Agencies that manage mobile photo enforcement (MPE) programmes must decide where and when to send their limited resources to monitor compliance with speed limits. Usually, the goal is to select locations based on a number of concerns (i.e., high collision sites, high speed violation sites, school zones, etc.), which, in most cases, is conflicting. If certain locations are given more MPE resources, then by definition, other locations will receive less attention, and vice versa. This paper aims to provide insights about such MPE programme trade-offs. We present a systematic procedure for interpreting the results of a multiobjective MPE resource allocation problem. The procedure consists of three steps: (a) Pareto front (PF) generation, (b) front representation, and (c) trade-off analysis. First, in generating a PF, we sequentially apply two well-known scalar optimization methods to obtain a comprehensive set of Paretooptimal solutions. Second, the K-medoids clustering algorithm and the silhouette index are adopted to partition the generated PF into similar-sized clusters, in order to help MPE programme agencies choose from a reduced set of solutions on the PF. Third, we use the response surface method to determine trade-off patterns on the PF. The results of the front generation analysis showed that applying two optimization methods together resulted in a nearly complete PF with a relatively uniform and dense spread of solutions. Consequently, the identified set of solutions (i.e., 13,210 cases) was further partitioned into 12 clusters by silhouette index and K-medoids. With the aim of reducing decision fatigue for agencies, each cluster's representative solution is considered a possible MPE resource allocation candidate. The trade-off analysis indicated how much one must sacrifice in the other objectives in order to increase attainment of one particular objective. Finally, the trade-off rate and elasticity were used to explore the quantitative relationship between the considered objectives. KEYWORDS mobile photo enforcement programme planning, multiobjective optimization, Pareto front analysis, resource allocation, trade-off analysis
