氏名: DE LEON BERMUDEZ HECTOR DANIEL

論文題目: SIMPLIFIED METHOD FOR CALCULATING WAITING TIME OF RIDE-HAILING SYSTEM 
指導教員: 加藤 哲平

This thesis presents a simplified method for calculating waiting times in ride-hailing systems,
addressing the challenges posed by urbanization and the increasing demand for efficient transportation solutions. Ride-hailing services have emerged as a flexible alternative to traditional public transportation, with waiting time being a critical factor influencing user satisfaction. Existing methods for analyzing waiting times are often complex and computationally intensive, limiting their practical application. This research proposes a network-based approach that represents urban areas as interconnected nodes, utilizing the Floyd-Warshall algorithm to calculate the shortest travel times between nodes. The method incorporates supply-demand imbalances and employs stochastic analysis to estimate waiting times under various scenarios.
The study develops a mathematical model to calculate waiting times at demand nodes, considering factors such as travel time, vehicle distribution, and supply-demand ratios. The model is implemented using Python, and a stochastic analysis is conducted with 10,000 iterations to capture system variability. The results are visualized through histograms, providing insights into the distribution of waiting times across different nodes. The analysis identifies patterns in waiting time behavior, offering a basis for optimizing vehicle allocation and reducing waiting times.
Key findings indicate that nodes with higher connectivity and shorter travel times experience lower waiting times, while isolated nodes with limited access face significantly longer waits. The study also explores the impact of increasing vehicle supply at specific nodes, demonstrating that strategic allocation of resources can effectively reduce waiting times. The proposed method offers a computationally efficient and accessible framework for analyzing ride-hailing systems, enabling rapid decision-making and optimization. This research contributes to the broader discourse on enhancing ride-hailing services and their integration with urban transportation networks, providing valuable insights for future studies and practical applications.