Atelecommunication company (telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the IP backbone infrastructure of such entities spanning densely urban spaces and widely rural areas, provides nowadays a unique opportunity to collect immense amounts of mobility data that can provide valuable insights for road traffc management and avoidance. In this paper we outline the components of the Traffc-TBD (Traffc Telco Big Data) architecture, which aims to become an innovative road traffc analytic and prediction system with the following desiderata: i) provide micro-level traffc modeling and prediction that goes beyond the current state provided by Internet-based navigation enterprises utilizing crowdsourcing; ii) retain the location privacy boundaries of users inside their mobile network operator, to avoid the risks of exposing location data to third-party mobile applications; and iii) be available with minimal costs and using existing infrastructure (i.e., cell towers and TBD data streams are readily available inside a telco). Road traffc understanding, management and analytics can minimize the number of road accidents, optimize fuel and energy consumption, avoid unexpected delays, contribute to a macroscopic spatio-temporal understanding of traffc in cities but also to "smart" societies through applications in city planning, public transportation, logistics and fleet management for enterprises, startups and governmental bodies.
|Original language||English (US)|
|Title of host publication||Proceedings of the International Workshop on Real-Time Business Intelligence and Analytics, BIRTE 2017|
|Publisher||Association for Computing Machinery|
|State||Published - Aug 28 2017|
|Event||11th International Workshop on Real-Time Business Intelligence and Analytics, BIRTE 2017 - Munich, Germany|
Duration: Aug 28 2017 → …
|Name||ACM International Conference Proceeding Series|
|Other||11th International Workshop on Real-Time Business Intelligence and Analytics, BIRTE 2017|
|Period||8/28/17 → …|
Bibliographical noteFunding Information:
This work was supported in part by the University of Cyprus, an industrial sponsorship by MTN Cyprus and EU COST Action IC1304. The third author’s research is supported by the Alexander von Humboldt-Foundation, Germany. The last author’s research is supported by NSF grants IIS-0952977, IIS-1218168, IIS-1525953, CNS-1512877.
© 2017 Association for Computing Machinery.
- Big Data
- Data Analytics
- Road Trffc