The urban expressway system serves as a key role in the roadway transportation system. It provides an efficient and comfortable approach for long-distance travel within the city. However, the safety status of the urban expressways is becoming a critical issue as the high-frequent traffic crashes have severely influenced the traffic operations. Among the safety influencing factors, including traffic operational parameters (such as traffic speed and volume), geometric features and traffic participants’ characteristics (such as vehicle roadway usage patterns), the traffic operational parameters and geometric features have been widely investigated. However, the impacts of traffic participants’ characteristics on traffic safety have never been examined. This unprecedented study aims to link vehicles’ roadway usage patterns with traffic safety through crash frequency analyses. First, the roadway usage patterns were identified using Latent Class Cluster Analysis (LCCA) based on their traveling rates. Then, the hourly-based crash frequency analysis data were formulated with traffic operational parameters, geometric features and crash data. Finally, crash frequency analysis models were developed to unveil the relationships between the crash occurrence and their influencing factors. The modeling results showed that the Random Effects Hurdle Negative Binomial Model (REHNBM) provided better goodness-of-fit. And it concluded that higher proportions of vehicles with low-level roadway usage pattern would substantially enhance the possibility of crash occurrence; while the proportions of vehicles with the medium-high-level roadway usage pattern had negative impacts on crash occurrence probability. Finally, safety improvement recommendations and strategies based on the modeling results were put forward.

Geosynchronous orbit synthetic aperture radar (GEO SAR) has a long integration time and a large imaging scene. Therefore, various nonideal factors are easily accumulated, introducing phase errors and degrading the imaging quality. Within the long integration time, tropospheric status changes with time and space, which will result in image shifts and defocusing. According to the characteristics of GEO SAR, the modeling, and quantitative analysis of background troposphere and turbulence are conducted. For background troposphere, the accurate GEO SAR signal spectrum, which takes into account the time-varying troposphere, is deduced. The influences of different rates of changing (ROC) of troposphere with time are analyzed. Finally, results are verified using the refractive index profile data from Fengyun (FY) 3C satellite and the tropospheric zenith delays data from international GNSS service (IGS). The time-space changes of troposphere can cause image shifts which only depend on the satellite beam-foot velocity and the linear ROC of troposphere. The image defocusing is related to the wavelength, resolution requirement, and the second and higher orders of ROC. The short-wavelength GEO SAR systems are more susceptible to impacts, while L-band GEO SAR will be affected when the integration time becomes longer. Tropospheric turbulence will cause the amplitude and phase random fluctuations resulting in image defocusing. However, in the natural environment, radio waves are very weakly affected by turbulence, and the medium-inclined GEO SAR of L- to C-band will not be affected, while the Xband will be influenced slightly.