Quantile Regression with Telematics Information to Assess the Risk of Driving above the Posted Speed Limit
Abstract
:1. Objective
2. Background
3. Methods
3.1. Quantile Regression
3.2. The Data
4. Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
Parameter Estimate (p-Value) | |
---|---|
Intercept | 0.6397 (<0.0001) |
Km_1000 | 0.0292 (<0.0001) |
Km_10002 | 0.0035 (<0.0001) |
Pkdr_vurba | −0.0137 (<0.0001) |
Pkdr_nocturn | 0.0018 (0.485) |
Age | −0.0079 (0.149) |
Gender | 0.2295 (<0.0001) |
R2 | 43.20% |
50th Percentile (p-Value) | 75th Percentile (p-Value) | 90th Percentile (p-Value) | 95th Percentile (p-Value) | 97.5th Percentile (p-Value) | 99th Percentile (p-Value) | |
---|---|---|---|---|---|---|
Intercept | 0.1812 (0.0003) | 0.3845 (<0.0001) | 0.3681 (0.0805) | 0.4940 (0.0643) | 0.1147 (0.7889) | 0.8439 (0.1271) |
Km_1000 | 0.0113 (0.0399) | 0.0257 (0.0010) | 0.0595 (0.0001) | 0.0839 (<0.0001) | 0.0887 (0.0084) | 0.0632 (0.0243) |
Km_10002 | 0.0035 (<0.0001) | 0.0056 (<0.0001) | 0.0079 (<0.0001) | 0.0087 (<0.0001) | 0.0107 (<0.0001) | 0.0138 (<0.0001) |
Pkdr_vurba | −0.0031 (<0.0001) | −0.0082 (<0.0001) | −0.0136 (<0.0001) | −0.0177 (<0.0001) | −0.0200 (<0.0001) | −0.0248 (<0.0001) |
Pkdr_nocturn | 0.0023 (0.0164) | 0.0010 (0.4777) | −0.0022 (0.6037) | 0.0028 (0.6140) | 0.0055 (0.5539) | 0.0095 (0.4052) |
Age | −0.0027 (0.1316) | −0.0001 (0.9749) | 0.0143 (0.0623) | 0.0216 (0.0266) | 0.0480 (0.0019) | 0.0416 (0.0725) |
Gender | 0.1132 (<0.0001) | 0.1734 (<0.0001) | 0.1975 (<0.0001) | 0.1510 (0.0082) | 0.1428 (0.1198) | 0.2360 (0.1646) |
Goodness-of-fit criterion | 23.62% | 33.45% | 43.70% | 49.62% | 54.10% | 59.67% |
Driver 1 | Driver 2 | Driver 3 | |
---|---|---|---|
Km | 12,000 | 8000 | 5500 |
Pkdr_vurba | 80 | 75 | 80 |
Pkdr_noctur | 14 | 11 | 10.5 |
Age | 25 | 25 | 25 |
Gender | 1 | 1 | 1 |
Estimated conditional percentile 1 | 45th | 78th | 96th |
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Variable | Description |
---|---|
Tolerkm | Number of kilometers driven at speeds above the posted limit during 2010. |
Km | Total number of kilometers driven during 2010. |
Lnkm | Logarithm of the total number of kilometers driven during 2010. |
Pkdr_vurba | % of kilometers driven on urban roads during 2010. |
Pkdr_nocturn | % of kilometers driven at night (between midnight and 6 am.) during 2010. |
Age | Age of the driver at the beginning of 2010. |
Gender | 1 = Male, 0 = Female |
Variable | Min | 1st Qu | Median | Mean | 3rd Qu | Max | St. Dev. | Skewness |
---|---|---|---|---|---|---|---|---|
Tolerkm | 0.00 | 282.40 | 689.20 | 1398.20 | 1701.60 | 23,500.20 | 1995.37 | 3.64 |
