An Integrated Framework for the Determination of Vehicle Sensor Deployment Strategy and Vehicular Trip Origin-Destination Matrix

  • 劉 瀚聰

Student thesis: Doctoral Thesis


A trip origin-destination (O-D) matrix in a vehicular network is one of the important components in network sciences and this information provides vehicular spatiotemporal patterns to describe traffic loading situations in a given network In the past four decades estimating network O-D matrices from relatively easily collected link flows and path flows has been developed and studied to overcome the problems associated with traditional network O-D matrix survey approaches including time consuming labor intensive and sampling errors in the survey process Because of the rapid development of information and communication technologies (ICTs) advanced technologies in sensor surveillance have been widely used in intelligent transportation systems (ITS) related applications Hence traffic information obtained from heterogeneous sensors including link and path flows as input data for the network O-D matrix estimation gradually becomes cost-effective and has its potential for improving estimation performance However in practice highway management agencies always face a budgetary constraint issue related to implementing a sensor deployment plan and a full-scale sensor deployment strategy is usually not available As a result how to strategically deploy heterogeneous traffic sensors for the purpose of vehicular tip O-D matrix estimation becomes essential in transportation network sciences The main purpose of this research is to develop an integrated model framework consisting of a heterogeneous sensor deployment strategy and vehicular trip O-D matrix estimation under a budgetary constraint The proposed integrated framework can efficiently provide desirable solutions in terms of an acceptable level of accuracy of network O-D estimated matrix estimate and has an implication for strategic sensor deployment strategy
Date of Award2015 Feb 12
Original languageEnglish
SupervisorShou-Ren Hu (Supervisor)

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