The traditional methods are lack of seat allocation as per user requirement, and lack of generation of good income to rental car or auto owner. The customers going daily for office, may not have purchasing capability of a bike or car. The difficulties of existing approaches are technology unaware, and dynamic fares based on calendar events. The integrated method would provide sophisticated data structure, and auto-calendar recordings from GPS service subscription. The unique features provide such as alerting when change in the fare is detected, fare is applied based on personalized chosen of the seat, and personalized tracking of the car or auto. The approaches such as FCFS, Seat Ranking have limitations and drawbacks such as missing of royal customer who generates the revenue as well as bearing total trip cost by individual. The usage of data visualization and intelligent computing over these would benefit the customers in selection of seat that results optimal service. The evaluation of the proposed system is compared against FCFS, Seat Ranking, Greedy, Dynamic Programming, Simulated Annealing, Genetic Algorithms, Integer Linear Programming, etc. The services guaranteed are personalized pricing based on selection, and fare rise or drop by the driver based on demanding scenarios. The auto-calendar activities are always turned on in the application that alerts on the price fixing for ensuring optimal revenue.