UC Irvine

Experience of daily commuters shows that stationary traffic patterns can be observed during peak periods in urban freeway networks. Such stationary states play an important role in various traffic flow studies. Conceptually, studies on the impact of capacity drop and design of traffic control strategies have been built on the assumption of stationarity. Mathematically, the existence and stability of stationary states in general road networks have been proved. Empirically, near-stationary states have been utilized for calibration of fundamental diagrams and investigation of traffic features at freeway bottlenecks. Therefore, an imperative need for real-world near-stationary data has been realized to better understand, investigate, and explore such above studies. However, there lacks an efficient method to identify near-stationary states.

To fill the gap, in this research, an automated method has been developed to efficiently identify near-stationary states from large amounts of inductive loop-detector data. The method consists of four steps: first, a data pre-processing technique is performed to select healthy datasets, fill in missing values, and normalize vehicle counts and occupancies; second, a PELT changepoint detection method is adopted to detect changes in means and partition time series into candidate intervals; third, informative characteristics of each candidate, including duration and gap, are defined and calculated; finally, near-stationary states are selected from candidates through duration and gap criteria.

A game theory approach is further designed to directly calibrate parameters of the above method. First, a multi-objective optimization problem is formulated to consider the quantity and quality of near-stationary states as the objective functions. Then the problem is converted into a non-cooperative game with at least one Nash equilibrium. To solve the game and obtain a unique solution, an alternated hill-climbing search algorithm is developed.

Furthermore, two calibration schemes for multi-lane and multi-class fundamental diagrams are respectively designed by utilizing near-stationary states. Such multi-commodity fundamental diagrams possess unifiable and non-FIFO properties and can capture interaction among different commodities. Calibration and validation results show that both the calibrated unifiable multi-lane and multi-class fundamental diagrams are well-fitted, physically meaningful, and have robust performance on the estimation of commodity flow-rates.