چكيده به لاتين
Transportation has consistently played a crucial role in economic representations of geographical space, and over time, technological advancements have significantly shaped its characteristics. Throughout history, various technological developments have revolutionized transportation, altering its fundamental features and capabilities. However, ultimately, each mode of transportation is a response to various characteristics and constraints imposed by space and location, including land functions, available resources, settlement patterns, population distribution, physical and human limitations, distance, time, cost, management, topography, and more. In general, land use has been a key factor influencing transportation choices and infrastructure development. Indeed, another influential factor that has often been overlooked is the streetscape. The design and layout of streets can have a significant impact on the behavior of road users. By creating well-designed spaces that provide a sense of limitation, streetscapes can effectively discourage risky behaviors and promote safer transportation practices. Recognizing the role of streetscape design can contribute to a comprehensive understanding of transportation dynamics and facilitate the development of strategies for enhancing safety and efficiency in urban areas. Every day, a significant number of individuals sustain injuries on the roads of Iran, resulting in diverse economic, social, cultural, environmental, health, and human consequences. The impact of road accidents encompasses a wide range of dimensions. To address this issue, it is crucial to implement effective measures that enhance traffic flow, prevent accidents, and ensure the safety of all road users. The aim of this research is to examine the influence of streetscape and land use on urban accidents that occurred in Mashhad between the years 2017 and 2021. To achieve this objective, the study focused on analyzing accidents in specific areas, namely the 1st, 9th, and 11th urban zones. It also considered the land use types adjacent to both closed and open streets, including residential, commercial, and other land uses. The research employed various surveys to gather the necessary data and insights related to the targeted areas. In this research, analysis and modeling were conducted using machine learning algorithms implemented in the Python programming language. Several models were employed, and the best models were selected based on their performance and accuracy, which include Random Forest Regression (RFR), Multilayer Neural Network Perceptron Regression (MLP) and Extreme Boost Gradient Regression (XGBoost). Among the mentioned land use categories, commercial land uses have experienced the highest number of accidents, with their share being approximately three times that of accidents in residential areas. Additionally, 75% of all accidents took place in areas with open streetscape, whereas accidents in areas with enclosed view accounted for one third of the number of accidents in open streetscape areas. The accuracy of the machine learning models which successfully predicted future outcomes was as follows: Random Forest Regression (RFR) achieved 84% accuracy, Extreme Boost Gradient Regression (XGBoost) achieved 80% accuracy, and finally, Neural Network Multilayer Perceptron Regression (MLP) achieved 80% accuracy.
