39,764 research outputs found
Hybrid multilane models for highway traffic
We study effects of lane changing rules on multilane highway traffic using
the Nagel-Schreckenberg cellular automaton model with different schemes for
combining driving lanes (lanes used by default) and overtaking lanes. Three
schemes are considered: a symmetric model, in which all lanes are driving
lanes, an asymmetric model, in which the right lane is a driving lane and the
other lanes are overtaking lanes, a hybrid model, in which the leftmost lane is
an overtaking lane and all the other lanes are driving lanes. In a driving lane
vehicles follow symmetric rules for lane changes to the left and to the right,
while in an overtaking lane vehicles follow asymmetric lane changing rules. We
test these schemes for three- and four-lane traffic mixed with some low-speed
vehicles (having a lower maximum speed) in a closed system with periodic
boundary conditions as well as in an open system with one open lane. Our
results show that the asymmetric model, which reflects the "Keep Right Unless
Overtaking" rule, is more efficient than the other two models. An extensible
software package developed for this study is free available.Comment: 7 pages, 9 figure
Macroscopic Dynamics of Multi-Lane Traffic
We present a macroscopic model of mixed multi-lane freeway traffic that can
be easily calibrated to empirical traffic data, as is shown for Dutch highway
data. The model is derived from a gas-kinetic level of description, including
effects of vehicular space requirements and velocity correlations between
successive vehicles. We also give a derivation of the lane-changing rates. The
resulting dynamic velocity equations contain non-local and anisotropic
interaction terms which allow a robust and efficient numerical simulation of
multi-lane traffic. As demonstrated by various examples, this facilitates the
investigation of synchronization patterns among lanes and effects of on-ramps,
off-ramps, lane closures, or accidents.Comment: For related work see
http://www.theo2.physik.uni-stuttgart.de/helbing.htm
Line-of-Sight Obstruction Analysis for Vehicle-to-Vehicle Network Simulations in a Two-Lane Highway Scenario
In vehicular ad-hoc networks (VANETs) the impact of vehicles as obstacles has
largely been neglected in the past. Recent studies have reported that the
vehicles that obstruct the line-of-sight (LOS) path may introduce 10-20 dB
additional loss, and as a result reduce the communication range. Most of the
traffic mobility models (TMMs) today do not treat other vehicles as obstacles
and thus can not model the impact of LOS obstruction in VANET simulations. In
this paper the LOS obstruction caused by other vehicles is studied in a highway
scenario. First a car-following model is used to characterize the motion of the
vehicles driving in the same direction on a two-lane highway. Vehicles are
allowed to change lanes when necessary. The position of each vehicle is updated
by using the car-following rules together with the lane-changing rules for the
forward motion. Based on the simulated traffic a simple TMM is proposed for
VANET simulations, which is capable to identify the vehicles that are in the
shadow region of other vehicles. The presented traffic mobility model together
with the shadow fading path loss model can take in to account the impact of LOS
obstruction on the total received power in the multiple-lane highway scenarios.Comment: 8 pages, 11 figures, Accepted for publication in the International
Journal of Antennas and Propagation, Special Issue on Radio Wave Propagation
and Wireless Channel Modeling 201
Traffic flow modeling and forecasting using cellular automata and neural networks : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand
In This thesis fine grids are adopted in Cellular Automata (CA) models. The fine-grid models are able to describe traffic flow in detail allowing position, speed, acceleration and deceleration of vehicles simulated in a more realistic way. For urban straight roads, two types of traffic flow, free and car-following flow, have been simulated. A novel five-stage speed-changing CA model is developed to describe free flow. The 1.5-second headway, based on field data, is used to simulate car-following processes, which corrects the headway of 1 second used in all previous CA models. Novel and realistic CA models, based on the Normal Acceptable Space (NAS) method, are proposed to systematically simulate driver behaviour and interactions between drivers to enter single-lane Two-Way Stop-Controlled (TWSC) intersections and roundabouts. The NAS method is based on the two following Gaussian distributions. Distribution of space required for all drivers to enter intersections or roundabouts is assumed to follow a Gaussian distribution, which corresponds to heterogeneity of driver behaviour. While