Modelling Autonomous Demand Responsive Transport (DRT) For KTH Campus

This thesis explores the network design and simulation of an Autonomous Demand Responsive Transport (DRT) system for the KTH Royal Institute of Technology campus in Stockholm, Sweden. The study addresses current mobility challenges within the campus, particularly to the Albanova campus, which has limited accessibility. Using a data-driven approach, the research integrates e-scooter trip data and SL smart data for public transport to construct an O-D matrix characterizing mobility demands within the campus. A simulation framework was developed using VIPsim, allowing for the evaluation of multiple dispatch and routing algorithms under varying fleet sizes and demand scenarios. Five algorithms were tested across morning and afternoon peak periods, with performance assessed based on key indicators such as average waiting time, vehicle utilization, and passenger service rates. Results show that DRT can significantly improve campus mobility, offering a flexible and efficient transportation alternative, particularly in serving the Albanova campus. During morning peak hours, 15 vehicles were enough to achieve reasonable waiting times, while only 10 vehicles were needed for afternoon peak hours. However, in the afternoon peak hour, 10 vehicles were not enough to minimize waiting time according to user preference of less than 5 minutes; therefore, increasing vehicle capacity is necessary. In terms of algorithm performance, the Max Pass algorithm, which assigns vehicles based on maximizing the number of passengers, performed best in both the morning and afternoon peak hours compared to the Min Max algorithm, which assigns vehicles based on minimizing waiting time.