Highway traffic modeling.

详细信息   

• 作者：Yang ; Jun.
• 学历：Ph.D.
• 年：2010
• 导师：Sun, Lu,eadvisorRegalia, Phillip,eadvisorPao, Hsien Pingecommittee memberLucko, Gunnarecommittee memberJudge, Johnecommittee member
• 毕业院校：The Catholic University of America
• Department：Civil Engineering
• ISBN：9781109770827
• CBH：3403512
• Country：USA
• 语种：English
• FileSize：2640692
• Pages：193

文摘

With the growth of the number of vehicles around the world, the amount of congestion, pollution, and accidents is increasing. To solve this problem, highway traffic modeling, as one of the key components in traffic management, is becoming more important. In this dissertation, a methodological framework is first developed to deal with traffic-stream modeling based on data mining, steepest-ascend algorithm, and genetic algorithm. The new method is adaptive in nature and has greater flexibility and generality compared with existing methods. Secondly, a new method is developed to estimate and predict macroscopic traffic conditions in the area where no existing traffic information is available. The new method is based on shock wave theory. Unlike widely used data-driven methods, the proposed method has a clear traffic explanation and gives an accurate estimate and prediction of traffic flow. Based on the estimation and prediction of traffic conditions, travel time is the next information that needs to be estimated and predicted in traffic management. A piecewise truncated quadratic trajectory is proposed here to mimic the unknown speed trajectory between point detectors. The basis functions of the new method consist of quadratic and constant functions of time. Using the actual travel time obtained from field experiments, the new method yields a more accurate travel time estimate than other trajectory-based methods. Finally, for the microscopic level of traffic modeling, a new car-following model is proposed to solve problems in the application of existing Gipps car-following models. Gipps car-following models are based on the assumption that in car following behavior, drivers always attempt to get the maximum speed that is safe to prevent rear-end collisions in the event of an emergency stop. Since this assumption may not always be true during driving, it causes imaginary numbers due to the square root function in the model. This study introduces a new model without square root by using a nonlinear braking rate that was not adopted in the existing car-following models.