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Open AccessFeature PaperArticle

Estimating Freeway Level-of-Service Using Crowdsourced Data

1
Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USA
2
Tennessee Department of Transportation, Nashville, TN 37243, USA
*
Author to whom correspondence should be addressed.
Informatics 2021, 8(1), 17; https://doi.org/10.3390/informatics8010017
Received: 8 February 2021 / Revised: 26 February 2021 / Accepted: 1 March 2021 / Published: 5 March 2021
(This article belongs to the Special Issue Big Data and Transportation)
In traffic operations, the aim of transportation agencies and researchers is typically to reduce congestion and improve safety. To attain these goals, agencies need continuous and accurate information about the traffic situation. Level-of-Service (LOS) is a beneficial index of traffic operations used to monitor freeways. The Highway Capacity Manual (HCM) provides analytical methods to assess LOS based on traffic density and highway characteristics. Generally, obtaining reliable density data on every road in large networks using traditional fixed location sensors and cameras is expensive and otherwise unrealistic. Traditional intelligent transportation system facilities are typically limited to major urban areas in different states. Crowdsourced data are an emerging, low-cost solution that can potentially improve safety and operations. This study incorporates crowdsourced data provided by Waze to propose an algorithm for LOS assessment on an hourly basis. The proposed algorithm exploits various features from big data (crowdsourced Waze user alerts and speed/travel time variation) to perform LOS classification using machine learning models. Three categories of model inputs are introduced: Basic statistical measures of speed; travel time reliability measures; and the number of hourly Waze alerts. Data collected from fixed location sensors were used to calculate ground truth LOS. The results reveal that using Waze crowdsourced alerts can improve the LOS estimation accuracy by about 10% (accuracy = 0.93, Kappa = 0.83). The proposed method was also tested and confirmed by using data from after coronavirus disease 2019 (COVID-19) with severe traffic breakdown due to a stay-at-home policy. The proposed method is extendible for freeways in other locations. The results of this research provide transportation agencies with a LOS method based on crowdsourced data on different freeway segments, regardless of the availability of traditional fixed location sensors. View Full-Text
Keywords: crowdsourced data; big data; Level-of-Service; traffic; machine learning crowdsourced data; big data; Level-of-Service; traffic; machine learning
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MDPI and ACS Style

Hoseinzadeh, N.; Gu, Y.; Han, L.D.; Brakewood, C.; Freeze, P.B. Estimating Freeway Level-of-Service Using Crowdsourced Data. Informatics 2021, 8, 17. https://doi.org/10.3390/informatics8010017

AMA Style

Hoseinzadeh N, Gu Y, Han LD, Brakewood C, Freeze PB. Estimating Freeway Level-of-Service Using Crowdsourced Data. Informatics. 2021; 8(1):17. https://doi.org/10.3390/informatics8010017

Chicago/Turabian Style

Hoseinzadeh, Nima; Gu, Yangsong; Han, Lee D.; Brakewood, Candace; Freeze, Phillip B. 2021. "Estimating Freeway Level-of-Service Using Crowdsourced Data" Informatics 8, no. 1: 17. https://doi.org/10.3390/informatics8010017

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