SafeGo: An Intelligent and Inclusive Safety-Centric Ride-Hailing Platform with Machine Learning-Based Risk Prediction

Ride-hailing apps are built mainly to book a ride, match a driver and reach the destination quickly. Safety and accessibility are usually added afterwards as a separate SOS button or a support-ticket system, so women, elderly riders and persons with disabilities (PWD) are often poorly served. This paper presents SafeGo, a ride-hailing platform that brings passenger-specific ride modes, machine-learning-based safety-risk prediction, accessibility-aware driver matching, PIN-based ride verification, live monitoring, SOS emergency response, transparent fare calculation and an administrative command center into one system. SafeGo provides four ride modes (Normal, Pink, Elderly and PWD), and a Random Forest classifier uses temporal, geographical, trip-level and mode-related features to place each trip in the Stable, Cautious or High Priority category. On a held-out synthetic test set of 2,000 samples the classifier achieved 99.80% accuracy, 99.87% macro precision, 99.78% macro recall and 99.82% macro F1-score, with a mean prediction time of 32.27 ms. Scenario-based tests show that predictions change with the ride mode and trip context. Because the dataset is synthetic, these figures describe model behaviour on the evaluation data and are not a claim about real-world accuracy