## 4 Conclusions  
In this paper, an intelligent real-time learning framework for the last mile delivery of mobility as a service based on safe use of infrastructure by users. A proposed intelligent real-time modelling system was proposed and applied to the Northumbria region of England's northeast. It consists of four units; (a) Input Learning Unit (ILU), (b) Safety Predictive Toolkit (SPT), (c) Variable causation Processing Unit (VPU), and d) Last-Mile service Delivery Unit (LMDU). A knowledge-driven solution based on the specific user rather than generalized safe mobility practices were used for modelling. This was achieved through a hybrid approach integrating mathematics and supervised machine learning techniques. A predictive dynamic safety model was developed, and the interaction of various variables affecting a rider's safety for the last-mile delivery was modelled. Users' characteristics of age, which influence mode and route selection, were simulated.  
The real-time intelligent learning system was applied as a case study on the northeast of England. The learning model's average distinguishing power to reliably predict the riskiest age group was 95%, with a standard deviation of 0.02, suggesting a strong prediction accuracy across all age groups. According to the results, different infrastructural networks represent varying risks to riders of various ages. The rider's age impacts how other road users engage with them. The regional diversity in trip intent and traffic flow conditions were significant elements influencing the safe use of infrastructure for a specific age group. Through the application of the route choice toolkit, on the Newcastle city centre, it was found that cyclists do not only consider the minimum path algorithm. The important elements influencing cycling route choices include perceived safety, lighting circumstances, meteorological conditions, degree of separation, traffic flow, pedestrian conflicts, cyclist flow, and unique infrastructure characteristics.  
The study's findings can significantly influence road legislation, design, and planning. The rider's age and interaction with the infrastructure under varying traffic flow, environmental, lighting, and meteorological conditions were important factors influencing a cyclist's safety. These findings provide further encouragement for choosing the last-mile route. The time difference will be merely a few seconds or at most a couple of minutes because of altering the route. However, this can significantly increase both perceived and actual safety. This can be crucial for mode selection and creating confidence in using a bicycle as a means of transportation. The constructed model, which integrated the rider's fragility, sensitivity to externalities, and the varied safety impact dependent on its features, may even be used for infrastructure that is still in the planning/design phase. This work provides an in-depth understanding of the last-mile sustainable delivery of MaaS, which can be integrated into the presently researched concept of smart cities, mobility hubs, and fifteen-minute cities. It is envisaged that this research would aid in adopting sustainable (green) transportation options and the last-mile delivery of mobility as a service. Future work should aim to uncover the sensitivities of a rider from different countries and make a baseline comparison scenario.  
Conceptualization, methodology, software, validation, writing, F.A.M., L.D. and K.B.; data collection and review, M.K. and F.A.M., and supervision, funding acquisition, K.B. and L.D. 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.  
The authors declare no conflict of interest.