###### Abstract  
In the field of smart mobility, Artificial Intelligence (AI) approaches are influential and can make a highly beneficial contribution. Our project aims to develop a real-time ecological map of road traffic. This map will allow electric vehicles (EVs) and thermal vehicles (TVs) to display the cost of energy consumption and CO\({}_{2}\) emissions on different road sections. In urban environments, road traffic emissions are a significant contributor to environmental pollution, with vehicle emissions being a major component. Addressing these impacts requires a thorough understanding of the operational behavior of vehicles on different road infrastructures within the region. This paper presents a novel, comprehensive dataset, the Vehicle Activity Dataset (VAD), designed to assess the emissions and fuel consumption characteristics of vehicles about their actual operating environment. Constructed from a large number of real-world driving scenarios, VAD incorporates emission data collected by an industrial Portable Emission Measurement System (PEMS), road scenes captured by an RGB camera, and the detection of different object classes within these images. The primary objective of VAD is to provide a comprehensive understanding of the relationship between vehicle emissions and the diverse range of objects present on the road. Experimental results in real road traffic environments through different studies demonstrate the robustness of the developed dataset. Eco-routing; Vehicle Specific Power; Portable Emission Measurement; Road scenes; Artificial Intelligence; Computer vision; Smart mobility +
Footnote †: journal: Applied  
0000-0001-1438-2233-2333-2333-2333-2333-2333-23333-2333-2333-2333-2333-2333-23333-2333-2333-2333-2333-2333-2333-2333-23333-23333-2333-23333-2333-2333-23333-2333-2333-2333-23333-23333-23333-23333-23333-2333-2333-information can be used to monitor traffic congestion, identify accident-prone areas, and optimize navigation systems to provide drivers with the most efficient routes. Vehicles also emit data about their speed, acceleration, and deceleration patterns. By analyzing these data, traffic engineers can gain insight into traffic flow dynamics, identify bottlenecks, and implement adaptive traffic signal control systems to improve overall traffic efficiency. In addition, these data can be used to develop predictive models that anticipate traffic patterns and provide early warnings of potential congestion or accidents. Another important aspect of vehicle-generated data is the measurement of gas emissions. As sustainable transport becomes an increasingly important goal, monitoring and reducing greenhouse gas emissions is of paramount importance. By collecting real-time data on gas emissions from individual vehicles, transport authorities can assess the environmental impact of the transport system, identify high-emission zones, and implement targeted measures to promote cleaner and greener mobility options. The integration of vehicle-generated data into road scene understanding opens up new possibilities for creating a more sustainable and efficient transport ecosystem. By harnessing these data, we can pave the way for eco-friendly mobility solutions, reducing our environmental footprint, and creating a greener future for transport. As technology continues to advance, the potential for data-driven eco-mobility innovations is becoming increasingly promising, offering a transformative shift towards a more sustainable and intelligent transport system. This paper presents an innovative and comprehensive dataset, Vehicle Activity Dataset (VAD), which combines vehicle data and road scene information, aiming to drive advancements in eco-mobility and sustainable transport solutions. By integrating these two different data sources, we provide a valuable resource for studying the complex relationship between vehicle emissions, road conditions, and smart mobility strategies.