Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity
Abstract
:1. Introduction
2. Background
3. Materials and Methods
3.1. Data Description
3.2. Bias Identification
3.3. Bayesian Networks
- The qualitative aspect of the Bayesian Networks given by a directed acyclic network generally denoted as DAG (V, E), consisting of nodes (V) representing the variables related with directed edges (E), denoting the dependencies between the variables;
- The quantitative aspect consists of the conditional probability of each node, where every node has parents and has a conditional probability table expressing the dependencies of the father nodes. Therefore, the joint probability distribution is expressed as follows:
3.4. Network Validation
4. Results
4.1. Validation of Bayesian Network
4.2. Sensitivity Analysis
4.2.1. Assessment of the Influence of Technology-Based Distractions on Drivers’ Infractions
4.2.2. Assessment of the Influence of Technology-Based Distractions on Drivers’ Infractions Considering the Age and Gender of the Drivers
4.2.3. Assessment of the Influence of Technology-Based Distractions on Drivers’ Infractions Considering the Zone and Type of the Vehicle
4.2.4. Assessment of the Influence of Drivers’ Infractions on Traffic Accident Severity
5. Discussions
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Variables | Number of Cases | |||||
---|---|---|---|---|---|---|
Traffic Accident | Vehicle Type | 2016 | 2017 | 2018 | 2019 | Total |
Car, van, all-terrain vehicles | 120,831 | 120,261 | 119,755 | 118,491 | 479,338 | |
Motorcycles, quads, quadricycles | 35,222 | 36,051 | 36,319 | 37,467 | 145,059 | |
Heavy vehicles | 8906 | 8901 | 9176 | 8873 | 35,856 | |
Other vehicles | 922 | 759 | 1039 | 3531 | 6251 | |
Total | 165,881 | 165,972 | 166,289 | 168,362 | 666,504 | |
Drivers Demographics | Age | |||||
Y < 25 | 21,983 | 21,350 | 20,707 | 21,432 | 85,472 | |
25 ≤ Y ≤ 40 | 63,188 | 61,476 | 61,191 | 60,538 | 246,393 | |
40 ≤ Y ≤ 60 | 59,857 | 61,660 | 63,696 | 65,369 | 250,582 | |
Y > 60 | 17,399 | 18,102 | 18,333 | 19,010 | 72,844 | |
Unspecified | 3454 | 3384 | 2362 | 2013 | 11,213 | |
Total | 165,881 | 165,972 | 166,289 | 168,362 | 666,504 | |
Gender | ||||||
Male | 119,878 | 120,447 | 120,920 | 122,407 | 483,652 | |
Female | 445,44 | 44,225 | 44,772 | 45,407 | 178,948 | |
Unspecified | 1459 | 1300 | 597 | 548 | 3904 | |
Total | 165,881 | 165,972 | 166,289 | 168,362 | 666,504 |
Traffic Accident Severity | Severity Level | Number of Cases | Total | |||
2016 | 2017 | 2018 | 2019 | |||
M/NI | 151,446 | 151,595 | 152,757 | 155,161 | 610,959 | |
SI/F | 14,435 | 14,377 | 13,532 | 13,201 | 55,545 | |
Total | 165,881 | 165,972 | 166,289 | 168,362 | 666,504 |
Infractions | Drivers’ Infractions | Number of Cases | ||||
2016 | 2017 | 2018 | 2019 | Total | ||
Aberrant infractions | ||||||
No infractions | 54,405 | 52,131 | 52,054 | 62,566 | 221,156 | |
Aberrant infractions | 34,558 | 35,623 | 36,260 | 40,083 | 146,524 | |
Unspecified | 76,918 | 78,218 | 77,975 | 65,713 | 298,824 | |
Total | 165,881 | 165,972 | 166,289 | 168,362 | 666,504 | |
Speed infractions | ||||||
No speed infractions | 70,573 | 69,451 | 67,252 | 79,677 | 286,953 | |
Speed infractions | 8957 | 8154 | 8395 | 8117 | 33,623 | |
Unspecified | 86,351 | 88,367 | 90,642 | 80,568 | 345,928 | |
Total | 165,881 | 165,972 | 166,289 | 168,362 | 666,504 | |
Distracted Driving | Technology-based distractions | |||||
No distractions | 41,766 | 41,790 | 41,944 | 42,634 | 168,134 | |
Technology-based distractions | 881 | 1029 | 1024 | 1114 | 4048 | |
No technology-based distractions or unspecified | 123,234 | 123,153 | 123,321 | 124,614 | 494,322 | |
Total | 165,881 | 165,972 | 166,289 | 168,362 | 666,504 |
