Using big data and machine learning for microscopic delay modelling at construction work zones
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Morshedzadeh, Yeganeh
issued: 2024-05-03T08:18:27Z
issued: 2024
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Work zones are lane closures essential to protect workers during highway maintenance and construction activities; however, they lead to congested traffic movement. To mitigate delay effectively, it is essential to understand factors that impact delay and develop the ability to predict delays accurately. Given the rapid availability of big data, machine learning (ML) models are becoming increasingly popular in traffic flow prediction. Their ability to capture complex non-linear patterns can enhance predictions obtained using traditional tools. This thesis aims to employ ML models to predict delays observed at work zones. This is achieved through developing data-driven predictive models that leverage the growing usage of large-scale data in transportation. The vast dataset was assembled by integrating 15 million travel times observations from probe vehicles over a 700-km corridor with various factors such as weather conditions, work zone design, traffic conditions, and temporal elements gathered from a wide range of sources. The data was used to achieve two primary objectives: (i) identify relationships between various factors and work zone delay and (ii) compare usage of ML algorithms in accurately predicting work zone delays. To achieve the first objective, unsupervised clustering as well as ordinal logistic and mixed-effects regression were used. The findings highlight the proportional impact of the length of transition areas and work zones and the significant effects of environmental factors on work zone delays. To assess the use of ML models in predicting delays, a comparative evaluation of multiple techniques was conducted, highlighting the strengths and weaknesses of each approach. This included artificial neural networks (ANN), decision trees, gradient boosting regression, support vector regression, and ridge regression. Among the models evaluated, the decision tree and ANN achieved the best overall performance with the lowest RMSE of 0.05 and 0.150 minutes an…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88166
note: Using Big Data and MachineLearning for Microscopic DelayModelling at Construction WorkZonesbyYeganeh MorshedzadehB.Sc., Iran University of Science and Technology, 2021A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinThe College of Graduate Studies(Civil Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)April 2024© Yeganeh Morshedzadeh 2024The following individuals certify that they have read, and recommend to theCollege of Graduate Studies for acceptance, the thesis entitled:Using Big Data and Machine Learning for Microscopic Delay Mod-elling at Construction Work Zonessubmitted by Yeganeh Morshedzadeh in partial fulfillment of the requirementsfor the degree of Master of Applied Science.Dr. Suliman Gargoum, School of EngineeringSupervisorDr. Mahmudur Fatmi, School of EngineeringSupervisory Committee MemberDr. Qian Chen, School of EngineeringSupervisory Committee MemberDr. Wouter Bam, School of EngineeringUniversity ExamineriiAbstractWork zones are lane closures essential to protect workers during highwaymaintenance and construction activities; however, they lead to congested trafficmovement. To mitigate delay effectively, it is essential to understand factors thatimpact delay and develop the ability to predict delays accurately. Given the rapidavailability of big data, machine learning (ML) models are becoming increasinglypopular in traffic flow prediction. Their ability to capture complex non-linearpatterns can enhance predictions obtained using traditional tools.This thesis aims to employ ML models to predict delays observed at workzones. This is achieved through developing data-driven predictive models thatleverage the growing usage of large-scale data in transportation. The vast datasetwas assembled by integrating 15 million travel times observations from probevehicles over a 700-km corridor with various factors such as weather conditions,work zone design, traffic conditions, and temporal elements gath…
has type: Thesis/Dissertation
date issued: 2024-05
is shown at: 10.14288/1.0442099
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-ShareAlike 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-sa/4.0/
scholar level: Graduate
contributor: Gargoum, Suliman
type: Text
identifier URI: http://hdl.handle.net/2429/88166