CHAPTER 4.
DISCUSSION
The goal of this experiment was
to provide an initial answer to two questions asked by ATIS designers:
(1) How reliable must traffic
information be for motorists to trust and use it?
(2) How does the familiarity
of the setting influence trust and use of unreliable traffic information?
Results showed for several dependent
variables (e.g., penalty cost, trust, trust minus self-confidence) that while
100 percent accurate information yields best driver performance and subjective
opinion, information that is 71 percent accurate remains acceptable and useful.
Drivers are willing to tolerate some error in a simulated ATIS. However, when
information accuracy drops to 43 percent, driver performance and opinion suffer.
Thus, information accuracy above 71 percent is recommended to system designers.
Future research is needed to evaluate information accuracy between 44 percent
and 70 percent.
Drivers did not use simulated ATIS
accurate information as effectively in the familiar setting as in the unfamiliar
setting. Inaccurate traffic information was more harmful in the familiar setting.
These results may imply that commercial success for in-vehicle ATIS will be
easier to accomplish in unfamiliar settings, e.g., for use in rental vehicles
for visitors, than in one's home city. Because drivers have greater self-confidence
in familiar settings, they are more critical of ATIS advice and hold to a higher
standard of user acceptability when they know the area geography. Thus, to achieve
user acceptance, in-vehicle systems intended for purchase by a driver in a private
passenger vehicle will likely have to meet higher standards than systems intended
for commercial use.
Driver trust was decreased by inaccurate
traffic information but recovered when accurate information was received. However,
the more likely that information was inaccurate, the less the recovery. For
the 43 percent accuracy condition, T-SC became negative, implying that drivers
would prefer their own solutions to those offered by the simulated ATIS device.
So, although drivers do not demand perfect traffic information, high degrees
of inaccuracy will cause drivers to ignore system advice, especially in familiar
settings.
It is interesting to compare present
results with those of Bonsall and Parry (1991) and Allen et al. (1991) summarized
in the Introduction. When familiarity is defined as knowledge of a local geography,
we found that familiarity was harmful since penalty costs were higher; Allen
et al. (1991) found no effect of familiarity. When familiarity is defined as
learning over repeated trials, we found no decrement in driver trust over the
first two repeated trials. However, penalty cost did decrease on the second
trial. This conflicts with results of Bonsall and Parry who found drivers less
likely to accept advice over repeated trials. Note that experiments using artificial
networks to some extent necessarily confound familiarity with the traffic network
and familiarity with the simulated ATIS device, since the network is learned
by using the new device. While artificial networks can be useful microworlds
for the ergonomics researcher, we believe that they are most useful when topographically
matched to a real network as in this study.
Finally, we also note that this experiment manipulated information reliability in only one of several ways that might be meaningful to drivers. In this experiment, accuracy was based upon level of service. Drivers judged whether the actual level of service on a particular traffic link matched their expectations based upon information provided by the simulated ATIS. Other traffic dimensions may be equally salient for drivers. For example, Janssen and Van der Horst (1993) presented drivers in a simulator with length of congestion in kilometers, delays relative to normal travel times in minutes, and travel times in minutes. Reliability was manipulated by altering the variability of these estimates. They found that travel time information was most resistant to degradations in reliability. So the present results may not generalize to all of the possible formats and types of information traffic engineers can offer to drivers.