Optimal stochastic enforcement of parking regulations

Effective enforcement of no parking regulations, an essential operation in urban areas, is labor intensive and thus costly. Efficient, implementable enforcement strategies that can raise and keep compliance at a desired high level are needed. It is the objective of this work to determine such strategies. First a model relating enforcement (input) to compliance (output) was obtained on the basis of experimental data; its form is that of a homogeneous bilinear Markov process. Then several optimization problems that reflect different operating conditions were formulated and the corresponding enforcement strategies were derived via dynamic programming. Finally, simulations were used to analyze the practical implications of the results.