2015/11/17 by Daniël Reijsbergen, Reijsbergen, Daniël, Rajeev Ratan +1
Computer Science · Engineering · Social Sciences · #Computers and Society (cs.CY) #Data Management and Algorithms #FOS: Computer and information sciences #Formal Methods in Verification #Model-Driven Software Engineering Techniques #Software Engineering (cs.SE) #Traffic Prediction and Management Techniques #Transportation Planning and Optimization #cs.CY #cs.SE
paper · pdf · doi:10.48550/arxiv.1511.05363
arxiv created 2015/11/17 · openalex publication_date 2015/11/17 · arxiv updated 2015/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a data-driven methodology for evaluating the impact of the introduction of a speed limit on the punctuality of bus services. In particular, we use high-frequency Automatic Vehicle Location data to parameterise a model that represents the movement of a bus along predefined patches of the route. We fit the probability distributions of the time spent in each patch to two classes of probability distributions: hyper-Erlang distributions, for which we use the tool HyperStar, and a variation of the three-parameter gamma distributions recommended by the Traffic Engineering Handbook. In both cases we obtain models that can be expressed using the framework of Probabilistic Timed Automata, allowing us to evaluate bus punctuality using the model checking tool UPPAAL. We conduct a case study involving a proposed speed limit in Edinburgh. This is an extended version of a paper presented at ValueTools 2015.