This section presents the results and experimental analysis of the PTW road accident data mentioned as follows.
The PTW road accident data of 13 districts of Uttarakhand state is considered for analysis. We build decision tree using CART for all 13 districts and for entire data set (EDS). The confusion matrix obtained after building decision trees for all districts and EDS is shown in Table
4. The different values of classifier accuracy measures to illustrate the performance of decision tree classifier on 13 districts of Uttarakhand and EDS have been calculated from confusion matrix and shown in Table
5.
Table 3
Prediction accuracy of different classifiers
1 | Naïve Bayes | 74.14 | 77.84 |
2 | Decision Tree (CART) | 87.10 | 89.75 |
3 | Support vector machine | 79.79 | 80.63 |
Table 4
Confusion matrix for 13 districts and EDS of Uttarakhand
Almora | | KSI | SI | Bageshwar | | SI | KSI |
KSI | 17 | 14 | SI | 13 | 16 |
SI | 8 | 157 | KSI | 11 | 7 |
Chamoli | | KSI | SI | Champawat | | KSI | SI |
KSI | 23 | 11 | KSI | 16 | 4 |
SI | 3 | 86 | SI | 19 | 65 |
Dehradun | | KSI | SI | Haridwar | | SI | KSI |
KSI | 698 | 102 | SI | 1391 | 121 |
SI | 86 | 1664 | KSI | 116 | 560 |
Nainital | | KSI | SI | Pauri | | KSI | SI |
KSI | 707 | 158 | KSI | 128 | 72 |
SI | 42 | 1093 | SI | 53 | 834 |
Pithoragarh | | KSI | SI | Rudraprayag | | KSI | SI |
KSI | 51 | 58 | KSI | 28 | 12 |
SI | 15 | 380 | SI | 42 | 165 |
Tehri | | KSI | SI | US Nagar | | KSI | SI |
KSI | 124 | 49 | KSI | 1378 | 305 |
SI | 78 | 726 | SI | 73 | 2631 |
Uttarkashi | | KSI | SI | EDS | | KSI | SI |
KSI | 44 | 13 | KSI | 3591 | 1115 |
SI | 30 | 212 | SI | 1099 | 8904 |
The values of different parameters shown in Table
5 indicate the performance of CART to predict the severity of PTW accidents. The Dehradun, Haridwar, Nainital and Udham Singh Nagar districts which have the high PTW accident rate in Uttarakhand state. The decision tree classifier’s accuracy is found better than other remaining districts. In other districts, the performance of the classifier is not so accurate. The one reason can be the small size of the accident records. This certainly reveals the conclusion that if data set is not sufficiently large enough, then the decision tree algorithm may not be accurate as desired. The other reason for low accuracy is that the similar values for different attributes are there that predicts the KSI and SI both. The ROC plot is illustrated to show the performance of decision tree classifier for all 13 districts and EDS in Fig.
1.1 to Fig.
1.14. The AUC (Area under ROC curve) is shown in each figure. The AUC indicates that the decision tree classifier performs worst for Bageshwar district and best for Dehradun, Nainital, Hardiwar and Udham singh nagar district.
Further, decision rules are extracted from decision tree build for all districts and EDS. The relevant and interesting rules have been chosen to describe the patterns of each district and EDS. The description of decision rules are given as follows:
The decision rules for Almora, Bageshwar and Chamoli districts indicate that NOI, TOD, SUA and LIG are the main contributing accidents attributes that is involved in several PTW accidents. The decision rules revealed that PTW accidents that occurred during night time with no light conditions were KSI accidents. The locations where road light facilities were present during night time have SI accidents only. In other conditions, it is difficult to conclude between KSI and SI accidents, because similar attribute values were present for both KSI and SI accidents. The other attributes that were not available with the data such as speed and weather information may be the responsible factors for PTW accidents in these districts of Uttarakhand.
