| 19 Feb 2026
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.
General comments
The paper presents a model for capturing car flow patterns by iteratively computing optimal routes within a simplified nodal configuration of the urban traffic network. Based on the authors' impressive work, the model is developed and implemented for the London urban network. It shows the spatial distribution and diurnal variability of urban road traffic flow, but is limited to a selection of car-commuter classes.
The paper and the supplementary materials provide sufficient explanations of the model's development and validation. Therefore, the model could be replicated for other urban environments. Certainly, it is questionable if the validation steps followed for London would be sufficient for other urban cases. Nevertheless, the authors present appropriate indications and metrics to guide the validation process in other circumstances and specificities.
Specific comments
1. The authors appropriately emphasise the contributions of the proposed model compared to existing routing applications. Nevertheless, it would be worthwhile to have clearer explanations of how the presented model will address the stated goal of supporting urban climate modelling and environmental exposure assessments.
2. In the model evaluation, the route selection is limited to car commuters. The temporal and spatial evaluations are calibrated using historical traffic data from Google Maps. No correlations between person movements and traffic flow are discussed (e.g., modal share, car occupancy). Additionally, no impact of other types of road vehicles (e.g., freight vehicles, etc.) is considered.
3. The model is calibrated and validated based on historical traffic data from Google Maps. It is not demonstrated that it functions as a simulation model under changes to the considered urban structure or multimodal urban transport system (i.e., changes in modal choices for diurnal journeys).
Technical corrections
4. The paper is well structured, and the flow of the model description and implementation is coherent. Nevertheless, it would be useful to provide a synthetic overview of the methodology (before the model description), eventually including a flowchart of the study's main steps.
5. In the conclusion section, it would be helpful to explicitly redefine all the configurations referred to by #1, #2, … #7 (or at least add a reference to Table 3 for each of them).