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Federal Highway Administration Research and Technology
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Publication Number: FHWA-RD-96-145
Date: February 1998
Development of Human Factors Guidelines for Advanced Traveler Information Systems and Commercial Vehicle Operations: The Effects of Inaccurate Traffic Information on Driver Behavior and Acceptance of an Advanced In-Vehicle Traveler Information System
CHAPTER 1. INTRODUCTION
How reliable must traffic information be for motorists to trust and accept such advice? People are slow to accept and use new technology, even when the technology works reliably (Kantowitz, Becker, & Barlow, 1993). Can an in–vehicle Advanced Traveler Information System (ATIS) presenting real–time traffic information be commercially successful when some of the information it presents is incorrect?
Noise is inherent in most large systems and highway networks often suffer perturbations. Congestion, delays, and accidents can sometimes make traffic information provided to motorists unreliable when it is received inside the vehicle. Drivers may therefore discount, or even ignore, such information, just as alarm signals can fail to produce behavior intended by the system designer (Sorkin, 1988). In some domains, a single bad experience is sufficient to prevent people from using a machine or service. For example, few people continue to feed coins into a defective vending machine or parking meter. Empirical data are badly needed to help the highway engineer select a level of system reliability and accuracy that will maintain the driver's acceptance and use of route guidance information. The goal of this research is to provide data to aid the ATIS designer in designating an appropriate level of system reliability that will be accepted by drivers and help to achieve the goals of Intelligent Transportation Systems (ITS), e.g., reducing traffic congestion (IVHS America, 1992).
A route guidance system is a driver decision aid that uses knowledge about a traffic network to provide advice that facilitates travel between an origin and a destination. There are many possible algorithms and heuristics to provide such support. A simple static algorithm may only calculate the path providing the shortest distance. More sophisticated heuristics might take travel times into account based upon historical data. The most powerful systems use real–time communication between the vehicle and a traffic information center to provide frequent updates on travel times and network bottlenecks. Route guidance systems can plot travel routings for the driver and some can update them if traffic conditions change or if the driver diverts from the plotted path. Thus, it is important for the system designer to be able to estimate the conditions that will maximize the probability that a driver will trust and follow ATIS suggestions.
Part–task simulators are an effective tool for studying how operators interact with large systems in general (Kantowitz, 1988) and route guidance systems in particular (Bonsall, 1994). Bonsall and Parry (1991) used a simulated artificial traffic network to investigate the quality of advice defined as the ratio of the minimum time to reach a destination by means of the advised route to the minimum time by any route. They found that user acceptance declined with decreasing quality of advice in an unfamiliar network. As familiarity with the network increased, drivers were less likely to accept advice from the system. However, Allen, et al., (1991) found that familiarity did not affect route choice behavior. These researchers used a real traffic network, as opposed to the artificial network created by Bonsall and Parry (1991). Allen et al. (1991) explained their results by speculating that perhaps both familiar and unfamiliar driver populations may have been more similar than intended in that all drivers may have been unfamiliar with the environs of the Garden Grove Freeway in southern California. Thus, these two experiments yielded conflicting results about the effects of familiarity on driver choice. Since a comparison of these two experiments confounds familiarity with real vs. artificial traffic networks, additional research is required. The present experiment compares a familiar real traffic network with an unfamiliar artificial network that has been carefully matched to the topography of the real network.
Furthermore, the independent variable of familiarity was not operationally defined identically in both experiments. For Bonsall and Parry (1991), familiarity referred to learning the artificial network through repeated trials. For Allen et al. (1991), familiarity referred to the driver's mental conception of an existing real traffic network prior to the experiment proper. The present experiment also examines effects of repetition using a balanced experimental design. This design permits evaluation of both familiarity in the sense of a driver's mental model of a locale as well as in the sense of learning through repeated trials.
The basic issue of the effects of traffic information reliability was first studied by Kantowitz, Kantowitz, and Hanowski (1994) using the Battelle Route Guidance Simulator (RGS), a part–task simulator that provides the driver with continuous real–time information and traffic reports. This is an improvement in methodology over earlier simulator studies that used discrete traffic images either projected from slides (Allen et al., 1991) or on a small computer (Bonsall, 1994). When traffic information was 100 percent accurate, drivers were able to reduce penalty costs associated with non–optimal route selection relative to an unreliable condition with 77 percent accurate information. However, drivers continued to use the simulated ATIS even when the system was unreliable. In this first experiment, a real existing traffic network, Seattle and its environs, was simulated. The present experiment extends and replicates these results by using three levels of information accuracy and two traffic networks.
In a formal sense, the driver's decision about accepting the advice of an automated route guidance system is very similar to the dynamic allocation of function decision made by an operator controlling some industrial process (Kantowitz & Sorkin, 1987). In both cases, the system operator either lets the automation make the decision or manually makes the decision. The operator's subjective feelings about trust in the automated system play an important role in the dynamic allocation of function decision (Lee & Moray, 1991); indeed, a quantitative model of operator trust has been developed for process control. Additional research using this model (Lee & Moray, 1994) has shown that better predictions of operator behavior are made when subjective self–confidence is also taken into account. In general, when trust exceeds self–confidence, operators accept automated control. Conversely, when self–confidence exceeds trust, operators use manual control. The present study measures both operator trust and self–confidence to determine if these two factors are related to driver acceptance of traffic information. Several hypotheses may be formed using these subjective measures. For example, trust should decrease when ATIS reliability decreases. Furthermore, if self–confidence decreases in an unfamiliar setting while trust in the ATIS remains constant, drivers should be more willing to accept ATIS advice in the unfamiliar setting. Of course, since these subjective measures are correlational, inferences about causality must be made with great caution, if at all. It can be difficult to determine if operators make a decision because they trust the automation or if they trust the automation because they made a decision to use it.