According to the time-saving bias, drivers underestimate the time saved when increasing from a low speed and overestimate the time saved when increasing from a relatively high speed. Previous research used a specific type of task — drivers were asked to estimate time saved when increasing speed and to give a numeric response — to show this. The present research conducted two studies with multiple questions to show that the time-saving bias occurs in other tasks. Study 1 found that drivers committed the time-saving bias when asked to estimate (a) the time saved when increasing speed or (b) the distance that can be completed at a given time when increasing speed or (c) the speed required to complete a given distance in decreasing times. Study 2 showed no major differences in estimations of time saved compared to estimations of the remaining journey time and also between responses given on a numeric scale versus a visual analog scale. Study 3 tested two possible explanations for the time-saving bias: a Proportion heuristic and a Differences heuristic. Some evidence was found for use of the latter.

The aim of this research was to study the performance in a speed estimation task of a passenger travelling in a real car in different scenarios: a closed track used in previous experimental studies was compared with interurban traffic environment involving a secondary road and a highway. At the same time, the effect of sex and driving experience on speed estimation was analyzed. Thirty-six participants (18 male and 18 female, half of each group being drivers and half non-drivers) estimated the speed of the car in which they travelled as passengers. The actual speed values varied in the range of 40-100 km/h for the secondary road, 70-120 km/h for the highway condition, and 40-120 km/h for the track. The results obtained for the track in previous studies (Recarte & Nunes, 1996; Recarte, Conchillo, & Nunes, 2004, 2005) were replicated in the same condition and were also verified for the secondary road scenario. However, a different pattern of errors was found for the highway. From the viewpoint of psychophysics, the participants were more accurate on the without-traffic track than in real traffic conditions, considered as a whole. The differences found between road and highway are discussed. No effect was found for between- subject variables, sex, and driving experience.

In low-cost self-contained pedestrian navigation systems, traditional Pedestrian Dead Reckoning (PDR) solutions utilize accelerometers to derive the speed as well as the distance travelled, and obtain the walking heading from magnetic compasses or gyros. However, these measurements are sensitive to instrument errors and disturbances from ambient environment. To be totally different from these signals in nature, the electromyography (EMG) signal is a typical kind of biomedical signal that measures electrical potentials generated by muscle contractions from the human body. This kind of signal would reflect muscle activities during human locomotion, so that it can not only be used for speed estimation, but also disclose the azimuth information from the contractions of lumbar muscles when changing the direction of walking. Therefore, investigating how to utilize the EMG signal for PDR is interesting and promising. In this paper, a novel EMG-based speed estimation method is presented, including setup of the EMG equipment, pre-processing procedure, stride detection and stride length estimation. Furthermore, this method suggested is compared with the traditional one based on accelerometers by means of several field tests. The results demonstrate that the EMG-based method is effective and its performance in PDR can be comparable to that of the accelerometer-based method.