Traffic Congestion Estimating Device Using Inflow-Outflow Dynamics
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Solution Overview
Problem
Existing methods for estimating traffic congestion on sensor-uninstalled segments, such as highways, face challenges in precision due to uneven traffic volume distribution and insufficient GPS data, leading to difficulties in identifying congestion starting and end points accurately.
Innovation Solution
A traffic congestion estimating device that identifies congestion points based on vehicle position information, estimates inflow and outflow volumes using sensor data from upstream and downstream points, and updates congestion area boundaries dynamically using inflow and outflow calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stationary sensors are deployed on all road segments, then traffic condition measurement coverage is improved, but installation cost and system complexity increase significantly
Solution Approach 1:
The patent uses GPS-equipped vehicles as mobile measurement intermediaries to collect traffic data on road segments without stationary sensors. These vehicles act as moving measurement points, transmitting position and timing information to the server, which then infers traffic conditions in unmonitored areas based on the movement patterns of these mobile proxies.
Solution Approach 2:
The system creates virtual measurement points by tracking the positions and movements of GPS-equipped vehicles. Instead of physically deploying sensors everywhere, the system copies the measurement function to mobile devices, allowing traffic conditions to be inferred from the trajectories and timing data of these virtual measurement points.
2Ease of manufacture
If GPS in-vehicle devices are used for traffic condition measurement, then cost burden is reduced and measurement coverage is improved, but measurement precision deteriorates due to GPS errors and insufficient data
Solution Approach 1:
The patent merges data from multiple GPS-equipped vehicles with traffic volume information from stationary sensors at key locations. By combining these different data sources, the system compensates for the precision limitations of individual GPS measurements and creates a more accurate picture of traffic conditions through data fusion and correlation analysis.
Solution Approach 2:
The system uses feedback from stationary sensor measurements at upstream and downstream locations to calibrate and validate the GPS-based traffic condition estimates. The measured traffic volumes from sensors provide ground truth data that helps correct and refine the GPS-derived measurements, improving overall measurement precision.
3Area of stationary object
If traffic congestion estimation is performed in sensor-uninstalled segments using GPS data alone, then measurement coverage is improved, but identification precision of congestion boundaries deteriorates
Solution Approach 1:
The system performs preliminary traffic volume measurement and correction coefficient calculation at stationary sensor locations before extending the estimation to sensor-uninstalled segments. By establishing baseline measurements and correction factors in monitored areas first, the system prepares the necessary reference data to improve the precision of boundary identification when estimating congestion in unmonitored areas.
Solution Approach 2:
The patent divides the road network into monitored segments (with stationary sensors) and unmonitored segments (relying on GPS data). By segmenting the measurement approach according to data availability, the system applies the most appropriate measurement method to each segment, using stationary sensor data where available and GPS-based estimation where sensors are absent, thereby optimizing overall measurement precision.
Data Source
AI summary
A traffic congestion estimating device includes: a traffic congestion starting point and end point identifying unit that identifies a traffic congestion end point and a traffic congestion starting point for defining a traffic congestion area, based on position information detected for each vehicle; an inflow/outflow estimation unit that estimates an inflow to the traffic congestion end point at every time period, as well as, an outflow from the traffic congestion starting point at that time period, based on a traffic volume that was measured by a sensor on the upstream side of the traffic congestion end point, a traffic volume that was measured by a sensor on the downstream side of the traffic congestion starting point, and the position information; and a traffic congestion area updating unit that updates the traffic congestion end point, based on the inflow, and updates the traffic congestion starting point, based on the outflow.