Knowing the locations of traffic crash hotspots can provide us with valuable insights into the root causes of crash occurrence over the area under study. This knowledge helps decision-makers to better assess the risk associated with road crashes and, as a result, help them to propose more effective countermeasures in order to reduce the annual crash rate. Nonetheless, identifying the areas with the highest potential of crash occurrence is a complicated task. In this regard and within this study, five various types of hotspot identification techniques, consisting of Average Nearest Neighbor, Getis-Ord Gi*, Global Moran’s I, kernel density estimation (KDE) and mean centre, were compared to each other, using three different performance measures, including Predictive Accuracy Index (PAI), Recapture Rate Index (RRI) and hit rate. According to the results, the most accurate model with the highest PAI values (PAI = 1.61 and 1.76), Moran’s I, had the third-highest reliability value (RRI = 1.003). On the other hand, while the Gi* method was the most precise and reliable technique with the highest RRI value (RRI = 1.121), it showed the second-lowest accuracy (PAI= 0.83 and 0.74). Overall, it seems that Moran’s I method is superior to other methods in locating hotspots, which is not only the most accurate technique but also precise enough to rely on.

Various surrogate safety measures have been introduced, of which Time-to-Collision (TTC) is the most well-known and commonly applied indicator. TTC first was defined as the time remaining prior to a collision if the path and speed of vehicles are maintained as a constant. Various modifications have been applied to this indicator to increase the efficiency of TTC. However, there is still no specific platform that shows how all of these modifications can be applied simultaneously. Thus, this paper first reviews the modifications that have been made to TTC; subsequently, a comprehensive framework is proposed for the application of this indicator for different types of conflicts, while considering all previous modifications. The framework is then applied for microscopic traffic data collected in Tehran. Results indicate that there is a great difference between the results of different versions of TTC for collision probability prediction. However, the proposed procedure might be beneficial in increasing the precision and accuracy of detecting dangerous encounters.

Time to collision is an important time-based safety indicator for detecting rear-end conflicts in traffic safety evaluations. A major weakness of the time to collision notion is the assumption of constant velocities during the course of an accident. In this paper we utilise equations of motion to develop a generalised formulation for time to collision by relaxing the assumption of constant velocity, constant acceleration and so on. This paper also illustrates how this concept can be applied to real-world data, and detailed data gathered in the NGSIM project are used. Then time to collision is calculated based on the assumption of constant speed, constant acceleration and linear acceleration for leading and following vehicles. Results indicate that in the third case (linear acceleration), the average duration of exposure to critical time to collision values is greater than the others. So, applying time to collision based on the assumption of linear acceleration in collision avoidance systems would decrease driver errors more than other cases.

Surrogate safety measures (SSM) are suitable tools to detect dangerous situations. These indicators can be applied as a warning strategy in collision avoidance systems (CAS). Time-to-collision (TTC) and post-encroachment time (PET) are two important time-based SSM that identify the probability of a rear-end collision. TTC refers to the imminent danger, and PET implies the potential danger. However, sometimes the results from each indicator are inconsistent. An appropriate warning strategy for CAS can be developed using a new index that combines the properties of both TTC and PET. For this purpose, a new mixed index (MI) is proposed. In order to develop this MI, three main microscopic parameters, clearance, speed and the relative speed, are simultaneously applied to the leading vehicle. To calibrate MI, based on a fuzzy inference system (FIS), a value would be determined by a combination of TTC and PET at each instant and then by regression analysis the model parameters would be determined. Finally, MI, TTC and PET values for real car-following scenarios on the I-80 freeway are determined and compared. The results show that MI may be more suitable in detecting the rear-end collision risk within the proper time and with less errors