## 8 Conclusions  
The main goal of this study was to apply advanced data analytics methods to develop and evaluate crash prediction models that can be used in near-real-time. The application of several machine learning models for crash likelihood and crash severity prediction has been demonstrated in a case study of two interstate highways in New Jersey. The analysis dataset from which the explanatory variables and the response variables were derived included detailed crash data from the crash records database, basic roadway geometry data, synthetic vehicle volume and road segment capacity data, probe-vehicle traffic speed data, and weather data from the National Weather Service. In addition to crash records, the analysis included non-crash cases that were generated using the matched case-control methodology. To deal with the data imbalance between the non-injury (PDO) crashes and injury/fatal crashes, the study employed the random oversampling examples (ROSE) method. The relative importance of explanatory variables was evaluated using the RF model and they were ranked based on mean decrease accuracy (MDA). The RBLR model further revealed (or rather confirmed) the significance of each explanatory variable in the crash prediction model. In addition to RBLR, the RF, GBM, GNB, and KNN models were also estimated for both crash likelihood and crash severity  
Figure 5: Real-time crash risk map-based system. modeling. The predictive performance of the presented models was evaluated using the performance metrics that included overall accuracy, sensitivity, specificity, and the AUC value. The estimation results showed that the RF model outperformed all the other investigated models. However, none of the models based solely on the proactive data readily available to the transportation agencies performed sufficiently well for a meaningful and reliable application as operational models. The reason for such underperformance may be the lack of information and noise in the data used for model development. To address the potential shortcoming due to missing data features, a combined modeling framework has been presented that includes the data reflecting both proactive and reactive factors. The proposed method produced an improvement in the predictive performance of the injury severity model by incorporating the reactive data on driver age and vehicle age. This is achieved through implementation of a modeling framework that evaluates injury severities for different crash classes defined for different combinations of driver age and vehicle age. It is postulated that the results of this study could help the transportation agencies and decision-makers in advancing the design and implementation of more effective operational decision support systems for roadway safety management. Such systems would assist the traffic operations personnel in implementation of operational countermeasures and tactics to reduce the likelihood of crashes, such as proactive activation of advanced warnings on variable message sign (VMS), adjustments of variable speed limits (VSL), ramp metering (RM), or deployment of highway patrol and law enforcement resources to roadway segments with higher crash risk. The proposed modeling framework also provide a basis for further research in crash risk prediction, considering the emerging datasets. Such datasets include driving behavior records collected by the vehicle telemetry and user-based insurance (UBI) systems, which are already being offered to the policy holders by the major insurance companies. Another possible source of useful data are naturalistic driving studies [38], which can also be incorporated in the analysis to achieve more accurate predictive models. Using this rich source of data will enable the prediction of crash likelihood and injury severity by simultaneous use of proactive data and reactive data, such as driver and vehicle characteristics.