Details
| Original language | English |
|---|---|
| Title of host publication | The Web Conference 2019 |
| Subtitle of host publication | Companion of the World Wide Web Conference, WWW 2019 |
| Editors | Ling Liu, Ryen White |
| Place of Publication | New York |
| Pages | 137-145 |
| Number of pages | 9 |
| ISBN (electronic) | 9781450366755 |
| Publication status | Published - 13 May 2019 |
| Event | 2019 World Wide Web Conference, WWW 2019 - San Francisco, United States Duration: 13 May 2019 → 17 May 2019 |
Abstract
With the increasing availability of mobility-related data, such as GPS-traces, Web queries and climate conditions, there is a growing demand to utilize this data to better understand and support urban mobility needs. However, data available from the individual actors, such as providers of information, navigation and transportation systems, is mostly restricted to isolated mobility modes, whereas holistic data analytics over integrated data sources is not sufficiently supported. In this paper we present our ongoing research in the context of holistic data analytics to support urban mobility applications in the Data4UrbanMobility (D4UM) project. First, we discuss challenges in urban mobility analytics and present the D4UM platform we are currently developing to facilitate holistic urban data analytics over integrated heterogeneous data sources along with the available data sources. Second, we present the MiC app - a tool we developed to complement available datasets with intermodal mobility data (i.e. data about journeys that involve more than one mode of mobility) using a citizen science approach. Finally, we present selected use cases and discuss our future work.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
Sustainable Development Goals
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The Web Conference 2019: Companion of the World Wide Web Conference, WWW 2019. ed. / Ling Liu; Ryen White. New York, 2019. p. 137-145.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Data4Urbanmobility
T2 - 2019 World Wide Web Conference, WWW 2019
AU - Tempelmeier, Nicolas
AU - Kruegel, Tina
AU - Rietz, Yannick
AU - Mumm, Olaf
AU - Lishchuk, Iryna
AU - Carlow, Vanessa Miriam
AU - Dietze, Stefan
AU - Demidova, Elena
N1 - Funding Information: This work was partially funded by the Federal Ministry of Education and Research (BMBF), Germany, "Data4UrbanMobility" project, grant ID 02K15A040.
PY - 2019/5/13
Y1 - 2019/5/13
N2 - With the increasing availability of mobility-related data, such as GPS-traces, Web queries and climate conditions, there is a growing demand to utilize this data to better understand and support urban mobility needs. However, data available from the individual actors, such as providers of information, navigation and transportation systems, is mostly restricted to isolated mobility modes, whereas holistic data analytics over integrated data sources is not sufficiently supported. In this paper we present our ongoing research in the context of holistic data analytics to support urban mobility applications in the Data4UrbanMobility (D4UM) project. First, we discuss challenges in urban mobility analytics and present the D4UM platform we are currently developing to facilitate holistic urban data analytics over integrated heterogeneous data sources along with the available data sources. Second, we present the MiC app - a tool we developed to complement available datasets with intermodal mobility data (i.e. data about journeys that involve more than one mode of mobility) using a citizen science approach. Finally, we present selected use cases and discuss our future work.
AB - With the increasing availability of mobility-related data, such as GPS-traces, Web queries and climate conditions, there is a growing demand to utilize this data to better understand and support urban mobility needs. However, data available from the individual actors, such as providers of information, navigation and transportation systems, is mostly restricted to isolated mobility modes, whereas holistic data analytics over integrated data sources is not sufficiently supported. In this paper we present our ongoing research in the context of holistic data analytics to support urban mobility applications in the Data4UrbanMobility (D4UM) project. First, we discuss challenges in urban mobility analytics and present the D4UM platform we are currently developing to facilitate holistic urban data analytics over integrated heterogeneous data sources along with the available data sources. Second, we present the MiC app - a tool we developed to complement available datasets with intermodal mobility data (i.e. data about journeys that involve more than one mode of mobility) using a citizen science approach. Finally, we present selected use cases and discuss our future work.
UR - http://www.scopus.com/inward/record.url?scp=85066890570&partnerID=8YFLogxK
U2 - 10.48550/arXiv.1903.12064
DO - 10.48550/arXiv.1903.12064
M3 - Conference contribution
AN - SCOPUS:85066890570
SP - 137
EP - 145
BT - The Web Conference 2019
A2 - Liu, Ling
A2 - White, Ryen
CY - New York
Y2 - 13 May 2019 through 17 May 2019
ER -