Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia
Abstract
:1. Introduction
2. Background and Literature Review
2.1. Causes of RTCs
2.2. Machine Learning (ML) Models in RTC Analyses
3. Materials and Methods
3.1. Study Site
3.2. Materials
Preprocessing Data
- Getting the data to know: This step studied the various attribute types, which included nominal, binary, ordinal, and numeric attributes. Basic descriptive statistics are used to learn more about each attribute’s values. Knowing basic statistics makes it easier to fill in missing values, smooth noisy values, and spot outliers in the data preprocessing stage. Knowing attributes and their values can also help deal with inconsistencies incurred during data integration. Visualization of the RTC data provided information on the trend of the main attributes used in modeling.
- Checking the completeness: This step was carried out by checking the completeness of the main attributes of crash occurrences, such as the crash type, road and weather conditions, number of casualties and injuries, crash reasons and remarks, and road geometry. Some missing data were imputed with information available from other attributes, but some could not. For example, 943 out of 3439 cases were missing “road geometry” attributes that could not be imputed and coded as ‘unknown’ or ‘other’ in the variables used to describe roadway geometry.
- Imputing missing data: Data with missing values for some attributes are quite common. There are various methods for handling the problem of missing values in data. Here, using the most probable value to fill in the missing value was preferred and determined with decision tree induction using non-missing crash attributes in the data set [46].
- Normalization: Data normalization gives all attributes an equal weight, where the values are scaled to a smaller range, such as 0.0 to 1.0. Normalization benefits classification algorithms such as neural networks or distance-based models. Normalizing the values for each attribute included in the training set helps speed up the learning phase when using the neural network backpropagation algorithm for classification. For distance-based methods, normalization prevents attributes with large ranges (e.g., AADT, min = 2083 and max = 60,244) from outweighing small-range attributes (e.g., binary variables). There are several methods used for normalization. The min-max normalization was selected in this study. Min-max normalization transforms a value x of a numeric variable V to in the range (0, 1), as shown in Equation (1) below.
3.3. Methods
3.3.1. Analysis of RTCs
3.3.2. Modeling of RTCs
4. Results and Discussion
4.1. Descriptive Statistics and Visualization of RTC Factors
4.2. Modeling Results
4.2.1. Results of BNLOGREG
4.2.2. Comparison of BNLOGREG with Machine-Learning Algorithms
4.2.3. Discussion of the Results and Research Limitations
4.3. Crash Prevention and Mitigation Strategies
4.3.1. Designing Safe Roads and Maintaining Work Zone Safety
4.3.2. Driver Education and Awareness
4.3.3. Application of Advance Technologies
4.3.4. In-Vehicle Technologies and Autonomous Driving
4.3.5. Legislation and Enforcement of Traffic Regulations
4.3.6. Benefits of Academic Studies and Research on Traffic Safety
5. Summary and Conclusions
- The performance of all models is comparable, so they are found to be suitable for predicting the probability of driver errors in the occurrence of RTCs and understanding the role of the input variables in explaining the model outcomes.
- The two most influential variables are ASF and AADT in the RF model. In line with the findings of previous research conducted in a similar study context, an increase in the number of lanes (NL) and daily average speed of traffic flow (ASF) reduces the likelihood of the RTCs caused by driver errors. This finding is also supported by the results of previous studies [32,67]. In contrast, an increase in traffic volume (AADT) and the road geometry features (straight sections and horizontal curves) significantly contributed to driver errors leading to RTCs.
- Straight road sections and the sections with horizontal curves increase the probability of driver-error-related RTCs by more than two and three folds (odds ratios = 2.280 and 3.555 yielded by BNLOGREG).
