are located in the center of the graph and occur in several scenarios, implying that they are the most important factors influencing motorcycle rider injury severity. In addition, heavy motor-vehicle drivers’ violations including driving substandard vehicle, speeding, travelling in the opposite direction, changing lanes illegally, and unlicensed driving are found significant both in two high-risk traffic scenarios. Therefore, it is critical to curb heavy motor-vehicle drivers’ illegal behavior especially in high-risk traffic scenarios and to improve motorcycle riders’ helmet use rate for mitigating motorcycle riders’ injury severity. 5.1. The improvement of heavy motor-vehicle drivers’ performance Two motorcycle high-risk traffic scenarios identified are: (1) motorcycle riders interacting with heavy motor-vehicle drivers who commit violations, especially driving substandard vehicle, speeding, travelling in the opposite direction, changing lanes illegally, and unlicensed driving at weekends; (2) motorcycle riders interacting with heavy motor-vehicle drivers aged 18–34 or 45–54 who commit the same types of violations as the scenario one on weekdays. An integrated strategy including enforcement, education, engineering together with management and technologies are suggested to curb heavy motorvehicle drivers’ violations in high-risk traffic scenes. Before the development of enforcement measures, the understanding of the existing applicable laws and regulations is needed. In China, some specific driving laws about various types of violations were made in ‘‘Road Traffic Safety Law of the People’s Republic of China‘‘ and ‘‘Regulation on the Implementation of the Road Traffic Safety Law of the People’s Republic of

Fig. 2. Motorcycle high-risk traffic scenarios and related factors.

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China”. For example, the punishment for drivers who operate substandard vehicles, violate traffic facilities, overtake illegally, drive in the wrong direction, change lanes illegally, and speeding is a fine between 20 and 200 RMB (about 2.88–28.8 USD) and/or a deduction of 0–2 points from 12 points which is the maximum score deducted before the revocation of driver license. In terms of unlicensed driving, road users who drive without obtaining a license or during the period when the driving license is revoked will be fined 200–2000 RMB (28.8–288 United States Dollars) and detained for 15 days. Although the punishment for unlicensed driving is relatively severe, the cost of violations is quite low compared to per capita disposable income of 23102.71 RMB (about 3500 USD) in 2017. It is thus necessary to increase the cost of drivers’ violations in China to curb drivers’ violation behavior. To ensure the effectiveness of laws and regulations, strict enforcement is necessary. Given the fact that the police cannot catch all offenders, the success of laws depends on deterring potential offenders by creating the public perception of apprehension which is more important in deterring offenders than the severity of punishment. The key to creating this perception is sustained and well-publicized enforcement, which can be achieved by setting highly visible checkpoints intended to deter potential offenders. In terms of police’s law enforcement, it includes a variety of methods that use both technology and personnel to raise awareness and educate drivers about their driving behaviors and how they relate to the safety rules. However, many enforcement programs may be only effective during the period when it is conducted. If the program is stopped, the violation rate is likely to go back to the high level. Therefore, sustainably effective law enforcement mechanism should be established to ensure the long-term effectiveness of enforcement activities, which we can refer to the procedures of improving enforcement effectivity proposed by Pedestrian and Bicycle Information Centre. Due to the limitation of financial and human resources, allocating police or installing more traffic cameras only on roads where there are many heavy motorvehicles and motorcycles is recommended to deter heavy motor-vehicle drivers from illegal behavior especially at weekends. Apart from laws and strict enforcement, more publicity and educations are also expected to curb violations of heavy motor-vehicle drivers aged 18–34 or 45–54 in particular. In addition, there are other specific interventions available for various violations. For example, regarding the substandard vehicle, ongoing maintenance is required to maintain a heavy vehicle in a safe and roadworthy condition over its lifetime. Concerning vehicles travelling in the opposite direction, road facility strategies such as median strip and central traffic islands that separates opposing lanes of traffic on divided roadways are suggested. In addition to curbing heavy motor-vehicle drivers’ violations, new technologies and traffic management can also improve motorcycle related safety. Although the penetration of several crash avoidance technologies into large trucks has been slow, the technologies have a great potential of reducing large truck crashes (Jermakian, 2012). From the view of management, an available and effective countermeasure to reduce fatal and severe crashes is to segregate heavy vehicles from other road users, especially vulnerable riders from the perspective of time and space. In terms of the time dimension, there is a time restriction to heavy vehicles in many cities of China, like Changsha (Changsha Public Security Bureau, 2014). With regards to space, one measure usually used is to limit heavy vehicles on the suburban roads or roads in remote areas. The other is to set exclusive motorcycle lanes which can help avoid the conflicts between heavy motor-vehicles and motorcycles. However, because setting exclusive motorcycle lane is at the cost of other motor-vehicles’ travelling space, its applicability needs specified assessment at the background of traffic congestions in China. 