Km | 0.69 | 7530.56 | 11,697.82 | 13,063.71 | 17,337.00 | 57,756.98 | 7715.80 | 1.08 |
Lnkm | −0.37 | 8.93 | 9.37 | 9.27 | 9.76 | 10.96 | 0.75 | −1.87 |
Pkdr_vurba | 0.00 | 15.60 | 23.39 | 26.29 | 34.32 | 100.00 | 14.18 | 1.03 |
Pkdr_nocturn | 0.00 | 2.48 | 5.31 | 7.02 | 9.84 | 78.56 | 6.13 | 1.67 |
Age | 18.11 | 22.66 | 24.63 | 24.78 | 26.88 | 35.00 | 2.82 | 0.11 |
Parameter Estimate (p-Value) | |
---|---|
Intercept | −8082.506 (<0.0001) |
Lnkm | 1064.506 (<0.0001) |
Pkdr_vurba | −21.868 (<0.0001) |
Pkdr_nocturn | 7.536 (0.0101) |
Age | −1.131 (0.8565) |
Gender | 328.009 (<0.0001) |
R2 | 25.96% |
50th Percentile (p-Value) | 75th Percentile (p-Value) | 90th Percentile (p-Value) | 95th Percentile (p-Value) | 97.5th Percentile (p-Value) | 99th Percentile (p-Value) | |
---|---|---|---|---|---|---|
Intercept | −4496.53 (<0.0001) | −6250.34 (<0.0001) | −6418.11 (<0.0001) | −6009.63 (<0.001) | −5137.24 (<0.0001) | −2451.17 0.5780 |
Lnkm | 597.60 (<0.0001) | 892.80 (<0.0001) | 1074.66 (<0.0001) | 1094.57 (<0.0001) | 1119.94 (<0.0001) | 1180.21 (<0.001) |
Pkdr_vurba | −9.19 (<0.0001) | −22.26 (<0.0001) | −39.59 (<0.0001) | −53.44 (<0.0001) | −68.58 (<0.0001) | −87.12 (<0.0001) |
Pkdr_nocturn | 5.41 (<0.0001) | 6.71 (0.0363) | 21.76 (0.0226) | 37.49 (0.0086) | 20.01 (0.4266) | 43.86 (0.4014) |
Age | −2.56 (0.1632) | 1.84 (0.7298) | 5.16 (0.7419) | 40.29 (0.2086) | 71.28 (0.1094) | 36.87 (0.7009) |
Gender | 206.76 (<0.0001) | 377.94 (<0.0001) | 574.08 (<0.0001) | 755.87 (<0.0001) | 1070.06 (<0.0001) | 1091.38 (0.0624) |
Goodness-of-fit criterion | 14.19% | 18.26% | 20.23% | 20.27% | 20.56% | 20.06% |
Driver 1 | Driver 2 | Driver 3 | |
---|---|---|---|
Km | 12,000 | 8000 | 5500 |
Pkdr_vurba | 80 | 75 | 80 |
Pkdr_noctur | 14 | 11 | 10.5 |
Age | 25 | 25 | 25 |
Gender | 1 | 1 | 1 |
Estimated conditional percentile 1 | 50th | 75th | 90th |
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Share and Cite
Pérez-Marín, A.M.; Guillen, M.; Alcañiz, M.; Bermúdez, L. Quantile Regression with Telematics Information to Assess the Risk of Driving above the Posted Speed Limit. Risks 2019, 7, 80. https://doi.org/10.3390/risks7030080
Pérez-Marín AM, Guillen M, Alcañiz M, Bermúdez L. Quantile Regression with Telematics Information to Assess the Risk of Driving above the Posted Speed Limit. Risks. 2019; 7(3):80. https://doi.org/10.3390/risks7030080
Chicago/Turabian StylePérez-Marín, Ana M., Montserrat Guillen, Manuela Alcañiz, and Lluís Bermúdez. 2019. "Quantile Regression with Telematics Information to Assess the Risk of Driving above the Posted Speed Limit" Risks 7, no. 3: 80. https://doi.org/10.3390/risks7030080