Traffic Prediction Model for Route Overlap Avoidance
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Solution Overview
Problem
Existing trip planning systems fail to provide efficient and accurate route planning for individual drivers due to reliance on outdated data and inability to predict future traffic congestion, leading to increased travel times and resource waste in congested urban areas.
Innovation Solution
A computer-implemented method for trip planning that aggregates traffic demand data to build a traffic prediction model, allowing for real-time route optimization by avoiding route and travel time overlaps with other planned trips, and updates routes based on actual departure times and live traffic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing trip planning systems stream all traffic data across the entire area along the route, then users receive comprehensive traffic information, but application performance and usability deteriorate due to transmitting unnecessary data
Solution Approach 1:
The system extracts and transmits only the essential traffic data relevant to the user's specific route and time window, filtering out unnecessary information from the broader traffic network. This selective data extraction maintains information completeness for the user's purpose while significantly improving application performance by reducing data transmission volume.
Solution Approach 2:
The system applies local quality by customizing the traffic data content based on the user's specific route geometry, departure time, and destination. Instead of uniformly streaming all traffic data, the system tailors the data stream to match the user's actual travel needs, improving both performance and information relevance.
2Loss of time
If existing systems use live traffic data that is 5-10 minutes old, then the system can provide timely updates, but the data accuracy deteriorates because traffic conditions change rapidly
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical traffic data and patterns in advance, enabling it to predict future traffic conditions rather than merely reporting past conditions. This allows the system to provide accurate forecasts for the user's departure time even when real-time data is not immediately available.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor actual traffic conditions and compare them against predictions, adjusting future predictions based on observed deviations. This feedback loop improves measurement precision by correcting inaccuracies as they occur, ensuring increasingly accurate predictions as the system learns from actual traffic behavior.
3Reliability
If existing trip planning systems provide route suggestions based on historical data, then they can identify common routes, but they cannot predict future congestions accurately
Solution Approach 1:
The system transitions from static route suggestions based on historical averages to dynamic predictions that account for time-varying traffic patterns, events, and conditions. The route planning becomes adaptive, adjusting recommendations based on predicted future congestion patterns rather than merely repeating historical routes, thereby improving both reliability and future prediction capability.
Data Source
AI summary
A method for providing trip planning and route update is provided. The method includes receiving traffic demand data including a plurality of planned trips, each including an origin location, a destination location, a planned route, a planned departure time, and an end time. The method further includes building a traffic prediction model by aggregating the traffic demand data, and generating a traffic prediction for a specified trip. Related apparatus, systems, techniques and articles are also described.