  Parking has been a painful problem for urban drivers. The parking pain
exacerbates as more people tend to live in cities in the context of global
urbanization. Thus, it is demanding to find a solution to mitigate d rivers'
parking headaches. Many solutions tried to resolve the parking issue by
predicting parking occupancy. Their focuses were on the accuracy of the
theoretical side but lacked a standardized model to evaluate these proposals in
practice. This paper develops a Driver Side and Traffic Based Evaluation Model
(DSTBM), which provides a general evaluation scheme for different parking
solutions. Two common parking detection methods, fixed sensing and mobile
sensing are analyzed using DSTBM. The results indicate first, DSTBM examines
different solutions from the driver's perspective and has no conflicts with
other evaluation schemes; second, DSTBM confirms that fixed sensing performs
better than mobile sensing in terms of prediction accuracy.
