## 1 Introduction
In smart cities, the shared economy is expected to emerge as a sustainable consumption model, promoting servicing rather than traditional car ownership [5]. Modelling for future-ready smart cities is further complicated by the concepts of shared living/living streets, which are being promulgated to improve urban living and social interactions, both with the built environment and the associated geographies. Bicycles are being explored in the LMS literature with the broader MaaS. These include studies on bicycles in Belgium (Antwerp) [6], Austria (Vienna) [7], and the UK (Cambridge) [8]. The fundamental advantage of such bike networks is that they may reach users even in places with restricted access, such as pedestrian zones and regions with limited parking [7]. Another advantage is that, compared to other modes, these tend to improve the required degree of physical fitness. Therefore, such a system can lead to a sustainable transportation system, and an improved healthy lifestyle for the user. In the current literature, the last mile delivery is primarily focused on providing e-commerce logistic support. The COVID-19 pandemic has enhanced this need. However, this should not let us lose sight of the potential benefits of the LMS in providing intelligent, sustainable, and greener mobility. Such mobility form can ensure that our cities are future-ready, and those future generations can continue to reap the benefits of globalization while minimizing its associated harmful impact. Consequently, given how swiftly and unpredictably last-mile logistics are evolving to meet customer demand for on-demand logistic systems, transportation planners must develop quantifiable network indicators. The age of the bicycle rider is a crucial criterion influencing a rider's safe infrastructure use. A trip maker's route selection is influenced by both personal attributes and the behaviour of other road users [9]. Personal characteristics include age, gender, and experience [10]. [PERSON] and [PERSON] (2008) promoted cycling for the future by urging that cycling be made safe, convenient, and feasible for people of all ages and genders, while building a case for American municipalities to learn from European countries and embrace cycling [11]. The naturalistic investigation of cyclists discovered that the rider's age group directly impacts the safe use of the infrastructure. The naturalistic investigation conducted on British roadways [12] revealed that motorists exhibit behavioural sensitivity to the appearance of bicyclists. As a result, age is commonly documented in the literature as a crucial road safety variable for cyclists, which functions in conjunction with cyclist flow and other road users' behavioural sensitivity to affect safety in terms of crash frequency and perceived safety. Therefore, the research aims to develop a green intelligent real-time learning framework for the last-mile delivery of mobility as a service, based on a user's safe infrastructure usage. This aim will be achieved through the following set of objectives:  
1. Develop an intelligent real-time learning system for the delivery of the last-mile delivery
2. Develop a hybrid methodology that can model the safety of a particular bicyclist. 3. Create a predictive dynamic safety model that includes age as an output variable. 4. Develop a statistical variable interaction model for a rider's age and safety. Through a hybrid approach integrating statistics and supervised machine learning techniques, a knowledge-driven solution based on the specific user rather than generalized safe mobility practices is suggested. One of the most important aspects influencing travel mode and route selection, and safe infrastructure use, is the age of the user (see [13; 14; 15]). Still, relatively few studies deal with such modelling or the learning system. The envisioned learning system will include both hardware and software components.