A data-driven decision support tool for public transport service analysis and provision

Document Type

Journal Article

Publication Date


Subject Area

place - europe, planning - methods, planning - service improvement, planning - service level


Public transport service, Policy action, High-dimensional supervised classification, Data-driven, Jenks algorithm, Mahalanobis TOPSIS


Public transport service (PTS) analysis and provision is an important and challenging issue for public transport agencies. The results of the PTS analysis help transport planners to identify the areas in need of PTS improvement. Furthermore, relevant policy actions need to be determined for service provision to reach the desired level of PTS improvement in the identified areas. Without an appropriate decision support tool, planners need to apply several blind trials to find a policy action which improves the PTS in the examined areas. This paper introduces a data-driven decision support tool for PTS analysis and provision. The proposed framework combines a potentially large number of PTS measures while taking the correlation among the investigated measures into account and develops high-dimensional supervised classification models that predict the PTS levels for different policy actions. With this approach, planners can identify and prioritize the areas in need of PTS improvement, determine what policy actions should be targeted to improve the PTS in the identified areas, and predict the PTS impacts of these policy actions in the examined areas. The application of the proposed framework is demonstrated in detail through a case study of Budapest, Hungary, which is followed by a hypothetical policy implementation. The results show that mostly outskirts are in need of PTS improvement. Furthermore, the underlying reasons behind the areas with poor overall PTS are studied to target the relevant policy actions that improve the PTS in the identified areas. The PTS impacts of the targeted policy actions are studied by using the developed high-dimensional supervised classification models.


Permission to publish the abstract has been given by Elsevier, copyright remains with them.


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