The analysis of catchment areas of metro stations using trajectory data generated by dockless shared bikes

2019 ◽  
Vol 49 ◽  
pp. 101598 ◽  
Author(s):  
Diao Lin ◽  
Yongping Zhang ◽  
Ruoxin Zhu ◽  
Liqiu Meng
Keyword(s):  
Trajectory Data ◽  
Catchment Areas

scholarly journals Evaluating the Accessibility of Healthcare Facilities Using an Integrated Catchment Area Approach

2018 ◽  
Vol 15 (9) ◽  
pp. 2051 ◽  
Author(s):  
Xiaofang Pan ◽  
Mei-Po Kwan ◽  
Lin Yang ◽  
Shunping Zhou ◽  
Zejun Zuo ◽  
...  
Keyword(s):  
Travel Behavior ◽  
Catchment Area ◽  
Healthcare Facility ◽  
Trajectory Data ◽  
Healthcare Facilities ◽  
Utilization Of Services ◽  
The Core ◽  
Model Based ◽  
Catchment Areas ◽  
Gps Trajectory Data

Accessibility is a major method for evaluating the distribution of service facilities and identifying areas in shortage of service. Traditional accessibility methods, however, are largely model-based and do not consider the actual utilization of services, which may lead to results that are different from those obtained when people’s actual behaviors are taken into account. Based on taxi GPS trajectory data, this paper proposed a novel integrated catchment area (ICA) that integrates actual human travel behavior to evaluate the accessibility to healthcare facilities in Shenzhen, China, using the enhanced two-step floating catchment area (E2SFCA) method. This method is called the E2SFCA-ICA method. First, access probability is proposed to depict the probability of visiting a healthcare facility. Then, integrated access probability (IAP), which integrates model-based access probability (MAP) and data-based access probability (DAP), is presented. Under the constraint of IAP, ICA is generated and divided into distinct subzones. Finally, the ICA and subzones are incorporated into the E2SFCA method to evaluate the accessibility of the top-tier hospitals in Shenzhen, China. The results show that the ICA not only reduces the differences between model-based catchment areas and data-based catchment areas, but also distinguishes the core catchment area, stable catchment area, uncertain catchment area and remote catchment area of healthcare facilities. The study also found that the accessibility of Shenzhen’s top-tier hospitals obtained with traditional catchment areas tends to be overestimated and more unequally distributed in space when compared to the accessibility obtained with integrated catchment areas.

Incorporating Low-Stress Bicycling Connectivity into Expanded Transit Service Coverage

2021 ◽  
pp. 036119812199895
Author(s):  
Ting Zuo ◽  
Heng Wei ◽  
Na Chen
Keyword(s):  
Low Income ◽  
Network Connectivity ◽  
Potential Effect ◽  
Trajectory Data ◽  
Coverage Area ◽  
Transit Service ◽  
Catchment Areas ◽  
Transit Services ◽  
Service Coverage ◽  
Hamilton County

The speed advantage in bicycling over walking is believed to ease first-and-last mile (F&LM) travel and expand transit service coverage. To quantitatively investigate the potential effect of using bicycle as a F&LM connector, the paper measures and compares the impacts of walking and bicycling F&LM access on transit service coverage. In the estimation of transit service coverage, F&LM travel decay functions representing the attractiveness of public transit that declines with increasing walking/biking time to access transit facilities and the spatial boundaries of transit catchment areas are developed using GPS trajectory data collected from the latest Cincinnati Household Travel Survey in Hamilton County, Ohio. Level of traffic stress is used to evaluate the bicycle suitability of streets and bike network connectivity. Based on the F&LM distance decay functions and low-stress bike network connectivity, the transit service coverage area as well as the transit-served population and employment in Hamilton County, Ohio, are estimated. Results show that more population can reach transit services and therefore employment by bicycling than walking. Meanwhile, disadvantaged groups, that is, low-income and zero-car population, can be better served by transit if using bicycle as the F&LM connector. In addition, low-stress bicycling connectivity is a significant factor determining the bicycle-transit service coverage, and a well-connected low-stress bike network with quality bikeways is crucial to guaranteeing that. These findings can be used as references to assist planners in their decision-making process to achieve better mobility and accessibility.

Path Choice Behavior Analysis Based on Vehicle Trajectory Data

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Zuyao Zhang ◽  
Li Tang ◽  
Yifeng Wang ◽  
Xuejun Zhang
Keyword(s):  
Behavior Analysis ◽  
Choice Behavior ◽  
Trajectory Data ◽  
Vehicle Trajectory

A Heading Maintaining Oriented Compression Algorithm for GPS Trajectory Data

Informatica ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 33-52 ◽  
Author(s):  
Pengfei HAO ◽  
Chunlong YAO ◽  
Qingbin MENG ◽  
Xiaoqiang YU ◽  
Xu LI
Keyword(s):  
Compression Algorithm ◽  
Trajectory Data ◽  
Gps Trajectory Data ◽  
Gps Trajectory

