The impact of land use on passenger flow within the influence scope of urban rail transit stations has different spatiotemporal differentiation characteristics. To explore the complex nonlinear relationship between land use and passenger flow at different stations, this paper proposed a differentiation identification method based on the spatial distribution of land use variables, and through time-phased multiscale geographic weighted regression, station clustering indicators that can characterize the spatiotemporal changing characteristics of land use impact on passenger flow were obtained. K-means++ algorithm was used to divide the stations into four categories, and the complex nonlinear relationship between land use and railway passenger flow under different categories was explored based on the improved gradient boosting decision tree model. Research shows that the accuracy of nonlinear model can be effectively improved by capturing the spatiotemporal heterogeneity of the relationship between the land use and passenger flow and classifying the stations properly. According to the output results, the key factors are different for each category. For the first category, the bus station number and sidewalk density have top relative importance value of 61.35% and 30.08% respectively; the key factors are the same for the fourth, but with an importance value decreasing from 61.35% to 30.31% for the bus station number. For the second category, the building densityhas the greatest impact with a relative ratio of 66.57%, and on the contrary, which only accounts for 5.59% for the third one. Meanwhile, there are significant and varying threshold effects on the relationship between land use and rail transit passenger flow. The result shows that different types of stations should put different emphasis on land use development, and land use design indicators should be controlled within a reasonable range. This research will provide theoretical support and quantitative guidance for the formulation of differentiated land use development strategies around stations.

In view of the problem that passengers motivate ride-hailing drivers in the form of red packet or dispatching fees to realize self-scheduling, this paper studied the interactive relationship between passenger-ride-hailing matching decision and incentive strategy choice. Based on the matching equilibrium in taxi-sharing, this paper designed a matching equilibrium model for taxi-sharing with peer-passenger incentive mechanism, taking the maximization of total passenger surplus as the goal and considering the constraints such as matching, equilibrium and cost. From the perspective of passengers, it designed the passenger incentive strategy, ride-hailing incentive strategy, passenger and ride-hailing incentive strategy, and the three incentive strategies were embedded into the column generation algorithm to solve the model and to achieve matching equilibrium and pricing equilibrium. By empirical analysis of Dalian taxi data, the results show that, compared with with only motivating drivers from the supply side in the form of random dispatching fees, the implementation of incentive strategies from the demand side and the supply side can promote taxi-share, and the passenger surplus can be increased by 12.6%. When the demand is larger than the supply, about 26% of incentive is transferred among peer passengers for more taxi-share matches. The fare discount rate also affects the flow of incentives. Using incentive strategies and discount strategies simultaneously can avoid malicious competition and ineffective incentives. By increasing the number of taxi-sharing trips, we can simultaneously reduce travel costs for passengers and boost driver incomes.