Recently, the online car sharing is on the rise which improves the car resource utilization. In order to stimulate car sharing, the study on travel demand and user experience is essential. This paper measures the aggregation degree of travel demand in urban city through DBSCAN algorithm, which verifies the feasibility of car sharing. Mathematical models are provided based on Logit model to capture user experience and car utilization, and the user selection is predicted. Both real data and the survey result are leveraged to make the measurement and model realistic. The evolution of urban traffic mode is observed and analyzed, and it is found that the quantity of the travel demand and the traffic aggregation degree are the main factors influencing the evolution. The evolution can reach a steady state if and only if the travel demand reaches some certain value. The higher the traffic aggregation degree is, the more travelers will participate in car sharing and gain higher utilities. All the travelers will participate in the car sharing when the travel demand is larger than 290 as well as the traffic aggregation degree is larger than 0.9. Ultimately, the evolution of Beijing traffic mode is analyzed based on the real travel data from CAR Inc., which shows that the traffic mode will not reach a steady state now if car sharing is implemented without the involvement of economic factors and policies.