Data-Driven Optimization of Automated Speed Enforcement Logistics

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dc.contributor.advisorNourinejad, Mehdi
dc.contributor.authorHedayati Mobarakeh, Mandana
dc.date.accessioned2025-04-10T10:48:06Z
dc.date.available2025-04-10T10:48:06Z
dc.date.copyright2024-11-13
dc.date.issued2025-04-10
dc.date.updated2025-04-10T10:48:05Z
dc.degree.disciplineCivil Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractCanada’s collision fatalities are about 2000 lives a year, decreasing in the last decade, reaching 1745 in 2020 due to initiatives like Vision Zero. Among municipalities’ priorities is to enforce speed limits to reduce speeding-induced traffic fatalities, constituting 27 % of all traffic fatalities in Canada. An emerging strategy toward this goal is the deployment of , which detects violators through speed cameras positioned alongside designated roads. Empirical evidence from existing Automated Speed Enforcement (ASE) practices shows that the number of citations drops each month as driver become aware of camera locations and lower their driving speeds. Hence, ASE cameras are often relocated in cycles to expand their reach to more places and further deter speeding violations. The complexities of deployment lie in choosing camera locations and cycle duration, which have the highest deterrence impact on speeding during a planning period. This study proposes a data-driven model to classify camera site locations based on the effectiveness of ASE enforcement. Then, a Markov decision process optimization model is presented to find the optimal camera locations at each cycle and the length of the cycles for minimizing speed violations across the entire transportation network, considering limitations such as the number of available cameras.
dc.identifier.urihttps://hdl.handle.net/10315/42797
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subject.keywordsAutomated Speed Enforcement
dc.subject.keywordsSpeed Camera
dc.subject.keywordsTransportation
dc.subject.keywordsASE
dc.subject.keywordsMunicipality
dc.subject.keywordsSafety
dc.subject.keywordsTraffic
dc.subject.keywordsData-driven
dc.subject.keywordsEnforcement
dc.subject.keywordsOptimization
dc.subject.keywordsCommunity
dc.subject.keywordsSchool zones
dc.subject.keywordsCommunity safety zones
dc.subject.keywordsTraffic safety
dc.subject.keywordsSpeed violation
dc.subject.keywordsSafety prediction
dc.subject.keywordsScheduling
dc.subject.keywordsASE scheduling
dc.subject.keywordsMachine learning
dc.subject.keywordsSpatial analysis
dc.subject.keywordsEffectivity
dc.subject.keywordsImprovement
dc.subject.keywordsCollisions
dc.subject.keywordsSafer School Act
dc.subject.keywordsPractical
dc.subject.keywordsCity of Vaughan
dc.subject.keywordsArcGis
dc.subject.keywordsMapBox
dc.subject.keywordsHeuristic
dc.subject.keywordsMarkov decision process
dc.subject.keywordsMDP
dc.subject.keywordsK_means
dc.subject.keywordsSpeed distribution
dc.subject.keywordsVision Zero
dc.subject.keywordsSafety values
dc.titleData-Driven Optimization of Automated Speed Enforcement Logistics
dc.typeElectronic Thesis or Dissertation

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