Edge Computing Traffic Control via Mobile Crowdsourcing
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Solution Overview
Problem
Current urban traffic control systems face challenges in generating real-time optimal signal timing plans and route guidance due to the need for extensive vehicle detectors, high computational loads, and inefficiencies in route planning tools, which result in traffic congestion and increased operational costs.
Innovation Solution
A bidirectional interactive traffic-control management system with improved operational architecture through edge computation, where the traffic network information and urban traffic control subsystems are centralized, and the road-users' route guidance subsystem is decentralized on terminal devices. This system divides the urban traffic network into intersection groups and uses real-time travel information to generate optimal signal timing plans and route guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle detectors are installed at all intersections to obtain real-time traffic flow information, then the accuracy of traffic control is improved, but the cost of installation and maintenance increases significantly
Solution Approach 1:
The patent introduces mobile devices (phones, tablets) as intermediary carriers to collect traffic flow information. Instead of installing detectors at every intersection, the system uses mobile devices carried by road users to gather traffic data, thereby reducing installation costs while maintaining information accuracy through crowdsourced data collection
Solution Approach 2:
The system enables road users to participate in data collection themselves through their mobile devices. The mobile application collects traffic flow information from users' own devices, turning ordinary road users into active participants in the traffic monitoring system, which eliminates the need for expensive infrastructure installation
2Speed
If route planning tools perform simple route calculation without signal timing information, then the calculation speed is improved, but the accuracy of route guidance deteriorates
Solution Approach 1:
The patent segments the route calculation process into two parts: (1) rapid preliminary route calculation using standard navigation algorithms, and (2) precise signal timing adjustment using real-time traffic flow data from mobile devices. This segmentation allows the system to achieve both speed and accuracy by performing detailed calculations only for critical segments of the route
3Adaptability or versatility
If the central server performs real-time optimal signal timing calculation for all intersections, then the comprehensiveness of traffic control is improved, but the computing load increases excessively
Solution Approach 1:
The patent divides the traffic control system into a central server for overall coordination and mobile devices for local real-time calculations. Each mobile device independently calculates optimal signal timing for its location based on real-time traffic data, eliminating the need for the central server to process every intersection simultaneously and dramatically reducing computing power requirements
4Measurement precision
If the system collects real-time travel information from all road users, then the accuracy of route guidance is improved, but the data processing complexity increases
Solution Approach 1:
The patent applies local quality processing by having each mobile device process and filter its own traffic data locally before transmitting to the central server. Each device only processes information relevant to its location and route, reducing the overall data processing burden while maintaining high accuracy through localized data quality control
Data Source
AI summary
This invention provides a bidirectional interactive traffic-control management system with improved operational architecture through edge computation. The system comprises a server including a road and traffic network information subsystem, and an urban traffic control subsystem; and a plurality of terminal devices. For the purpose of edge computing, the plurality of terminal devices, located respectively at a road user end or a roadway end, is independently provided from a central server. The urban traffic control subsystem further includes an intersection grouping module for grouping adjacent intersections according to a correlation nature of their approach arrival-flow patterns, wherein the intersections in the digital urban traffic-control road network are divided into signal groups including isolated intersection groups, arterial intersection groups, and network intersection groups according to the correlation nature of their approach arrival-flow patterns, either high or low, between adjacent intersections in the urban traffic control network.