The congestion causes of parking lot were analyzed with causal chain method and a questionnaire about individual demand of parking users was carried out. The results show that more than 90% of the users hope parking spaces can be automatically assigned to them when it's difficult to find a parking space. The layout principles of WiFi in parking lot were provided. The automatic assignment mechanism of parking lot was given considering the individual demand of parking users and the avoidance of traffic conflicts. Some attribute decision factors such as lane occupancy conditions, travel distance, walking distance, and the occupancy situation of parking space on both sides were selected and optimal parking lot assignment model was established. Optimal paths were calculated through Dijkstra algorithm, and the information about the assignment location and path was sent to the drivers' cell phones. The driver's compliance was evaluated by comparing the driver's parking trajectory with system recommended path. Finally, a large parking lot in Beijing was taken as an example. The results can offer constructive suggestions on parking route design and parking assignment mechanism, which can make the use of the limited parking resources more effectively.

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This paper excavates tourist decision-making mechanism under the real-time tourist flow diversion scheme (RTFDS) and evaluates the tourist flow diversion effect of RTFDS. To meet the objectives, the stated preference survey and tourist flow survey of the Summer Palace were implemented. The tourist behavior adjustment model and tourist flow diversion simulation model were established. The results show that: (a) For core tourist spots, 66.5% and 16.5% of tourists will choose “behavior adjustment” and “no longer adjustment” under RTFDS, these behavior adjustments all shorten tourists’ residence time in tourist spots; (b) When the tourist congestion perception degree equals 4 or 5, tourists tend to adopt behavior adjustment or the individuals adopt no longer adjustment instead of cognitive adjustment when they face low tourist congestion perception degree, which equals 1 or 2; (c) When core tourist spots’ residence time is reduced by 10% and 20%, there are 60% and 73% time nodes where core tourist spots’ tourist flow density is less than or equal to the condition of null information, there are 73% and 60% time nodes where periphery tourist spots’ density is more than the condition of null information. The simulation results showed that some tourists could be guided from core tourist spots to periphery tourist spots through releasing RTFDS information. The research can provide theoretical support for the implementation of RTFDS, and alleviate the congestion inside the tourist attraction.