OD Traffic Volume Estimation Using Coarse-to-Fine Time Frame Segmentation
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Solution Overview
Problem
Existing methods for estimating origin-destination (OD) traffic volume require extensive processing time due to large matrix sizes and repeated simulations, especially when estimating OD traffic volume in fine time frames.
Innovation Solution
The proposed method involves performing initial traffic simulations in coarse time frames to determine OD traffic volume, then distributing this volume into finer time frames using predetermined ratios, reducing the number of repetitions needed to achieve an optimal solution by adjusting OD traffic volumes in subsequent simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traffic simulation is performed repeatedly for each fine time frame to estimate OD traffic volume, then measurement precision of OD traffic volume is improved, but processing time increases enormously
Solution Approach 1:
The time frame is segmented into multiple sub-time frames. The method performs traffic simulation for the entire time frame first, then distributes the simulated OD traffic volume to each sub-time frame based on predetermined ratios. This segmentation allows the system to achieve fine time frame resolution without performing repeated simulations for each sub-time frame, thereby reducing processing time while maintaining estimation accuracy.
Solution Approach 2:
The method performs preliminary traffic simulation for the entire time frame before distributing results to sub-time frames. By conducting the simulation once for the full time frame and then allocating results proportionally to sub-time frames, the system avoids performing multiple repeated simulations, thus reducing processing time while preserving measurement precision through the distribution algorithm.
2Productivity
If traffic simulation is performed for fine time frames with small time widths, then productivity of traffic analysis is improved, but device complexity increases due to large matrix sizes
Solution Approach 1:
The method segments the time frame into sub-time frames and performs simulation at the coarser time frame level, then distributes results to finer sub-time frames. This approach maintains productivity by providing fine time frame analysis capability while reducing device complexity by avoiding the need to handle large matrices for each fine time frame separately.
Solution Approach 2:
The method transitions from performing simulations in the fine time frame dimension to performing simulation in the coarser time frame dimension, then distributing results across fine time frames using predetermined ratios. This dimensional change reduces the complexity of matrix operations while maintaining the ability to analyze traffic at fine time frame resolution.
Data Source
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AI summary
An origin destination (OD) determination method includes: performing a first traffic simulation that estimates a traffic volume for each link included in a path from a first point to a second point, by using a first OD traffic volume that has departed from the first point and reached the second point in a first time frame, for each first time frame obtained by dividing a target period; determining an OD traffic volume for each first time frame, for a pair of the first point and the second point, by repeatedly performing the first traffic simulation while adjusting the first OD traffic volume, so as to reduce an error between the estimated traffic volume of the link and a traffic volume observed for the link; calculating a second OD traffic volume that has departed from the first point and reached the second point in a second time frame, for each second time frame obtained by dividing the first time frame, based on the determined OD traffic volume for each first time frame; performing a second traffic simulation that estimates a traffic volume for each link, for each second time frame, by using the calculated second OD traffic volume; and determining an OD traffic volume for each second time frame, for the pair of the first point and the second point, by repeatedly performing the second traffic simulation while adjusting the second OD traffic volume, so as to reduce an error between the estimated traffic volume of the link and a traffic volume observed for the link.