🤖 AI Summary
This study addresses the critical need for high-resolution, vehicle-class-specific origin–destination (OD) data in urban traffic simulation. While mobile signaling data offer broad spatial coverage, they lack mode differentiation and suffer from representativeness bias; conversely, toll station counts provide accurate yet sparse observations. To bridge this gap, the authors propose a machine learning framework that fuses sparse, precise toll counts with large-scale mobile signaling data by modeling spatiotemporal features and incorporating route assignment logic to learn their nonlinear mapping. This approach enables, for the first time, the calibration of aggregated mobility data using minimal sensor inputs to generate hourly, vehicle-class-disaggregated macroscopic OD matrices. Validated in a bus depot expansion case study in Trondheim, Norway, the method successfully produced length-based vehicle OD estimates, significantly enhancing the accuracy and scalability of traffic flow inference—particularly in data-scarce environments.
📝 Abstract
Traffic simulations, essential for planning urban transit infrastructure interventions, require vehicle-category-specific origin-destination (OD) data. Existing data sources are imperfect: sparse tollbooth sensors provide accurate vehicle counts by category, while extensive mobility data from cellular network activity captures aggregated crowd movement, but lack modal disaggregation and have systematic biases. This study develops a machine learning framework to correct and disaggregate cellular network data using sparse tollbooth counts as ground truth. The model uses temporal and spatial features to learn the complex relationship between aggregated mobility data and vehicular data. The framework infers destinations from transit routes and implements routing logic to distribute corrected flows between OD pairs. This approach is applied to a bus depot expansion in Trondheim, Norway, generating hourly OD matrices by vehicle length category. The results show how limited but accurate sensor measurements can correct extensive but aggregated mobility data to produce grounded estimates of background vehicular traffic flows. These macro-scale estimates can be refined for micro-scale analysis at desired locations. The framework provides a generalisable approach for generating origin-destination data from cellular network data. This enables downstream tasks, like detailed traffic simulations for infrastructure planning in data-scarce contexts, supporting urban planners in making informed decisions.