Spatial and Temporal Analysis of Mismatch between Planned Road Infrastructure and Traffic Demand in Large Cities

Ivanchev, Jordan; Aydt, Heiko; Knoll, Alois · 2015 · Crossref

DOI: 10.1109/itsc.2015.239

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Summary

This paper addresses the mismatch between planned road infrastructure capacity and actual traffic demand in large cities. As urban environments evolve, previously optimal road networks often become suboptimal due to shifting commuter behaviors and traffic patterns. The authors argue that evaluating this mismatch is crucial for authorities to identify underutilized or congested infrastructure and react appropriately. The study focuses on intersections as the fundamental units of analysis, examining whether the physical lane distribution aligns with the probabilistic routing choices of commuters. To quantify this mismatch, the authors propose a mathematical framework that calculates the deviation between actual turning probabilities and ideal turning probabilities derived from road capacities. The method involves estimating Origin-Destination (OD) matrices and simulating agent routing choices. The core metric, $\Delta_i$, measures the discrepancy between the ratio of lanes on successor roads and the ratio of traffic demand for those turns. To account for the discrete nature of lane counts, an absolute mismatch measure ($m_i$) is also defined, indicating the number of lanes that would need redistribution to achieve optimal performance. These measures are weighted by a congestion factor to prioritize high-throughput intersections and normalized to allow for cross-city comparisons. The methodology was applied to a case study of Singapore using data from the 2012 Household Interview Travel Survey. The researchers generated approximately 300,000 synthetic agents with itineraries based on survey data, incorporating stochastic routing preferences for distance, time, and comfort. The simulation covered the entire day in 30-minute intervals. The analysis focused on intersections with a daily throughput exceeding 10,000 vehicles. Results indicated that the distribution of deviation values followed a log-normal pattern, with a peak around 0.3. Only about 100 intersections showed a perfect or near-perfect match (deviation < 0.1), while approximately 1,500 intersections clustered around the peak deviation. The maximum observed deviation was 0.8, indicating significant misalignment between infrastructure and demand at certain locations. The significance of this work lies in providing a universal, invariant measure for evaluating road network utilization. By quantifying the spatial and temporal profile of infrastructure-demand mismatch, the proposed method enables a more precise assessment of network performance than traditional topological or flow-based analyses. This approach allows for the identification of specific problematic intersections and supports data-driven decisions for infrastructure adjustments, such as lane redistribution, to improve overall traffic efficiency.

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• mental demand