Demand Responsive Transport (DRT) emerges as an alternative solution to deliver a cost-effective Public Transport (PT) service, particularly in low-demand areas. DRT is a flexible version of PT, which operates based on passengers' specific demand requests. This approach aims to enhance the core role of PT, which is to provide accessible, efficient, and sustainable mobility for users. The primary aim of this research is developing and designing a real-world size DRT system that integrates with the existing urban PT network and investigate on the impact of DRT on equitable distribution of PT service facilities. DRT is modelled as a Dial-a-Ride Problem (DARP), representing a specific form of the vehicle routing problem. Comparing real-scale problems with benchmarks in previous studies reveals a gap between the real-world problem scales and the scale of DARP models in those studies. Therefore, the first phase of this thesis proposed method to deal with challenges related to modelling real-world setting DRT applications. The primary contributions within this phase are divided into two core sections: First, formulating an initialisation method, ensuring solution feasibility at any point in the optimisation process's termination, Second, enhancement of the Deterministic Annealing algorithm to expedite the attainment of near-optimal solutions. In the second phase, the study confronts challenges intrinsic to implementing DRT as an alternative to conventional PT. This entails accommodating local trips from any origin to any destination throughout the service area and ensuring connectivity with the main PT network and adherence to its timetables. Additionally, prevailing research tends to focus on singular objectives when delineating the design and operational facets of DRT. This predominantly single-objective optimisation approach restricts decision-makers by compartmentalising objectives rather than integrating varied and sometimes conflicting goals. This thesis leverages the advantages of adopting a multi-objective approach to optimise the operational plan based on factors such as operational costs, passenger travel time, fuel consumption, and equity considerations. The inclusion of an equity component aims to minimise the standard deviation of passengers' ratios of excess travel times over direct travel, thereby ensuring equitable treatment in terms of individual travel times among passengers.