AitF: Collaborative Research: Modeling movement on transportation networks using uncertain data

AitF：协作研究：使用不确定数据对交通网络上的运动进行建模

基本信息

批准号：
1637576
负责人：
Carola Wenk
金额：
$ 31.77万
依托单位：
Tulane University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637576&HistoricalAwards=false
关键词：
AitF Collaborative Research Modeling movement

项目摘要

In the current data-centered era, there are many highly diverse data sources that provide information about movement on transportation networks. Examples include GPS trajectories, social media data, and traffic flow measurements. Much of this movement data is challenging to utilize due to the inherent uncertainty caused by infrequent sampling and sparse coverage. The goal of this project is to develop a unified framework that uses as many available data sources as possible to extract meaningful traffic and movement information automatically from the data. Probabilistic network movement models will be developed that capture movement probabilities and traffic volume on a network over time. The results will impact a range of applications that rely on capturing population movements, such as urban planning, geomarketing, traffic management, and emergency management. Educational activities will be integrated throughout the project. Students will be closely involved in research and practical implementations, and will be trained in spatio-temporal data management, algorithms development, and (trajectory) data analysis. The combination of such skills is increasingly important in spatial data science. Topics involved in this project will enrich the course material and curriculum development at both institutions. The objective of this project is to create a unified framework for aggregating and analyzing diverse and uncertain movement data on road networks, with the aim to provide tools for querying and predicting traffic volume and movement. Probabilistic movement models on the network will be developed that can handle heterogeneous data sources, including GPS trajectories, geo-tagged social media data, bike-share data, public transport data, and traffic volume data. The diversity and spatio-temporal uncertainty of this data will be addressed with a Bayesian traffic pattern learning approach that first trains the movement models with the more certain data, which in turn will be used to fill gaps in the more uncertain data. The project will advance the state-of-the-art in theoretical communities (computational geometry, data mining) as well as in applied communities (spatial databases, location science). The results of the research will available on the project website (movementanalytics.org), and will be disseminated in prestigious venues through presentations and demonstrations at conferences, and through publications in journals.

在当前以数据为中心的时代，有许多高度多样化的数据源可提供有关运输网络运动的信息。示例包括GPS轨迹，社交媒体数据和交通流量测量值。由于不经常采样和稀疏覆盖范围引起的固有不确定性，因此该运动数据的大部分是具有挑战性的。该项目的目的是开发一个统一的框架，该框架使用尽可能多的可用数据源来自动从数据中提取有意义的流量和移动信息。将开发概率网络运动模型，以捕获网络上的移动概率和流量。结果将影响一系列依赖捕获人口运动的应用程序，例如城市规划，地域营销，交通管理和应急管理。教育活动将在整个项目中整合。学生将密切参与研究和实际实施，并将接受时空数据管理，算法开发和（轨迹）数据分析的培训。在空间数据科学中，这种技能的结合越来越重要。该项目涉及的主题将丰富两个机构的课程材料和课程开发。该项目的目的是创建一个统一的框架，用于在道路网络上汇总和分析多样化和不确定的运动数据，以提供查询和预测交通量和移动的工具。将开发网络上的概率运动模型，该模型可以处理异质数据源，包括GPS轨迹，地理标记的社交媒体数据，自行车共享数据，公共交通数据和流量量数据。该数据的多样性和时空不确定性将通过贝叶斯交通模式学习方法来解决，该方法首先使用更多的数据训练运动模型，而这些数据又将用于填补更不确定的数据中的空白。该项目将推进理论社区（计算几何，数据挖掘）以及应用社区（空间数据库，位置科学）中的最先进。该研究的结果将在项目网站（Movemanalalytics.org）上获得，并通过会议上的演讲和示威以及通过期刊的出版物在享有声望的场地中传播。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Global Curve Simplification

全局曲线简化

DOI：
10.4230/lipics.esa.2019.67
发表时间：
2019
期刊：
27th Annual European Symposium on Algorithms (ESA 2019
影响因子：
0
作者：
van de Kerkhof, Mees;Kostitsyna, Irina;Löffler, Maarten;Mirzanezhad, Majid;Wenk, Carola
通讯作者：
Wenk, Carola

Simplification of Indoor Space Footprints

室内空间足迹的简化

DOI：
发表时间：
2019
期刊：
1st ACM SIGSPATIAL International Workshop on Spatial Gems (SpatialGems 2019
影响因子：
0
作者：
Kim, Joon-Seok;Wenk, Carola
通讯作者：
Wenk, Carola

A Unified Framework to Predict Movement

预测运动的统一框架

DOI：
10.1007/978-3-319-64367
发表时间：
2017
期刊：
International Symposium on Spatial and Temporal Databases
影响因子：
0
作者：
Gkountouna, Olga;Pfoser, Dieter;Wenk, Carola;Zuefle, Andreas
通讯作者：
Zuefle, Andreas

Graph Sampling for Map Comparison

DOI：
10.1145/3662733
发表时间：
2023-12
期刊：
ACM Transactions on Spatial Algorithms and Systems
影响因子：
1.9
作者：
J. Aguilar;K. Buchin;M. Buchin;Erfan Hosseini Sereshgi;Rodrigo I. Silveira;C. Wenk
通讯作者：
J. Aguilar;K. Buchin;M. Buchin;Erfan Hosseini Sereshgi;Rodrigo I. Silveira;C. Wenk

Shortest-Path Diversification through Network Penalization: A Washington DC Area Case Study

DOI：
10.1145/3357000.3366137
发表时间：
2019-11
期刊：
Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science
影响因子：
0
作者：
Danhong Cheng;Olga Gkountouna;Andreas Züfle;D. Pfoser;C. Wenk
通讯作者：
Danhong Cheng;Olga Gkountouna;Andreas Züfle;D. Pfoser;C. Wenk