Enhancing Driver Performance: A Closed Track Experiment

Enhancing Driver Performance: A Closed Track Experiment


Erlend Aakre, NTNU Traffic Engineering Research Centre, Arvid Aakre, NTNU Traffic Engineering Research Centre, Torbjørn Haugen, NTNU Traffic Engineering Research Centre


A closed track experiment studying potential effects of in-car ITS applications on individual driver performance and overall traffic flow. Main focus will be on saturation flow rate and start-up lost times in signalized intersections.


Short abstract

Signalized intersection operations are important for urban mobility, safety, travel times and environmental issues. Saturation flow rate and start-up lost time are two of the most important parameters when it comes to calculating signalized intersection capacity. Consequently, it is not surprising that countless studies have aimed towards estimating saturation flow rates and start up lost times at signalized intersections. Reports on saturation flow rates reveal large variations between different cities and countries. Values of 1800-2000 veh/h are common, but ranges from 1500-2500 veh/h have been observed.

Driver performance is often regarded to be a result of the prevailing conditions, such as intersection geometry, grade, vehicle attributes, percentage of heavy vehicles and weather. Population characeristics, degree of familiar drivers (commuters) and traffic pressure are other factors that are reported to affect flow rates and lost times. As a result, most traffic management strategies are not aimed towards enhancing driver performance. This paper takes a different approach: How can we enhance driver performance, and more specifically; to what extent can behavior change increase signalized intersection capacity? The potential efficiency gains will be studied by conducting a field trial on a closed track. Unlike other studies, this paper will examine individual driver performance as well as overall traffic flow.


It is reasonable to believe that in most cases, there is a gap between optimal and prevailing driver behavior. When it comes to traffic safety, several measures have been implemented in order to reduce this gap.

Current ATM strategies regarding capacity and flow in congested areas usually focus on traffic signal timing and optimization, ramp metering, lane management or rerouting drivers. In essence, these measures influence the time or the space where drivers can go. Dynamic speed limits are an exception, as this measure’s main objective is to influence driver behavior. In intersections, measures influencing driver behavior are rare or non-existing. As long as drivers perform in a suboptimal manner, the full potential of the network will not be achieved.

Emerging technologies allow communication between drivers, vehicles and infrastructure through cooperative ITS. Suijs et al (2015) have indicated that in-car speed advice can reduce phantom jam occurrence through micro simulation experiments. Also, Risto (2014)’s driving simulator and real road experiments showed promising results concerning drivers ability and willingness to follow in-car advice. Gaps of individual drivers were reduced, but, unfortunately, overall traffic flow effects based on multiple drivers were not studied. In-car ITS applications can provide drivers with real time information or advice specific to their location and traffic conditions, thereby enhancing situational awareness and motivation.


A closed track experiment involving approximately 30 cars will be conducted at the motor arena at Hell, Norway. The drivers will first be instructed to drive normally, as if they were on their daily commute. Start-up lost times, saturation flow rates and individual headways, along with acceleration and trajectory data, will be recorded in a series of signal cycles. When the lost time and saturation flow rate has been determined, a subset of the drivers will be instructed to drive as efficiently as they feel is safe, thereby minimizing these drivers’ contribution to lost time and their time headways. Finally, all drivers in the experiment will receive these instructions. Behavior that may increase the risk of rear end crashes will also be investigated. After the experiment, the drivers will repeat the normal driving style, in order to determine the bias due to learning effects.

Policy implications

The most important contribution to the research area will be knowledge about the potential gain in individual driver performance and overall traffic flow due to informing and motivating drivers. The authors believe that there exists a significant potential for capacity increases in signalized intersections. This may delay or even eliminate the need for infrastructure measures. Also, more efficient traffic flow may provide more spare capacity for transit priority.

Although the main concern of this paper is traffic flow and driver behavior, traffic safety issues may never be ignored. Smaller gaps could lead to an increase in rear-end crashes. Still, it is the belief of the authors that efficiency gains due to increased attention and motivation may not increase the risk of accidents. Risto’s (2014) studies of cooperative in-vehicle advice support this view.

Finally, this study aims to affect the future drivers in Norway; the experiments will be conducted in cooperation with North University, who provide the Norwegian driving instructor education.


Association for European Transport