A novel generative adversarial network for estimation of trip travel time distribution with trajectory data. (November 2019)

• Record Type: Journal Article
• Authors: Zhang, Kunpeng; Jia, Ning; Zheng, Liang; Liu, Zijian
• Is Part Of: Transportation research. Volume 108(2019)
• Page Start: 223
• Page End: 244
• Publication Date: 2019-11
• Subjects: Travel time distribution -- Generative adversarial networks -- Wasserstein distance -- Mutual information -- Spatiotemporal correlation; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2019.09.019
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: This study proposes a novel TTD estimation method based on GAN. The proposed T-InfoGAN shows a well estimation performance of trip TTDs. T-InfoGAN can capture network-wide spatiotemporal dependences of link travel times. DCWD can characterize traffic state transitions and group link pairs into various clusters. Abstract: Knowledge of trip travel times serves an important role in transportation management and control. Existing travel time estimation approaches generally cover empirical ones, statistical ones and hybrid ones. Despite strong tractability, the empirical approaches cannot sufficiently capture diverse travel time distributions (TTDs) and often encounter some issues (e.g., assumption of a predefined distribution, failure of significance tests). Statistical and hybrid methods possess better generalization in estimating heterogeneous TTDs, but fail to model the network-wide spatiotemporal correlations, which have been found useful in the TTD estimation. To address these drawbacks, this paper proposes a deep learning based Trip Information Maximizing Generative Adversarial Network (T-InfoGAN). In this method, the trip TTD is estimated by modeling the joint distribution of travel times of two successive links with the consideration of network-wide spatiotemporal correlations. Meanwhile, a dynamic clustering with Wasserstein distance (DCWD) algorithm is used to explore the traffic state transitions for link pairs and cluster the link pairs with similar TTDs into one group, which benefits the training and estimation processes of T-InfoGAN. Then, based on GPS trajectory data from Didi Chuxing in Chengdu city, China, numerical results show that the T-InfoGAN with DCWD can well estimate three mini trip TTDs with various features, and performs better than three other counterparts (i.e., Convolution method, MC-Grid method, and MC-GMMS method) in estimating the TTDs of two longer trips. In summary, this study is the first successful try to estimate trip TTDs within the framework of Generative Adversarial Networks (GANs), and the deep learning based T-InfoGAN is a promising approach to estimate heterogeneous trip TTDs with the better generalization and flexibility in the big data era.