Digital Tour Route Planning for Historic Neighborhoods Driven by the Combination of BD and Intelligent Algorithms
Bibliographic record
Abstract
With the development of big data (BD) technology, tourism route planning of historical blocks relies on a large amount of real-time data.The existing research data sources are limited and dif icult to integrate, which cannot meet the personalized needs of tourists.This paper combined BD and intelligent algorithms to realize personalized tourism route planning of historical blocks.By collecting tourists' behavioral data, scenic spot spatial data and real-time traf ic information, the paper built tourist portraits and used the neural collaborative iltering algorithm to make personalized scenic spot recommendations.It used genetic algorithms (GAs) to optimize routes, taking into account factors such as tourists' interests, distances between scenic spots, and traf ic conditions.With the help of the real-time data streaming platform Apache Ka ka, the paper dynamically adjusted routes to deal with sudden traf ic or crowded attractions, thereby improving the tourist experience.The experimental results analyze the consumption preferences and behavioral characteristics of different tourists.Tourist 1002 spent 500 yuan on shopping, and high-end shopping malls and food courts were recommended for him.Tourist ID 1005 preferred "snacks and coffee" in terms of dining, and showed no interest in souvenir consumption.This tourist preferred to stay in leisure places for a longer time rather than a compact travel route.The neural coordination iltering algorithm + GA performed well in terms of total travel time of 4.2 hours, total walking distance of 7.8 kilometers, and traf ic congestion coef icient of 0.35, which was better than other algorithms, showing its signi icant advantages in digital tourism route planning in historical blocks.This method combines BD and intelligent algorithms to improve the tourist experience through personalized recommendations and route optimization, optimize the traf ic management of scenic spots, lexibly respond to emergencies, promote the intelligent and re ined management of historical district tourism, and provide innovative ideas for future tourism route planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".