Print Email Facebook Twitter Car-Following Model using Machine Learning Techniques Title Car-Following Model using Machine Learning Techniques: Approach at Urban Signalized Intersections with Traffic Radar Detection Author Echaniz Soldevila, Ignasi (TU Delft Civil Engineering and Geosciences; TU Delft Geoscience and Engineering) Contributor Hoogendoorn, Serge (mentor) Knoop, Victor (graduation committee) Steenbakkers, Jeroen (graduation committee) Alonso Mora, Javier (graduation committee) Degree granting institution Delft University of Technology Date 2017-10-12 Abstract 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. Subject Car-Following modelsLongitudinal driver behaviorMachine LearningGausssian Process RegressionNon-parametric modelsUrban signalized intersectionsTraffic light To reference this document use: http://resolver.tudelft.nl/uuid:6f864003-8f63-4be3-8837-77656ed620d0 Embargo date 2018-12-31 Coordinates 52.357927, 4.841711 Part of collection Student theses Document type master thesis Rights © 2017 Ignasi Echaniz Soldevila Files PDF Thesis_Ignasi_Echaniz_4516680.pdf 18.6 MB Close viewer /islandora/object/uuid:6f864003-8f63-4be3-8837-77656ed620d0/datastream/OBJ/view