With technological advancements in recent years, a series of intelligent transportation system (ITS) products have now become available to the transportation agencies to collect data and manage traffic conditions on the roadway network. Among ITS products, the advanced traffic management system (ATMS) has been effectively serving as the central nervous system of a traffic management center. ATMS serves as an integrated application for a wide variety of purposes ranging from data collection to implementing traffic management strategies. Owing to commercial popularity, a series of ATMS products are now available to transportation agencies and there is no consensus on selecting the best-suited product based on tailored requirements. Making a decision for a decision-critical item such as ATMS products on qualitative evidence can add risk to the decision-makers to justify their decision of choice. In this study, a multi-criteria decision analysis framework was proposed for quantitative evaluation of ATMS alternatives that can consider multiple and conflicting decision-making criteria using a real-world example. Moreover, the proposed framework was evaluated for different scenarios related to different applications of ATMS products to provide flexibility to the user in evaluating the ATMS alternatives. Results indicated that the proposed method can be considered as a viable alternative in contrast to a qualitative evidence-based decision-making strategy to minimize the risk associated with the decision-makers. Using the proposed quantitative framework, decision-makers can examine the weights of different criteria under consideration and evaluate multiple ATMS alternatives based on their jurisdiction-specific requirements. The proposed framework can be easily applied to other ITS technology selection processes.


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    Title :

    Quantitative Evaluation of Advanced Traffic Management Systems using Analytic Hierarchy Process


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2021-08-08




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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