Dummy Variable Technology-Based Distractions | States | Number of Cases | Total | Percentage | SI/F | |||
2016 | 2017 | 2018 | 2019 | |||||
Presence or absence of technology-based distractions | 42,647 | 42,819 | 42,968 | 43,748 | 172,182 | 25.83% | 10.86% | |
Unknown | 123,234 | 123,153 | 123,321 | 124,614 | 494,322 | 74.17% | 7.40% | |
Total | 165,881 | 165,972 | 166,289 | 168,362 | 666,504 | / |
Variables | Accident Severity | Aberrant Infractions | Speed Infractions | Technology-Based Distraction | ||||
---|---|---|---|---|---|---|---|---|
States | SI/F | M/NI | No | Yes | No | Yes | No | Yes |
AUC scores | 0.62 | 0.62 | 0.88 | 0.77 | 0.90 | 0.77 | 0.99 | 0.93 |
Technology-Based Distractions | Aberrant Infractions | Speed Infractions | ||
---|---|---|---|---|
States | No | Yes | No | Yes |
No | 76.43% | 23.57% | 92.22% | 7.78% |
Yes | 33.25% | 66.75% | 85.55% | 14.45% |
Demographics | Technology-Based Distractions | Aberrant Infractions | Speed Infractions | ||
Age | States | No | Yes | No | Yes |
Y < 25 | No | 76.65% | 23.35% | 85.66% | 14.34% |
Yes | 32.00% | 68.00% | 72.86% | 27.14% | |
25 ≤ Y ≤ 40 | No | 76.50% | 23.50% | 91.39% | 8.61% |
Yes | 33.18% | 66.82% | 84.10% | 15.90% | |
40 ≤ Y ≤ 60 | No | 76.61% | 23.39% | 93.96% | 6.04% |
Yes | 33.82% | 66.18% | 88.94% | 11.06% | |
Y > 60 | No | 75.54% | 24.46% | 95.39% | 4.61% |
Yes | 33.66% | 66.34% | 91.72% | 8.28% | |
Gender | Technology-Based Distractions | Aberrant Infractions | Speed Infractions | ||
States | No | Yes | No | Yes | |
Male | No | 76.38% | 23.62% | 91.45% | 8.55% |
Yes | 33.21% | 66.79% | 84.25% | 15.75% | |
Female | No | 76.56% | 23.44% | 94.08% | 5.92% |
Yes | 33.45% | 66.55% | 88.91% | 11.09% |
Variables | Technology-Based Distractions | Aberrant Infractions | Speed Infraction | ||
Vehicle Type | States | No | Yes | No | Yes |
Car/Van/All Terrain | No | 73.73% | 26.27% | 92.02% | 7.98% |
Yes | 31.09% | 68.91% | 85.80% | 14.20% | |
Motorcycles/quads/quadricycles | No | 86.58% | 13.42% | 93.46% | 6.54% |
Yes | 43.83% | 56.17% | 84.56% | 15.44% | |
Heavy vehicles | No | 78.28% | 21.72% | 90.44% | 9.56% |
Yes | 39.70% | 60.30% | 84.73% | 15.27% | |
Other vehicles | No | 85.48% | 14.52% | 95.67% | 4.33% |
Yes | 34.90% | 65.10% | 85.37% | 14.63% | |
Zone | Technology-Based Distractions | Aberrant Infractions | Speed Infraction | ||
States | No | Yes | No | Yes | |
Road | No | 76.19% | 23.81% | 89.03% | 10.97% |
Yes | 41.06% | 58.94% | 85.64% | 14.36% | |
Street or similar | No | 76.74% | 23.26% | 96.97% | 3.03% |
Yes | 18.89% | 81.11% | 85.50% | 14.50% | |
Highway | No | 83.82% | 16.18% | 94.19% | 5.81% |
Yes | 20.20% | 79.80% | 60.41% | 39.59% |
Drivers’ Infractions | Severity of Traffic Accidents | |
---|---|---|
Aberrant infractions | M/NI | SI/F |
No | 90.62% | 9.38% |
Yes | 90.31% | 9.69% |
Speed infractions | ||
No | 90.97% | 9.03% |
Yes | 82.11% | 17.89% |
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García-Herrero, S.; Febres, J.D.; Boulagouas, W.; Gutiérrez, J.M.; Mariscal Saldaña, M.Á. Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity. Int. J. Environ. Res. Public Health 2021, 18, 7155. https://doi.org/10.3390/ijerph18137155
García-Herrero S, Febres JD, Boulagouas W, Gutiérrez JM, Mariscal Saldaña MÁ. Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity. International Journal of Environmental Research and Public Health. 2021; 18(13):7155. https://doi.org/10.3390/ijerph18137155
Chicago/Turabian StyleGarcía-Herrero, Susana, Juan Diego Febres, Wafa Boulagouas, José Manuel Gutiérrez, and Miguel Ángel Mariscal Saldaña. 2021. "Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity" International Journal of Environmental Research and Public Health 18, no. 13: 7155. https://doi.org/10.3390/ijerph18137155