Traffic Sensor Utility Scoring for Bottleneck Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traffic flow monitoring systems rely heavily on sensor data, but the accuracy and utility of this data can vary significantly based on location, context, and type of sensor, leading to inefficiencies in bottleneck detection and route planning, especially in situations with limited connectivity or processing capabilities.
Innovation Solution
A system that assigns utility values to sensor data based on its relevance in identifying bottlenecks and congestion, allowing for prioritization and filtration of data for transmission and analysis, and uses these values to optimize sensor placement and maintenance within traffic flow systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large sets of sensors are used to monitor traffic flow, then the accuracy of traffic monitoring and prediction is improved, but the system complexity and cost increase
Solution Approach 1:
The patent extracts and identifies only the critical sensor locations that provide the most valuable data for bottleneck detection and traffic prediction. By using utility values to rank sensor importance, the system selectively processes data from key locations rather than treating all sensor data equally, thereby maintaining accuracy while reducing the effective complexity of the sensor network.
Solution Approach 2:
The patent applies local quality by assigning different utility values to different sensor locations based on their specific importance for detecting bottlenecks and predicting traffic flow. Sensors at critical locations (such as those near known bottleneck areas or high-traffic intersections) are given higher utility values, allowing the system to focus computational resources on the most informative local measurements rather than uniformly processing all sensor data.
2Reliability
If sensors are positioned at all potential bottleneck locations, then bottleneck detection accuracy is improved, but the cost and complexity of the sensor deployment increases
Solution Approach 1:
The patent performs preliminary analysis to identify which sensor locations will be most useful for bottleneck detection before deploying or activating sensors. By pre-calculating utility values based on historical traffic patterns, road topology, and known bottleneck locations, the system determines the optimal subset of sensor positions in advance, avoiding the need to deploy sensors at all potential locations.
Solution Approach 2:
The patent uses historical traffic data and simulated bottleneck scenarios to create a model of which sensor positions would be most effective. This virtual modeling allows the system to identify critical sensor locations without physically testing every possible sensor placement, thereby reducing deployment complexity while maintaining detection reliability.
3Loss of information
If all sensor data is transmitted and processed, then the completeness of traffic information is improved, but the processing load and energy consumption increase
Solution Approach 1:
The patent applies partial action by processing only the portion of sensor data that is most valuable for traffic monitoring and prediction. Using utility values, the system selectively processes data from sensors with high utility scores while minimizing or skipping processing of data from low-utility sensors, thereby reducing energy consumption while maintaining the essential completeness of traffic information needed for accurate bottleneck detection and prediction.
Data Source
AI summary
Methods for decision making about sensor location/configuration for traffic sensing and routing are described. Construction of predictive models via machine learning that infer variance of road speeds, in general or for specific contexts (e.g., rush hours for a traffic system) occurs. The predictive models for road reliability are built from libraries of data about sensed variances and road segments. The datasets include information for road segments monitored by fixed sensors/moving probes, road segment properties, geometric relationships among road segments, and proximal resources. Road segments are labeled by the sensed variance seen in traffic speeds over similar contexts. A model is created that can apply estimates of the variance of the traffic speed for a segment, including non-sensed segments via generalization to non-sensed road segments. Methods are described for employing the predictive models of variance, along with demand and propagation models, to make decisions about configuration of sensors.