## 1 Introduction
The present need of the transportation system requires cycling mode share to increase significantly. The major hurdle in this process is insufficient evidence to understand the relationship between cyclist safety and the identified parameters [23]. The literature is abundant with evidence of variables affecting cycling safety, which are presently not modelled in the prevalent general framework. The Italian crash study [24] demonstrated that the variable gender is a significant primary variable affecting the risk faced in terms of road type, type of interacting vehicle, riskiest vehicle manoeuvres, collision type, time, day, and season of the journey. The varied safety implication of gender is compounded by varied meteorological conditions that present different risks to different genders for varied infrastructure types [25]. Similarly, different lighting conditions also have different safety implications [26]. The needs of the cyclists are peculiar while interacting with other road users and infrastructure and its corresponding safety implications. The present road safety modelling framework implies a trade-off between prediction accuracy and knowledge of the causal link between the essential variables [27]. A practical model for examining road safety should determine causation, be highly predictive, and be scalable to large data sets [28]. This gap in the literature needs to be filled so that effective cycling infrastructure design and planning is not compromised. The transportation sector is at the brink of the fourth industrial revolution, with the movement toward autonomous/semi-autonomous vehicles and infrastructure to start soon. It is high time that the work on real-time embedded safety systems is explored. Presently, very few works in the literature undertake the development of an embedded learning system that can accurately model cyclist safety. Hence, the study aims to develop an intelligent embedded learning system for modelling cycling infrastructure. Such a system will be able to take the input data in real-time, model the variable, and result in an output of the predicted safety. As a result, the objectives are to:  
1. Develop an intelligent framework to construct a real-time embedded learning system that accurately models cyclist safety. 2. Apply this learning system as a case study on an investigation area. 3. Construct a nanoscopic model for a cyclist to predict the safety for a particular age and gender. Identify and quantify the significance of the variable affecting the unsafeness of the rider based upon the personal attribute. The proposed embedded learning system will consist of specific hardware and software. This will result in a composite system that can continuously take the data and undertake modelling to present the policymakers/city planners with the final desired output that is ready to use. Such a proactive approach is necessary to achieve the 2030 vision of zero road traffic fatalities and embark on a pathway towards a smart, green, and integrated transportation system. It is high time that intelligent embedded systems are incorporated into transportation research as well as practice. The proposed real-time embedded learning system is described in the next section, followed by the results in Section 3, and the conclusions are drawn in Section 4.