Multisource Data-Driven Modeling Method for Estimation of Intercity Trip Distribution
Traditional intercity trip distribution modeling methods are merely derived from household travel survey due to its limitation to partial or inaccurate information. With the development of information construction, reliable historical data can be easily collected from different sources, such as sens...
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2018-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2018/8948676 |
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doaj-10392a1cf54840e38162e11415bedff92020-11-24T21:36:52ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/89486768948676Multisource Data-Driven Modeling Method for Estimation of Intercity Trip DistributionYilin Li0Haiquan Wang1Jiejie Zhao2Bowen Du3College of Software, Beihang University, Beijing 100044, ChinaCollege of Software, Beihang University, Beijing 100044, ChinaState Key Laboratory of Software Development Environment, Beihang University, Beijing 100044, ChinaState Key Laboratory of Software Development Environment, Beihang University, Beijing 100044, ChinaTraditional intercity trip distribution modeling methods are merely derived from household travel survey due to its limitation to partial or inaccurate information. With the development of information construction, reliable historical data can be easily collected from different sources, such as sensor and statistical data. In this study, a data-driven method based on Poisson distribution theory is proposed to estimate intercity trip distribution using sensor data and various city features. A Poisson model, which reveals the deep correlation between city feature variables and trip distribution, is initially formulated. The L1-norm approach and the coordinate descent algorithm are then adopted in selecting related features and estimating model parameters, respectively, to reduce the complexity of the model. Finally, a k-means clustering method is used to analyze the latent correlation between city features and improve the availability of the model. The methodology is tested on a realistic dataset containing the highway trips of 17 cities in Shandong Province, China. The city feature variables have 66 dimensions, including economic index and population indicator. In comparison with traditional gravity model, which regards population as the most important factor affecting city attraction, our result shows that one of the core positive factors is the economic feature, such as gross regional domestic product. Moreover, the dimension of city features in the developed model decreases from 66 to 13 dimensions. The model developed in this study performs well in replicating the observed intercity origin-destination matrix.http://dx.doi.org/10.1155/2018/8948676 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yilin Li Haiquan Wang Jiejie Zhao Bowen Du |
spellingShingle |
Yilin Li Haiquan Wang Jiejie Zhao Bowen Du Multisource Data-Driven Modeling Method for Estimation of Intercity Trip Distribution Mathematical Problems in Engineering |
author_facet |
Yilin Li Haiquan Wang Jiejie Zhao Bowen Du |
author_sort |
Yilin Li |
title |
Multisource Data-Driven Modeling Method for Estimation of Intercity Trip Distribution |
title_short |
Multisource Data-Driven Modeling Method for Estimation of Intercity Trip Distribution |
title_full |
Multisource Data-Driven Modeling Method for Estimation of Intercity Trip Distribution |
title_fullStr |
Multisource Data-Driven Modeling Method for Estimation of Intercity Trip Distribution |
title_full_unstemmed |
Multisource Data-Driven Modeling Method for Estimation of Intercity Trip Distribution |
title_sort |
multisource data-driven modeling method for estimation of intercity trip distribution |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2018-01-01 |
description |
Traditional intercity trip distribution modeling methods are merely derived from household travel survey due to its limitation to partial or inaccurate information. With the development of information construction, reliable historical data can be easily collected from different sources, such as sensor and statistical data. In this study, a data-driven method based on Poisson distribution theory is proposed to estimate intercity trip distribution using sensor data and various city features. A Poisson model, which reveals the deep correlation between city feature variables and trip distribution, is initially formulated. The L1-norm approach and the coordinate descent algorithm are then adopted in selecting related features and estimating model parameters, respectively, to reduce the complexity of the model. Finally, a k-means clustering method is used to analyze the latent correlation between city features and improve the availability of the model. The methodology is tested on a realistic dataset containing the highway trips of 17 cities in Shandong Province, China. The city feature variables have 66 dimensions, including economic index and population indicator. In comparison with traditional gravity model, which regards population as the most important factor affecting city attraction, our result shows that one of the core positive factors is the economic feature, such as gross regional domestic product. Moreover, the dimension of city features in the developed model decreases from 66 to 13 dimensions. The model developed in this study performs well in replicating the observed intercity origin-destination matrix. |
url |
http://dx.doi.org/10.1155/2018/8948676 |
work_keys_str_mv |
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