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Developing Algorithms To Detect Incidents On Freeways From Loop Detector And Vehicle Re-Identification Data, Biraj Adhikari
Developing Algorithms To Detect Incidents On Freeways From Loop Detector And Vehicle Re-Identification Data, Biraj Adhikari
Civil & Environmental Engineering Theses & Dissertations
A new approach for testing incident detection algorithms has been developed and is presented in this thesis. Two new algorithms were developed and tested taking California #7, which is the most widely used algorithm to date, and SVM (Support Vector Machine), which is considered one of the best performing classifiers, as the baseline for comparisons. Algorithm #B in this study uses data from Vehicle Re-Identification whereas the other three algorithms (California #7, SVM and Algorithm #A) use data from a double loop detector for detection of an incident. A microscopic traffic simulator is used for modeling three types of incident …
Deep Reinforcement Learning Approach For Lagrangian Control: Improving Freeway Bottleneck Throughput Via Variable Speed Limit, Reza Vatani Nezafat
Deep Reinforcement Learning Approach For Lagrangian Control: Improving Freeway Bottleneck Throughput Via Variable Speed Limit, Reza Vatani Nezafat
Civil & Environmental Engineering Theses & Dissertations
Connected vehicles (CVs) will enable new applications to improve traffic flow. The focus of this dissertation is to investigate how reinforcement learning (RL) control for the variable speed limit (VSL) through CVs can be generalized to improve traffic flow at different freeway bottlenecks. Three different bottlenecks are investigated: A sag curve, where the gradient changes from negative to positive values causes a reduction in the roadway capacity and congestion; a lane reduction, where three lanes merge to two lanes and cause congestion, and finally, an on-ramp, where increase in demand on a multilane freeway causes capacity drop. An RL algorithm …
Latent Choice Models To Account For Misclassification Errors In Discrete Transportation Data, Lacramioara Elena Balan
Latent Choice Models To Account For Misclassification Errors In Discrete Transportation Data, Lacramioara Elena Balan
Civil & Environmental Engineering Theses & Dissertations
One of the most fundamental tasks when it comes to analyzing data using statistical methods is to understand the relationship between the explanatory variables and the outcome. Misclassification of explanatory variables is a common risk when using statistical modeling techniques. In this dissertation, we define ‘misclassification,’ as a response that is reported or recorded in the wrong category; for example, a variable is registered as a one when it should have the value zero. Misclassification can easily happen in any data; for example, in an interview setting where the respondent misunderstands the question or the interviewer checks the wrong box. …