- The inferences concerning the effects of crash attributes are in agreement with the findings in the literature. Thus, the paper sufficiently contributes to insufficient knowledge of the factors in RTCs on major roads within the context of this case study.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Station 1 | Road No. | Road Type 2 | Speed Limit | No. of Lanes | Weather Cond. | Road Cond. | No. of Deaths | No. of Injured | No. of Vehicles | Road Geometry |
---|---|---|---|---|---|---|---|---|---|---|
1568 | 15 | fast | 120 | 3 | No rain | Dry | 1 | 10 | 3 | Straight |
1917 | 15 | double | 110 | 2 | No rain | Dry | 0 | 0 | 2 | Straight |
1553 | 15 | fast | 120 | 3 | No rain | Dry | 0 | 0 | 1 | Straight |
1754 | 15 | fast | 110 | 4 | No rain | Dry | 0 | 0 | 2 | Straight |
1599 | 15 | fast | 120 | 3 | No rain | Dry | 0 | 6 | 2 | Straight |
1753 | 15 | fast | 110 | 4 | No rain | Dry | 0 | 1 | 2 | Straight |
1754 | 15 | fast | 110 | 4 | No rain | Dry | 0 | 3 | 1 | Straight |
1725 | 15 | fast | 120 | 3 | No rain | Dry | 0 | 0 | 2 | Straight |
1731 | 15 | fast | 120 | 3 | No rain | Dry | 0 | 5 | 1 | Straight |
1602 | 15 | fast | 120 | 3 | No rain | Dry | 0 | 0 | 1 | Straight |
Crash Causes | Crashes | Deaths | Injuries | |||
---|---|---|---|---|---|---|
Number | Percent | Number | Percent | Number | Percent | |
Driver-related crashes | ||||||
Speeding | 1028 | 29.9 | 102 | 0.099 | 933 | 0.908 |
Distracted driving/loss of control | 816 | 23.7 | 132 | 0.162 | 810 | 0.993 |
Reckless driving | 353 | 10.3 | 29 | 0.082 | 341 | 0.966 |
Driver asleep | 272 | 7.9 | 53 | 0.195 | 284 | 1.044 |
Other | 99 | 2.9 | 15 | 0.269 | 88 | 1.129 |
Subtotal | 2568 | 74.7 | 331 | 0.129 | 2456 | 0.956 |
Vehicle-related crashes | ||||||
Tire-blowout | 361 | 10.5 | 51 | 0.141 | 343 | 0.950 |
Mechanical/electrical malfunction | 114 | 3.3 | 10 | 0.088 | 56 | 0.491 |
Overloading/misloading | 12 | 0.3 | 0 | 0.000 | 14 | 1.167 |
Subtotal | 487 | 14.2 | 61 | 0.125 | 413 | 0.848 |
Other factors | ||||||
Road/traffic conditions-related | 20 | 0.6 | 0 | 0.000 | 16 | 0.800 |
Weather-related | 10 | 0.3 | 1 | 0.100 | 9 | 0.900 |
Animal crossing | 74 | 2.2 | 10 | 0.135 | 53 | 0.716 |
Other/undetermined | 280 | 8.0 | 27 | 0.096 | 270 | 0.964 |
Subtotal | 384 | 11.1 | 38 | 0.099 | 348 | 0.906 |
Variable | Mean | Std. Dev. | Min. | Max. | Sum (No. of Cases) 2 |
---|---|---|---|---|---|
RTCs by causes and consequences | |||||
Annual number of RTCs | 1146.33 | 141.11 | 1048 | 1308 | 3439 |
RTCs caused by driver error | 856.67 | 32.52 | 820 | 882 | 2570 |
RTCs caused by other factors | 289.67 | 118.25 | 215 | 426 | 869 |
Annual number of total casualties | 143.33 | 7.09 | 137 | 151 | 430 |
Casualties due to driver-errors | 110.33 | 13.61 | 115 | 121 | 331 |
Casualties due to other factors | 33 | 19.92 | 21 | 56 | 99 |
Number of casualties per all crashes | 0.1258 | 0.01 | 0.1154 | 0.1355 | 0.1250 |
Casualties per driver-error crashes | 0.1293 | 0.02 | 0.1077 | 0.1476 | 0.1288 |
Casualties per other factor crashes | 0.1086 | 0.02 | 0.0921 | 0.1315 | 0.1139 |
Annual number of total injuries | 1072.33 | 100.27 | 980 | 1179 | 3217 |
Injuries due to driver-errors | 1018.33 | 131.98 | 855 | 1017 | 2800 |