5.2. The enhancement of helmet usage The protective effect of helmets has been widely emphasized by traffic safety researchers and relevant organizations. In a traffic crash, unhelmeted riders are three times as likely as helmeted ones to suffer brain injuries which often result in lifelong disabilities or death (National Highway Traffic Safety Administration, 2008). World Health Organization (2018b) stated that riders’ risk of death and severe injury can decrease by about 40% and over 70%, respectively if a quality-standard motorcycle helmet is correctly used. However, China is identified as a country without a good helmet law, because riders are not required to fasten helmets properly in China (World Health Organization, 2018c). According to Li, Li, Cai, Zhang, and Lo (2008), the prevalence of helmet correct use, the figure is quite low, 32.3% and 15.3% for motorcycle drivers and passengers, respectively. According to the interview of motorcycle riders, although 90% acknowledged the benefits of wearing helmets, the majority (70%) wore helmets to ‘‘cope with police” instead of preventing or alleviating head injury, which may explain the much lower rate of correct usage. Furthermore, some interviewees reported that wearing a helmet is uncomfortable and block their vision. However, a 1994 study found that wearing helmets does not restrict the ability to hear horn signals or to see a vehicle in an adjacent lane prior to initiating a lane change (Mcknight & Mcknight, 1994). Education and enforcement are two main measures suggested to improve the motorcycle usage rate. However, as Li et al. (2008) stated that most motorcyclists in China have realized the benefits of helmets but still use poor vision or discomfort as an excuse for not wearing a helmet. As such, we suggest using strengthened enforcement rather than education as the most important way to improve the prevalence of helmet use in China. It is believed that helmet-wearing rate can increase to over 90% when motorcycle helmet laws are strictly enforced. In support of this statement, we can refer to the changes of Arkansas’ helmet wearing rate in the year after the helmet law weakened, falling from 97% to 52% (Preusser, Hedlund, & Ulmer, 2000). However, the enforcement of helmet laws is not strictly enacted in China (Li et al., 2008) and the cost of violating helmet law is quite low. For example, the penalty for the absence of a safety helmet is only 50 China Yuan (less than 8 United States Dollars) in Hunan compared to per capita disposable income of 23102.71 China Yuan (about 3500 United States Dollars) in 2017. Therefore, it is imperative to improve the helmet law by requiring riders over 44 in particular to wear helmets

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correctly, undertake more high visibility enforcement activities on second-class highways and urban highways, and increase the cost of law violation in China. 6. Conclusions Based on the 2015–2017 crash data from Hunan Province of China, this study intended to identify motorcycle high-risk traffic scenes through the interactive risk analysis of high-risk riding behaviors and other traffic characteristics. To reveal a variety of highly correlated explanatory factors, a classification tree rather than a statistical model technique was applied. The results showed that the variable for collision object is the most important determinant of rider fatality and serious injury, followed by the other driver’s violation, helmet usage, and rider age. These variables interact with each other, forming motorcycle high-risk traffic scenarios as following: (1) motorcycle riders interacting with heavy motor-vehicle who commit violations, especially driving substandard vehicle, speeding, travelling in the opposite direction, changing lanes illegally, and unlicensed driving on the weekend; (2) motorcycle riders interacting heavy motor-vehicle drivers aged 18–34 or 45–54 who commit the same types of violations as the scenario one on weekdays. The results highlight the importance of both heavy motor-vehicle drivers’ behavior and helmet use in motorcycle KSI crashes, which is helpful to propose evidence-based and targeted interventions to reduce the injury levels of motorcycle riders. The finding also suggests that more attention should be paid to curbing other motor-vehicles’ improper behavior rather than just motorcycle riders’ which is the research focus of previous studies. Regarding drivers’ violations, increase in the penalty of drivers’ violations, sustainably strengthened enforcement at weekends, and education