research-article

coSense: Collaborative Urban-Scale Vehicle Sensing Based on Heterogeneous Fleets

Authors:

Desheng ZhangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 4

Article No.: 196, Pages 1 - 25

https://doi.org/10.1145/3287074

Published: 27 December 2018 Publication History

Abstract

The real-time vehicle sensing at urban scale is essential to various urban services. To date, most existing approaches rely on static infrastructures (e.g., traffic cameras) or mobile services (e.g., smartphone apps). However, these approaches are often inadequate for urban scale vehicle sensing at the individual level because of their static natures or low penetration rates. In this paper, we design a sensing system called coSense to utilize commercial vehicular fleets (e.g., taxis, buses, and trucks) for real-time vehicle sensing at urban scale, given (i) the availability of well-equipped commercial fleets sensing other vehicles by onboard cameras or peer-to-peer communication, and (ii) an increasing trend of connected vehicles and autonomous vehicles with periodical status broadcasts for safety applications. Compared to existing solutions based on cameras and smartphones, the key features of coSense are in its high penetration rates and transparent sensing for participating drivers. The key technical challenge we addressed is how to recover spatiotemporal sensing gaps by considering various mobility patterns of commercial vehicles with deep learning. We evaluate coSense with a preliminary road test and a large-scale trace-driven evaluation based on vehicular fleets in the Chinese city Shenzhen, including 14 thousand taxis, 13 thousand buses, 13 thousand trucks, and 10 thousand regular vehicles. We compare coSense to infrastructure and cellphone-based approaches, and the results show that we increase the sensing accuracy by 10.1% and 16.6% on average.

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Cao ZZhao DSong HYuan HWang QMa HTong JTan C(2024) F 3 VeTrac: Enabling Fine-grained, Fully-road-covered, and Fully-individual penetrative Vehicle Trajectory Recovery IEEE Transactions on Mobile Computing10.1109/TMC.2023.3301871(1-16)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3301871
Das DBhattacharjee SChakraborty SMitra BDas S(2024)Early Detection of Driving Maneuvers for Proactive Congestion Prevention2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494436(135-142)Online publication date: 11-Mar-2024
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Index Terms

coSense: Collaborative Urban-Scale Vehicle Sensing Based on Heterogeneous Fleets
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Location based services
2. Networks
  1. Network types
    1. Cyber-physical networks
      1. Sensor networks

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 4

December 2018

1169 pages

EISSN:2474-9567

DOI:10.1145/3301777

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Copyright © 2018 ACM.

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Publication History

Published: 27 December 2018

Accepted: 01 October 2018

Revised: 01 October 2018

Received: 01 August 2018

Published in IMWUT Volume 2, Issue 4

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Cited By

Cao ZZhao DSong HYuan HWang QMa HTong JTan C(2024) F 3 VeTrac: Enabling Fine-grained, Fully-road-covered, and Fully-individual penetrative Vehicle Trajectory Recovery IEEE Transactions on Mobile Computing10.1109/TMC.2023.3301871(1-16)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3301871
Das DBhattacharjee SChakraborty SMitra BDas S(2024)Early Detection of Driving Maneuvers for Proactive Congestion Prevention2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494436(135-142)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerCom59722.2024.10494436
Gao JPan YZhang XHan QHu Y(2024)Sharing instant delivery UAVs for crowdsensingComputers and Industrial Engineering10.1016/j.cie.2024.110100191:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.cie.2024.110100
Tan HYuan YZhong SYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Joint Rebalancing and Charging for Shared Electric Micromobility Vehicles with Energy-informed DemandProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614942(2392-2401)Online publication date: 21-Oct-2023
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Yan HWang HZhang DYang YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Identifying Regional Driving Risks via Transductive Cross-City Transfer Learning Under Negative TransferProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614924(2877-2886)Online publication date: 21-Oct-2023
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Yan HYang Y(2023)Large-scale trajectory prediction via relationship-aware adaptive hierarchical graph learningCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-023-00133-w5:4(351-366)Online publication date: 15-Jun-2023
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Xie XHong ZQin ZFang ZTian YZhang D(2022)TransRiskProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35345816:2(1-19)Online publication date: 7-Jul-2022
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Fang ZYang YYang GXian YZhang FZhang D(2021)CellSenseProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34780875:3(1-22)Online publication date: 14-Sep-2021
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