Injuries due to other factors | 139 | 20.07 | 125 | 162 | 417 |
Number of injuries per all crashes | 0.9378 | 0.0378 | 0.9014 | 0.9769 | 0.9354 |
Injuries per driver-error crashes | 0.9919 | 0.0739 | 0.9093 | 1.0518 | 0.9911 |
Injuries per other factor crashes | 0.7362 | 0.1295 | 0.6491 | 0.8850 | 0.7710 |
Traffic flow characteristics | |||||
Annual average daily traffic (AADT) 1 | 13,756.08 | 14,377.92 | 2083 | 60,244 | (3327) 2 |
Daily average speed of traffic flow (ASF) in kph 1 | 96.77 | 8.86 | 70.5 | 116 | (3327) 2 |
85th percentile speed in kph | 117.56 | 10.17 | 101 | 151 | (3320) 2 |
Road geometry characteristics | |||||
Number of lanes (NL) 1 | 2.93 | 0.65 | 2 | 4 | (3435) 2 |
Speed limit (km/h) | 116.91 | 4.63 | 100 | 120 | (3435) 2 |
Categorical variables | |||||
Causes of RTCs (DV) | 1 = driver-error (count = 2570) | (2852) 2 | |||
0 = otherwise (count = 869) | (587) 2 | ||||
RG1: Straight segment 1 | 1 = straight (count = 2438) | (2438) 2 | |||
0 = otherwise (count = 1001) | (1001) 2 | ||||
RG2: Horizontal curve 1 | 1 = horizontal curve (count =39) | (3400) 2 | |||
0 = otherwise (count = 3400) | (39) 2 |
Variable | Q1 | Q3 | Q3 − Q1 | z-Scores 1 | |||
---|---|---|---|---|---|---|---|
Min. | No. of Cases | Max. | No. of Cases | ||||
ASF | 88.5 | 102.2 | 13.7 | −2.9658 > −2.698 * | 43 | 2.1715 < 2.698 * | N/A 2 |
AADT | 6794 | 12,904 | 6110 | −0.8119 < −2.698 * | N/A 1 | 3.2333 > 2.698 * | 230 |
Unweighted Cases | N | Percent | |
---|---|---|---|
Selected Cases | Included in Analysis | 3327 | 96.7 |
Missing Cases | 112 | 3.3 | |
Total | 3439 | 100.0 | |
Unselected cases | 0 | 0.0 | |
Total | 3439 | 100.0 |
Unweighted Cases | N | Percent | |
---|---|---|---|
RG1: Straight Section | Straight section (1) | 2344 | 70.5 |
Other (0) | 983 | 29.5 | |
RG2: Horizontal Curve | Horizontal curve (1) | 39 | 1.2 |
Other (0) | 3288 | 98.8 |
Variable in the Equation | |||||||
---|---|---|---|---|---|---|---|
Model | B | S.E. | Wald | df | Sig. | Exp(B) | |
Model 0 | Constant 1 | 1.055 | 0.040 | 709.766 | 1 | <0.001 | 2.873 |
Predicted | |||||||
CRTC | Percentage | ||||||
Observed | Otherwise = 0 | Driver error = 1 | Correct 2 | ||||
Model 0 | CRTC | Otherwise = 0 | 0 | 859 | 0.0 | ||
Driver error = 1 | 0 | 2468 | 100.0 | ||||
Overall percentage | 74.2 |
Model | Chi-Square | df | Sig. | |
---|---|---|---|---|
Step 5 | Model 0 | 10.109 | 1 | 0.001 |
Model 1 | 133.384 | 5 | <0.001 |
Predicted | ||||||
---|---|---|---|---|---|---|
CRTC | Percentage | |||||
Observed | Otherwise = 0 | Driver Error = 1 | Correct 2 | |||
Model 1 1 | CRTC | Otherwise = 0 | 16 | 843 | 1.9 | |
Driver error = 1 | 7 | 2461 | 99.7 | |||
Overall percentage/accuracy rate | 74.5 |
Coefficient | Std. Error | Wald | df | Sig. | ||
---|---|---|---|---|---|---|
Constant | 4.375 | 0.622 | 49.495 | 1 | <0.001 | 79.463 |
NL | −0.501 | 0.101 | 24.782 | 1 | <0.001 | 0.606 |
ASF | −0.027 | 0.005 | 28.831 | 1 | <0.001 | 0.973 |
AADT. 10−3 | 0.016 | 0.005 | 12.580 | 1 | <0.001 | 1.017 |
RG1 (Straight = 1) | 0.824 | 0.086 | 92.068 | 1 | <0.001 | 2.280 |
RG2 (Horz.curve = 1) | 1.268 | 0.451 | 7.904 | 1 | 0.005 | 3.555 |
