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.

Connected and automated vehicles (CAVs) are poised to revolutionize mobility. The relocation feature of CAVs enhances parking convenience for the public. Users can instruct CAVs to arrive at their work destination, drop them off, and then assign CAVs to a cost-effective parking facility through an optimized itinerary. However, realizing the benefits of CAVs depends on user acceptance, and the impact of relocation features on CAV acceptance remains an area that is yet to be explored. This study introduces a novel acceptance model to mainly investigate the effects of relocation-related factors on CAV acceptance through 717 valid responses. The results indicate that the perceived convenience of relocation (PCOR) indirectly increases human acceptance through three determinants, initial trust, perceived usefulness (PU), and perceived ease of use (PEOU), while initial trust, PU, and PEOU directly increase human acceptance. The public expectations of saving on parking fees (EOSPF) can enhance PCOR. Additionally, a multigroup analysis revealed that PCOR exerts a more positive impact on PU or PEOU in subgroups including males, pre-Generation-Z individuals, experienced drivers, and those with autopilot riding experience. The findings on mediators are also discussed. This study provides valuable insights for further research and the practical adoption of emerging CAVs.

There are significant differences in the utilization efficiency of parking spaces in different spatial locations within the complex parking lots, which reduces the utilization efficiency of parking resources. For the above problem, a parking spaces supply demand characteristics indexes system was constructed. The Metro City complex was taken as an example, and its parking demand utilization characteristics were analyzed to judge the problem of parking spaces utilization. On this basis, a model of the dynamic allocation of parking spaces for parking spaces was constructed to improve drivers’ degree of degree of satisfaction and balance the occupancy rates for parking spaces in different zones. The simulation results show that after the implementation of the dynamic allocation of parking spaces, the differences of the parking spaces’ demand characteristic indexes between two different parking zones are significantly reduced. It was specifically observed that the differences between parking zones A and B in terms of turnover number, total parking time and average parking time were reduced from 2.24 times to 0.03 times, 1.3 h to 0.6 h and 2.2 h to 0.1 h, respectively, and the average interval time of parking spaces became smaller and more evenly distributed. It can be seen that this model can improve the overall utilization efficiency of the complex parking lot and drivers’ degrees of satisfaction.