目录
目录Readme.md

2020 IEEE Transactions on Industrial Informatics

AttractRank: District Attraction Ranking Analysis Based on Taxi Big Data

Our taxi statistic data and codes are made publicly available for academic research usage.

source.csv

Data Introduction:

Period:2017.02.01–2017.03.31

Rows : 2306690

Position:Guangzhou, China

Data Structure:
Column Explanation
id Primary key
from_idx The ordinal of departure place’s
to_idx The ordinal of destination place’s
date_month month
date_day day
date_hour hour
route_weight The count of record in this hour
Data sample:
id from_idx to_idx date_month date_day date_hour route_weight
0 0 0 2 1 0 9
1 0 1 2 1 0 1
2 0 2 2 1 0 2
3 0 3 2 1 0 1
4 0 4 2 1 0 3
5 0 5 2 1 0 3
6 0 7 2 1 0 2
7 0 8 2 1 0 3
8 0 9 2 1 0 4
9 0 10 2 1 0 1
10 0 11 2 1 0 10

attractrank.py

This is the code of our attractrank algorithm.

Data_visualization_application_system

This is the open source code of our visualization application system.

distance_matrix.csv

This is the distance matrix required by our algorithm.

districts_details.csv

This is the detailed data about districts of the Guangzhou that we divided, including the latitude and longitude point of the center of each area, and the set of latitude and longitude points of the outer contour of each area, etc.

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AttractRank: District Attraction Ranking Analysis Based on Taxi Big Data

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