Analysis of Risk Factors Affecting Urban Truck Traffic Accident Severity in Korea
Abstract
:1. Introduction
2. Literature Review
3. Research Structure and Regression Model
3.1. Research Structure
3.2. Regression Model
3.2.1. Ordinal Probit Regression
3.2.2. Poisson and Negative Binomial Regression
4. Model Estimation
4.1. Ordinal Probit Regression Analysis of Driver and Environmental Factors
4.1.1. Data Collection
4.1.2. Definition of Logistics Facility’s Influence Area
4.1.3. Result of Driver and Environmental Factors
4.2. Poisson and Negative Binomial Regression of Traffic Condition Factors
4.2.1. Data Collection
4.2.2. Result of Traffic Condition Factors
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Section | Factor | Variable | Total | Fatal | Seriously Injured | Slightly Injured | ||||
---|---|---|---|---|---|---|---|---|---|---|
Number of Accidents | Injuries | Number of Accidents | Number of Persons | Number of Accidents | Number of Persons | Number of Accidents | Number of Persons | |||
Temporal factors | Season | Spring (March, April, May) | 633 | 914 | 11 | 11 | 209 | 237 | 413 | 666 |
Summer (June, July, August) | 603 | 858 | 21 | 22 | 167 | 189 | 415 | 647 | ||
Autumn (September, October, November) | 668 | 954 | 17 | 18 | 217 | 249 | 434 | 687 | ||
Winter (December, January, February) | 602 | 888 | 18 | 18 | 198 | 222 | 386 | 648 | ||
Total | 2506 | 3614 | 67 | 69 | 791 | 897 | 1648 | 2648 | ||
Day of week | Monday–Friday | 2035 | 2864 | 54 | 55 | 644 | 716 | 1337 | 2093 | |
Saturday, Sunday | 471 | 750 | 13 | 14 | 147 | 181 | 311 | 555 | ||
Total | 2506 | 3614 | 67 | 69 | 791 | 897 | 1648 | 2648 | ||
Time | Morning peak (6:00 a.m.–10:00 a.m.) | 484 | 627 | 16 | 16 | 152 | 165 | 316 | 446 | |
Daytime (10:00 a.m.–5:00 p.m.) | 1119 | 1610 | 28 | 28 | 343 | 391 | 748 | 1191 | ||
Evening peak (5:00 p.m.–9:00 p.m.) | 509 | 745 | 6 | 6 | 148 | 172 | 355 | 567 | ||
Nighttime (9:00 p.m.–6:00 a.m.) | 394 | 632 | 17 | 19 | 148 | 169 | 229 | 444 | ||
Total | 2506 | 3614 | 67 | 69 | 791 | 897 | 1648 | 2648 | ||
Type of accident | Type of accident | Vehicle to Vehicle | 1963 | 3037 | 35 | 37 | 503 | 601 | 1425 | 2399 |
Vehicle to Pedestrian | 530 | 562 | 30 | 30 | 280 | 288 | 220 | 244 | ||
Single-vehicle | 13 | 15 | 2 | 2 | 8 | 8 | 3 | 5 | ||
Total | 2506 | 3614 | 67 | 69 | 791 | 897 | 1648 | 2648 | ||
Road and weather conditions | Road surface condition | Aridity | 2292 | 3295 | 59 | 61 | 725 | 820 | 1508 | 2414 |
Wetting/Freezing | 214 | 319 | 8 | 8 | 66 | 77 | 140 | 234 | ||
Total | 2506 | 3614 | 67 | 69 | 791 | 897 | 1648 | 2648 | ||
Weather condition | Lucidity | 2299 | 3296 | 59 | 61 | 728 | 818 | 1512 | 2417 | |
Raining/Snowing/Fogging | 207 | 318 | 8 | 8 | 63 | 79 | 136 | 231 | ||
Total | 2506 | 3614 | 67 | 69 | 791 | 897 | 1648 | 2648 | ||
Road type | Intersection | 1285 | 1805 | 32 | 32 | 436 | 482 | 817 | 1291 | |
Non-intersected road | 1221 | 1809 | 35 | 37 | 355 | 415 | 831 | 1357 | ||
Total | 2506 | 3614 | 67 | 69 | 791 | 897 | 1648 | 2648 | ||
Logistics facility’s influence | Influencing area | 1103 | 1630 | 31 | 32 | 358 | 412 | 714 | 1186 | |
Non-influencing area | 1403 | 1984 | 36 | 37 | 433 | 485 | 934 | 1462 | ||
Total | 2506 | 3614 | 67 | 69 | 791 | 897 | 1648 | 2648 | ||
Truck driver behaviors | Driver gender | Male | 2456 | 3543 | 67 | 69 | 778 | 883 | 1611 | 2591 |
