Road traffic crashes are a global problem and represent a major cause of mortality among children and young people worldwide. Crash prediction models (CPM) play a crucial role in identifying crash-prone areas by predicting the expected number of crashes, thus facilitating the implementation of proactive measures. Previous studies have explored various CPM based on spatial analysis or modeling approaches. Nonetheless, a substantial gap still exists in exploring CPM that simultaneously address the combination of spatial units and modeling approaches, using of heterogeneous data at both the macroscopic and microscopic levels to develop models using Multi-Criteria Decision-Making and Statistical methods. Thus, the objective of this study is to develop multiple CPM using various spatial units of analysis and employing different modeling approaches, combine them, and conduct comparisons to determine the final model that best fits the data. A total of 7 models have been developed: three at the macroscopic level, utilizing Analytic Hierarchy Process (AHP), Negative Binomial (NB), and a combination of both; one at the microscopic level using Geographic Weighted Poisson Regression (GWPR). Additionally, three combined models bring together the previous from both levels, resulting in AHP+GWPR, NB+GWPR, and AHP+NB+GWPR models. These combined and individual models have been compared, and the best-performing has been selected through a validation process. The models were calibrated using data from a case study of a highway located in the northwest of Spain for the period 2016 to 2020. Subsequently, predictions for the year 2021 were made and compared to the actual crash occurrences during that year. The results indicate that the best model is the one that combines the macroscopic and microscopic levels using the approaches AHP+NB+GWPR, which presents a Mean Absolute Deviation (MAD) of 1.55, a Mean Square Error (MSE) of 2.89, does not underestimate any segments and shows a recall of 0.88. The results obtained confirm that the combination of models at the macroscopic and microscopic levels yields the best crashes predictions. This approach offers the advantage that our model, in addition to learning from historical data using statistical methods, also fits to the importance weights assigned by knowledgeable experts on the crashes propensity of the highway in the case study area using the Analytical Hierarchical Process.

Forward Collision Warning (FCW) system constantly measures the relative position of the vehicle ahead and then predicts collisions. This paper proposes a new cost-effective and computationally efficient FCW method that uses a time-of-flight (ToF) camera to measure relevant distances to the front vehicle based on license plate detection. First, a Yolo V7 model is used to detect license plates to identify vehicles in front of the ego vehicle. Second, the distance between the front vehicle and the ego vehicle is determined by analyzing the captured depth map by the time-of-flight camera. In addition, the relative speed of the vehicle can be calculated by the direct distance change between the license plate and the camera between two consecutive frames. With a processing speed of 25–30 frames per second, the proposed FCW system is capable of determining relative distances and speeds within 26 meters in the real-time.

Data acquisition in forensics science must be performed in a fast and an efficient way, so that the data acquired is maximized at the same time that disturbance and time on the scene are minimized. For this reason, the use of indoor mapping systems appears as a key solution, in contrast with static systems, either laser or photogrammetry based, in which representing big and complex scenes requires acquisition from a high number of positions, and long-time dedication for data processing. This paper presents a methodology for the segmentation of point clouds acquired with a mobile indoor mapping system, and their conversion to 3-D models in CAD format, based on parameterized geometric elements from the scene. This way, all the information required in forensic sciences is stored in an adequate digital format, enabling its availability in the future, and minimizing time dedication in both data acquisition and processing steps.