The extent and severity of traffic and environment impacts of commercial vehicles is currently not well understood due to the lack of an advanced surveillance system that provides necessary information about the nature and variation of their travel behavior. This is particularly important in considering the impacts of major investments in trade national corridor improvements, including access and egress for the Ports of Long Beach and Los Angeles.

This research study will develop a new commercial vehicle vector classification system based on a prototype implementation of a new high-fidelity inductive loop sensor that is relatively easy to install and has the potential to yield highly detailed vehicle inductive signatures. This new classification system will provide an unprecedented level of detail on commercial vehicles. The initial results show the potential of using such an inductive sensor to provide a more comprehensive commercial vehicle data profile based on its ability to extract both axle configuration information as well as high fidelity undercarriage profiles within a single sensor technology.

The objective of this research is to understand stationary and dynamic bottleneck effects of lane-changing traffic from a macroscopic point of view. It is well known that disruptive lane-changes in merging and weaving areas can cause substantial capacity reduction and drop. In this research we attempt to develop a coherent theory for such critical bottleneck effects with the help of a new fundamental diagram and corresponding kinematic wave models. With NGSIM data, we first establish lane-changing intensity as a function of road geometry, proportion of lane-changing traffic, and other lane-changing behavior characteristics and then derive a fundamental diagram of lane-changing traffic flow, from which we analyze how the number of lanes and proportion of lane-changing traffic would impact capacity reduction in a lane-changing area. This research can improve our understandings of fundamental properties of lane-changing bottlenecks and their impacts on traffic congestion and environments. Insights obtained from the research could help improve lane-management, variable speed limits, ramp metering, and other management and control strategies.

The complex interplay among merging, lane-changing, and accelerating behaviors plays an important role in determining the performance of a congested merging area. Especially, once a merging bottleneck is activated, the discharging flow-rate can drop by 10% (about 800 vph on a four-lane freeway); such a capacity drop can lead to excessive traffic queues and stop-and-go traffic patterns and increase fuel consumption and GHG emissions. The objective of this research is to analyze the performance and design the control parameters for both pretimed and traffic-responsive on-ramp metering of congested merging bottlenecks. In this research we will (1) quantify the congestion mitigation effects of different ramp metering algorithms at an active merging bottleneck, (2) design control parameters for efficient and robust traffic responsive ramp metering algorithms, (3) identify demand patterns when ramp metering algorithms are effective, and (4) develop a set of simple decision-support tools for ramp metering with both kinematic wave models and microscopic simulations. The research will help Caltrans to make decisions on the necessity, priority, algorithm, and parameter tuning related to ramp metering.