After experiencing many years of shrinkage, bike mode share has enjoyed a large-scale renaissance since 2016 with the birth of dockless bikeshare. The rapid expansion of dockless bikeshare, however, is coincident with the serious oversupply of bikes and chaos of parking on the streets. Predicting the right level of dockless bike use is essential to maintain the order of the road space. This study aims to control the number of dockless public bikes in each neighborhood. With data obtained from a smartphone app, MoBike, this study examines factors associated with dockless bikeshare. A generalized additive mixed model is applied to investigate associations between dockless bike trip density and various factors. The results are: floor area ratio, which represents density, is positively associated with dockless bike trip density; percentages of residential, industrial, and green spaces, and degrees of mixed land use, are positively related to dockless bike trip density; densities of primary and secondary roads are positively related to dockless bike trip density, while the density of intersections is negatively associated with dockless bike trip density; females and children are less likely to ride dockless bikes; and dockless bikes are more often used during peak hours, on sunny or cloudy days, and on weekdays. To promote dockless bike use, the right level supply for different weather, time, and location conditions should be identified, encouraging dense urban development in establishing a friendly biking environment, and improving street connectivity to reduce the impedance of biking in intersection dense areas.

Trucks play a vital role in promoting regional freight transportation and economic development, but truck-involved collisions often have more severe consequences and create greater losses for society. This study examined the relationships between injury severity and various explanatory factors in truck-involved collisions to identify preventive countermeasures for safety improvement. Los Angeles’ collision records from 2010 to 2018 were analyzed. A cumulative link mixed model was applied, where the heterogeneities among drivers were highlighted. Our findings confirmed that various driving mistakes, such as speeding, improper driving, and drinking alcohol, contributed to severe injuries. Male drivers were more likely to be severely injured, while female occupants were more likely to be severely injured. The use of safety equipment always helped mitigate injury severity. Collisions at night on dark roads with no streetlights and collisions on slippery road surfaces had higher risks of causing severe injuries. In addition, collisions on ramps were more likely to result in severe injuries. Drivers in old trucks were also at a higher risk of suffering from severe injuries. Freight companies are encouraged to monitor drivers’ performance using remote cameras. Policy-wise, local agencies should regulate improper driving behavior and safety equipment use for truck drivers. Improving lighting conditions, periodically testing the skid resistance of road surfaces, adjusting speed limits, and applying weigh-in-motion technologies may greatly help mitigate injury severity. Old trucks should be brought in for frequent tests or abandoned after many years of usage.