Traffic analysis zone-based mobility patterns

Citation Author(s):
zheng
zhang
Beijing University of Technology
Submitted by:
zheng zhang
Last updated:
Thu, 06/06/2019 - 03:05
DOI:
10.21227/a8hb-2n59
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Abstract 

The rawdata.csv profile indicates the traffic analysis based mobility patterns. we extract human trips from Call Records Detail data. Combining traffic analysis zone dataset, we map each trip record to the zones with the same origin zones and destination zones. After  this, we can obtain this dataset. This dataset stores the hourly number of departure and arrival trips in each traffic analysis zone.

The POI-importance.csv profile indicates the term frequency-inverse doument frequency(TF-IDF) of each category of poi the in each traffic analysis zone.

Instructions: 

The rawdata.csv profile indicates the traffic analysis based mobility patterns. 

1. We extract human trips(origin, destination and timestamps for each trip) from Call Records Detail data.

2. Combining traffic analysis zone layer, the longitude and latitude of the origin and destination points, 

we map each trip record to the zones. 

3. After  this, we can integrate the trips with the same origin and destination. Finally, we can obtain this dataset. 

Fields in the dataset.

This dataset stores the hourly number of departure and arrival trips in each traffic analysis zone.

Row indicates the traffic analysis zone id and column indicates the time windows.

The POI-importance.csv profile indicates the term frequency-inverse doument frequency(TF-IDF) of each category of poi the in each traffic analysis zone.

Comments

thank you

Submitted by Sai Sravan Kantem on Fri, 06/26/2020 - 09:08

Thank

Submitted by Niranjan Khakurel on Thu, 02/23/2023 - 21:19