Current visible-infrared cross-modality person re-identification research has only focused on exploring the bi-modality mutual retrieval paradigm, and we propose a new and more practical mix-modality retrieval paradigm. Existing Visible-Infrared Person Re-Identification (VI-ReID) methods have achieved some results in the bi-modality mutual retrieval paradigm by learning the correspondence between visible and infrared modalities. However, significant performance degradation occurs due to the modality confusion problem when these methods are applied to the new mix-modality paradigm. Therefore, this article proposes a Mix-Modality Person Re-Identification (MM-ReID) task, explores the influence of modality mixing ratio on performance, and constructs mix-modality test sets for existing datasets according to the new mix-modality testing paradigm. To solve the modality confusion problem in MM-ReID, we propose a Cross-Identity Discrimination Harmonization Loss (CIDHL) adjusting the distribution of samples in the hyperspherical feature space, pulling the centers of samples with the same identity closer, and pushing away the centers of samples with different identities while aggregating samples with the same modality and the same identity. Furthermore, we propose a Modality Bridge Similarity Optimization Strategy (MBSOS) to optimize the cross-modality similarity between the query and queried samples with the help of the similar bridge sample in the gallery. Extensive experiments demonstrate that compared to the original performance of existing cross-modality methods on MM-ReID, the addition of our CIDHL and MBSOS demonstrates a general improvement.

Because the strategy of stopping bus lines during an epidemic can negatively impact residents, this study proposes a bus passenger flow control model to optimize the safety of and access to bus transport. The information interaction environment can provide a means for the two-way regulation of buses and passengers. In this model, passengers first request their pick-up and drop-off location, and then the bus feeds back information on whether it accepts the request. Through this method, passenger flow control can be realized through complete information interaction. The study aimed to establish a multi-objective function that minimizes the weighted total cost of the safety cost, the passenger travel cost, and the bus travel cost during an epidemic. The constraints were the full load and riding rates of urban buses in peak periods under the condition of epidemic prevention and control. The results showed that, in the morning peak period, the passenger flow control scheme reduced the passenger infection probability by 17.89%, compared with no passenger flow control scheme. The weighted total cost of the epidemic safety cost, the passenger travel cost, and the bus operation cost was reduced by 8.04%. The optimization effect of the passenger flow control scheme of this model is good, and not only reduces the probability of passengers being infected, but also meets the requirements of epidemic prevention and the travel needs of residents.

Bus guidance information supplied by advanced traveler information systems provide a reference for route selection by bus passengers. This study analyzes the interaction between bus passengers’ route-choice behavior and the relationship in a game. The game revenue of the passengers waiting on the bus platform is quantified by parameters such as the number of passengers waiting on the platform, the number of passengers on the bus, and the passenger capacity of the bus. Based on the characteristics of passenger-bounded rationality, an evolutionary game model of bus choice behavior under information-induced conditions is established. Through the evolutionary game model, the distribution of the proportion of passengers receiving the guidance strategy is obtained when the evolution is stable. Finally, according to the distribution results, the passenger guidance strategy is discussed to achieve a win-win situation between passengers and bus operators.