Aiming at the data noise filtering problem and the importance problem of different POIs in POI (Point-Of-Interest)recommendation research, a POI recommendation algorithm, named RecSI (Recommendation by Spatiotemporal information and POI Importance), was proposed. First, the geographic information and the mutual attraction between the POIs were used to filter out the data noise, so as to narrow the range of candidate set. Second, the user’s preference score was calculated by combining the user’s preference on the POI category at different time periods of the day and the popularities of the POIs. Then, the importances of different POIs were calculated by combining social information and weighted PageRank algorithm. Finally, the user’s preference score and POI importances were linearly combined in order to recommend TOP- K POIs to the user. Experimental results on real Foursquare sign-in dataset show that the precision and recall of the RecSI algorithm are higher than those of baseline GCSR (Geography-Category-Socialsentiment fusion Recommendation) algorithm by 12.5% and 6% respectively, which verify the effectiveness of RecSI algorithm.

The large number of duplicate images in the database not only affects the performance of the learner, but also consumes a lot of storage space. For massive image deduplication, a duplicate detection algorithm for massive images was proposed based on pHash (perception Hashing). Firstly, the pHash values of all images were generated. Secondly, the pHash values were divided into several parts with the same length. If the values of one of the pHash parts of the two images were equal to each other, the two images might be duplicate. Finally, the transitivity of image duplicate was discussed, and corresponding algorithms for transitivity case and non-transitivity case were proposed. Experimental results show that the proposed algorithms are effective in processing massive images. When the similarity threshold is 13, detecting the duplicate of nearly 300000 images by the proposed transitive algorithm only takes about two minutes with the accuracy around 53%.

In view of the optimization of high-speed train connection in passenger transport hub under the condition of high-speed railway network, the concept of transfer satisfaction of medium and long distance passenger flow was proposed by analyzing the passenger transfer process in hub, and a high-speed train connection optimization model for large passenger transport hub based on transfer orientation was proposed with the average transfer satisfaction and the arrival and departure equilibrium of trains at hub stations as the optimization objective and with the constraint conditions of reasonable originating time of large stations, reasonable terminating time, station operation interval time, passenger transfer time and station arrival and departure line capacity. A genetic algorithm with improved chromosome coding mode and selection strategy was designed to solve the example. Compared with the basic genetic algorithm and the basic simulated annealing algorithm, the improved genetic algorithm increases the average transfer satisfaction in the objective function by 5.10% and 2.93% respectively, and raises the equilibrium of arrival and departure of trains at hub stations by 0.27% and 2.31% respectively. The results of the example verify the effectiveness and stability of the improved genetic algorithm, which indicates that the proposed method can effectively optimize the quality of the high-speed train connection in large passenger transport hub.

In order to improve the quality and efficiency of railway crew routing plan, the problem of crew routing plan was abstracted as a Multi-Traveling Salesman Problem (MTSP) with single base and balanced travel distance, and a equilibrium factor was introduced to establish a mathematical model aiming at less crew routing time and balanced tasks between sub-crew routings. A dual-strategy ant colony optimization algorithm was proposed for this model. Firstly, a solution space satisfying the space-time constraints was constructed and pheromone concentration was set for the node of the crew section and the continuation path respectively, then the transitional probability of the dual-strategy state was adopted to make the ant traverse all of the crew segments, and finally the sub-crew routings that meet the crew constraint rules were found. The designed model and algorithm were tested by the data of the intercity railway from Guangzhou to Shenzhen. The comparison with the experimental results of genetic algorithm shows that under the same model conditions, the number of crew routing in the crew routing plan generated by double-strategy ant colony optimization algorithm is reduced by about 21.74%, the total length of crew routing is decreased by about 5.76%, and the routing overload rate is 0. Using the designed model and algorithm to generate the crew routing plan can reduce the crew routing time of crew plan, balance the workload and avoid overload routing.

Focusing on the issue that emergency resource assignment for multiple demand points and multiple supply points in railway emergencies, an emergency resource assignment model of multiple rescue targets was established, which was based on the concept of "soft time window". The maximum fairness and minimum total assignment cost were considered as the optimization objectives, and parallel selected genetic algorithm was used to solve the model. The population was equally divided into subpopulations by the algorithm. Subpopulations' number was equal to the number of objective functions. An objective function was assigned to each divided subpopulation and the selection work was done independently, by which individuals with high fitness were selected from each subpopulation to form a new population. Crossover and mutation were done to generate the next generation of population. The computing cases show that the parallel selected genetic algorithm reduces the variance of resource satisfaction degree of all demand points by 93.88% and 89.88% respectively, and cuts down the cost by 5% and 0.15% respectively, compared with Particle Swarm Optimization (PSO) and two-phase heuristic algorithm. The proposed algorithm can effectively reduce the variance of the resource satisfaction degree of all demand points, that is, it improves the fairness of each demand point and reduces the cost at the same time, and can obtain higher quality solution when solving multiple objective programming problem.