| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000432504 |
| 005 | | 20200224131648 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085747134 |
| 035 | |
▼a (MiAaPQ)AAI13898511 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 385 |
| 100 | 1 |
▼a Zhu, Wenbo. |
| 245 | 10 |
▼a A Connected Vehicle Based Coordinated Adaptive Navigation System. |
| 260 | |
▼a [S.l.]:
▼b University of Washington.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 167 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: A. |
| 500 | |
▼a Advisor: Wang, Yinhai. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Washington, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 506 | |
▼a This item must not be added to any third party search indexes. |
| 520 | |
▼a Real-time data communication and analysis are important to support many smart transportation applications. The connected vehicle (CV) technology leads to a system in which vehicles can communicate with other vehicles, transportation infrastructure, and other devices with communication capabilities. With the increase of data availability, there is a great need for algorithms to process and utilize the data to improve the system efficiency and mobility.This study developed an adaptive navigation algorithm based on the data collection and communication functions in the connected vehicle system. Specifically, the algorithm will utilize real-time traffic information to adaptively recommend the optimal path considering both the user cost and system impact. To quantify the travel cost associated with different paths, a link cost function is developed to estimate both the link travel time and delay at the downstream intersection. An empirical intersection delay function is derived from the stochastic queueing theory models. The developed function can support link cost estimation for interrupted traffic flow on local streets, which is a limitation for previous navigation algorithms. Based on the CV communication capabilities, two specific dynamic navigation algorithms have been developed to suggest the optimal paths that dynamically minimize the user cost and system impact, respectively.The developed navigation algorithms have been implemented in a microscopic simulation model using VISSIM application programming interface (API) functions. Multiple experiments have been conducted to test the CV navigation algorithms in a virtual traffic environment based on the urban street network in downtown Bellevue, WA. Experiment results reveal that CV navigation algorithms are effective in reducing both the user and system cost compared to the static navigation used by non-CVs. The benefits of adaptive navigation algorithms will increase with the CV market penetration, and the maximum benefit is achieved when the CV penetration rate reaches around 60%. In the studied network, the marginal benefit of using the dynamic system optimum navigation over the dynamic user equilibrium navigation is relatively small (e.g., around 1%) comparing with the total user cost. Further experiments show that the developed CV navigation algorithms can work effectively during non-recurrent congestions through properly balancing historical and real-time traffic information. |
| 590 | |
▼a School code: 0250. |
| 650 | 4 |
▼a Transportation. |
| 690 | |
▼a 0709 |
| 710 | 20 |
▼a University of Washington.
▼b Civil and Environmental Engineering. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04A. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0250 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15491947
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |