Characterizing Road Conditions Via Smart Mobile Crowd Sourcing

Dustin Shorter; Sami Alshammari; Sejun Song

doi:10.1109/ISC251055.2020.9239020

Characterizing Road Conditions Via Smart Mobile Crowd Sourcing

Dustin Shorter
, Sami Alshammari
, Sejun Song

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

Bad road conditions can cause vehicle damage and create hazardous driving conditions. Current reporting methods for bad road conditions is a burden for the reporter, so much so that these conditions might not be reported. With current smartphone technology this reporting method can be greatly improved. Smartphones come with an accelerometer and GPS positioning on-board. Utilizing these sensors, once the application is installed and run, a smartphone user can gather bad road conditions automatically while driving. Then the user can upload the data to the server. These road conditions are then classified and optionally displayed on a map while the user is driving. The potential for finding bad road conditions is greatly increases with crowd sourcing. When the road condition data is analyzed an algorithm is used to determine the size of the bad road condition and how many samples are needed. Even though the smartphone might not have the sampling rate of a dedicated accelerometer, its potential for gather large amounts of data using crowd sourcing and ease of use outweighs this deficiency.

Original language	English (US)
Title of host publication	2020 IEEE International Smart Cities Conference, ISC2 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728182940
DOIs	https://doi.org/10.1109/ISC251055.2020.9239020
State	Published - Sep 28 2020
Externally published	Yes
Event	2020 IEEE International Smart Cities Conference, ISC2 2020 - Piscataway, United States Duration: Sep 28 2020 → Oct 1 2020

Publication series

Name	2020 IEEE International Smart Cities Conference, ISC2 2020

Conference

Conference	2020 IEEE International Smart Cities Conference, ISC2 2020
Country/Territory	United States
City	Piscataway
Period	9/28/20 → 10/1/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 11 Sustainable Cities and Communities

Keywords

Accelerometer
Internet of Things
Potholes
Road maintenance

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Transportation
Urban Studies
Artificial Intelligence
Energy Engineering and Power Technology

Access to Document

10.1109/ISC251055.2020.9239020

Cite this

Characterizing Road Conditions Via Smart Mobile Crowd Sourcing. / Shorter, Dustin; Alshammari, Sami; Song, Sejun.
2020 IEEE International Smart Cities Conference, ISC2 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9239020 (2020 IEEE International Smart Cities Conference, ISC2 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shorter, D, Alshammari, S & Song, S 2020, Characterizing Road Conditions Via Smart Mobile Crowd Sourcing. in 2020 IEEE International Smart Cities Conference, ISC2 2020., 9239020, 2020 IEEE International Smart Cities Conference, ISC2 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE International Smart Cities Conference, ISC2 2020, Piscataway, United States, 9/28/20. https://doi.org/10.1109/ISC251055.2020.9239020

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title = "Characterizing Road Conditions Via Smart Mobile Crowd Sourcing",

abstract = "Bad road conditions can cause vehicle damage and create hazardous driving conditions. Current reporting methods for bad road conditions is a burden for the reporter, so much so that these conditions might not be reported. With current smartphone technology this reporting method can be greatly improved. Smartphones come with an accelerometer and GPS positioning on-board. Utilizing these sensors, once the application is installed and run, a smartphone user can gather bad road conditions automatically while driving. Then the user can upload the data to the server. These road conditions are then classified and optionally displayed on a map while the user is driving. The potential for finding bad road conditions is greatly increases with crowd sourcing. When the road condition data is analyzed an algorithm is used to determine the size of the bad road condition and how many samples are needed. Even though the smartphone might not have the sampling rate of a dedicated accelerometer, its potential for gather large amounts of data using crowd sourcing and ease of use outweighs this deficiency.",

keywords = "Accelerometer, Internet of Things, Potholes, Road maintenance",

author = "Dustin Shorter and Sami Alshammari and Sejun Song",

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doi = "10.1109/ISC251055.2020.9239020",

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AU - Shorter, Dustin

AU - Alshammari, Sami

AU - Song, Sejun

PY - 2020/9/28

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N2 - Bad road conditions can cause vehicle damage and create hazardous driving conditions. Current reporting methods for bad road conditions is a burden for the reporter, so much so that these conditions might not be reported. With current smartphone technology this reporting method can be greatly improved. Smartphones come with an accelerometer and GPS positioning on-board. Utilizing these sensors, once the application is installed and run, a smartphone user can gather bad road conditions automatically while driving. Then the user can upload the data to the server. These road conditions are then classified and optionally displayed on a map while the user is driving. The potential for finding bad road conditions is greatly increases with crowd sourcing. When the road condition data is analyzed an algorithm is used to determine the size of the bad road condition and how many samples are needed. Even though the smartphone might not have the sampling rate of a dedicated accelerometer, its potential for gather large amounts of data using crowd sourcing and ease of use outweighs this deficiency.

AB - Bad road conditions can cause vehicle damage and create hazardous driving conditions. Current reporting methods for bad road conditions is a burden for the reporter, so much so that these conditions might not be reported. With current smartphone technology this reporting method can be greatly improved. Smartphones come with an accelerometer and GPS positioning on-board. Utilizing these sensors, once the application is installed and run, a smartphone user can gather bad road conditions automatically while driving. Then the user can upload the data to the server. These road conditions are then classified and optionally displayed on a map while the user is driving. The potential for finding bad road conditions is greatly increases with crowd sourcing. When the road condition data is analyzed an algorithm is used to determine the size of the bad road condition and how many samples are needed. Even though the smartphone might not have the sampling rate of a dedicated accelerometer, its potential for gather large amounts of data using crowd sourcing and ease of use outweighs this deficiency.

KW - Accelerometer

KW - Internet of Things

KW - Potholes

KW - Road maintenance

UR - https://www.scopus.com/pages/publications/85097164973

UR - https://www.scopus.com/pages/publications/85097164973#tab=citedBy

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M3 - Conference contribution

AN - SCOPUS:85097164973

T3 - 2020 IEEE International Smart Cities Conference, ISC2 2020

BT - 2020 IEEE International Smart Cities Conference, ISC2 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Smart Cities Conference, ISC2 2020

Y2 - 28 September 2020 through 1 October 2020

ER -

Characterizing Road Conditions Via Smart Mobile Crowd Sourcing

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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