A commercial traffic site selection optimization method based on multi-source data fusion

By integrating multi-source data to generate comprehensive feature representations and establishing a multi-objective optimization model, the problem of single data source in traditional commercial traffic location selection methods is solved, and a comprehensive characterization of candidate points and multi-objective trade-offs are achieved, providing a more reasonable location selection scheme.

CN121235218BActive Publication Date: 2026-06-23CHENGKE ERA (BEIJING) NETWORK TECH CO LTD
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Patent Information

Application Number
CN202511737345.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-11-25
Publication Date
2026-06-23
Estimated Expiration
2045-11-25

AI Technical Summary

Technical Problem

Traditional commercial traffic location optimization methods often rely on a single data source, neglecting the balance between transportation convenience, consumption potential, and competitive pressure, leading to biased location results.

Method used

By integrating spatial geographic data, spatiotemporal traffic data, and consumer behavior data, an adaptive weight allocation mechanism is used to generate a comprehensive feature representation. A multi-objective optimization model is established by combining causal inference methods, outputting the comprehensive score and ranking results of candidate points, and outputting the site selection scheme through a visualization platform.

Benefits of technology

It achieves a more comprehensive characterization of candidate sites, improves the accuracy and robustness of prediction and optimization, avoids the one-sidedness of traditional single-objective optimization, and provides a more reasonable business site selection solution.

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Abstract

The application discloses a kind of commercial traffic site selection optimization method based on multi-source data fusion, it is related to big data processing and multi-source data fusion technical field, including the following steps: obtaining the multi-source data of the region to be measured, constructs multi-source feature, and through adaptive weight distribution mechanism multi-source feature cross-modal fusion, generate comprehensive feature representation;Based on comprehensive feature representation, through causal inference method, output candidate point in different scene passenger flow trend and causal contribution;Passenger flow trend and causal contribution are input, a multi-objective optimization model is established, and is solved, and the comprehensive score and ranking result of candidate point are output;According to comprehensive score and ranking result, combine causal contribution result, construct interpretive index matrix, and output site selection scheme through visual platform;The application is fused spatial geographic data, space-time flow data and consumer behavior data, solve the problem that data source is single in prior art, candidate point is not comprehensive.
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