The Traffic Conflict Technique may provide a framework for the assessment of individual differences in risk perception. In this work, video scenes of traffic conflicts were analysed and different objective parameters of these scenes were determined. These parameters were calculated from the vehicles’ movement on the video images. A single measure for every scene was used as a score to rate its dangerousness. This measure was the Minimum Time-To-Collision. The Minimum Time-To-Collision (MTTC from now on) was adopted as a criterion to set the dangerousness of an interaction (see Hydén, 1987; Van der Horst, 1990, 1991). According to these studies, we agree that MTTC describes how dangerous the collision risk was across the interaction. The lower the TTC value, the higher was the collision risk. Although all of the scenes show a particular common situation in urban road traffic —a preceding vehicle and a following one, both aproaching an intersection-, some of them reached low MTTC values —less than 1.5 seconds. A set of these scenes was presented to a number of participants which had to perform three tasks: in the first one, they had to score the dangerousness of the whole interaction in a 5-point Likert scale. The scale was labelled as “No risk at all” (1) and “Very high risk” (5). The dangerousness was not explained to the participants, allowing them to rate the scenes according to their own concept of risk. The results showed that participants tend to rate the dangerousness of every scene in a way that reflects its MTTC value. In the second task, subjects were instructed to make a motor response —press the space bar- in the moment “they think the collision risk just started”. No indications were given about what information had to be considered for making the judgement. Latency values were recorded for the participants’ responses related to the moment TTC reach 2.5 seconds until the moment they press the button. The absence of response in these scenes was taken as the maximum latency —the time from the moment in which TTC reaches 2.5 seconds until the end of the scene. The results in the second task indicates that remarkable individual differences can arise in the responses to some scenes. In general, participants with more than 40.000 Kms driven -average = 66.000 Kms- had lower latencies than participants with less experience —average = 11.000 Kms- indicating that the amount of driving experience can influence the skill to recognise the collision risk on traffic conflicts. Finally, we proposed a third task to the participants. A different version of the scenes were obtained by editing the video footage according to a given value of TTC. Thus, four sets of 36 scene fragments were made. These sets were presented as a Signal Detection task in which participants had to respond “Yes” or “No” the collision risk had really started “at the very last moment of the scene fragment”. The four sets presents half of the fragments (18) which ends with a particular value of TTC below 3 seconds (Signal) and the other half of the fragments (18) which ends with a value of 3.2 seconds (Noise). The Signal video-scene fragments ended with a value of 1.4 secs. (Version A), 1.9 secs. (Version B), 2.3 secs. (Version C) and 2.7 seconds (Version D), while the Noise video-scene fragments allways ended with the same TTC value -3.2 secs.- in every version. The results showed that participants could easily recognise the difference between signal and noise when the Signal scenes ended with a TTC value lower than 2 seconds. This task was a little harder when this TTC value was 2.3 seconds while detectability was significantly less -nearly zero- when TTC was equal to 2.7 seconds. This supports the idea of a “thershold value” in the detection of collision risk, at least in the kind of video scenes used in this study, that could be given by a TTC value of 2.5 seconds. (Author/publisher) This publication may be accessed by Internet users at: http://www.ictct.org/workshop.php?workshop_nr=25
Abstract