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You searched for subject:(Urban signalized intersections). Showing records 1 – 2 of 2 total matches.

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Delft University of Technology

1. Echaniz Soldevila, Ignasi (author). Car-Following Model using Machine Learning Techniques: Approach at Urban Signalized Intersections with Traffic Radar Detection.

Degree: 2017, Delft University of Technology

This master thesis aims to gain new empirical insights into longitudinal driving behavior by means of the enumeration of a new hybrid car-following (CF) model which combines parametric and non parametric formulation. On one hand, the model, which predicts the drivers acceleration given a set of variables, benefits from innovative machine learning techniques such as Gaussian process regression (GPR) to make predictions when there exist correlation between new input and the training dataset. On the other hand, it uses existent traditional parametric CF models to predict acceleration when no similar situations are found in the training dataset. This formulation guarantees a complete and continues model and deals with the challenges of new available types of dataset in the transport field: noisy and incomplete yet with large amount of data. Multiple models have been trained using the Optimal Velocity Model (OVM) as a basis parametric model and a dataset collected in the PPA project in Amsterdam by traffic radar detection in stop and go traffic conditions. The other main innovation of this thesis is that variables rarely included in any CF model such as the status and the distance of drivers to the traffic light are also analyzed. Results show that the GPR model formulation is robust as the model performs better than OVM alone according to the main KPI, but still collisions occasionally occur. Moreover, results depict that traffic light status actively influences driver behavior. Overall, this thesis gives insights into new powerful mathematical techniques that can be applied to describe longitudinal driving behavior or any modeled process. Advisors/Committee Members: Hoogendoorn, Serge (mentor), Knoop, Victor (graduation committee), Steenbakkers, Jeroen (graduation committee), Alonso Mora, Javier (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Car-Following models; Longitudinal driver behavior; Machine Learning; Gausssian Process Regression; Non-parametric models; Urban signalized intersections; Traffic light

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APA (6th Edition):

Echaniz Soldevila, I. (. (2017). Car-Following Model using Machine Learning Techniques: Approach at Urban Signalized Intersections with Traffic Radar Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6f864003-8f63-4be3-8837-77656ed620d0

Chicago Manual of Style (16th Edition):

Echaniz Soldevila, Ignasi (author). “Car-Following Model using Machine Learning Techniques: Approach at Urban Signalized Intersections with Traffic Radar Detection.” 2017. Masters Thesis, Delft University of Technology. Accessed April 13, 2021. http://resolver.tudelft.nl/uuid:6f864003-8f63-4be3-8837-77656ed620d0.

MLA Handbook (7th Edition):

Echaniz Soldevila, Ignasi (author). “Car-Following Model using Machine Learning Techniques: Approach at Urban Signalized Intersections with Traffic Radar Detection.” 2017. Web. 13 Apr 2021.

Vancouver:

Echaniz Soldevila I(. Car-Following Model using Machine Learning Techniques: Approach at Urban Signalized Intersections with Traffic Radar Detection. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Apr 13]. Available from: http://resolver.tudelft.nl/uuid:6f864003-8f63-4be3-8837-77656ed620d0.

Council of Science Editors:

Echaniz Soldevila I(. Car-Following Model using Machine Learning Techniques: Approach at Urban Signalized Intersections with Traffic Radar Detection. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:6f864003-8f63-4be3-8837-77656ed620d0


Portland State University

2. Kendrick, Christine M. Improving the Roadside Environment through Integrating Air Quality and Traffic-Related Data.

Degree: PhD, Environmental Sciences and Resources, 2016, Portland State University

