The main purpose of this work is to develop a single vehicle noise emission model that uses speed as input variable and returns as output a parameter directly referable to the noise source, namely the source sound power level (Lw). The model was tested on three light-duty vehicles with different motorizations: diesel, gasoline and gasoline-electric hybrid. Field measurements were conducted on a straight road and for different speed values (10-90 km/h). The influence of the engaged gear on the noise at different constant speed values was also explored for gasoline and diesel vehicles using one-way analysis of variance (ANOVA). Results revealed that the source power level emitted by different typologies of cars against speed followed significantly different trends, more evident at speeds lower than 40 km/h. In such cases, the contribution of the engine on the noise is prevalent and ANOVA test confirmed that the gear choice influenced the noise at low speeds. At higher speed values such difference disappears.

The application of automation in vehicles has increased rapidly over the years, especially with regard to automated driver support systems. Vehicle automation is proclaimed as a remedy for improved road safety, mobility and cleaner environment. The claim for cleaner environment is based on more efficient driving, so-called “tank to wheel”. What is ignored in this claim is the increase of data required to allow automation. If this data requirement is included and translated into required additional energy production, vehicle automation may jeopardize the environment by causing considerable amounts of additional CO2 and pollutant particle emissions. This article will provide details in the calculation and the magnitude of these effects. Keywords: Automation, data, emission

A new design of a user-centric Emission Trading Systems (ETS) and its implementation as a carbon Blockchain framework for Smart Mobility Data-market (cBSMD) is presented. The cBSMD allows for individual transactions of token-based GHG emission quantities when realizing a trip in a multimodal setting as well as the management of system-wide emission performance data. The cBSMD design is here applied to an ETS framework where individual travellers receive a certain amount of emission credits in the form of tokens. Travellers spend tokens every time they emit GHG when travelling in a multi-modal network through cBSMD transactions. This design instance of cBSMD is then applied to a case-study of 24hours of mobility for 3,187 travelers. The cBSMD performs with a very low latency and high throughput for this number of travelers. To showcase cBSMD data management features, socio-demographic and trip features regarding token usage and emission performance are also analyzed. Our proposed system sets the first implementation step towards the design of future user-centric and practice-ready ETS frameworks.