Simon Ollander

Global navigation satellite systems provide accurate positioning nearly worldwide. However, in the urban canyons of dense cities, buildings block and reflect the signals, causing multipath errors. To mitigate multipath errors, knowledge of the distribution of the reflection delays is important. Measurements of this distribution have been done in several dense cities, but it is unknown how the delay distribution depends on the depth of the urban canyon. To fill this gap, we simulated reflection scenarios in 12 different environments: from suburban to deep urban canyon. Subsequently, we analyzed the resulting delay distributions. This paper presents these distributions, and a method to estimate them using the number of received satellites. According to our simulation, the multipath delays follow gamma distributions, whose shape parameters decrease when the urban canyon depth increases. A quadratic model can estimate the shape parameter using the number of received satellites. Consequently, depending on the number of received satellites, the distribution of the reflection delays can be estimated. This information can be combined with prior knowledge from other methods for improved multipath delay estimation. In the future, for more realistic results, the effects on signals that are reflected multiple times and environments other than urban canyons should be simulated.

This article addresses the question of passengers’ experience through different transport modes. It presents the main results of a pilot study, for which stress levels experienced by a traveller were assessed and predicted over two long journeys. Accelerometer measures and several physiological signals (electrodermal activity, blood volume pulse and skin temperature) were recorded using a smart wristband while travelling from Grenoble to Bilbao. Based on user’s feedback, three events of high stress and one period of moderate activity with low stress were identified offline. Over these periods, feature extraction and machine learning were performed from the collected sensor data to build a personalized regressive model, with user’s stress levels as output. A smartphone application has been developed on its basis, in order to record and visualize a timely estimated stress level using traveler’s physiological signals. This setting was put on test during another travel from Grenoble to Brussels, where the same user’s stress levels were predicted in real time by the smartphone application. The number of correctly classified stress-less time windows ranged from 92.6% to 100%, depending on participant’s level of activity. By design, this study represents a first step for real-life, ambulatory monitoring of passenger’s stress while travelling.