4,395 research outputs found
A two-dimensional data-driven model for traffic flow on highways
Based on experimental traffic data obtained from German and US highways, we
propose a novel two-dimensional first-order macroscopic traffic flow model. The
goal is to reproduce a detailed description of traffic dynamics for the real
road geometry. In our approach both the dynamic along the road and across the
lanes is continuous. The closure relations, being necessary to complete the
hydrodynamic equation, are obtained by regression on fundamental diagram data.
Comparison with prediction of one-dimensional models shows the improvement in
performance of the novel model.Comment: 27 page
The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions
Ramp metering, a traditional traffic control strategy for conventional
vehicles, has been widely deployed around the world since the 1960s. On the
other hand, the last decade has witnessed significant advances in connected and
automated vehicle (CAV) technology and its great potential for improving
safety, mobility and environmental sustainability. Therefore, a large amount of
research has been conducted on cooperative ramp merging for CAVs only. However,
it is expected that the phase of mixed traffic, namely the coexistence of both
human-driven vehicles and CAVs, would last for a long time. Since there is
little research on the system-wide ramp control with mixed traffic conditions,
the paper aims to close this gap by proposing an innovative system architecture
and reviewing the state-of-the-art studies on the key components of the
proposed system. These components include traffic state estimation, ramp
metering, driving behavior modeling, and coordination of CAVs. All reviewed
literature plot an extensive landscape for the proposed system-wide coordinated
ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE
- ITSC 201
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
Two-way multi-lane traffic model for pedestrians in corridors
We extend the Aw-Rascle macroscopic model of car traffic into a two-way
multi-lane model of pedestrian traffic. Within this model, we propose a
technique for the handling of the congestion constraint, i.e. the fact that the
pedestrian density cannot exceed a maximal density corresponding to contact
between pedestrians. In a first step, we propose a singularly perturbed
pressure relation which models the fact that the pedestrian velocity is
considerably reduced, if not blocked, at congestion. In a second step, we carry
over the singular limit into the model and show that abrupt transitions between
compressible flow (in the uncongested regions) to incompressible flow (in
congested regions) occur. We also investigate the hyperbolicity of the two-way
models and show that they can lose their hyperbolicity in some cases. We study
a diffusive correction of these models and discuss the characteristic time and
length scales of the instability
Hybrid stochastic kinetic description of two-dimensional traffic dynamics
In this work we present a two-dimensional kinetic traffic model which takes
into account speed changes both when vehicles interact along the road lanes and
when they change lane. Assuming that lane changes are less frequent than
interactions along the same lane and considering that their mathematical
description can be done up to some uncertainty in the model parameters, we
derive a hybrid stochastic Fokker-Planck-Boltzmann equation in the
quasi-invariant interaction limit. By means of suitable numerical methods,
precisely structure preserving and direct Monte Carlo schemes, we use this
equation to compute theoretical speed-density diagrams of traffic both along
and across the lanes, including estimates of the data dispersion, and validate
them against real data
Cellular Automata Models of Road Traffic
In this paper, we give an elaborate and understandable review of traffic
cellular automata (TCA) models, which are a class of computationally efficient
microscopic traffic flow models. TCA models arise from the physics discipline
of statistical mechanics, having the goal of reproducing the correct
macroscopic behaviour based on a minimal description of microscopic
interactions. After giving an overview of cellular automata (CA) models, their
background and physical setup, we introduce the mathematical notations, show
how to perform measurements on a TCA model's lattice of cells, as well as how
to convert these quantities into real-world units and vice versa. The majority
of this paper then relays an extensive account of the behavioural aspects of
several TCA models encountered in literature. Already, several reviews of TCA
models exist, but none of them consider all the models exclusively from the
behavioural point of view. In this respect, our overview fills this void, as it
focusses on the behaviour of the TCA models, by means of time-space and
phase-space diagrams, and histograms showing the distributions of vehicles'
speeds, space, and time gaps. In the report, we subsequently give a concise
overview of TCA models that are employed in a multi-lane setting, and some of
the TCA models used to describe city traffic as a two-dimensional grid of
cells, or as a road network with explicitly modelled intersections. The final
part of the paper illustrates some of the more common analytical approximations
to single-cell TCA models.Comment: Accepted for publication in "Physics Reports". A version of this
paper with high-quality images can be found at: http://phdsven.dyns.cx (go to
"Papers written"
Steady-state traffic flow on a ring road with up- and down- slopes
This paper studies steady-state traffic flow on a ring road with up- and
down- slopes using a semi-discrete model. By exploiting the relations between
the semi-discrete and the continuum models, a steady-state solution is uniquely
determined for a given total number of vehicles on the ring road. The solution
is exact and always stable with respect to the first-order continuum model,
whereas it is a good approximation with respect to the semi-discrete model
provided that the involved equilibrium constant states are linearly stable. In
an otherwise case, the instability of one or more equilibria could trigger
stop-and-go waves propagating in certain road sections or throughout the ring
road. The indicated results are reasonable and thus physically significant for
a better understanding of real traffic flow on an inhomogeneous road
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