Simulation of highway driving was traditionally the domain of virtual physics based models. Yet, traffic simulation is incomplete without considering the drivers\u27 conscious strategic and tactical behavior. These aspects can be naturally simulated through an agent-based driver model. In this paper, we describe a model of the strategic lane preferences of the drivers, with a special attention to the optimal lane positioning for a safe exit. Our experiments show that the simulated traffic of Orlando\u27s Highway 408 matches well with the real world traffic data. The increased simulation detail can be applied to crash prediction and the control of intelligent transportation system devices, such as variable speed limits. © 2012 Springer-Verla...
This paper describes how work zones account for 24 percent of nonrecurring congestion, 2 percent of ...
Drivers continuously evaluate the surrounding traffic and the roadway environment, and make decision...
Recent advances in Deep Reinforcement Learning have sparked new interest in many different research ...
Simulation of highway driving was traditionally the domain of virtual physics based models. Yet, tra...
Current state-of-the-art highway traffic flow simulators rely extensively on models using formulas s...
Current state-of-the-art highway traffic flow simulators rely extensively on models using formulas s...
A car following model for dynamic traffic simulation is applied to a bottleneck situation on a two l...
High-fidelity driving simulators immerse a driver in a highly realistic virtual environment for the ...
International audienceWe present a multi-agent traffic simulation to improve the validity of traffic...
Quantifying and encoding occupants’ preferences as an objective function for the tactical decision m...
International audienceThe aim of this paper is to improve the validity of traffic simulations in urb...
Abstract Traffic simulation has been being an interesting research subject for transport engineer a...
Advanced Driving Assistance Systems (ADAS) have huge potential for improving road safety and travel ...
TRB 2008 Annual Meeting CD-ROM Paper revised from original submittal. 2 In this paper, we present a ...
2015Final ReportYang, C.Y. DavidPDFTech ReportFHWA-HRT-15-082CamerasDilemma zoneRed light runningSim...
This paper describes how work zones account for 24 percent of nonrecurring congestion, 2 percent of ...
Drivers continuously evaluate the surrounding traffic and the roadway environment, and make decision...
Recent advances in Deep Reinforcement Learning have sparked new interest in many different research ...
Simulation of highway driving was traditionally the domain of virtual physics based models. Yet, tra...
Current state-of-the-art highway traffic flow simulators rely extensively on models using formulas s...
Current state-of-the-art highway traffic flow simulators rely extensively on models using formulas s...
A car following model for dynamic traffic simulation is applied to a bottleneck situation on a two l...
High-fidelity driving simulators immerse a driver in a highly realistic virtual environment for the ...
International audienceWe present a multi-agent traffic simulation to improve the validity of traffic...
Quantifying and encoding occupants’ preferences as an objective function for the tactical decision m...
International audienceThe aim of this paper is to improve the validity of traffic simulations in urb...
Abstract Traffic simulation has been being an interesting research subject for transport engineer a...
Advanced Driving Assistance Systems (ADAS) have huge potential for improving road safety and travel ...
TRB 2008 Annual Meeting CD-ROM Paper revised from original submittal. 2 In this paper, we present a ...
2015Final ReportYang, C.Y. DavidPDFTech ReportFHWA-HRT-15-082CamerasDilemma zoneRed light runningSim...
This paper describes how work zones account for 24 percent of nonrecurring congestion, 2 percent of ...
Drivers continuously evaluate the surrounding traffic and the roadway environment, and make decision...
Recent advances in Deep Reinforcement Learning have sparked new interest in many different research ...