## 4 Conclusions  
This paper proposes a real-time nanoscopic rider safety system based on the varied infrastructure variables. Such a system will help improve safety, hence the attractiveness of the mode of travel. An embedded real-time learning system was developed, whose knowledge processing unit is primarily based upon deep neural networks. This is applied as a case study on the Northumbria region in England's northeast. Two accurate prediction safety models were developed that can predict the riskiest age and gender groups.  
An average accuracy of 92% was obtained in these prediction models. This accuracy is several times higher than the available models in the literature. In addition, a variable interaction model was developed that ranks the input variable and estimates the variable importance.  
The research's primary contribution is developing a comprehensive embedded system that models safety in real-time. The system includes an input unit, knowledge processing unit, and output unit. Not only prediction models are developed, but an understanding of the critical variables was determined and established. Presently, very few such systems exist in the literature that have affected infrastructure systems' design and planning. The present models in the literature are primarily based upon the probabilistic functions of human error, which model variables such as miles traversed, speed limits, intoxication, and others. These variables model road users' behaviour at an aggregate level, such as a city or a county. The learning system developed in this work models the road user at a nanoscopic level. The local authorities and city planners can directly apply such a system. Through the application of the learning system on the north-east of England, it is established that the varied types of infrastructure impose differing risks to riders of various ages. The interaction between cyclists and infrastructure can cause both physical and cognitive stressors, to which riders of different ages and genders respond differently, allowing us to estimate the riskiest age and gender group depending on the specific input factors. Furthermore, certain infrastructural characteristics are hazardous to all bicycles. However, the rider's personal attributes such as age and gender determine the amount of danger that any infrastructure poses. The critical variable affecting the safe usage of infrastructure for females is the rider manoeuvre that they are required to perform. This is due to the varying physical and cognitive abilities that females possess compared with males. However, for males, it is a sudden change in the road hierarchy level that presents the highest level of risk. The sudden change in the road hierarchy requires a shift in how cyclists need to interact with the infrastructure and other road users.  
The study's findings will assist in improving safety and contribute to the development of a sustainable integrated bicycle transportation system. The study's findings will aid in better understanding the risk variance of various infrastructure types. Following validation, the developed real-time learning system is interoperable in several situations, including present heterogeneous and future semi-autonomous and autonomous transportation systems. The embedded learning system will improve the attractiveness of cycling as a mode of travel and contribute to a smart and green transportation system.  
Funding acquisition, L.D. and K.B.; investigation, F.A.M.; methodology, F.A.M.; supervision, L.D. and K.B.; writing--original draft, F.A.M., L.D. and K.B.; writing--review & editing, F.A.M., L.D. and K.B. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank Northumbria University for sponsoring the research through the research development fund and Gateshead City Council for accessing the TADU crash database.  
Not applicable.  
The is no conflict of interest to declare.