Route Recommendation System Based on Group Preferences Using Analytic Hierarchy Process as Criteria Weight Calculation

The COVID-19 pandemic has significantly increased reliance on online shopping, leading to a surge in demand for courier delivery services. However, navigating unfamiliar delivery areas remains a challenge for many couriers. This study develops a route recommendation system based on group preferences, utilizing criteria such as traffic conditions, weather (including temperature and humidity), road length, travel time, and vehicle variability. Driving preferences were gathered from 544 drivers through questionnaires, and the importance level of each criterion was calculated using the Analytical Hierarchy Process (AHP). These weights were applied to recommend optimal routes for drivers. The system generates routes within 11.514 to 12.758 seconds for different types, utilizing less than 20% of CPU resources and 63.153 to 73.597% of memory. It also allows drivers to navigate intersections more efficiently by minimizing the weight of road segments to the destination. This efficient and scalable system offers a practical solution for improving courier navigation. Future research could integrate real-time traffic and weather data to further enhance route optimization. This study contributes to intelligent transportation systems and addresses critical logistical challenges in last-mile delivery.