programs for heavy motorvehicle drivers aged 18–34 or 45–54 are suggested. In addition, segregating vulnerable motorcycles from heavy motorvehicle drivers and crash avoidance system are proposed to improve motorcycle traffic safety. Considering the protection of helmets, improving helmet law by requiring riders to wear helmet correctly and highly visible enforcement activities are the right strategy to mitigate motorcycle rider injury severity. The study is based on the crash data reported by traffic police and focuses on the fatal and severe injury probability of post-crash which is actually a conditional probability. Police crash report is the available official data, covering the most detailed crash records in China, based on which many policies and measures are developed by relevant government departments. Based on the illegal behavior of relevant drivers in the crash and other factors recorded in the police report, the study explored the contribution of illegal behavior to KSI motorcycle crashes and how it interacts with other contributing factors. Although this is an empirical study and cannot provide the precise causative relationship, it can be a complement and support to previous studies about intrinsic factors exploration from both policies and data point. Besides, the results show that the illegal traffic behavior of the other party is proved to make more contributions to the riders’ serious injury and fatality than riders’ violations, which suggests that counterpart drivers’ behavior should not be ignored as in previous studies but be emphasized during the motorcycle crash severity analysis. In addition, the results also provide a new direction for observation of motorcycle riders’ behavior. While researchers focus on the observation of driving behavior of motorcycle riders themselves, more attention should be paid to other road users’, especially the violations of heavy vehicle drivers. There are still some limitations about the police-report crash data used in this study, for example, the possibility of underreporting of less severe crashes, and the unavailability of relevant variables such as collision speed and traffic volume. Therefore, the integration of police-reported data with other data sources (e.g. questionnaire surveys, field observations, driving simulations, accident reconstruction simulation) is recommended to achieve a more explicit understanding of the causal mechanism of motorcycle crashes. Acknowledgements This work was supported by the Joint Research Scheme of National Natural Science Foundation of China/Research Grants Council of Hong Kong (Project No. 71561167001 & N_HKU707/15), and the Natural Science Foundation of China (Project No. 71371192). Author Contributions: Fangrong Chang collected the data and wrote this paper; Hanchu Zhou designed the model; Pengpeng Xu provided writing assistance and language help; Jaeyoung Lee and Helai Huang made editing corrections. References Abegaz, T., Berhane, Y., Worku, A., Assrat, A., & Assefa, A. (2014). Effects of excessive speeding and falling asleep while driving on crash injury severity in Ethiopia: A generalized ordered logit model analysis. Accident Analysis & Prevention, 71, 15–21. Antoniazzi, D., & Klein, R. (2019). Risky riders: A comparison of personality theories on motorcyclist riding behaviour. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 33–44. Aworemi, J. R., Abdul-Azeez, I. A., & Olabode, S. O. (2010). Analytical study of the causal factors of road traffic crashes in southwestern Nigeria. Educational Research, 1(4), 118–124. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. Chambers, J. M., & Hastie, T. J. (Eds.). (1992). Tree-based models. Statistical models in S. New York: Chapman & Hall. Chapter 9. Chang, L. Y., & Chien, J. T. (2013). Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Safety Science, 51(1), 17–22.

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Chang, F., Li, M., Xu, P., Zhou, H., Haque, M. M., & Huang, H. (2016). Injury severity of motorcycle riders involved in traffic crashes in Hunan, China: A mixed ordered logit approach. International journal of environmental research and public health, 13(7), 714. Chang, L. Y., & Wang, H. W. (2006). Analysis of traffic injury severity: An application of nonparametric classification tree techniques. Accident Analysis & Prevention, 38(5), 1019–1027. Changsha Public Security Bureau (2014). Notice on restrictions on the passage of freight vehicles in parts of urban areas and road sections. . Chen, C. F., & Chen, C. W. (2011). Speeding for fun? Exploring the speeding behavior of riders of heavy motorcycles using the theory of planned behavior and psychological flow theory. Accident Analysis & Prevention, 43(3), 983–990. Cheng, A. S. K., & Ng, T. C. K. (2010). Development of a Chinese motorcycle rider driving violation questionnaire. Accident Analysis & Prevention, 42(4), 1250–1256. Chung, Y., Song, T. J., & Yoon, B. J. (2014). Injury severity in delivery-motorcycle to vehicle crashes in the Seoul metropolitan area. Accident Analysis & Prevention, 62, 79–86. De Lapparent, M. (2006). Empirical Bayesian analysis of accident severity for motorcyclists in large French urban areas. Accident Analysis & Prevention, 38(2), 260–268. Elliott, M. A., Baughan, C. J., & Sexton, B. F. (2007). Errors and violations in relation to motorcyclists’ crash risk. Accident Analysis & Prevention, 39(3), 491–499. Elvik, R., & Mysen, A. B. (1999). Incomplete accident reporting: Meta-analysis of studies made in 13 countries. Transportation Reseach Record, 1665, 133–140. Haworth, N. (2012). Powered two wheelers in a changing world—challenges and opportunities. Accident Analysis & Prevention, 44(1), 12–18. Hosseinpour, M., Yahaya, A. S., & Sadullah, A. F. (2014). Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: Case studies from Malaysian Federal Roads. Accident Analysis & Prevention, 62, 209–222. Huang, H., Peng, Y., Wang, J., Luo, Q., & Li, X. (2018). Interactive risk analysis on crash injury severity at a mountainous freeway with tunnel groups in China. Accident Analysis & Prevention, 111, 56–62. Hunan Provincial Bureau of Statistics (2017). Hunan Statistical Yearbook 2016. China Statistics Press. Jermakian, J. S. (2012). Crash avoidance potential of four large truck technologies. Accident Analysis and Prevention, 49, 338–346. Jung, S., Qin, X., & Oh, C. (2016). Improving strategic policies for pedestrian safety enhancement using classification tree modeling. Transportation Research Part A: Policy and Practice, 85, 53–64. Kim, J. K., Kim, S., Ulfarsson, G. F., & Porrello, L. A. (2007). Bicyclist injury severities in bicycle–motor-vehicle accidents. Accident Analysis & Prevention, 39(2), 238–251. Li, L. P., Li, G. L., Cai, Q. E., Zhang, A. L., & Lo, S. K. (2008). Improper motorcycle helmet use in provincial areas of a developing country. Accident Analysis & Prevention, 40(6), 1937–1942. Manan, M. M. A., Ho, J. S., Arif, S. T. M. S. T., Ghani, M. R. A., & Várhelyi, A. (2017). Factors associated with motorcyclists’ speed behaviour on Malaysian roads. Transportation research part F: Traffic Psychology and Behaviour, 50, 109–127. Mcknight, A. J., & Mcknight, A. S. (1994). The effects of motorcycle helmets upon seeing and hearing Report no. DOT HS-808-399. Washington, DC: National Highway Traffic Safety Administration. National Highway Traffic Safety Administration (2008). Traffic safety facts, laws: Motorcycle helmet use laws Report no. DOT HS-810-887W. Washington, DC: U.S. Department of Transportation. Pai, C. W. (2009). Motorcyclist injury severity in angle crashes at T-junctions: Identifying significant factors and analysing what made Drivers fail to yield to motorcycles. Safety Science, 47, 1097–1106. Pai, C. W., & Saleh, W. (2007). An analysis of motorcyclist injury severity under various traffic control measures at three-legged junctions in the UK. Safety Science, 45, 832–847. Pai, C. W., & Saleh, W. (2008). Modelling motorcyclist injury severity by various crash types at T-junctions in the UK. Safety Science, 46, 1234–1247. Papadimitriou, E., Theofilatos, A., Yannis, G., Cestac, J., & Kraïem, S. (2014). Motorcycle riding under the influence of alcohol: Results from the SARTRE-4 survey. Accident Analysis & Prevention, 70, 121–130. Pedestrian and Bicycle Information Centre. What police can do to enforce the laws. (accessed 21.11.2018). . Preusser, D. F., Hedlund, J. H., & Ulmer, R. G. (2000). Evaluation of motorcycle helmet law repeal in Arkansas and Texas. Washington, DC: National Highway Traffic Safety Administration. Quddus, M. A., Noland, R. B., & Chin, H. C. (2002). An analysis of motorcycle injury and vehicle damage severity using ordered probit models. Journal of Safety Research, 33, 445–462. Rifaat, S. M., Tay, R., & Barros, A. D. (2012). Severity of motorcycle crashes in Calgary. Accident Analysis & Prevention, 49, 44–49. Savolainen, P., & Mannering, F. (2007). Probabilistic models of motorcyclists’ injury severities in single-and multi-vehicle crashes. Accident Analysis & Prevention, 39(5), 955–963. Shaheed, M. S., & Gkritza, K. (2014). A latent class analysis of single-vehicle motorcycle crash severity outcomes. Analytic Methods in Accident Research, 2(2), 30–38. Shaheed, M. S. B., Gkritza, K., Zhang, W., & Hans, Z. (2013). A mixed logit analysis of two-vehicle crash severities involving a motorcycle. Accident Analysis & Prevention, 61, 119–128. Treat, J. R., Tumbas, N. S., McDonald, S. T., Shinar, D., Hume, R. D., Mayer, R. E., ... Catellan, N. J. (1979). Tri-level study of the causes of traffic accidents: Volume I: Causal factor tabulations and assessment (Document NO. DOT HS-805 085). Washington, DC: National Highway Traffic Safety Administration, USDOT. Turner, C., McClure, R., & Pirozzo, S. (2004). Injury and risk-taking behavior—a systematic review. Accident Analysis & Prevention, 36(1), 93–101. Washington, S. (2000). Iteratively specified tree-based regression: Theory and trip generation example. Journal of Transportation Engineering, 126(6), 482–491. Wong, J. T., Chung, Y. S., & Huang, S. H. (2010). Determinants behind young motorcyclists’ risky riding behavior. Accident Analysis & Prevention, 42(1), 275–281. World Health Organization (2018a). Global status report on road safety 2018 (accessed 20.11.2018). . World Health Organization (2018b). Road Safety. (accessed 20.11.2018). . World Health Organization (2018c). Death on the roads. (accessed 20.11.2018). . Xu, S., & Huang, H. (2016). Traffic crash liability determination: Danger and Dodge model. Accident Analysis & Prevention, 95, 317–325.