Female | 50 | 71 | 0 | 0 | 13 | 14 | 37 | 57 | ||
Total | 2506 | 3614 | 67 | 69 | 791 | 897 | 1648 | 2648 | ||
Driver age group | Under 30 | 180 | 252 | 7 | 7 | 64 | 68 | 109 | 177 | |
30s | 341 | 508 | 6 | 7 | 105 | 115 | 230 | 386 | ||
40s | 539 | 791 | 17 | 17 | 201 | 222 | 321 | 552 | ||
50s | 837 | 1185 | 20 | 20 | 255 | 295 | 562 | 870 | ||
Over 60 | 609 | 878 | 17 | 18 | 166 | 197 | 426 | 663 | ||
Total | 2506 | 3614 | 67 | 69 | 791 | 897 | 1648 | 2648 | ||
Type of violation | Non-compliance of a safety distance | 245 | 373 | 1 | 1 | 57 | 70 | 187 | 302 | |
Violation of cross traffic rules | 117 | 161 | 1 | 1 | 31 | 33 | 85 | 127 | ||
Lane violation | 144 | 208 | 2 | 2 | 48 | 59 | 94 | 147 | ||
Signal violation | 309 | 509 | 7 | 7 | 143 | 170 | 159 | 332 | ||
Non-compliance of safe driving | 1558 | 2220 | 51 | 53 | 447 | 499 | 1060 | 1668 | ||
Violation of pedestrian protection | 133 | 143 | 5 | 5 | 65 | 66 | 63 | 72 | ||
Total | 2506 | 3614 | 67 | 69 | 791 | 897 | 1648 | 2648 |
Section | Factor | Variable | B | Std. Error | Hypothesis Test | ||
---|---|---|---|---|---|---|---|
Wald Chi-Square | df | Sig. | |||||
Temporal factors | Season | Spring (March, April, May) | Set to zero because this parameter is redundant. | ||||
Summer (June, July, August) | −0.131 | 0.126 | 1.079 | 1 | 0.299 | ||
Autumn (September, October, November) | −0.035 | 0.121 | 0.083 | 1 | 0.774 | ||
Winter (December, January, February) | 0.018 | 0.125 | 0.022 | 1 | 0.883 | ||
Day of week | Saturday, Sunday | Set to zero because this parameter is redundant. | |||||
Monday-Friday | −0.018 | 0.114 | 0.025 | 1 | 0.874 | ||
Time | Evening peak (5:00 p.m.–9:00 p.m.) | Set to zero because this parameter is redundant. | |||||
Daytime (10:00 a.m.–5:00 p.m.) | 0.151 | 0.121 | 1.571 | 1 | 0.210 | ||
Morning peak (6:00 a.m.–10:00 a.m.) | 0.214 | 0.142 | 2.285 | 1 | 0.131 | ||
Nighttime (9:00 p.m.–6:00 a.m.) | 0.482 | 0.148 | 10.664 | 1 | 0.001 * | ||
Type of accident | Type of accident | Vehicle to Vehicle | Set to zero because this parameter is redundant. | ||||
Vehicle to Pedestrian | 1.441 | 0.117 | 152.213 | 1 | 0.000 * | ||
Single-vehicle | 2.287 | 0.587 | 15.199 | 1 | 0.000 * | ||
Road and weather conditions | Road surface condition | citation in the main text | citation in the main text | ||||
Wetting/Freezing | 0.089 | 0.280 | 0.101 | 1 | 0.751 | ||
Weather condition | Lucidity | Set to zero because this parameter is redundant. | |||||
Raining/Snowing/Fogging | −0.104 | 0.285 | 0.132 | 1 | 0.716 | ||
Road type | Intersection | Set to zero because this parameter is redundant. | |||||
Non-intersected road | 0.035 | 0.099 | 0.123 | 1 | 0.726 | ||
Logistics facility’s influence | Non-influencing area | Set to zero because this parameter is redundant. | |||||
Influencing area | 0.225 | 0.090 | 6.318 | 1 | 0.012 * | ||
Truck driver behaviors | Driver gender | Female | Set to zero because this parameter is redundant. | ||||
Male | 0.543 | 0.339 | 2.577 | 1 | 0.108 | ||
Driver age group | Under 30 | Set to zero because this parameter is redundant. | |||||
30 s | −0.354 | 0.199 | 3.174 | 1 | 0.075 | ||
40 s | −0.006 | 0.183 | 0.001 | 1 | 0.972 | ||
50 s | −0.287 | 0.176 | 2.647 | 1 | 0.104 | ||
Over 60 | −0.460 | 0.184 | 6.256 | 1 | 0.012 * | ||
Type of violation | Non-compliance of a safety distance | Set to zero because this parameter is redundant. | |||||
