A Physics-Based Algorithm for Dynamic CO2 Emissions Estimation in Demand-Responsive Transport and Ride-Hailing Services

As road transport is a major contributor to anthropogenic CO2 emissions, the importance of sustainable mobility planning and fleet management becomes very clear. Therefore, this article proposes a physics-based mathematical framework for dynamic estimation of CO2 emissions. The proposed framework is very flexible and enables CO2 assessment for different types of vehicles (i.e., combustion engine and electric vehicles), traffic and operating conditions. The proposed software prototype is evaluated through representative urban and peri-urban simulation scenarios. These scenarios involve conventional public transport, private vehicles, and demand-responsive ride-hailing services. The simulation results show that vehicle occupancy is one of the main factors impacting specific CO2 emissions. In this context, in low-passenger-demand and dispersed travel conditions, demand-responsive mobility services can achieve lower emissions per passenger-kilometer than conventional public transport. In contrast, when occupancy levels are sufficiently high, public transport remains the most efficient option. These results indicate that there is no universally optimal transport mode and that emission efficiency is the result of a matching between vehicle capacity and passenger demand. Therefore, the proposed framework delivers a transparent and practical decision-support tool for transport mobility services.