Видео

Overview of Curb Space Operations in Downtown Ann Arbor, Michigan, featuring unauthorized parking (i.e., parking in a space dedicated to a different activity). The simulations are conducted in SUMO.

Overview of Curb Space Operations in Downtown Ann Arbor, Michigan, with a high level of background traffic. The simulations are conducted in SUMO.

Overview of Curb Space Operations in Downtown Ann Arbor, Michigan, with a low level of background traffic. The simulations are conducted in SUMO.

Curb space operations simulation in downtown Ann Arbor, Michigan

This video covers six trajectory planning scenarios over two intersections. The trajectory of the subject vehicle is denoted in red, and the trajectories of the surrounding vehicles and pedestrians are displayed in blue and orange, respectively. In each scenario a 5-sconds long trajectory is planned for the subject vehicle.

Full paper: www.researchgate.net/publication/353851429_A_Learning-Based_Method_for_Predicting_Heterogeneous_Traffic_Agent_Trajectories_Implications_for_Transfer_Learning

Based on the paper titled "Anomaly detection in connected and automated vehicles using an augmented state formulation", published in the 2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), 156-161.

Using subsidies to stabilize peer-to-peer ridesharing markets with role assignment

A brief presentation of the paper "V2XSim: A V2X Simmulator for Connected and Automated Vehicle Environment Simulation", published in the proceedings of ITSC 2020

For source code and user manual, visit github.com/next-generation-mobility-systems-lab/v2xsim

Connected vehicle (CV) technology facilitates communication among vehicles, the infrastructure, and other road users. CV technology enables forming virtual trains of vehicles, called “platoons”. Due to constant communication, vehicles in a platoon are able to travel with small gaps between them. Platoon formation can introduce many benefits including (i) creating energy efficiency through reduc...

This video visualizes the deployment of a single shuttle out of 20 to serve on-demand requests in Benton Harbor, MI on March 3, 2017 from 7 a.m to 10 a.m. The size of circles corresponds to the number of on-board customers and its color changes when it switches to an alternative route. Red and blue pins represent the pick-up and drop-off locations of customers, respectively.

In this video we visualize the deployment of 20 shuttles with capacity 10 to serve on-demand requests in Benton Harbor, MI on March 3, 2017 from 7 a.m to 10 a.m. The size of circles corresponds to the number of on-board customers.

This video visualizes the deployment of a single shuttle out of 100 to serve on-demand requests in the Manhattan area on June 4, 2016 from 6 p.m to 9 p.m. The size of circles corresponds to the number of on-board customers and its color changes when it switches to an alternative route. Red and blue pins represent the pick-up and drop-off locations of customers, respectively.

100 shuttles, Manhattan (A Data-driven Optimization Approach For Dynamic Shuttle Dispatching)