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

共 50 条

[31] Mobile Crowdsensing with Imagery Tasks
Dautaras, Justas
Matskin, Mihhail
PROCEEDINGS OF 2021 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY WORKSHOPS AND SPECIAL SESSIONS: (WI-IAT WORKSHOP/SPECIAL SESSION 2021), 2021, : 54 - 61
[32] Noise-Aware Optimization for Mobile Crowdsensing-Based Travel Time Estimation
Guo, Xiaoyu
Xing, Weiwei
Fang, Jun
Chen, Jia
Chen, Xi
Gao, Ruipeng
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2024, 73 (03) : 4067 - 4080
[33] RTS: road topology-based scheme for traffic condition estimation via vehicular crowdsensing
Shao, Lu
Wang, Cheng
Liu, Lu
Jiang, Changjun
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2017, 29 (03):
[34] AI-based Security Design of Mobile Crowdsensing Systems: Review, Challenges and Case Studies
Zhang, Yuegian
Kantarci, Burak
2019 13TH IEEE INTERNATIONAL CONFERENCE ON SERVICE-ORIENTED SYSTEM ENGINEERING (SOSE) / 10TH INTERNATIONAL WORKSHOP ON JOINT CLOUD COMPUTING (JCC) / IEEE INTERNATIONAL WORKSHOP ON CLOUD COMPUTING IN ROBOTIC SYSTEMS (CCRS), 2019, : 17 - 26
[35] Preserving Privacy in Mobile Crowdsensing within Intelligent Transportation System: Current Research and Future Challenges
Maqour, Zaina
El Bakkali, Hanan
Benhaddou, Driss
Benbrahim, Houda
El Gadi, Hajar
Abou-Zbiba, Wahiba
2023 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, IWCMC, 2023, : 307 - 312
[36] Mobile AR Depth Estimation: Challenges & Prospects
Ganj, Ashkan
Zhao, Yiqin
Su, Hang
Guo, Tian
PROCEEDINGS OF THE 2024 THE 25TH INTERNATIONAL WORKSHOP ON MOBILE COMPUTING SYSTEMS AND APPLICATIONS, HOTMOBILE 2024, 2024, : 21 - 26
[37] Toward Efficient Mechanisms for Mobile Crowdsensing
Zhang, Xinglin
Yang, Zheng
Liu, Yunhao
Li, Jianqiang
Ming, Zhong
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2017, 66 (02) : 1760 - 1771
[38] Maximum Profit Routing for Mobile Crowdsensing
Li, Zhiyao
Zhang, Jiale
Gao, Xiaofeng
Chen, Guihai
2022 21ST ACM/IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN 2022), 2022, : 441 - 450
[39] A Reference Architecture for Mobile Crowdsensing Platforms
Diniz, Herbertt B. M.
Silva, Emanoel C. G. F.
Nogueira, Thomas C. C.
Gama, Kiev
PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL 2 (ICEIS), 2016, : 600 - 607
[40] Detecting Mobile Crowdsensing Context in the Wild
Agarwal, Rachit
Chopra, Shaan
Christophides, Vassilis
Georgantas, Nikolaos
Issarny, Valerie
2019 20TH INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (MDM 2019), 2019, : 170 - 175

← 1 2 3 4 5 →