Identifying primary public transit corridors using multi-source big transit data

ABSTRACT

Effective public transit planning needs to address realistic travel demands, which can be illustrated by corridors across major residential areas and activity centers. It is vital to identify public transit corridors that contain the most significant transit travel demand patterns. We propose a two-stage approach to discover primary public transit corridors at high spatio-temporal resolutions using massive real-world smart card and bus trajectory data, which manifest rich transit demand patterns over space and time. The first stage was to reconstruct chained trips for individual passengers using multi-source massive public transit data. In the second stage, a shared-flow clustering algorithm was developed to identify public transit corridors based on reconstructed individual transit trips. The proposed approach was evaluated using transit data collected in Shenzhen, China. Experimental results demonstrated that the proposed approach is a practical tool for extracting time-varying corridors for many potential applications, such as transit planning and management.     

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.     

Rainfall interception of typical ecosystems in the Heihe River Basin: Based on high temporal resolution soil moisture data

Journal of Geographical Sciences - Rainfall interception is of great significance to the fully utilization of rainfall in water limited areas. Until now, studies on rainfall partitioning process of...     

人海关系地域系统视角下海洋本体的解构与研究重点

在揭示人海辩证关系与海洋社会属性的基础上,从人海关系地域系统的视角,解析了海洋的构成。第一海洋（自然海洋）是人海关系地域系统中自然存在而非人为构筑的海洋部分,其核心要素是地理位置、物质构成和空间形态。第二海洋（人工海洋）是人类海洋实践中所建造的设施及相应的物质、能量及信息流动,包括基地设施、人类主体之间的物质信息交流和人类与海洋之间的采集排放活动。第三海洋（关系海洋）是不同人类主体在海洋实践中形成的互动关系,核心内容是主体构成、内容组成以及表现形式。第四海洋（观念海洋）是人类对海洋及海洋实践活动的认知,核心是在处理人类与海洋、陆地与海洋和人类主体之间关系时所秉持的理念。现实中,自然海洋、实践性的人工海洋和关系海洋与构想性的观念海洋相互作用,形成了丰富多样的海洋世界。随着人类海洋实践的深化,关系海洋和观念海洋与自然海洋和人工海洋一并成为了研究与实践的重点。     

1983—2017年宁夏盐池县生态治理政策的类型与变化——基于政策工具视角

政策工具是政府实施政策的手段。结合盐池县1983-2017年出台的生态治理政策,运用内容分析法,从政策工具视角,对盐池县生态治理政策的类型与变化进行了分析,并深入探讨其原因。结果表明:强制型和混合型政策工具占主导地位,其中,规制、直接提供、信息与劝诫、补贴与奖励4种工具使用频率最高,自愿型工具的使用还很缺乏。政策工具的运用受到中央政策的显著影响。以后应当开发更多可利用的政策工具,并对不同政策工具进行优化组合。通过本研究可深入了解生态脆弱区地方政府生态政策的政策工具使用情况,为生态脆弱区生态保护的政策工具选择与优化组合提供参考。     

齐大山铁矿尾矿矿砂对环境污染的风洞实验

在开发矿产资源的同时又面临着环境资源的破坏,以及环境的污染问题,如在采矿和冶炼中产生的弃土和尾矿等废弃物,如处理不当在风力的作用下会产生对环境的污染.本文针对齐大山铁矿尾矿进行了有关方面的风洞吹砂实验;1、干尾矿砂(含水率为0.14%),与大于起砂风速的2次方呈正比关系;2、尾矿砂的输砂率随含水量的增加而减少非常明显.因此,给尾矿砂加入一定量的水分,使其保持不低于3-4%的水分,能有效的降低尾矿砂的吹扬量。     

On the Fréchet differentiability of the one-dimensional magnetotellurics problem

Summary. This paper proves that, subject to certain assumptions, the one-dimensional magnetotelluric response function and the associated electric fields are Fréchet differentiable with respect to the conductivity function. The flaw in the proof offered by an earlier author is explained.     

APPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNIQUES TO REMOTE SENSING*

Remote sensing is a geographic analysis tool capable of producing large quantities of data in the spectral, temporal and spatial domains. Techniques for automating the image analysis process would be advanced by the inclusion of artificial intelligence techniques in the design of image processing systems. The remote sensing application which show promise for successful implementation of artificial intelligence techniques are intelligent onboard processing, advanced database interrogation, and the automated analysis of multispectral imagery.