The severity of PTW accidents in Champawat and Hardiwar districts were mainly affected by NOI, TOD, ROF and LIG attributes. The decision rules for Champawat district reveals that intersection were mainly involved in KSI accidents in during TOD values T1and T6 whereas for other TOD values the accidents were SI. The decision rules for Haridwar district indicate that Intersections in no light condition was more prone to KSI accidents. Other road features such as curve and slope was found to have similar effect on PTW accidents in all lightning conditions for SI accidents with 2 or more victims involved in accidents. Some PTW accidents were KSI accidents that involved 1 victim injured in day light conditions in slope road feature.
Table 5
CART performance metrics for 13 districts and EDS
Almora | 0.548 | 0.048 | 0.952 | 0.680 | 0.548 | 0.607 | 0.547 | 0.776 | KSI |
0.952 | 0.452 | 0.548 | 0.918 | 0.952 | 0.935 | 0.547 | 0.776 | SI |
Bageshwar | 0.389 | 0.552 | 0.448 | 0.304 | 0.389 | 0.341 | −0.158 | 0.506 | KSI |
0.448 | 0.611 | 0.389 | 0.542 | 0.448 | 0.491 | −0.158 | 0.506 | SI |
Chamoli | 0.676 | 0.034 | 0.966 | 0.855 | 0.676 | 0.767 | 0.704 | 0.819 | KSI |
0.966 | 0.324 | 0.676 | 0.887 | 0.966 | 0.925 | 0.704 | 0.819 | SI |
Champawat | 0.800 | 0.226 | 0.774 | 0.457 | 0.800 | 0.582 | 0.479 | 0.860 | KSI |
0.774 | 0.200 | 0.8 | 0.942 | 0.774 | 0.850 | 0.479 | 0.860 | SI |
Dehradun | 0.873 | 0.049 | 0.951 | 0.890 | 0.873 | 0.881 | 0.828 | 0.943 | KSI |
0.951 | 0.128 | 0.873 | 0.942 | 0.951 | 0.947 | 0.828 | 0.943 | SI |
Haridwar | 0.828 | 0.080 | 0.92 | 0.822 | 0.828 | 0.825 | 0.747 | 0.915 | KSI |
0.920 | 0.172 | 0.828 | 0.923 | 0.920 | 0.921 | 0.747 | 0.915 | SI |
Nainital | 0.817 | 0.037 | 0.963 | 0.944 | 0.817 | 0.876 | 0.799 | 0.912 | KSI |
0.963 | 0.183 | 0.817 | 0.874 | 0.963 | 0.916 | 0.799 | 0.912 | SI |
Pauri | 0.640 | 0.060 | 0.94 | 0.707 | 0.640 | 0.672 | 0.604 | 0.817 | KSI |
0.940 | 0.360 | 0.64 | 0.921 | 0.940 | 0.930 | 0.604 | 0.817 | SI |
Pithoragarh | 0.468 | 0.038 | 0.962 | 0.773 | 0.468 | 0.583 | 0.525 | 0.708 | KSI |
0.962 | 0.532 | 0.468 | 0.868 | 0.962 | 0.912 | 0.525 | 0.708 | SI |
Rudraprayag | 0.700 | 0.203 | 0.797 | 0.400 | 0.700 | 0.509 | 0.406 | 0.796 | KSI |
0.797 | 0.300 | 0.7 | 0.932 | 0.797 | 0.859 | 0.406 | 0.796 | SI |
Tehri | 0.717 | 0.097 | 0.903 | 0.614 | 0.717 | 0.661 | 0.584 | 0.831 | KSI |
0.903 | 0.283 | 0.717 | 0.937 | 0.903 | 0.920 | 0.584 | 0.831 | SI |
US Nagar | 0.819 | 0.027 | 0.973 | 0.950 | 0.819 | 0.879 | 0.818 | 0.921 | KSI |
0.973 | 0.181 | 0.819 | 0.896 | 0.973 | 0.933 | 0.818 | 0.921 | SI |
Uttarkashi | 0.772 | 0.124 | 0.876 | 0.595 | 0.772 | 0.672 | 0.590 | 0.870 | KSI |
0.876 | 0.228 | 0.772 | 0.942 | 0.876 | 0.908 | 0.590 | 0.870 | SI |
EDS | 0.763 | 0.110 | 0.890 | 0.766 | 0.763 | 0.764 | 0.654 | 0.889 | KSI |
0.890 | 0.237 | 0.763 | 0.889 | 0.890 | 0.889 | 0.654 | 0.889 | SI |
The Dehradun district that has the highest PTW road accidents in Uttarakhand state was mainly affected by NOI, TOD, ROF, SUA and LIG road accident attributes. The decision rules certainly reveal some interesting information. According to decision rules, most of the KSI accidents have occurred in no light conditions in intersections near markets, residential area and agriculture land. Curve on road near forest area was also KSI prone area for PTW accidents with 1 victim involved. Other values of different attributes were usually involved in SI accidents.
