A Microservice–based Big Trajectory Data Processing Platform for Multimodal Trip Planning
- DOI
- 10.2991/acsr.k.191223.009How to use a DOI?
- Keywords
- trip planning, big trajectory data processing, multimodal routing, microservices
- Abstract
Trip planning is an essential function provided by an intelligent transportation system. As many different transport modes have been emerging recently, it becomes a common requirement to mix available transport types to find the most comfort and fastest solutions for getting given destinations. A key challenge for achieving such a desired trip plan is to get quality estimations of all possible traveling services rapidly and accurately. Thanks to the rapid growth of mobile computing, lots of real-time data about traffic status can be collected throw smartphones. Based on such continuously acquired data, we would get quality estimations more accurately and efficiently. Thus, we design and implement a big trajectory data processing platform based on microservices for supporting multimodal trip planning. The paper introduces not only the services and service processes for dealing with the big trajectory data but also the implementation and distributed deployment based on microservices deployed in multiple Dockers. Regarding the loosely-coupled nature of microservices, this platform can provide a flexible way to support trip planning applications consider new available travel modes quickly.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jun Na AU - Xiaowei Wang AU - Yuting Xu AU - Wenzhi Luo PY - 2019 DA - 2019/12/24 TI - A Microservice–based Big Trajectory Data Processing Platform for Multimodal Trip Planning BT - Proceedings of the 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019) PB - Atlantis Press SP - 36 EP - 39 SN - 2352-538X UR - https://doi.org/10.2991/acsr.k.191223.009 DO - 10.2991/acsr.k.191223.009 ID - Na2019 ER -