Efficiently Simulating Personal Vehicle Energy Consumption in Mesoscopic Transport Models

Mesoscopic transport models can efficiently simulate complex travel behavior and traffic patterns over large networks, but simulating energy consumption in these models is difficult with traditional methods. As mesoscopic transport models rely on a simplified handling of traffic flow, they cannot provide the second-by-second measurement of vehicle speeds and accelerations that are required for accurately estimating energy consumption. Here we present extensions to the TripEnergy model that fill in the gaps of low-resolution trajectories with realistic, contextual driving behavior. TripEnergy also includes a vehicle energy model capable of simulating the impact of traffic conditions on energy consumption and CO2 emissions, with inputs in the form of widely available calibration data, allowing it to simulate thousands of different real-world vehicle makes and models. This design allows TripEnergy to integrate with mesoscopic transport models and to be fast enough to run on a large network with minimal additional computation time. We expect it to benefit from and enable advances in transport simulation, including optimizing traffic network controls to minimize energy, evaluating the performance of different vehicle technologies under wide-scale adoption, and better understanding the energy and climate impacts of new infrastructure and policies.

Applying a Mesoscopic Transport Model to Analyse the Effects of Urban Freight Regulatory Measures on Transport Emissions—An Assessment

Sustainable urban freight management is a growing challenge for local authorities due to social pressures and increasingly more stringent environmental protection requirements. Freight and its adverse impacts, which include emissions and noise, considerably influence the urban environment. This calls for a reliable assessment of what can be done to improve urban freight and meet stakeholders’ requirements. While changes in a transport system can be simulated using models, urban freight models are quite rare compared to the tools available for analysing private and public transport. Therefore, this article looks at ways to extend Gdynia’s existing mesoscopic transport model by adding data from delivery surveys and examines the city’s capacity for reducing CO2 emissions through the designation of dedicated delivery places. The results suggest that extending the existing model by including freight-specific data can be justified when basic regulatory measures are to be used to improve freight transport. There are, however, serious limitations when an exact representation of the urban supply chain structure is needed, an element which is required for modelling advanced measures.