Navigation route recommendation is one of the important functions of intelligent transportation. However, users frequently deviate from recommended routes for various reasons, with personalization being a key problem in the field of research. This paper introduces a personalized route recommendation method based on user historical navigation data. First, we formulate route sorting as a pointwise problem based on a large set of pertinent features. Second, we construct route features and user profiles to establish a comprehensive feature dataset. Furthermore, we propose a Deep-Cross-Recurrent (DCR) learning model aimed at learning route sorting scores and offering customized route recommendations. This approach effectively captures recommended navigation routes and user preferences by integrating DCN-v2 and LSTM. In offline evaluations, our method compared with the minimum ETA (estimated time of arrival), LightGBM, and DCN-v2 indicated 8.72%, 2.19%, and 0.9% reduction in the mean inconsistency rate respectively, demonstrating significant improvements in recommendation accuracy.

The proliferation of human-AI ecosystems involving human interaction with algorithms, such as assistants and recommenders, raises concerns about large-scale social behaviour. Despite evidence of such phenomena across several contexts, the collective impact of GPS navigation services remains unclear: while beneficial to the user, they can also cause chaos if too many vehicles are driven through the same few roads. Our study employs a simulation framework to assess navigation services' influence on road network usage and CO2 emissions. The results demonstrate a universal pattern of amplified conformity: increasing adoption rates of navigation services cause a reduction of route diversity of mobile travellers and increased concentration of traffic and emissions on fewer roads, thus exacerbating an unequal distribution of negative externalities on selected neighbourhoods. Although navigation services recommendations can help reduce CO2 emissions when their adoption rate is low, these benefits diminish or even disappear when the adoption rate is high and exceeds a certain city- and service-dependent threshold. We summarize these discoveries in a non-linear function that connects the marginal increase of conformity with the marginal reduction in CO2 emissions. Our simulation approach addresses the challenges posed by the complexity of transportation systems and the lack of data and algorithmic transparency.

The European Space Agency (ESA) has launched an ambitious new project to build a sat-nav and communication satellite network in orbit around the moon. This new infrastructure could one day turn our natural satellite into the'eighth continent' as humanity spreads its wings and builds cities on the lunar surface. ESA says the project, known as Moonlight, will support the Lunar Gateway space station, multiple agencies working on moon missions and human exploration. In what will be the world's first commercial service of its kind, a number of British firms have won contracts to investigate how it might work, worth over £2 million. 'We are entering a new phase - the systematic exploration of our "eighth continent", the Moon,' ESA's David Parker told BBC News.

Artificial Intelligence (AI) is taking over the technological world. There are many ways AI is being adopted in our day-to-day lives. It is applied in almost all the sectors from agriculture and healthcare to banking, manufacturing, and production. Artificial Intelligence acts as the main driver of emerging technologies like robotics, the Internet of Things (IoT), and data and will continue to be the technological innovator for the future. The objectives of AI include perception, reasoning, and learning.