Changing Lanes - Using Advance Sensor Technology to Understand Driver Behavior

Ohio State University, The, Columbus OH

Investigators

Abstract

Traffic congestion impedes US competitiveness, needlessly slowing the movement of most persons and goods. The impacts of congestion are diverse, ranging from safety issues to increased costs of goods and services. The total financial cost of congestion in 2011 was $121 billion. To address this challenge we need a deeper understanding of how traffic flows (and at times does not flow). Freeway traffic is inherently difficult to study because there are thousands of vehicles interacting over several miles. While it is clear that lane change maneuvers are an important factor generating turbulence that impedes traffic, the details of how this turbulence forms and grows are beyond the resolution of existing traffic monitoring tools. This research will advance sensor technology to develop the right tools to understand and model the lane change process in detail. With this better understanding, operating agencies will be able to manage the freeways more efficiently and reduce congestion without building more facilities. Thus, using the existing infrastructure more efficiently to reduce the costs of congestion. This research seeks to greatly advance the understanding and microscopic modeling of where, when, how and why vehicles undertake lane change maneuvers on freeways. A lane change maneuver can take over a mile from start to finish before the traffic stream fully adjusts to the change, with driver behavior dependent upon only a few feet difference in the spacing between vehicles. Given the large number of vehicles interacting over long distances it has been virtually impossible to observe the microscopic details of the lane change maneuver process with conventional tools. This research will use advanced sensing technology to develop a deeper understanding of the microscopic factors that give rise to congestion. The work will use hundreds of hours of instrumented probe vehicle data that include positioning and ranging data for the ambient vehicles around the probe out to 80 m, collected on an urban freeway during peak periods with recurring and non-recurring congestion. Preliminary review indicates that these data include over 10,000 lane change maneuvers. This large quantity of microscopic vehicle interactions will be used to develop a deeper understanding and more accurate models of the lane change maneuver process to help develop the right tools necessary to mitigate traffic congestion. Models of lane changing behavior will be crucial as connected vehicle infrastructures are constructed and autonomous vehicles are introduced into manually operated traffic streams.