Research On The Mechanism Of Multi - Source Fuzzy Travel Information Based On Driving Simulation

With the socio-economic development and motorized travel aggravation, urban traffic congestion has gradually evolved into a common phenomenon, which is seriously affecting residents’ travel efficiency and the quality of life, and alleviating traffic congestion becomes a pressing task. Intelligent Transportation System based on real time information of the road network providing service to residents has become a powerful measure to ease urban traffic congestion. Travel information is the key element of Intelligent Transportation System. Therefore, it is necessary to conduct the research on drivers’ information behavior characteristic under travel information environment in order to improve the pertinence and effectiveness of the planning of travel information environment. In this context, drivers’ information behavior characteristic was analyzed, and travel information mechanism test was conducted. On the basis of test data, rout choice behavior and congnitive load under different travel information environment was analyzed, and the impact of travel information congnitive on drive operation was dissected.First, the characteristic of travel information was resolved on the basis of previous studies. In order to obtain drivers’ information behavior characteristic in travel information environment, information behavior questionnaires for different information equipment were designed, and drivers’ information behavior characteristics was analyzed by merging data of RP and SP. This paper reveals that drivers have a sustainable increased willingness to pay for personalized information service. Market penetration of vehicle navigation rose continuely, which is closely related to drivers’ economy attribute; Significantly positive correlation was found between willingness to pay and service level and significantly negative correlation was found between failure frequency and service level, utilization load and service level; Mobile application was superior to traffic radio in dynamic traffic information service, but traffic radio is still an important source of supplementary information.Second, to obtain information on travel behavior characteristic under real travel information environment, the test scenarios of travel information were developed with driving simulation system, and planning path data, cognitive load measurement data and vehicle status data were obtained after the travel information mechanism test. Driver’s route choice model was analyzed with the consideration of travel time prediction, and rout choice behavior was studied based on the data from test. The results reveal that commuting drivers prefer the road which allow high-speed and almost no random interference. Traffic radio can help drivers avoid serious delays road, but its prediction accuracy of travel time is not enough. Variable message signs with high prediction accuracy for travel time work within a limited area, and there is a close relationship between the grading degree of speed information and prediction accuracy. Multi-source travel information environment improve drivers’prediction accuracy for travel time and individual travel costs can be significantly reduced.Finaly, the congnitive processe of travel information was analyzed based on congnitive psychology while cognitive load theory foundation was elaborated, then the formation mechanism of cognitive load was elaborated based on the sources resolving of intrinsic cognitive load, extraneous cognitive load and germane cognitive load while making a review of the measuring methods for the drivers’cognitive load. Based on the test data drivers’cognitive load of travel information under different scenarios was assessed, and the impact of information congnitive on driving operation was analyzed. The study suggests that travel information environment and travel experience have significant impact on the driver’s cognitive load, and the impacts of theose are independent. Both mental workload and time workload have significant relationships with travel information environment respectively. Driver’s cognitive load inrease accordingly when travel information environment is very complex, and drivers may reduce the speed to ensure ability to control vehicle.