TY - JOUR
T1 - Model on empirically calibrating stochastic traffic flow fundamental diagram
AU - Wang, Shuaian
AU - Chen, Xinyuan
AU - Qu, Xiaobo
N1 - Funding Information:
Xiaobo Qu is a Chair Professor with the Department of Architecture and Civil Engineering, Chalmers University of Technology in Sweden, and is also affiliated with the School of Vehicle and Mobility, Tsinghua University, China. His research is focused on improving large, complex and interrelated urban mobility systems by integrating with emerging technologies. To date, Prof. Qu has secured research funding well above 9 million Euros from the Australian Research Council, Swedish Innovation Agency Vinnova, STINT, and European Union. He has published over 120 journal articles published at top tier journals in the area of transportation. He is an elected member in Academia Europaea-The Academy of Europe.
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/12
Y1 - 2021/12
N2 - This paper addresses two shortcomings of the data-driven stochastic fundamental diagram for freeway traffic. The first shortcoming is related to the least-squares methods which have been widely used in establishing traffic flow fundamental diagrams. We argue that these methods are not suitable to generate the percentile-based stochastic fundamental diagrams, because the results generated by least-squares methods represent weighted sample mean, rather than percentile. The second shortcoming is widespread use of independent modeling methodology for a family of percentile-based fundamental diagrams. Existing methods are inadequate to coordinate the fundamental diagrams in the same family, and consequently, are not in alignment with the basic rules in probability theory and statistics. To address these issues, this paper proposes a holistic modeling framework based on the concept of mean absolute error minimization. The established model is convex, but non-differentiable. To efficiently implement the proposed methodology, we further reformulate this model as a linear programming problem which could be solved by the state-of-the-art solvers. Experimental results using real-world traffic flow data validate the proposed method.
AB - This paper addresses two shortcomings of the data-driven stochastic fundamental diagram for freeway traffic. The first shortcoming is related to the least-squares methods which have been widely used in establishing traffic flow fundamental diagrams. We argue that these methods are not suitable to generate the percentile-based stochastic fundamental diagrams, because the results generated by least-squares methods represent weighted sample mean, rather than percentile. The second shortcoming is widespread use of independent modeling methodology for a family of percentile-based fundamental diagrams. Existing methods are inadequate to coordinate the fundamental diagrams in the same family, and consequently, are not in alignment with the basic rules in probability theory and statistics. To address these issues, this paper proposes a holistic modeling framework based on the concept of mean absolute error minimization. The established model is convex, but non-differentiable. To efficiently implement the proposed methodology, we further reformulate this model as a linear programming problem which could be solved by the state-of-the-art solvers. Experimental results using real-world traffic flow data validate the proposed method.
KW - Speed distributions
KW - Stochastic fundamental diagram
KW - Traffic control
UR - http://www.scopus.com/inward/record.url?scp=85130310121&partnerID=8YFLogxK
U2 - 10.1016/j.commtr.2021.100015
DO - 10.1016/j.commtr.2021.100015
M3 - Journal article
AN - SCOPUS:85130310121
SN - 2772-4247
VL - 1
JO - Communications in Transportation Research
JF - Communications in Transportation Research
M1 - 100015
ER -