Abstract:

Federal agencies will enforce tougher security standards at airport check-ins, federal buildings, military installations and nuclear sites at the beginning in 2020. This requirement is posing a challenge against the NCDOT-DMV to plan enough site locations to handle the increased workload of the North Carolina driver license (NC REAL ID) in all parts of the state including urban and rural areas. One of the most important decision-making processes is to optimally locate new physical and mobile locations to address the demand of prospect customers. This is an important concern as these locations will face influx of ID conversion applications to meet the NC Read ID deadlines. The objective of this study is to provide an integrated approach for selecting the optimal NCDMV driver license locations using expert knowledge, data mining, Analytic Hierarchy Process (AHP), Geographical Information System (GIS) and Maximal Covering Location Problem (MCLP). The proposed approach identifies the two-level location criteria through experts’ input as part of the AHP process, yielding demographic attributes, flexibility, efficiency, cost and access to public facilities. Following the weight assessment for all criteria and sub-criteria, normalized weights are used for location suitability analysis in ArcGIS. Based on our projections for the demand and related geospatial data, alternative DMV locations are determined and visualized through ArcGIS. Finally, the alternative locations are evaluated by AHP weights and the multi-criteria location selection problem is optimized to maximize the coverage across the state. Abstract Federal agencies will enforce tougher security standards at airport check-ins, federal buildings, military installations and nuclear sites at the beginning in 2020. This requirement is posing a challenge against the NCDOT-DMV to plan enough site locations to handle the increased workload of the North Carolina driver license (NC REAL ID) in all parts of the state including urban and rural areas. One of the most important decision-making processes is to optimally locate new physical and mobile locations to address the demand of prospect customers. This is an important concern as these locations will face influx of ID conversion applications to meet the NC Read ID deadlines. The objective of this study is to provide an integrated approach for selecting the optimal NCDMV driver license locations using expert knowledge, data mining, Analytic Hierarchy Process (AHP), Geographical Information System (GIS) and Maximal Covering Location Problem (MCLP). The proposed approach identifies the two-level location criteria through experts’ input as part of the AHP process, yielding demographic attributes, flexibility, efficiency, cost and access to public facilities. Following the weight assessment for all criteria and sub-criteria, normalized weights are used for location suitability analysis in ArcGIS. Based on our projections for the demand and related geospatial data, alternative DMV locations are determined and visualized through ArcGIS. Finally, the alternative locations are evaluated by AHP weights and the multi-criteria location selection problem is optimized to maximize the coverage across the state.