Route Planning Using Historical Traffic OD Graphs
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Solution Overview
Problem
Traditional navigation systems fail to account for dynamic traffic conditions, leading to inefficient route selection and increased travel times, causing driver frustration and productivity loss.
Innovation Solution
A system that analyzes historical traffic patterns in an origin-destination matrix to determine optimal routes by generating network graphs based on historical traffic values, determining desired routes, and using machine learning to predict efficient paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional navigation systems use static maps and predefined routes, then the system complexity is low and ease of operation is high, but the route optimization accuracy deteriorates and travel time increases
Solution Approach 1:
The system performs preliminary analysis of historical traffic data to generate pre-computed optimal routes stored in an OD matrix. This preliminary action enables the system to quickly retrieve and recommend optimized routes without complex real-time calculations, thus improving route optimization accuracy while maintaining relatively simple system complexity.
Solution Approach 2:
The system dynamically updates route recommendations by integrating real-time traffic conditions with historical data from the OD matrix. This dynamic approach allows the system to adapt to changing traffic patterns while leveraging pre-computed optimal paths, thereby improving route accuracy without proportionally increasing system complexity.
2Productivity
If navigation systems account for real-time traffic conditions at the start of trip, then the route efficiency improves, but the reliability deteriorates when traffic conditions change during the trip
Solution Approach 1:
The system continuously monitors real-time traffic conditions and compares them with historical patterns from the OD matrix. When deviations are detected, the system provides feedback to update route recommendations, ensuring both high route efficiency through real-time awareness and high reliability by validating against established historical patterns.
Solution Approach 2:
The system pre-computes multiple alternative optimal routes based on historical traffic patterns and stores them in the OD matrix. When real-time conditions change during a trip, the system can switch to pre-computed alternative routes, maintaining route efficiency and reliability without requiring complex real-time re-optimization.
3Loss of time
If drivers follow popular routes based on local knowledge or navigation recommendations, then the ease of operation is high, but the travel time increases due to traffic congestion on these popular routes
Solution Approach 1:
The system replaces manual route selection based on local knowledge with an automated intelligent system that analyzes historical traffic data and real-time conditions. This substitution reduces travel time by identifying optimal routes that avoid congestion, while maintaining ease of operation through automated route recommendation and turn-by-turn navigation instructions.
Solution Approach 2:
The system changes the selection criterion from popular routes (high traffic volume) to optimal routes (minimum travel time) by analyzing traffic flow parameters in the OD matrix. This parameter change enables the system to recommend routes that minimize travel time while still being easy to follow through clear navigation instructions.
Data Source
AI summary
Embodiments of the present disclosure disclose a system for determining an optimal route. The system obtains an origin-destination (OD) matrix comprising historical traffic values for predefined time slots for each route and generates network graphs based on the OD matrix. Each of the network graphs corresponds to one of the predefined time slots, and each of the network graphs comprise nodes, and edges having corresponding weight values. The system determines desired routes from the one or more routes based on the weight values, determines travel frequency data for each of the desired routes at least in the predefined time slots based on the network graphs; and determines a modal route based on the desired routes and the travel frequency data. Each of the desired routes is associated with one of the network graphs.