A technique for identifying transportation-disadvantaged populations, intended for use by local practitioners and designed with their skills and professional routines in mind, was piloted in five rural counties in North Carolina.
The findings are in agreement with previous research [ 5, 7, 10– 18] that shows that birth preparedness is not easy to achieve especially in developing countries, where the majority are relatively poor, few women identify transportation ahead of childbirth, few women put aside funds for transport in case of emergency, and few women are knowledgeable on danger signs or implement BPCR.
The study identified transportation, logistics and administrative jobs as the most vulnerable to automation.
Zheng et al.[1] analyze GPS logs with graph-based post-processing algorithm to identify transportation modes.
The school identified transportation, steel, iron ore, and electric power as key economic industries.
The GIS-based methods used in this study to identify transportation carbon emissions had several advantages over the questionnaire-type data sources commonly relied upon by product life cycle studies.
Women who identified transportation and child care as problems may be less able than others to resolve these issues, perhaps due to lack of resources or a reduced personal capacity.
The findings reveal that p allowed an improvement of the transportation planning processes in both boroughs, as it contributed to identifying specific transportation issues experienced by citizens.
The authors also proposed using mobile phones with a built-in GPS (Global Positioning System) receiver and an accelerometer for identifying individual transportation modes including whether an individual is stationary, walking, running, biking, or in motorized transport [ 19, 20].
