Transport modelling for dynamic urban climate studies: MATSDA-roads v2.0

Abstract. Representing dynamic patterns of people’s movement is crucial for modelling high-resolution urban systems with feedback to emissions (e.g., anthropogenic heat, pollutants) and exposure of individuals to environmental stressors. We have developed the transport model MATSDA (Movement And Transport Simulations using Dijkstra’s Algorithm) consists of MATSDA-roads and MATSDA-metro that iteratively computes optimised routes through a simplified nodal representation of urban transport networks. The approach can be used for different transport modes with routes being represented as a sequence of journeys between linked nodes. MATSDA-roads v2.0’s input network has a hierarchy of road types (motorway to local roads, UK) connected by junctions and intersections. MATSDA-roads v2.0 is assessed in London (UK) with data collected in real-time from the Google Maps Directions API over several weeks to capture day of week, time of day, route direction (outbound v. inbound) and route alternatives (total of >87,000 reference routes). A high level of road network detail, notably carriageway types (e.g., dual carriageways, slip roads), is critical to obtain travel times accurately to within 5–10 minutes during daytime, particularly for longer journeys. Model parameter choices are shown to impact model performance, with effective length of local roads and junction delay penalties increasing the modelled travel time. MATSDA-roads v2.0 captures the diurnal variability of urban traffic through its input data, including morning and evening rush hours but travel times are systematically underestimated late at night (between 22 h and 4 h). The model exhibits high skill at identifying major travel corridors (Fractions Skill Score ~0.7 at 500 m grid resolution), indicating its route choices are spatially realistic. This work provides a valuable tool for transport research, urban climate modelling and environmental exposure assessment that require dynamic human movement patterns.