# Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network
## 1 Introduction  
Road traffic accidents are one of the major causes of death, injury, and disability globally [1, 2, 3, 4, 5]. Up to 1.3 million people lose their lives, and another 50 million sustain serious injuries due to road accidents each year, and the numbers keep increasing [6]. Annual road accidents (ARAs), which cause serious and irreparable economic and social harm to nations, are thus one of the main concerns of transportation managers around the world [7]. Successful implementation of road infrastructural safety management requires sufficient funding in addition to regulations, institutional capacity, and the availability of safety data [8]. Road management authorities are faced with the difficult task of identifying and prioritizing new projects and maintenance actions. Several optimization tools for resource allocation in road asset management have been adopted for efficient and well-informed decision-making processes [9, 10, 11, 12, 7, 13]. Due to budget constraints, most of these optimizationtools focus on the best alternative solutions within available budget constraints [14]. The question of how much is enough to tackle a single objective is usually given little attention effectively. The multi-criteria solution orientation of these decision frameworks also contributes to this lack of focus [9]. Safety, political importance, economic impact, and functional and structural performance are among the key priority functions for road asset management. However, among these factors, only the safety aspect affects human lives directly. This study focuses on safe limits for the annual maintenance of the road network and new infrastructural development investments in terms of minimum system-wide road accidents for diverse demographic, economic, and transport systems. Numerous studies were conducted towards modeling the frequency of road accidents using artificial neural network (ANN) [15; 16; 17; 18; 19]. These studies focused on predicting accident frequency based on road features, environmental conditions, driver attributes, and specific types of road facilities. Traffic characteristics such as volume and vehicle mix proportions were also used as predicting variables [20]. Accident prediction models such as these are often useful in developing crash modification factors [21] and understanding and prioritizing accident risk indicators [16]. Some crash prediction models (CPMs) are incredibly helpful tools for quantitative road safety analysis and can be applied to road network screening to identify the network's most important portions and to more effectively guide in-depth studies [22]. Other road accident predictors, such as vehicle ownership, per capita gross domestic product (GDP), and the relative usage of transport modes, have also been investigated [23]. Moreover, integrating the safety of VRUs--pedestrians, cyclists, and motorcyclists, has also resulted in significant consideration when making judgments about the management of road infrastructure [24]. Previously, ANN models were applied successfully to forecast road traffic [25] and travel time [26]; to optimize signal timing at road intersections [27], road vehicle collision avoidance systems, and road sign detection and classification [28]; and to evaluate pedestrian road crossing behavior [29]. The major aim of this study is to model and critically analyze the relationship between the frequency of road accidents and investments to maintain existing roads and for new road construction using ANN and regression analysis. Programs to improve infrastructure-related safety are typically capital-intensive and call for management and planning. Despite being constructed to engineering standards, the road network, which consists of sections and junctions, will typically need renovations and adjustments over its lifespan due to changes, most notably in traffic volumes and additional safety requirements.