1673-P: The Neighborhood Deprivation Index and Provider Geocoding Identify Critical Catchment Areas for Diabetes Outreach Programs

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1673-P
Author(s):  
ASHBY F. WALKER ◽  
HUI HU ◽  
NICOLAS CUTTRISS ◽  
MICHAEL J. HALLER ◽  
C. JASON WANG ◽  
...  
Keyword(s):  
Deprivation Index ◽  
Neighborhood Deprivation ◽  
Outreach Programs ◽  
Catchment Areas

ESTIMASI HASIL AIR DARI DAERAH TANGKAPAN AIR DANAU RAWA PENING DENGAN MENGGUNAKAN MODEL INVEST

2017 ◽  
Vol 19 (2) ◽  
pp. 157
Author(s):  
Nunung Puji Nugroho
Keyword(s):  
Spatial Distribution ◽  
Ecosystem Services ◽  
Water Resources ◽  
High Water ◽  
Water Yield ◽  
Accurate Information ◽  
Yield Model ◽  
Catchment Areas ◽  
Integrated Valuation ◽  
Valuation Of Ecosystem Services

<p class="JudulABSInd"><strong>ABSTRAK</strong></p><p class="abstrak">Informasi hasil air dari suatu ekosistem sangat penting dalam pengelolaan sumber daya air. Dalam perencanaan kegiatan konservasi sumber daya air, informasi sebaran spasial hasil air diperlukan untuk menentukan prioritas wilayah terkait dengan alokasi anggaran. Hasil air dari suatu ekosistem atau daerah aliran sungai (DAS) dapat diestimasi dengan menggunakan model hidrologi. Penelitian ini bertujuan untuk mendapatkan informasi tentang hasil air, baik besaran maupun sebaran spasialnya, dari daerah tangkapan air (DTA) Danau Rawa Pening. Hasil air dari lokasi penelitian dihitung dengan menggunakan model hasil air pada InVEST (<em>the Integrated Valuation of Ecosystem Services and Tradeoffs</em>), yang didasarkan pada pendekatan neraca air. Hasil perhitungan menunjukkan bahwa volume hasil air di DTA Danau Rawa Pening pada tahun 2015 adalah sekitar 337 juta m<sup>3</sup>. SubDAS Galeh, sebagai subDAS terluas, merupakan penghasil air terbesar (72,4 juta m<sup>3</sup>) diikuti oleh subDAS Sraten (66,8 juta m<sup>3</sup>) dan Parat (62,4 juta m<sup>3</sup>). Secara spasial, hasil air di lokasi kajian mempunyai nilai antara 0 hingga 29.634,19 m<sup>3</sup>/ha. Wilayah hulu dan tengah subDAS Sraten secara umum mempunyai hasil air yang lebih tinggi, sedangkan wilayah danau dan sekitarnya serta hulu subDAS Galeh mempunyai hasil air yang lebih rendah dibandingkan dengan wilayah lainnya. Wilayah dengan hasil air tinggi dapat diprioritaskan dalam kegiatan konservasi sumber daya air untuk mendukung pasokan air ke Danau Rawa Pening.</p><p><strong><em>Kata kunci</em></strong><em>: hasil air, daerah tangkapan air, model InVEST, Danau Rawa Pening</em><em></em></p><p class="judulABS"><strong>ABSTRACT</strong></p><p class="Abstrakeng">Accurate information on water yield from an ecosystem is very important in the management of water resources. In the planning of water resources conservation activities, the information on the spatial distribution of water yield is needed to determine regional priorities related to budget allocations. The water yield from an ecosystem or watershed can be estimated using a hydrological model. This study aimed to obtain information about the water yield, both the magnitude and their spatial distribution, from the catchment areas of Lake Rawa Pening. The water yield from the study area was calculated using the water yield model in InVEST (the Integrated Valuation of Ecosystem Services and Tradeoffs), which based on the water balance approach. The results indicated that the volume of water yield in Lake Rawa Pening for 2015 is approximately 337 million m<sup>3</sup>. Galeh subwatershed, as the largest subwatershed, is the largest water producer (72.4 million m<sup>3</sup>), followed by Sraten subwatershed (66.8 million m<sup>3</sup>) and Parat subwatershed (62.4 million m<sup>3</sup>). Spatially, the water yield at the study site has a value between 0 to 29,634.19 m<sup>3</sup>/ha. Upstream and middle areas of Sraten subwatershed generally have higher water yield, while the lake and its surrounding areas as well as the upstream of Galeh subwatershed have lower water yield compared to other regions. The regions with high water yield can be prioritized in water resource conservation activities to support the supply of water to Lake Rawa Pening.</p><p><strong><em>Keywords</em></strong><em>: water yield, catchment areas, InVEST model, Lake Rawa Pening</em><em></em></p>

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

Trajectory data generalization based on local multi-hierarchy grid

2013 ◽  
Vol 33 (6) ◽  
pp. 1604-1607
Author(s):  
Guang YANG ◽  
Lei ZHANG ◽  
Fan LI
Keyword(s):  
Trajectory Data ◽  
Data Generalization
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