Violation of cross traffic rules | 0.109 | 0.269 | 0.163 | 1 | 0.686 | ||
Lane violation | 0.458 | 0.234 | 3.835 | 1 | 0.050 * | ||
Signal violation | 0.822 | 0.203 | 16.384 | 1 | 0.000 * | ||
Non-compliance of safe driving | 0.071 | 0.167 | 0.180 | 1 | 0.671 | ||
Violation of pedestrian protection | −0.118 | 0.263 | 0.200 | 1 | 0.655 | ||
Threshold | Slightly injured accidents (0) | ||||||
Seriously injured accidents (1) | 1.719 | 0.438 | 15.402 | 1 | 0.000 | ||
Fatal accidents (2) | 4.861 | 0.457 | 113.354 | 1 | 0.000 | ||
Likelihood Ratio Chi-Square | 264.245 | ||||||
Pearson Chi-Square | 3199.213 | ||||||
Sig. of Omnibus Test | <0.001 |
Factor | Slightly Injured Accidents (0) | Seriously Injured Accidents (1) | Fatal Accidents (2) | |
---|---|---|---|---|
Time | Nighttime (9:00 p.m.–6:00 a.m.) | −0.0395 | 0.0339 | 0.0056 |
Type of accident | Vehicle to Pedestrian | −0.2448 | 0.2142 | 0.0305 |
Single–vehicle | −0.4919 | 0.3568 | 0.1351 | |
Logistics facility’s influence | Influencing area | −0.0103 | 0.0089 | 0.0014 |
Driver age group | Over 60 | 0.0214 | −0.0189 | −0.0025 |
Type of violation | Lane violation | −0.0696 | 0.0634 | 0.0062 |
Signal violation | −0.1100 | 0.1018 | 0.0082 |
Variable | Definition | N | Minimum | Maximum | Mean | Std. Deviation | |
---|---|---|---|---|---|---|---|
Dependent variable | Number of deaths | The number of deaths caused by truck traffic accidents on an intermittent flow road in Incheon during 2017–2019 | 688 | 0.00 | 3 | 0.08 | 0.33 |
Number of serious injuries | The number of serious injuries caused by truck traffic accidents on an intermittent flow road in Incheon during 2017–2019 | 688 | 0.00 | 33 | 1.12 | 3.06 | |
Independent variable | Average length (km) | The average segment length of a road | 688 | 0.02 | 0.96 | 0.16 | 0.11 |
Average lanes | The average number of segment lanes of a road | 688 | 1.00 | 7.64 | 3.02 | 1.29 | |
Average max speed (km/h) | The average segment max-speed of a road | 688 | 30.00 | 80.00 | 41.28 | 11.18 | |
Average traffic signal light density | The number of traffic signal lights per kilometer | 688 | 0.00 | 27.78 | 3.53 | 3.12 | |
Average entry angle | The average entry angle of a road | 688 | 0.00 | 179.00 | 85.00 | 28.98 | |
Average exit angle | The average exit angle of a road | 688 | 0.00 | 179.00 | 87.45 | 30.66 | |
Number of road property-changing nodes | The complexity of the traffic network | 688 | 1.00 | 223.00 | 15.31 | 27.54 |
Independent Variables | Average Length | Average Lanes | Average Max Speed | Average Traffic Signal Light Density | Average Entry Angle | Average Exit Angle | Number of Road Property-Changing Nodes | |
---|---|---|---|---|---|---|---|---|
Average length | Pearson Correlation | 1 | ||||||
Sig. (2-tailed) | ||||||||
Average lanes | Pearson Correlation | −0.101 ** | 1 | |||||
Sig. (2-tailed) | 0.008 | |||||||
Average max speed | Pearson Correlation | 0.081 * | 0.421 ** | 1 | ||||
Sig. (2-tailed) | 0.033 | 0.000 | ||||||
Average traffic signal light density | Pearson Correlation | −0.269 ** | 0.232 ** | 0.070 | 1 | |||
Sig. (2-tailed) | 0.000 | 0.000 | 0.067 | |||||
Average entry angle | Pearson Correlation | 0.033 | 0.076 * | −0.018 | −0.002 | 1 | ||
Sig. (2-tailed) | 0.389 | 0.047 | 0.630 | 0.953 | ||||
Average exit angle | Pearson Correlation | 0.026 | 0.001 | 0.070 | −0.061 | −0.035 | 1 | |
Sig. (2-tailed) | 0.500 | 0.974 | 0.067 | 0.108 | 0.360 | |||
