Traffic control requires looking at traffic models of all types in finer details. This paper is to investigate lane-wise flow-density (or equivalently speed-density) relationship which is traditionally called Fundamental Diagram (FD) over a stretch of homogeneous freeway section using the microscopic NGSIM data. Particularly, it investigates how a homogenous traffic further drop (breakdown) thr...

Trajectory prediction of surrounding vehicles is a crucial capability intelligent driving vehicles. In scene, vehicle and its constitute an integral system, the vehicle's future motion trajectory affected by actions The influencing mode degree are hidden in relevant historical information neighbour vehicle. existing methods either do not consider confidence predicted trajectory, or accuracy req...

Vehicle-trajectory prediction is essential for intelligent traffic systems (ITS), as it can help autonomous vehicles to plan a safe and efficient path. However, still challenging task because existing studies have mainly focused on the spatial interactions of adjacent regardless temporal dependencies. In this paper, we propose spatial-temporal attentive LSTM encoder–decoder model (STAM-LSTM) pr...

General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: A key qualitative requirement for highway traffic models is the ability to replicate a type of traffic jam popularly referred to as a phantom jam, shock wave or stop-and-go wave. Despite over 50 years of modellin...

Queues at signalized intersections are the main cause of traffic delays and travel time variability in urban networks. In this article, we propose a method to estimate queue profiles that are traffic shockwave polygons in the time-space plane describing the spatiotemporal formation and dissipation of queues. The method integrates the collective effect of dispersed probe vehicle data with traffi...

The car-following models are the research basis of traffic flow theory and microscopic simulation. Among previous work, theory-driven dominant, while data-driven ones relatively rare. In recent years, related technologies Intelligent Transportation System (ITS) re- presented by Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing ITS, large-scale vehicle trajectory da...

This paper proposes a novel hybrid car-following model: the physics-informed conditional generative adversarial network (PICGAN), designed to enhance multi-step modeling in mixed traffic flow scenarios. model leverages strengths of both physics-based and deep-learning-based models. By taking advantage inherent structure GAN, PICGAN eliminates need for an explicit weighting parameter typically u...

Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially dense traffic, because the merging vehicle typically needs to interact with other vehicles identify or create gap safely merge into. In this paper, we consider problem of control forced scenarios. We propose novel game-theoretic controller, called Leader-Follower Game Controller (LFGC), which i...

This paper focuses on the unexplored problem of inferring motion of objects that are invisible to all cameras in a multiple camera setup. As opposed to methods for learning relationships between disjoint cameras, we take the next step to actually infer the exact spatiotemporal behavior of objects while they are invisible. Given object trajectories within disjoint cameras’ FOVs (field-ofview), w...

Car Following models have a critical role in all microscopic traffic simulation models. Current microscopic simulation models are unable to mimic the unsafe behaviour of drivers as most are based on presumptions about the safe behaviour of drivers. Gipps model is a widely used car following model embedded in different micro-simulation models. This paper examines the Gipps car following model to...