gdut-rp/2020_ieee_attractrank

Our taxi statistic data is made publicly available for academic research usage.

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.