According to World Health Organization (WHO), the leading cause of fatalities and injuries is rear-ending collision in vehicles. The critical challenge of the technologically rich transportation system is to reduce the chances of accidents between vehicles. For this purpose, it is especially important to analyze the factors that are the cause of accidents. Based on these factors’ results, this paper presents a driver assistance system for collision avoidance. There are many factors involved in collisions in the existing literature from which we identified some factors which can affect the accident occurrence probability. However, with advancements in the technologies of autonomous vehicles, these factors can be controlled using an onboard driver assistance system. We used MATLAB’s Fuzzy Inference System Tool to analyze the categories of accident contributing factors. Fuzzy results are validated using the VOMAS agent in the NetLogo simulation model. The proposed system can inform the vehicle’s automated system when chances of an accident are higher so that the vehicle may take control from the driver. The proposed research is extremely helpful in handling various kinds of factors involved in accidents. The results of the experiments demonstrated that multi-factor-enabled vehicles could better avoid collision as compared to other vehicles.

Precipitation not absorbed by the soil or local vegetation and remain on the surface leading to stormwater can cause soil erosion, flooding, property damage, and overflow to wastewater treatment facilities. This paper introduces a novel multicriteria decision-making model to choose among various sustainable solutions that can help in managing stormwater. This model is intended to help decision-makers in handling stormwater through proper utilization of precipitation while ensuring public safety and adhering to runoff regulations. The model also aims to present sustainable technologies that can help in reducing harmful stormwater overflows. As a way of constructing and validating the model, precipitation and other relevant data from the North-Eastern region of the United States were used. The model can be altered though to suit other regions in the world. The model was further validated by seeking the opinion of a group of experts on its constructs. Statistical analysis identified high item-to-total correlations for model constructs and a model Cronbach’s alpha value of 0.84 leading to conclude that the model is valid. Yet, green solutions presented in this study and the developed model should be considered as a first step in determining sustainable stormwater solutions and further research in this area is needed.

Visual object tracking is still considered a challenging task in computer vision research society. The object of interest undergoes significant appearance changes because of illumination variation, deformation, motion blur, background clutter, and occlusion. Kernelized correlation filter- (KCF) based tracking schemes have shown good performance in recent years. The accuracy and robustness of these trackers can be further enhanced by incorporating multiple cues from the response map. Response map computation is the complementary step in KCF-based tracking schemes, and it contains a bundle of information. The majority of the tracking methods based on KCF estimate the target location by fetching a single cue-like peak correlation value from the response map. This paper proposes to mine the response map in-depth to fetch multiple cues about the target model. Furthermore, a new criterion based on the hybridization of multiple cues i.e., average peak correlation energy (APCE) and confidence of squared response map (CSRM), is presented to enhance the tracking efficiency. We update the following tracking modules based on hybridized criterion: (i) occlusion detection, (ii) adaptive learning rate adjustment, (iii) drift handling using adaptive learning rate, (iv) handling, and (v) scale estimation. We integrate all these modules to propose a new tracking scheme. The proposed tracker is evaluated on challenging videos selected from three standard datasets, i.e., OTB-50, OTB-100, and TC-128. A comparison of the proposed tracking scheme with other state-of-the-art methods is also presented in this paper. Our method improved considerably by achieving a center location error of 16.06, distance precision of 0.889, and overlap success rate of 0.824.

EVALUATING THE OPERATIONAL AND THE ENVIRONMENTAL BENEFITS OF A SMART ROUNDABOUT

Using public transport is an important part of daily routine of many people in large cities and consumes considerable time and financial resources. Enhancing public transport can help in encouraging less use of privately owned motor vehicles and, therefore, in emitting less greenhouse gases. Hence, there is a need to improve public transport and make it more attractive than private motor vehicles. This study aims to assess commuters opinion on several aspects related to using public buses in Istanbul with the ultimate aim of making them more attractive than private motor vehicles. A total of 620 randomly selected bus commuters responded to a questionnaire specially prepared for this purpose. Satisfaction rates with most aspects related to public buses were found to be high. Nevertheless, non-parametric test results revealed that female commuters recorded significantly lower satisfaction rates than male commuters on many aspects related to public buses. Results also revealed that a significant minority of commuters walk for long distances to the nearest bus stop or wait for very long times for the next bus to come. Moreover, results revealed that commuters of older ages and higher income levels recorded significant higher satisfaction rates than others. Results also show that the majority of commuters support increasing bus fees during peak hours in order to minimize crowdedness during these hours. Implications of these results for decision makers in megacities on ways to encourage the use of public buses are discussed at the end.