Cut-Off Value | Observed | Predicted | Percent Correct | Accuracy (%) | Sensitivity (TPR) (%) | Specificity (1-FPR) (%) | Youden Index | ||
---|---|---|---|---|---|---|---|---|---|
0 | 1 | ||||||||
0.60 | CRTC | 0 | 127 | 732 | 14.8 | 74.5 1 | 95.3 1 | 14.8 1 | 10.1 1 |
1 | 116 | 2352 | 95.3 | ||||||
0.59/ 0.58 | CRTC | 0 | 124 | 735 | 14.4 | 74.8 ↑ | 95.8 ↑ | 14.4 ↓ | 10.2 ↑ |
1 | 104 | 2364 | 95.8 | ||||||
0.55 | CRTC | 0 | 84 | 775 | 9.8 | 74.8 ↑ | 97.4 ↑ | 9.8 ↓ | 7.2 ↓ |
1 | 65 | 2403 | 97.4 | ||||||
0.50 | CRTC | 0 | 16 | 843 | 1.9 | 74.5 | 99.7 ↑ | 1.9 ↓ | 1.6 ↓ |
1 | 7 | 2461 | 99.7 |
Coefficient | Wald | df | Sig. |
---|---|---|---|
Accident hour | 20.477 | 23 | 0.613 > 0.05 |
Weekday | 5.187 | 6 | 0.520 > 0.05 |
Month | 5.271 | 11 | 0.219 > 0.05 |
Weather | 3.853 | 5 | 0.247 > 0.05 |
Speed limit | 0.899 | 1 | 0.343 > 0.05 |
Variables | VIF | NL | ASF | AADT. 10−3 | RG1 = 1 | RG2 = 1 |
---|---|---|---|---|---|---|
NL | 2.273 | 1.000 | ||||
ASF | 1.349 | 0.245 | 1.000 | |||
AADT. 10−3 | 2.148 | −0.658 | 0.133 | 1.000 | ||
RG1 (Straight = 1) | 1.049 | −0.012 | −0.132 | 0.023 | 1.000 | |
RG2 (Horz.crv = 1) | 1.031 | −0.036 | −0.021 | 0.019 | 0.121 | 1.000 |
Model | AUC | Accuracy (CA) | Balanced Accuracy (BAC) | F1 1 | Precision | LogLoss |
---|---|---|---|---|---|---|
Dataset (1): 3439 cases (no data imputing) and models were tested using 10-fold cross-validation. | ||||||
RF 2 | 0.701 | 0.760 | 0.593 | 0.853 | 0.787 | 0.512 |
kNN 3 | 0.612 | 0.745 | 0.600 | 0.839 | 0.792 | 4.557 |
BNLOGREG 4 | 0.607 | 0.747 | 0.500 | 0.855 | 0.747 | 0.548 |
Dataset (2): Cases with missing values removed (69.44% data used) and models tested using 10-fold cross-validation. | ||||||
RF 2 | 0.653 | 0.787 | 0.500 | 0.881 | 0.787 | 0.490 |
kNN 3 | 0.595 | 0.724 | 0.556 | 0.829 | 0.809 | 2.365 |
BNLOGREG 4 | 0.554 | 0.787 | 0.500 | 0.881 | 0.787 | 0.510 |
Dataset (3): Model-based imputer (simple tree) used to replace missing values (100% data used), and models tested using 10-fold cross-validation. | ||||||
RF 2 | 0.712 | 0.762 | 0.598 | 0.854 | 0.789 | 0.503 |
kNN 3 | 0.643 | 0.755 | 0.547 | 0.851 | 0.794 | 2.154 |
BNLOGREG 4 | 0.608 | 0.747 | 0.500 | 0.855 | 0.747 | 0.547 |
Predicted | ||||||
---|---|---|---|---|---|---|
CRTC | Percentage | |||||
Observed | Otherwise = 0 | Driver error = 1 | Correct | |||
RF model 1 | CRTC | Otherwise = 0 | 229 | 640 | 26.4 | |
Driver error = 1 | 178 | 2392 | 93.1 | |||
Overall percentage/accuracy rate | 76.2 |
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Akin, D.; Sisiopiku, V.P.; Alateah, A.H.; Almonbhi, A.O.; Al-Tholaia, M.M.H.; Al-Sodani, K.A.A. Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia. Sustainability 2022, 14, 16654. https://doi.org/10.3390/su142416654
Akin D, Sisiopiku VP, Alateah AH, Almonbhi AO, Al-Tholaia MMH, Al-Sodani KAA. Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia. Sustainability. 2022; 14(24):16654. https://doi.org/10.3390/su142416654
Chicago/Turabian StyleAkin, Darcin, Virginia P. Sisiopiku, Ali H. Alateah, Ali O. Almonbhi, Mohammed M. H. Al-Tholaia, and Khaled A. Alawi Al-Sodani. 2022. "Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia" Sustainability 14, no. 24: 16654. https://doi.org/10.3390/su142416654