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Turning Movements Estimation Using Data with Perturbations

Ahmad, Qurashi
http://orcid.org/0000-0002-2197-9719
http://rave.ohiolink.edu/etdc/view?acc_num=akron1597337871546389

Abstract Details

2020, Doctor of Philosophy, University of Akron, Civil Engineering.
The study of turning movements (TM) at intersections is vital in many ways. Applications of TM include advanced applications such as dynamic traffic assignment, adaptive signal control, and transportation planning, in addition to roadway designs, signage and signal design. This research mainly explored and improved on the origin-destination (OD) model outcomes and TM errors that generate from perturbations in the input data. It considered a four-legged intersection with one shared lane coming from each direction that represents the worst-case scenario for estimating TM. The data was collected at the intersections using video image processing (VIP) systems. This research also examined the use of the OD linear equations system and real-time data for specific TMs to find the remaining unknown TMs. This research used three input TM combinations to solve the determinant linear OD model to obtain real-time TMs at intersections: (ET, NL, NR, WT, and WL), (SR, ST, ER, NR, and WR), and (SR, SL, ER, NR, and WR). In addition, this research studied the input variable combinations under three levels of volumes: low, medium, and high. From historical data, we found that VIP systems can measure thru movements and total incoming and exiting vehicles from an intersection with up to 2% errors. However, the VIP systems measure right and left TMs with up to 15% errors. The results showed that under a range of ±5% input errors, the weighted output TMs had an average error of 1.5% in thru movements and an average error of 7% in other TMs. Not far from the range of ±5% input errors, the range of ±15% input errors produced an average weighted error of 2% in thru movements and an average weighted error of 8% in other TMs. The weighted error technique produced results that showed a 75% improvement in output TM errors.
Ping Yi (Advisor)
Qindan Huang (Committee Member)
Anil Patnaik (Committee Member)
Curtis Clemons (Committee Member)
Chen Ling (Committee Member)
59 p.
Civil Engineering; Electrical Engineering; Mathematics
Turning Movements; Turning Movements Estimation; Real-Time Turning Movements

Recommended Citations

Citations

  • Ahmad, Q. (2020). Turning Movements Estimation Using Data with Perturbations [Doctoral dissertation, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1597337871546389

    APA Style (7th edition)

  • Ahmad, Qurashi. Turning Movements Estimation Using Data with Perturbations. 2020. University of Akron, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1597337871546389.

    MLA Style (8th edition)

  • Ahmad, Qurashi. "Turning Movements Estimation Using Data with Perturbations." Doctoral dissertation, University of Akron, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1597337871546389

    Chicago Manual of Style (17th edition)

akron1597337871546389
357
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This open access ETD is published by University of Akron and OhioLINK.