Measuring delay at signalized intersections

Subscribers:
24,000
Published on ● Video Link: https://www.youtube.com/watch?v=YANGym2z5vE



Duration: 15:05
135 views
1


A typical Dutch signalized intersection has over 50 detectors: induction loops and buttons. The detectors are used to determine where to give green light and for how long. The information from detectors and signal phases is logged in a standardized format (VLOG). This makes it possible to estimate things like intensity over a period of time, vehicles that crossed during red, maximum waiting time, and so on.

CodingConnected has developed a server-client application (YAVC) that gathers and analyzes this kind of logging data. The software is used by multiple municipalities (like Rotterdam and Nijmegen) and engineering companies. Alongside the sever-client application, CodingConnected also develops a stand-alone desktop app that can read VLOG data from disk (YAVV).

One much sought after indicator for the quality of the controlling process is time-loss. Given the nature of the data, time-loss can only be estimated based on VLOG. Floating car data would allow for a much better calculation, however, this kind of data is still relatively rare and considered privacy sensitive. Therefore, I am working on an algorithm to calculate and estimation of time-loss based on VLOG data. To get an idea of the quality of the algorithm, SUMO was used to gather actual time-loss values from a simulation. These are then compared with time-loss values based on logging data from the same simulation.




Other Videos By Eclipse Foundation


2021-10-11Take Your Microservices to the Next Level with Helidon and Verrazzano
2021-10-11Welcome and Introduction
2021-10-06Simulation based method for the analysis of energy-efficient driving algorithms using SUMO
2021-10-06Optimising Low-Emissions Zone Geofencing Strategy using SUMO with Vehicle Co-Simulation
2021-10-06High-Fidelity Modeling of Curbside Driving Behavior in SUMO
2021-10-06Validating a parking lot assignment method by Eclipse SUMO
2021-10-06A comparison of SUMO’s count based and countless demand generation tools
2021-10-06Automated Calibration of Traffic Demand and Traffic Lights in SUMO Using Real-World Observations
2021-10-06Impact of Heavy Vehicles on Traffic
2021-10-06Augmenting SUMO with Ring-and-Barrier Structured Traffic Signal Controller Module
2021-10-06Measuring delay at signalized intersections
2021-10-06Using Deep Reinforcement Learning to Coordinate Multi-Modal Journey Planning with Limited ...
2021-10-06Last-Mile Logistics Simulation in SUMO
2021-10-06The Development of Pedestrian Gap Acceptance and Midblock Pedestrian Road Crossing Behavior ...
2021-10-06Calibrating spatio-temporal network states in microscopic traffic simulation on a global level
2021-10-06Investigating the Effect of Enabled Mesoscopic Settings within a Sensitivity Analysis
2021-10-06Ride-hailing and ride-pooling: State of the art, future research directions, and the role of ...
2021-10-06A Study of Applying Eco-Driving Speed Advisory on Transit Signal Priority
2021-10-06Cooperative traffic light control - considering cyclists with the aid of V2I
2021-10-06YASE - An Agnostic and Holistic Scenario Engine
2021-10-06Modeling Cellular Network Infrastructure in SUMO