On the Challenges of Mobile Crowdsensing for Traffic Estimation

被引：8

作者：

Gil, Daniela Socas ^{[1
,2
]}

d'Orey, Pedro M. ^{[3
]}

Aguiar, Ana ^{[3
]}

机构：

[1] Univ Simon Bolivar, Caracas, Venezuela

[2] Univ Porto, Porto, Portugal

[3] Univ Porto, Inst Telecomunicacoes, Porto, Portugal

来源：

PROCEEDINGS OF THE 15TH ACM CONFERENCE ON EMBEDDED NETWORKED SENSOR SYSTEMS (SENSYS'17) | 2017年

关键词：

Crowdsensing; traffic estimation;

D O I：

10.1145/3131672.3136958

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Traffic congestion adversely impacts our lives. Traffic estimation resorting to mobile (crowdsensing) probes is a challenging task. We present key challenges for accurate and real-time traffic estimation resorting to crowdsensing data, namely data sparsity, user trip diversity, population bias, data quality, among others. We propose solutions to address some of these issues and demonstrate the relevance of others through an exploratory data analysis.

引用

页数：2

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