Urban arterial corridors are landscapes that give rise to short and long-term exposures to transportation-related pollution. With high traffic volumes, congestion, and a wide mix of road users and land uses at the road edge, urban arterial environments are important targets for improved exposure assessment to traffic-related pollution. Applying transportation management strategies to reduce emissions along arterial corridors could be enhanced if the ability to quantify and evaluate such actions was improved. However, arterial roadsides are under-sampled in terms of air pollution measurements in the United States and using observational data to assess such effects has many challenges such as lack of control sites for comparisons and temporal autocorrelation. The availability of traffic-related data is also typically limited in air monitoring and health studies. The work presented here uses unique long-term roadside air quality monitoring collected at the intersection of an urban arterial in Portland, OR to characterize the roadside atmospheric environment. This air quality dataset is then integrated with traffic-related data to assess various methods for improving exposure assessment and the roadside environment. Roadside nitric oxide (NO), nitrogen dioxide (NO2), and particle number concentration (PNC) measurements all demonstrated a relationship with local traffic volumes. Seasonal and diurnal characterizations show that roadside PM2.5 (mass) measurements do not have a relationship with local traffic volumes, providing evidence that PM2.5 mass is more tied to regional sources and meteorological conditions. The relationship of roadside NO and NO2 with traffic volumes was assessed over short and long-term aggregations to assess the reliability of a commonly employed method of using traffic volumes as a proxy for traffic-related exposure. This method was shown to be insufficient for shorter-time scales. Comparisons with annual aggregations validate the use of traffic volumes to estimate annual exposure concentrations, demonstrating this method can capture chronic but not acute exposure. As epidemiology and exposure assessment aims to target health impacts and pollutant levels encountered by pedestrians, cyclists, and those waiting for transit, these results show when traffic volumes alone can be a reliable proxy for exposure and when this approach is not warranted. Next, it is demonstrated that a change in traffic flow and change in emissions can be measured through roadside pollutant concentrations suggesting roadside pollution can be affected by traffic signal timing. The effect of a reduced maximum traffic signal cycle length on measurements of degree of saturation (DS), NO, and NO2 were evaluated for the peak traffic periods in two case studies at the study intersection. In order to reduce bias from covariates and assess the effect due to the change in cycle length only, a matched sampling method based on propensity scores was used to compare treatment periods (reduced cycle length) with control… Advisors/Committee Members: Linda A. George.

Subjects/Keywords: Atmospheric nitrogen oxides  – Oregon  – Portland  – Measurement  – Case studies; Signalized intersections  – Environmental aspects  – Oregon  – Portland  – Case studies; Traffic flow  – Environmental aspects  – Oregon  – Portland  – Case studies; Roads  – Interchanges and intersections  – Environmental aspects; Air quality  – Oregon  – Portland  – Measurement; Urban pollution  – Environmental aspects; Environmental Health; Environmental Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kendrick, C. M. (2016). Improving the Roadside Environment through Integrating Air Quality and Traffic-Related Data. (Doctoral Dissertation). Portland State University. Retrieved from https://pdxscholar.library.pdx.edu/open_access_etds/3086

Chicago Manual of Style (16th Edition):

Kendrick, Christine M. “Improving the Roadside Environment through Integrating Air Quality and Traffic-Related Data.” 2016. Doctoral Dissertation, Portland State University. Accessed April 13, 2021. https://pdxscholar.library.pdx.edu/open_access_etds/3086.

MLA Handbook (7th Edition):

Kendrick, Christine M. “Improving the Roadside Environment through Integrating Air Quality and Traffic-Related Data.” 2016. Web. 13 Apr 2021.

Vancouver:

Kendrick CM. Improving the Roadside Environment through Integrating Air Quality and Traffic-Related Data. [Internet] [Doctoral dissertation]. Portland State University; 2016. [cited 2021 Apr 13]. Available from: https://pdxscholar.library.pdx.edu/open_access_etds/3086.

Council of Science Editors:

Kendrick CM. Improving the Roadside Environment through Integrating Air Quality and Traffic-Related Data. [Doctoral Dissertation]. Portland State University; 2016. Available from: https://pdxscholar.library.pdx.edu/open_access_etds/3086

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