The factors that affect the severity for PTW accidents in Nainital districts, in addition to other previously mentioned districts, has few more accident attribute responsible for accidents i.e. Age of victim and ROT. The rules reveals that curve on road are the main factor that contributes to KSI accidents at night and early morning duration. Also, in evening duration the KSI accidents on highway roads were involved with minor victims or victims less than 18 years of age.
For Udham singh nagar district, the factors that affect severity of road accidents were quite similar to those factors in Dehradun districts. The colonies and markets areas were the major location where lots of the accidents have occurred but most of these accidents were SI accidents only. The PTW KSI accidents were mainly occurred at a highway that goes through the agriculture land or the forest area. The YNG and ADU age group victim were mainly involved in KSI accidents. Very few KSI accidents were involved SNR and CHD group victims.
Rudraprayag, Tehri and Uttarkashi districts were not mainly affected by ROT, ROF and other important factors which were found for the previous districts. One common factor revealed by decision rules is the LIG condition. Most of the KSI accidents in these districts have occurred in DUSK lightning condition. Other lightning conditions were usually involved SI accidents. As the accident records for PTW accidents for these districts were comparatively low, some other factors remain hidden. The decision rules for Pauri and Pithoragarh districts revealed that these two districts have similar patterns for PTW accidents. In both districts, the KSI accidents mainly involved the AGE group CHD and SNR and the LIG condition as DUS. Also, these accidents were mainly happened in Q1 and Q4 months of the years. The SI accidents were mainly involved the AGE group ADU, whereas YNG age group was equally involved in both SI and KSI accidents.
Further, the rules for the EDS have been analyzed. It was found that for EDS almost all attributes except the MON (month) attribute were involved in KSI and SI accidents for PTW. Most of the KSI accidents were involved NOI values of 1 but very few KSI accidents involved NOI = +2 for EDS. For AGE attribute, the values YNG and ADU were mainly involved in KSI accidents, whereas the number of CHD victims was comparatively low. SNR victims were found to be involved in both KSI and SI accidents but these accidents are comparatively lower than accidents with other victims. The major road location where most of the KSI accidents have occurred was intersections on highways. Most of the intersections where KSI accidents have occurred were a part of highways. Also the curve on highways was found to be dangerous as it involves most of the KSI accidents than SI accidents. The SUA attribute values MAR and HIL are the locations where most of the accidents have occurred but the number of SI accidents was more in comparison to KSI accidents in these locations. The SUA values FOR and AGL was found to be dangerous for PTW accidents on local roads. For attribute LIG, around 10% of accidents have occurred in DUS condition in which 46% accidents were KSI, hence the DUS condition could be dangerous for PTW accidents. Although, lots of accidents have occurred in DLT condition but most of the accidents were SI accidents. In RLT condition, it is found that most of the PTW accidents were KSI accidents. Some of the PTW accidents have also occurred in NLT conditions but most of the accidents were SI accidents.
Therefore, it is found that a separate analysis of every district data and a complete analysis of entire data certainly reveal different but important information that can be utilized to understand the factors that involved in PTW road accidents. The different accident attributes have different impact on PTW accidents in every district. It can be concluded that the analysis of entire data can give you a broad overview of the information about the factors involved in road accidents of PTW accidents, whereas a separate analysis of each district can reveal factors associated with PTW accidents in those district only. Therefore, both type of analysis should be performed with EDS and each districts to get a broad and insight information about accident factors.