Models for vehicle detection, classification, and counting based on computer vision and artificial intelligence are constantly evolving. In this study, we present the Yolov4-based vehicle detection, classification, and counting model approach. The number of vehicles was calculated by generating the serial number of the identity of each vehicle. The object is detected and classified, marked by the display of bounding boxes, classes, and confidence scores. The system input is a video dataset that considers the camera position, light intensity, and vehicle traffic density. The method has counted the number of vehicles: cars, motorcycles, buses, and trucks. Evaluation of model performance is based on accuracy, precision, and total recall of the confusion matrix. The results of the dataset test and the calculation of the model performance parameters had obtained the best accuracy, precision. Total recall values when the model testing was carried out during the day where the camera position was at the height of 6 m and the loss of 500 was 83%, 93%, and 94%. Meanwhile, the lowest total accuracy, precision, and recall were obtained when the model was tested at night. The camera position was at the height of 1.5 m, and 900 losses were 68%, 77%, and 78%.

Public transport safety is still an issue that needs to be studied by bureaucrats and researchers. This is because public bus accidents are still quite high. This is because many families, involved in traffic accidents, are shocked by the accident. Therefore, the problem of perception of a safe bus needs to be studied. The purpose of this study was to determine the perception model of a safe public bus. Mathematical modeling based on the parameters that have been studied was selected first. While the second objective was to determine the importance value of the parameters that are an indication of the perception of the safety of intercity bus public transportation. This research is a type of perceptual one where the data is taken from the relevant respondents. The method of data collection was carried out using a questionnaire with respondents from bus company owners, drivers, and passengers in the province of East Java, Indonesia. Respondents were asked to answer questions related to the variables of income, speed, comfort, and safety. The method of conjoint analysis is used. The first stage is the result of modeling the perception of a safe bus. Further analysis is carried out to obtain the importance value of the parameters. The result of this research is a utility model for the perception of a safe bus, which is expressed by the equation U, where the variables include income, speed, comfort, and safety. The highest level of importance is income 33.29 %, followed by the security variable with a weight of 25.39 %. This shows that the income factor is a top priority for drivers and management of bus company owners, while road safety is second only to income. In other words, respondents' perceptions are more concerned with income, while safety is still a non-priority factor.

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Road maintenance action program must begin with identification of road surface defects before compiling a work program. One method of identification of road defects is the Road Condition Index (RCI) method. This method is simpler than the other methods because the survey method is by visualizing. This study aims to identify road defects with the RCI method carried out by several surveyors and how defects occur on the Caruban-Ngawi road section.The method used in this study is by direct survey of primary data on road surface defects conditions. There were 3 surveyors who conducted a survey with normal and opposite directions along the road. Data slices are made at lengths of every 100 m to identify road defects. The data is processed by doing an average on each data which is then made a strip map of road defects image. Data processing was done by determining the percentage of defects categories ranging from good, moderate, light defects, and heavy defects.The results of the study showed that the survey conducted by several surveyors was good and the general results were not significantly different. This means that the surveyors have almost the same perception in terms of assessing the condition of road defectss with the RCI method. The condition of road pavement on the Caruban-Ngawi road in general can be said that the road is still in good condition where heavy defects road damage in the normal and opposite directions is only 1.13% and 0.28% respectively.