کاهش سر و صدا و ایمنی ترافیک: تجارت کردن موتورهای ساکت تر و پیاده رو در تشخیص خودرو

Road traffic sounds are a major source of noise pollution in urban areas. But recent developments such as low noise pavements and hybrid/electric engine vehicles cast an optimistic outlook over such an environmental problem. However, it can be argued that engine, tire, and road noise could be relevant sources of information to avoid road traffic conflicts and accidents. In this paper, we analyze the potential trade-offs of traffic-noise abatement approaches in an experimental study, focusing for the first time on the impact and interaction of relevant factors such as pavement type, vehicle type, listener's age, and background noise, on vehicle detection levels. Results reveal that vehicle and pavement type significantly affect vehicle detection. Age is a significant factor, as both younger and older people exhibit lower detection levels of incoming vehicles. Low noise pavements combined with all-electric and hybrid vehicles might pose a severe threat to the safety of vulnerable road users. All factors interact simultaneously, and vehicle detection is best predicted by the loudness signal-to-noise ratio.

Traffic related noise is nowadays the major source of environmental noise in most industrialized nations and developing regions. Its negative impact has been demonstrated at several instances, from health to school efficiency and overall emotional annoyance (e.g., Gorai and Pal, 2006, Passchier-Vermeer and Passchier, 2000, Sanz et al., 1993 and Freitas et al., 2012). It is therefore a matter of active concern for traffic-related researchers, public authorities in health and traffic, as well as transportation and road industries, to find quieter alternatives to the major sources of transportation noise. In a near future, we might expect a reduction of road traffic noise both by pavements that are more efficient and because of the growing popularity of hybrid and all-electric vehicles. Therefore, there is an optimistic outlook on health improvement and annoyance reduction due to a quieter road traffic environment, specifically for populations living in urban areas. However, in urban areas traffic noise could also be a key factor for the awareness of imminent conflicts by vulnerable road users. In other words, road, tire and engine noises might be used as meaningful signals by pedestrians and bicyclists: they can act as attentional triggers, allowing for a better perception of speed and proximity of incoming traffic and for timely reactions to avoid conflicts. Therefore, due to traffic noise abatement, we might face in the near future an increasing trade-off between the improvement of population's health and the rise of accidents involving vulnerable road users. Such trade-off analysis has never been approached from an experimental perspective. When compared to internal combustion vehicles, electric/hybrid engine vehicles have higher incidence of crashes involving pedestrians and bicyclists (Garay-Vega et al., 2010 and Hanna, 2009). On the one hand road users show substantial interest in driving quiet hybrid or all-electric cars; but on the other hand they are concerned with the reduced conspicuity of such vehicles (Wolgater et al., 2001). Some experimental studies have addressed this issue. Ashmead et al. (2012) analyzed the path identification of electric engine and internal combustion engine vehicles in quiet and noisy environments. They found that in quiet environments there were timely path identifications of the electrical vehicles, but not in noisy ones. They also found that these judgments were based on sound level, the main characteristic that is altered in electric/hybrid cars. Studies with visually impaired populations have also revealed lower vehicle detectability of hybrid and all-electric vehicles (Emerson et al., 2010). All these data have contributed to the official recognition by the U.S. National Highway Traffic Safety Administration that electric vehicles in low-speed operation may induce a safety issue for blind pedestrians (Garay-Vega et al., 2010). Other factors might affect traffic-related noise and hence vehicle conspicuity. In a previous study (Freitas et al., 2012) we have demonstrated that pavement type largely affects the levels of environmental noise and related subjective annoyance. However, the way pavement type affects vehicle detection is still not clear. In addition, age might be regarded as a relevant variable. Young pedestrians are more often involved in accidents than older people are, but while being rare, accidents with older people are the most severe (Martin, 2006). In experimental studies with children, the number of correctly identified vehicle sounds was significantly improved with age (Pfeffer and Barnecutt, 1996). Despite the strong evidence of the role of several traffic noise factors on vehicle conspicuity, there has never been a comprehensive study analyzing the main relevant variables (Barton et al., 2012). In this paper, we present for the first time such an integrated approach to traffic noise variables and related vehicle detection levels. We address the detection of approaching vehicles as a function of pavement, vehicle type, background noise and the age of the listener. Binaural pass-by noise samples were recorded using several combinations of pavement, vehicle and speed. These samples were then edited to create scenarios of approaching vehicles in noisy environments. Under controlled laboratory conditions, participants had to detect the approaching vehicles.

Our results clearly show a negative impact of traffic noise abatement on the detection of approaching vehicles. Detection is significantly lowered by low noise pavements and quieter vehicles. Interestingly, pavement type had a stronger effect than vehicle type on the detection levels. This might reveal that tire-road noise is a more relevant cue for vehicle detection than engine noise, namely at lower traffic velocities such as those used in this study. The analysis of this finding may become very complex since tire-road noise at low speeds is influenced not only by the type of road surface but also by tire characteristics such as pattern and wear. In this study, the tires of the hybrid vehicle were nearly new, therefore less noisy, while the tires of the other vehicles were worn. In this way the effect of the vehicle type was clearly differentiated. This finding should be taken into consideration in future studies on traffic noise abatement. Indeed, there is barely any research related to pavement type and specifically low noise pavements, when compared to the high data volume on vehicle engines. Our data strongly suggest that different asphalt mixtures will contribute differently to traffic conspicuity and vulnerable road users’ safety. Furthermore, a novel approach to tire effect on detection should be carried out. Also, age is a critical factor. Younger and particularly older participants are the most impaired. The worse detectability levels in older listeners most likely reflect the typical hearing loss associated with age. On the other hand, the decreased detectability in younger groups is congruent with data pointing out that as they grow older, children increase their accuracy in vehicle identification (Pfeffer and Barnecutt, 1996). Not only the variables revealed direct and separate effects on the vehicle detectability, but they also showed interactive effects. This fact points out to the need for comprehensive approaches that account for subject's age (or listening abilities), vehicle and pavement type, as well as background noise. These interactions might be regarded as a result of loudness additivity. Noisier cars and pavements should be more accurately identified by better listening groups. Loudness signal-to-noise ratio did indeed reveal some predictability, but it did not account for all variables. Cobble stones traffic sounds remain highly detected despite varying loudness levels, probably due to their spectral or rhythmic patterns. Nevertheless, the finding that loudness is the best acoustic measure to predict vehicle detection, against LAeq and LAmax, is consistent with our previous results, pointing to loudness as the best predictor of traffic-noise annoyance ( Freitas et al., 2012), and brings further support to the claim that environmental noise assessment should have this measure as a standard. One major concern standing out from this study relies on some age groups (younger and older participants) performing below a threshold of 75%, or even close to random, in several traffic scenarios. In the real world, the detection performance is likely to be even worse. On the one hand, we used a standard white noise background, while in everyday situations road traffic contributes heavily to the noise environment, thus reducing the conspicuity of the sound envelope of each vehicle. On the other hand, in our experiments, participants only had to detect one approaching vehicle at a time instead of simultaneously facing several targets, which would be the case in common urban scenarios. Moreover, transition periods between vehicle or pavement type are potentially very difficult and risky. Vulnerable road users will inevitably have to cope with a growing mix of vehicles and pavements, with varying degrees of conspicuity. In such a transition scenario, hybrid and all-electric vehicles, circulating on low noise pavements, might prove quite difficult to detect. Therefore, a trade-off between a more pleasant and healthy urban road environment and an increase of traffic conflicts and accidents involving pedestrians and bicyclists should be a matter of concern. In the next section we approach this matter from the decision-maker point of view.