Personalized Route Planning System Based on Driver Preference.

Area of Science:

• Computer Science
• Artificial Intelligence
• Transportation Engineering

Background:

• Current route navigation systems rely on limited attributes, failing to capture diverse driver needs.
• Optimal routes often involve multiple objectives and attributes, which existing systems do not adequately address.

Purpose of the Study:

• To propose a driver preference-based route planning (DPRP) model.
• To develop a system that recommends optimal routes by incorporating individual driver preferences.

Main Methods:

• Collected data on driver preferences.
• Developed an integrated algorithm to solve the DPRP model.
• Optimized the algorithm by simplifying road networks and removing invalid sub-routes to reduce computation cost.

Main Results:

• The proposed DPRP model effectively recommends optimal routes based on driver preferences.
• The integrated algorithm significantly speeds up the route recommendation process.
• Computational cost is reduced through network simplification and sub-route invalidation.

Conclusions:

• The DPRP model enhances route navigation systems by personalizing route recommendations.
• The developed algorithm offers an efficient and effective solution for preference-based route planning.
• This approach addresses the limitations of existing systems by considering multiple driver objectives and attributes.