A Geographically and Temporally Weighted Regression Model to Explore the Spatiotemporal Influence of Built Environment on Trip Distribution: A Case Study in Hangzhou-Reference-Cited by-同舟云学术

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A Geographically and Temporally Weighted Regression Model to Explore the Spatiotemporal Influence of Built Environment on Trip Distribution: A Case Study in Hangzhou

Published:2019-07-02 Issue: Volume: Page:
ISSN:
Container-title:CICTP 2019
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Author:

Zhou Yujia¹¹²,Shen Xinyi¹¹²,Jin Sheng¹¹²

Affiliation:

1. Institute of Intelligent Transportation Systems, Zhejiang Univ., B818 Anzhong Bldg., Zijingang Campus, Hangzhou 310058, China.

2. Institute of Intelligent Transportation Systems, Zhejiang Univ., B815 Anzhong Bldg., Zijingang Campus, Hangzhou 310058, China.

Publisher

American Society of Civil Engineers

Link

http://ascelibrary.org/doi/pdf/10.1061/9780784482292.407

Reference15 articles.

1. AMAP. http://Ibs.amap.com. Accessed July 3 2018.

2. Baidu Map Open Platform. http://Ibsyun.baidu.com. Accessed Jun 23 2018.

3. An improved model for trip distribution forecast;Deng M.;Journal of Transport Information & Safety.,2010

4. Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach;Ding C.;Accident Analysis and Prevention,2018

5. Google Maps APIs. https://developers.google.com/maps. Accessed Jun 28 2018.

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1. Exploring the Spatiotemporal Factors of Ridesplitting Demand Based on the Geographically and Temporally Weighted Regression;2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS);2021-12-10

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