arXiv:2509.24725v3 Announce Type: replace Abstract: Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management. Partial observability of vehicle flows complicates this task despite the availability of two privacy-preserving data sources: (i) aggregated vehicle counts from loop detectors near stop lines, and (ii) aggregated floating car data (aFCD) that provide segment-wise average speed measurements. However, how to integrate these sources with differing spatial and temporal resolutions for queue length estimation is rather unclear. Addressing this
The proliferation of various data sources in urban environments and advances in AI techniques allow for more sophisticated solutions to long-standing traffic management problems.
Improved queue length estimation enhances urban mobility, reduces congestion, and optimizes resource allocation in smart cities, affecting logistics and citizen quality of life.
The proposed Q-Net offers a more accurate method for integrating disparate traffic data, potentially leading to more effective real-time traffic signal optimization.
- · Smart city solution providers
- · Urban planners
- · Logistics companies
- · Commuters
- · Inefficient traffic management systems
- · Drivers idling in congestion
More precise traffic flow prediction and management at signalized intersections.
Reduced commute times and fuel consumption in urban areas.
Potential for dynamic infrastructure adjustments based on real-time traffic patterns, leading to more adaptive urban environments.
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Read at arXiv cs.LG