Driver Cooperation in Traffic: A Closed Track Experiment



Driver Cooperation in Traffic: A Closed Track Experiment

Authors

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

Description

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 traffic flow in bottlenecks such as lane drops, work-zones or traffic incidents.

Abstract

Short abstract

The term congestion usually relates to an excess of demand for transport at a specific site, compared to prevailing capacity. Hence, there are two ways of mitigating congestion; reducing (or redirecting) demand or increasing capacity. This paper focuses on the latter. In the field of increasing capacity, research and practice has come a long way. Still, emerging technologies have untapped potential.

An especially interesting field is in-car ITS applications targeting traffic flow-related driver behavior. Previous research on such systems has mainly been aimed at studying individual driver behavior only. In many cases, the effects on a larger scale remain unidentified. In this paper, traffic flow parameters (i.e. flow rates, density, average speed) will be analyzed together with individual driver behavior (i.e. space headways, time headways, trajectory and vehicle data) in order to seek answers to the following question: Can in-car ITS applications improve individual driver performance and overall traffic flow in bottlenecks?

Introduction

FHWA’s report on Traffic Congestion and Reliability identify three main categories of sources of congestion. These three categories consist of totally seven root causes of congestion. The categories (and root causes) are Traffic-influencing events (Traffic incidents, Work zones and Weather), Traffic demand issues (Fluctuations in normal traffic and Special events) and Physical highway features (Traffic control devices and Physical bottlenecks). They also emphasize that driver behavior is “a wild card”. For instance, commuters familiar with congestion are reported to drive more efficiently than drivers in less congested areas.

Several of the root causes may be linked to lower capacity at spots or short sections in the network. Kerner calls this phenomenon a spontaneous traffic breakdown, when there is free flow conditions upstream and downstream of the bottleneck. A hysteresis effect often leads to lower traffic flow rates at the return transition from congested to free flow conditions.

It is reasonable to believe that in most cases, there is a gap between optimal and prevailing driver behavior. As long as drivers perform in a suboptimal manner, the full potential of the network will not be achieved. When it comes to traffic safety, several measures have been implemented in order to reduce this gap. Driver assistance systems aiming to optimize traffic flow have not been as widespread yet.

Emerging technologies allow us to communicate with drivers in real time. 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. 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.

Methodology

A closed track experiment involving approximately 30 cars will be conducted at the motor arena at Hell, Norway. The experiment setup will consist of two oval 400 m long tracks. The two tracks join together at one point, creating a 50 m long merge/diverge bottleneck.

The drivers will first be instructed to drive normally, as if they were on their daily commute, circling the track until a steady-state condition is reached. A subset of the drivers will then be instructed to drive as efficiently as they feel is safe, thereby minimizing the individual headways of these drivers. Finally, all drivers in the experiment will receive these instructions. Afterwards, the drivers will repeat the normal driving style, in order to determine the bias due to learning effects.

Video images, GPS and vehicle data will be recorded continuously. Analyses will be made on macro and micro parameters related to traffic flow and driver behavior.

Policy implications

The most important contribution 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 incident-related bottlenecks, work zones, lane drops and merge sections. FHWA correctly describe driver behavior as a “wild card”. We seek to find out to what extent it may be controlled.

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 bottleneck efficiency gains due to short periods of increased attention and motivation from each driver may be feasible without increasing the risk of accidents.

Publisher

Association for European Transport