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Determining Intersection Turning Movements with Detection Errors

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2017, Master of Science, University of Akron, Civil Engineering.
This study investigates and develops a turning movement data estimation model to determine all intersection turning movements by using a combination of matrix calculation and detector data. Two models are corresponding to two general intersection geometric backgrounds: three-leg and four-leg. According to the flow conservation law, the summation of all turning movements from a direction equals the total arrival or departure vehicles as linear equation format. These linear equations can transform into matrix format by using the coefficient matrix times the variable matrix that equals the constant matrix. After checking the number of solutions for the non-homogenous linear equation, the rank of the coefficient matrix is equal to the rank of the augmented matrix which this system called over-determined system. The system utilizes the matrix simplification techniques and pseudoinverse concept to isolate unknown variable matrix and solve by the least square method in the MATLAB. The calculated answers are 100% accuracy compared with ground truth. Moreover, with the error consideration of input data, the error matrix have joined the system. The error percentage e_i is assigned randomly to a 10% error interval. The overall results indicate that all results are within 10% and the worst-case scenario is roughly 7%. However, with historical data, the through movements have less error percent than the left and right turning movements. Through movement only have 2% error rather than 10%. Furthermore, when applying our model to the different intersection, if current input variables hold large percentage error, the alternative system can change and flip input and output variable to avoid large error. With the historical data points, the final solutions are within the 6% error range. Additionally, the results also approve that change of input volume level will not affect the result under the same error interval.
Ping Yi, Dr. (Advisor)
Kevin L. Kreider, Dr. (Committee Member)
Jun Ye, Dr. (Committee Member)
65 p.

Recommended Citations

Citations

  • Feng, D. (2017). Determining Intersection Turning Movements with Detection Errors [Master's thesis, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1512746695445707

    APA Style (7th edition)

  • Feng, Dehua. Determining Intersection Turning Movements with Detection Errors. 2017. University of Akron, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1512746695445707.

    MLA Style (8th edition)

  • Feng, Dehua. "Determining Intersection Turning Movements with Detection Errors." Master's thesis, University of Akron, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1512746695445707

    Chicago Manual of Style (17th edition)