Search

[Display omitted] • A comparative study of urban expansion and WESs in two megacities was conducted. • In Chengdu, urban expansion had an edge-based pattern that encompassed city-wide. • In Wuhan, suburban expansion is growing rapidly due to leapfrogging development. • Urban expansion has larger negative effects on WY and WP in Wuhan's suburbs. • Ecological land in the megacities' center suffered from indirect loss of WESs. The megacity expansion that continues to occur in developing countries is an unavoidable trend at this stage, yet there exists a dearth of knowledge regarding the comprehensive impacts of urban expansion on water-related ecosystem services (WESs) within these areas. Taking Chengdu and Wuhan, two megacities in the upper and middle Yangtze River Economic Belt in China as the study area, this study first compare the magnitude, rate, spatial divergence, and the mode of urban expansion; then evaluate its impact on three typical types of WESs, i.e., water yield (WY), water purification (WP), and habitat quality (HQ) using spatial econometric models and bivariate Moran's I. The research findings show that Chengdu's suburbs tend to experience edge-based expansion along rivers, while Wuhan's city center also spreads outward in edge-based from the river junctions of the Yangtze and Han rivers. Urban expansion negatively impacts all three types of WESs, and its effect in Chengdu is greater than in Wuhan. WY in the suburbs of Wuhan is severely impacted by urban expansion, while WP in the suburbs of Chengdu is as well. HQ in Chengdu's city center suffers the largest negative impact. The study also reveals a spatial spillover effect of urban expansion, with extensive negative externalities on all WESs in the city centers of both cities and positive externalities on WY and WP in the southwestern suburbs of Chengdu. Based on these results, we suggest that megacities in developing countries should focus on protecting HQ in the city center and limiting cross-regional edge-based expansion in suburbs. Attention should be paid to the water-ecological protection of the suburb rivers. City managers should consider ecological zoning geared toward the conservation of WESs to address the adverse impacts of expansion. [ABSTRACT FROM AUTHOR]

Online ride-hailing plays an important role in modern urban transportation systems, and accurate short-term passenger demand prediction contributes to improving ride-hailing services. Many existing studies have made great achievements in zone-based demand prediction. However, origin–destination (OD)-based demand prediction has attracted little attention, even though it provides abundant information and is of high practical importance, e.g., it facilitates the routing and matching of ride-hailing services. Considering the importance of both types of demands, a conditional generative adversarial network (CGAN) with Wasserstein divergence (CWGAN-div) is proposed to jointly predict zone-based and OD-based demands. Residual blocks are utilized to capture internal spatiotemporal features, which benefit the training and prediction processes of the CWGAN-div model. Conditional information is also incorporated to characterize the external dependencies of the demands. Numerical experiments are performed by using GPS trajectory data from Didi Chuxing, Chengdu, China. The results show that the proposed CWGAN-div model yields good joint prediction performance and outperforms both classic models (historical average (HA) and convolutional neural network (CNN) models) and other prevailing GAN models (i.e., GAN, CGAN, Wasserstein GAN with a gradient penalty (WGAN-GP), and GAN with Wasserstein divergence (WGAN-div)). The proposed CWGAN-div model displays promise for the joint prediction of zone-based and OD-based passenger demands. • This study attempts to predict the OD-based and zone-based demand considering network-wide spatiotemporal correlations. • By combining CGANs and WGAN-div, the CWGAN-div model can provide qualified predictions with desirable training stability. • ResNets improve the efficiency of exploiting the spatiotemporal relationships and the convergence of the model. [ABSTRACT FROM AUTHOR]

• 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. 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. [ABSTRACT FROM AUTHOR]