Number of road property changing nodes | Pearson Correlation | −0.149 ** | 0.423 ** | −0.018 | −0.002 | 0.011 | −0.018 | 1 |
Sig. (2-tailed) | 0.000 | 0.000 | 0.630 | 0.963 | 0.766 | 0.635 |
Section | Number of Deaths | Number of Serious Injuries | |
---|---|---|---|
N | 688 | 688 | |
Poisson Parameter | Mean | 0.08 | 1.12 |
Variance | 0.110 | 9.331 | |
Most Extreme Differences | Absolute | 0.012 | 0.342 |
Positive | 0.012 | 0.342 | |
Negative | −0.007 | −0.059 | |
Kolmogorov–Smirnov Z | 0.303 | 8.972 | |
Asymp. Sig. (2-tailed) | 1.000 | 0.000 |
Section | Number of Deaths (Poisson Regression Model) | Number of Serious Injuries (Negative Binomial Regression Model) | ||
---|---|---|---|---|
Value | Value/df | Value | Value/df | |
Deviance | 183.600 | 0.270 | 518.033 | 0.762 |
Scaled Deviance | 183.600 | 518.033 | ||
Pearson Chi-Square | 474.816 | 0.698 | 721.034 | 1.060 |
Scaled Pearson Chi-Square | 474.816 | 721.034 | ||
Log Likelihood | −138.515 | −712.844 | ||
Akaike’s Information Criterion | 293.031 | 1441.689 | ||
Finite Sample-Corrected AIC | 293.243 | 1441.901 | ||
Bayesian Information Criterion | 329.301 | 1477.959 | ||
Consistent AIC | 337.301 | 1485.959 |
Dependent Variables | Independent Variables | B | Std. Error | Hypothesis Test | Exp(B) | ||
---|---|---|---|---|---|---|---|
Wald Chi-Square | df | Sig. | |||||
Number of deaths (Poisson Regression Model) | (Intercept) | −3.71 | 1.12 | 11.02 | 1.00 | 0.00 | 0.02 |
Average length | −3.81 | 2.68 | 2.03 | 1.00 | 0.15 | 0.02 | |
Average lanes | 0.30 | 0.12 | 6.57 | 1.00 | 0.01 * | 1.35 | |
Average max speed | 0.03 | 0.02 | 4.13 | 1.00 | 0.04 * | 1.03 | |
Average traffic signal light density | −0.04 | 0.07 | 0.31 | 1.00 | 0.58 | 0.96 | |
Average entry angle | −0.01 | 0.01 | 1.60 | 1.00 | 0.21 | 0.99 | |
Average exit angle | −0.01 | 0.01 | 1.34 | 1.00 | 0.25 | 0.99 | |
Number of road property-changing nodes | 0.02 | 0.00 | 68.06 | 1.00 | 0.00 * | 1.02 | |
Likelihood ratio Chi-Square | 122.104 | ||||||
sig. | <0.001 | ||||||
Number of serious injuries (Negative Binomial Regression Model) | (Intercept) | −2.84 | 0.44 | 41.49 | 1.00 | 0.00 | 0.06 |
Average length | 0.84 | 0.68 | 1.52 | 1.00 | 0.22 | 2.31 | |
Average lanes | 0.07 | 0.06 | 1.29 | 1.00 | 0.26 | 1.07 | |
Average max speed | 0.04 | 0.01 | 37.25 | 1.00 | 0.01 * | 1.04 | |
Average traffic signal light density | 0.02 | 0.03 | 0.68 | 1.00 | 0.41 | 1.02 | |
Average entry angle | 0.00 | 0.00 | 0.31 | 1.00 | 0.58 | 1.00 | |
Average exit angle | 0.00 | 0.00 | 1.15 | 1.00 | 0.28 | 1.00 | |
Number of road property–changing nodes | 0.03 | 0.00 | 116.00 | 1.00 | 0.00 * | 1.03 | |
Likelihood Ratio Chi-Square | 590.914 | ||||||
sig. | <0.001 |
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Chen, M.; Zhou, L.; Choo, S.; Lee, H. Analysis of Risk Factors Affecting Urban Truck Traffic Accident Severity in Korea. Sustainability 2022, 14, 2901. https://doi.org/10.3390/su14052901
Chen M, Zhou L, Choo S, Lee H. Analysis of Risk Factors Affecting Urban Truck Traffic Accident Severity in Korea. Sustainability. 2022; 14(5):2901. https://doi.org/10.3390/su14052901
Chicago/Turabian StyleChen, Maowei, Lele Zhou, Sangho Choo, and Hyangsook Lee. 2022. "Analysis of Risk Factors Affecting Urban Truck Traffic Accident Severity in Korea" Sustainability 14, no. 5: 2901. https://doi.org/10.3390/su14052901