distribution of space required for a single driver to enter an intersection or roundabout is assumed to follow another Gaussian distribution, which corresponds to inconsistency of driver behavior. The effects of passing lanes on single-lane highway traffic are investigated using fine grids CA. Vehicles entering, exiting from and changing lanes on passing lane sections are discussed in detail. In addition, a Genetic Algorithm-based Neural Network (GANN) method is proposed to predict Short-term Traffic Flow (STF) in urban networks, which is expected to be helpful for traffic control. Prediction accuracy and generalization ability of NN are improved by optimizing the number of neurons in the hidden layer and connection weights of NN using genetic operations such as selection, crossover and mutation
A realistic two-lane traffic model for highway traffic
A two-lane extension of a recently proposed cellular automaton model for
traffic flow is discussed. The analysis focuses on the reproduction of the lane
usage inversion and the density dependence of the number of lane changes. It is
shown that the single-lane dynamics can be extended to the two-lane case
without changing the basic properties of the model which are known to be in
good agreement with empirical single-vehicle data. Therefore it is possible to
reproduce various empirically observed two-lane phenomena, like the
synchronization of the lanes, without fine-tuning of the model parameters
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning
This paper introduces a method, based on deep reinforcement learning, for
automatically generating a general purpose decision making function. A Deep
Q-Network agent was trained in a simulated environment to handle speed and lane
change decisions for a truck-trailer combination. In a highway driving case, it
is shown that the method produced an agent that matched or surpassed the
performance of a commonly used reference model. To demonstrate the generality
of the method, the exact same algorithm was also tested by training it for an
overtaking case on a road with oncoming traffic. Furthermore, a novel way of
applying a convolutional neural network to high level input that represents
interchangeable objects is also introduced
Artificial potential functions for highway driving with collision avoidance
We present a set of potential function components to assist an automated or semi-automated vehicle in navigating a multi-lane, populated highway. The resulting potential field is constructed as a superposition of disparate functions for lane- keeping, road-staying, speed preference, and vehicle avoidance and passing. The construction of the vehicle avoidance potential is of primary importance, incorporating the structure and protocol of laned highway driving. Particularly, the shape and dimensions of the potential field behind each obstacle vehicle can appropriately encourage control vehicle slowing and/or passing, depending on the cars' velocities and surrounding traffic. Hard barriers on roadway edges and soft boundaries between navigable lanes keep the vehicle on the highway, with a preference to travel in a lane center
Order parameter model for unstable multilane traffic flow
We discuss a phenomenological approach to the description of unstable vehicle
motion on multilane highways that explains in a simple way the observed
sequence of the phase transitions "free flow -> synchronized motion -> jam" as
well as the hysteresis in the transition "free flow synchronized motion".
We introduce a new variable called order parameter that accounts for possible
correlations in the vehicle motion at different lanes. So, it is principally
due to the "many-body" effects in the car interaction, which enables us to
regard it as an additional independent state variable of traffic flow. Basing
on the latest experimental data (cond-mat/9905216) we assume that these
correlations are due to a small group of "fast" drivers. Taking into account
the general properties of the driver behavior we write the governing equation
for the order parameter. In this context we analyze the instability of
homogeneous traffic flow manifesting itself in both of the mentioned above
phase transitions where, in addition, the transition "synchronized motion ->
jam" also exhibits a similar hysteresis. Besides, the jam is characterized by
the vehicle flows at different lanes being independent of one another. We
specify a certain simplified model in order to study the general features of
the car cluster self-formation under the phase transition "free flow
synchronized motion". In particular, we show that the main local parameters of
the developed cluster are determined by the state characteristics of vehicle
motion only.Comment: REVTeX 3.1, 10 pages with 10 PostScript figure
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