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Trip generation modeling is essential in transportation planning activities. Previous modeling methods that depend on traditional data collection methods are inefficient and expensive. This paper proposed a novel data-driven trip generation modeling method for urban residents and non-local travelers utilizing location-based social network (LBSN) data and cellular phone data and conducted a case study in Nanjing, China. First, the point of interest (POI) data of the LBSN were classified into various categories by the service type, then, four features of each category including the number of users, number of POIs, number of check-ins, and number of photos were aggregated by traffic analysis zones to be used as explanatory variables for the trip generation models. We used a random tree regression method to select the most important features as the model inputs, and the trip models were established based on the ordinary least square model. Then, an exploratory approach was used to test the performance of each combination of the variables with various test methods to identify the best model for residents' and travelers' trip generation functions. The results suggest land use compositions have significant impact on trip generations, and the trip generation patterns are different between urban residents and non-local travelers.

A GNSS/INS integrated navigation system has been intensively developed and widely applied in multiple areas. It can provide high accuracy position, velocity and attitude for vehicle with appropriate data fusion algorithm. However, the overall performance of a low-cost GNSS/MEMS IMU frequently degrades in shaded environment. The traditional constraints GNSS/MIMU algorithm based on zero-velocity detection can effectively increase positioning performance, but easily be susceptible to false detection. This article aims to improve a ZUPT/DZUPT constraints model to improve the accuracy of navigation solutions during satellites signal blockages for different motion states. Firstly, we present a tightly coupled strategy to integrate GPS/BDS and INS by applying EKF. Then, a compositive static zero-velocity detection scheme is carried out by using the Vondrak low pass filter, GNSS/INS calculated velocity and the original data of INS. Meanwhile, a dynamic ZUPT constraint model is also constructed based on the motion characteristics of vehicle. An vehicle test was performed to validate the new algorithm. The results indicate that proposed method can effectively improve the success rate of zero-velocity detection. When the satellite signal is interrupted for 120 s, the position and velocity accuracy of the vehicle are improved by 74.7%similar to 96% and 47%similar to 86.2% respectively.