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dc.contributor.authorWang, Yinhai
dc.contributor.authorZhu, Wenbo
dc.contributor.authorZhu, Meixin
dc.date.accessioned2020-05-08T19:51:27Z
dc.date.available2020-05-08T19:51:27Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/1773/45554
dc.descriptionData Link: https://doi.org/10.7910/DVN/XAOW8Aen_US
dc.description.abstractConnected vehicle (CV) technology leads to a system in which vehicles can communicate with other vehicles, transportation infrastructure, and other devices with communication capabilities. With increases in data availability, there is a great need for algorithms to process and utilize the data to improve system efficiency and mobility. This study developed an adaptive navigation algorithm based on the data collection and communication functions in the CV based on user cost. To quantify the travel cost associated with different paths, a link cost function was developed to estimate both the link travel time and delay at the downstream intersection. An empirical intersection delay function was derived from stochastic queueing theory models. The developed function can support link cost estimation for interrupted traffic flow on local streets, which has been a limitation of previous navigation algorithms. On the basis of CV communication capabilities, a dynamic navigation algorithm as developed to suggest the optimal paths that dynamically minimize user cost. The developed navigation algorithms were implemented in a microscopic simulation model using VISSIM application programming interface (API) functions. Multiple experiments were conducted to test the CV navigation algorithms in a virtual traffic environment based on the urban street network in downtown Bellevue, Wash. Experiment results revealed that the CV navigation algorithms were effective at reducing user cost in comparison to the static navigation used by non-CVs. The benefits of adaptive navigation algorithms will increase with CV market penetration, and the maximum benefit will be achieved when the CV penetration rate reaches around 60 percent. In the studied network, the marginal benefit of using the dynamic system optimum navigation over dynamic user equilibrium navigation was negligible (e.g., around 1 percent) given traffic flow randomness. Further experiments showed that the developed CV navigation algorithms can work effectively during non-recurrent congestion by properly balancing historical and real-time traffic information.en_US
dc.description.sponsorshipPacific Northwest Transportation Consortiumen_US
dc.language.isoen_USen_US
dc.subjectConnected Vehicleen_US
dc.subjectNavigationen_US
dc.subjectUser Equilibriumen_US
dc.subjectTravel Timeen_US
dc.subjectShortest Pathen_US
dc.titleA Connected Vehicle-Based Adaptive Navigation Algorithmen_US
dc.typeTechnical Reporten_US


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