From an operation viewpoint, traffic breakdown (from free-flow) was defined as when the average speed of traffic drops below a certain threshold. It is known that traffic breakdown is a stochastic phenomenon which can happen even when the traffic flow is below the capacity. The capacity has many definitions, such as that in HCM or the average of maximum daily flow. This study investigates the probability of breakdown at certain locations of freeway. The motivation is to find a practical capacity for each freeway section for active traffic control/operation purposes, which could be different from previous viewpoints. Capacity is usually expressed in terms of flow rate. Nevertheless, it is well known that a particular value of flow rate could represent two different traffic states: uncongested and congested. Therefore, simply considering flow rates as the main factor is inadequate for operational purpose. In this study, a bivariate Weibull distribution is adopted to model the probability of breakdown as a function of both mean speed and occupancy of the incoming traffic. The methodology of constructing and calibrating the bivariate distribution is introduced. In addition, three case studies are performed to test the methodology proposed herein. The case studies are carried out by using three different datasets: PORTAL, PeMS, and BHL (Berkeley Highway Lab). PORTAL is an archived data source collected from freeways in Oregon, while the other two are collected from freeways in California PATH. The datasets measure and process flow rates, occupancies, and speeds of traffic from the loop stations on the freeways. Empirical results derived and their potential applications are discussed for developing various traffic control strategies including Variable Speed Limit (VSL) and ramp metering.

Density, speed and flow are the three critical parameters for traffic analysis. Traffic management and control with high performance require accurate estimation/prediction of distance mean speed and density for large spatial and temporal coverage. Speed, including time mean speed and distance mean speed, and flow estimation are relatively easy to be measured and estimated in the practical site, but accurate density estimation is very difficult. Inductive loop detector systems have been widely deployed, it makes better sense to fully adopt available infrastructure to achieve required traffic measurement. As a new promising technology for transportation system, Vehicle Infrastructure Integration (VII) is developing rapidly with the market penetration of cell phone and GPS systems. This report proposed a method for real-time estimation of density using synchronized loop detector data and VII probe vehicle data. Berkeley Highway Laboratory (BHL) loop detector data and the field collected Probe Vehicle data have been used in the method validation. Density estimated from the vehicle-by-vehicle trajectory tracking in Next Generation Simulation (NGSIM) data has also been used as the second data source for validating the algorithm. Comparison of the two results – that form the loop and VII probe vehicle data and that from NGSIM data, showed that they are very close except a small offset which needs further investigation.

This report describes the development of two of the three mobility applications that PATH is developing and evaluating under the sponsorship of the FHWA Exploratory Advanced Research Program, with cost share funding provided by PATH TO 6224. These applications are intended to use DSRC wireless communications among vehicles and between vehicles and the roadway infrastructure to improve mobility on limited-access highways. The first application combines ramp metering with variable speed limits to enhance control of traffic so that traffic flow breakdowns can be deferred or avoided at bottleneck locations. The second application uses vehicle-vehicle communication to improve the performance of adaptive cruise control systems so that they can operate safely with smaller longitudinal gaps and vehicle-roadside communication to provide adjustments to their set speed and gap settings to adapt to changes in local traffic conditions.