Abstract

Modeling capacity is an integral component towards multiple traffic engineering objectives such as design and evaluation of control strategies. Traffic dynamics at bottlenecks, both on freeways and on arterial networks, influenced by bounded acceleration and lane-changing, affect the capacity in intriguing ways. This research attempts to capture these impacts of the bounded acceleration behavior and its interplay with lane-changing, by constructing a modeling framework that accurately models traffic dynamics at bottlenecks.Towards this goal, first a modified Cell Transmission Model (CTM) is proposed, by substituting the traditionally constant demand function with a linearly decreasing function for congested traffic. The jam-density discharge flow-rate is introduced as an additional parameter to characterize the macroscopic bounded acceleration effects. Analytically the new model is shown to reproduce observed features in the discharge flow-rate and headway at signalized intersections. Calibration with observations from existing studies, as well as new observations, further suggests that the model can reasonably capture all traffic queue discharge features.The demand function is further modified by integrating macroscopic lane-changing effects on capacity. The Lane Changing Bounded Acceleration CTM (LCBA-CTM) thus developed, is shown to realistically model the capacity drop phenomenon at active freeway lane-drop bottlenecks in stationary states. The capacity drop magnitude is determined by macroscopic bounded acceleration and lane-changing characteristics. Constant loading problems are analytically solved to reveal the onset and recession processes of congestion.An addition to the framework connects microscopic acceleration profiles of vehicles to modified demand functions. This completes the framework presented by offering a mechanism to start from any acceleration model. Finally, two applications of the modified CTM are presented illustrating the use of the framework: a) to model impacts of improved vehicle acceleration on traffic dynamics at intersections; and b) to create Macroscopic Fundamental Diagrams (MFDs) for arterial networks and compare their accuracy with traditional CTM methods.This dissertation offers a systematic approach to incorporating bounded acceleration and lane-changing into the CTM demand functions. Such an approach is shown to capture important static and dynamic features at critical bottlenecks, including lost time and queue discharge features at signalized intersections, as well as capacity drop magnitude and the onset of capacity drop at active freeway bottlenecks. The consistency between the modified demand function and microscopic bounded acceleration models is also established.

Abstract

The purpose of this study is to develop a traffic estimation framework which combines different data sources to better reconstruct the traffic states on the freeways. The framework combines both traffic parameter and state estimation in the same work flow, which resolves the inconsistency issue of most existing traffic state estimation methods.

To examine the quality of the traffic sensor data, the study starts with proposing the network sensor health problem (NSHP). The optimal set of sensors is selected from all sensors such that the violation of flow conservation is minimized. The health index for individual detector is then calculated based on the solutions. We also developed a tailored greedy search algorithm to find the solutions effectively. The proposed method is tested using the loop detector data from PeMS on a stretch of the SR-91 freeway. We compared the results with PeMS health status and found considerable level of consistency.

Two different traffic state estimation methods are proposed based on the data availability and traffic states. The LoopReid method is derived from the Newell’s simplified kinematic wave model by assuming the whole road segment is fully congested. We formulate a least square optimization problem to find the initial states and traffic parameters based on the first-in-first-out principle and the congested part of the Newell’s model. While developing the LoopCT method, we derived a counterpart of the Newell’s kinematic wave model in the Lagrangian coordinates under Eulerian boundary conditions. This model also leads to a new method to estimate vehicle trajectories within a road segment. We formulate a least square optimization problem in initial states and traffic parameters which works for mixed traffic states. The two estimation methods turned out to be highly related and the LoopCT method degenerates to the LoopReid method when the traffic is fully congested. The two methods are validated using two datasets from the NGSIM project. Both methods achieved considerable level of accuracy at reconstructing the traffic states and parameters.