With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.

The urban transport sector has become one of the major contributors to global CO₂ emissions. This paper investigates the driving forces of changes in CO₂ emissions from the passenger transport sectors in different cities, which is helpful for formulating effective carbon-reduction policies and strategies. The logarithmic mean Divisia index (LMDI) method is used to decompose the CO₂ emissions changes into five driving determinants: Urbanization level, motorization level, mode structure, energy intensity, and energy mix. First, the urban transport CO₂ emissions between 1960 and 2001 from 46 global cities are calculated. Then, the multiplicative decomposition results for megacities (London, New York, Paris, and Tokyo) are compared with those of other cities. Moreover, additive decomposition analyses of the 4 megacities are conducted to explore the driving forces of changes in CO₂ emissions from the passenger transport sectors in these megacities between 1960 and 2001. Based on the decomposition results, some effective carbon-reduction strategies can be formulated for developing cities experiencing rapid urbanization and motorization. The main suggestions are as follows: (i) Rational land use, such as transit-oriented development, is a feasible way to control the trip distance per capita; (ii) fuel economy policies and standards formulated when there are oil crisis are effective ways to suppress the increase of CO₂ emissions, and these changes should not be abandoned when oil prices fall; and (iii) cities with high population densities should focus on the development of public and non-motorized transport.

For a decade, researchers have focused on the development and deployment of road automated mobility. In the development of autonomous driving embedded systems, several stages are required. The first one deals with the perception layers. The second one is dedicated to the risk assessment, the decision and strategy layers and the optimal trajectory planning. The last stage addresses the vehicle control/command. This paper proposes an efficient solution to the second stage and improves a virtual Cooperative Pilot (Co-Pilot) already proposed in 2012. This paper thus introduces a trajectory planning algorithm for automated vehicles (AV), specifically designed for emergency situations and based on the Autonomous Decision-Support Framework (ADSF) of the EU project Trustonomy. This algorithm is an extended version of Elastic Band (EB) with no fixed final position. A set of trajectory nodes is iteratively deduced from obstacles and constraints, thus providing flexibility, fast computation, and physical realism. After introducing the project framework for risk management and the general concept of ADSF, the emergency algorithm is presented and tested under Matlab software. Finally, the Decision-Support framework is implemented under RTMaps software and demonstrated within Pro-SiVIC, a realistic 3D simulation environment. Both the previous virtual Co-Pilot and the new emergency algorithm are combined and used in a near-accident situation and shown in different risky scenarios.

Ecodriving is known as a way to quickly and efficiently reduce fuel consumption for the concerned ecodriven vehicle. However, the impact of ecodriving on a whole road network at a large scale is unknown. In order to perform studies in a micro traffic simulation software, a fuel consumption model coupled to a gear behavior model are required. This study presents a gear shifting behavior model able to represent as well the variability of drivers as the difference between ecodriving and normal driving. This work, based on the evaluation of the real driver behaviors during 42 trips, has been partially validated with a result of 60% of time spend in the correct gear. Future works will be concentrated on a detailed validation of this model and on its implementation with a fuel consumption model.

For a decade, a lot of researches have focused on the development and the deployment of automated mobility services and means for road networks (urban, suburb, rural, highway). In the development of autonomous driving embedded systems, several stages are needed. The first one concerns the perception layers. The second one is dedicated to the risk assessment, the decision and strategy layers and the optimal path planning. The last one addresses the control/command parts. Based on the Autonomous Decision-Support Framework (ADSF) of the EU project Trustonomy, this paper proposes a risk assessment and decision-making framework to the second stage and improves an existing virtual co-pilot by combining a new emergency mode and corresponding trajectory planning algorithm. After introducing the project framework for risk management and the general co-pilot concept developed in the University Gustave Eiffel, the Decision-Support framework, implemented in RTMaps platform, is demonstrated within a realistic 3D simulation environment called Pro-SiVIC. Both the previous virtual copilot and the new emergency algorithm are combined and a switching strategy between the different modes is tested in a near-accident situation.