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You searched for +publisher:"Delft University of Technology" +contributor:("Steenbakkers, Jeroen"). One record found.

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

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

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