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Optimal bicycle trip impediments resolution by data fusion

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Abstract

We propose a method, whose purpose is to combine a set of GPS traces collected by bicyclists with a set of notifications of problematic situations to determine an optimal action plan for solving safety related problems in a traffic network. In particular, we use optimization to determine which problem locations to resolve under a given budget constraint in order to maximize the number of impediment free trips. The method aims to suggest a priority of impediments to resolve, which would be manually infeasible. The proposed method consists of two steps. First, problematic locations are clustered, where each cluster corresponds to a so-called impediment. Each impediment is associated with trips nearby using a distance function. The trip set is partitioned by matching each trip with the largest set of its affecting impediments. Solving all impediments associated with such a part induces a cost and makes the associated part of trips impediment free. The second step aims to find the set of impediments that can be solved with a given budget and that makes the maximum number of trips impediment free. A branch-and-bound optimizer for the second step is presented and evaluated. The clustering parameters affect the set of identified impediments and the extent of each of them. In order to evaluate the sensitivity of the result to the clustering parameters a technique is proposed to consistently estimate the impediment resolution cost. Our study aims to support the interactive urban designer to improve the urban bicycle road infrastructure. By providing a method to prioritize between impediments to resolve, it also aims to contribute to a safer and more attractive traffic situation for bicyclists.

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Acknowledgements

The research leading to this paper was partially supported by the Smarta Offentliga Miljöer II (SOM II) project of the Lund (Sweden) municipality by supplying data.

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Authors and Affiliations

  1. Transportation Research Institute (IMOB), Hasselt University, Agoralaan, 3590, Diepenbeek, Belgium

    Luk Knapen

  2. VU Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands

    Luk Knapen

  3. Department of Computer Science and Media Technology, Internet of Things and People Research Center, Malmö University, Malmö, 205 06, Sweden

    Johan Holmgren

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Correspondence to Luk Knapen.

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Knapen, L., Holmgren, J. Optimal bicycle trip impediments resolution by data fusion. J Ambient Intell Human Comput 12, 103–120 (2021). https://doi.org/10.1007/s12652-020-02854-7

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