6,686 research outputs found
Speed dependent stochasticity capacitates Newell model for synchronized flow and oscillation growth pattern
This paper has incorporated the stochasticity into the Newell car following
model. Three stochastic driving factors have been considered: (i) Driver's
acceleration is bounded. (ii) Driver's deceleration includes stochastic
component, which is depicted by a deceleration with the randomization
probability that is assumed to increase with the speed. (iii) Vehicles in the
jam state have a larger randomization probability. Two simulation scenarios are
conducted to test the model. In the first scenario, traffic flow on a circular
road is investigated. In the second scenario, empirical traffic flow patterns
in the NGSIM data induced by a rubberneck bottleneck is studied, and the
simulated traffic oscillations and synchronized traffic flow are consistent
with the empirical patterns. Moreover, two experiments of model calibration and
validation are conducted. The first is to calibrate and validate using
experimental data, which illustrates that the concave growth pattern has been
quantitatively simulated. The second is to calibrate and cross validate
vehicles' trajectories using NGSIM data, which exhibits that the car following
behaviors of single vehicles can be well described. Therefore, our study
highlights the importance of speed dependent stochasticity in traffic flow
modeling, which cannot be ignored as in most car-following studies.Comment: 19 page
From Behavioral Psychology to Acceleration Modeling: Calibration, Validation, and Exploration of Drivers Cognitive and Safety Parameters in a Risk-Taking Environment
We investigate a utility-based approach for driver car-following behavioral
modeling while analyzing different aspects of the model characteristics
especially in terms of capturing different fundamental diagram regions and
safety proxy indices. The adopted model came from an elementary thought where
drivers associate subjective utilities for accelerations (i.e. gain in travel
times) and subjective dis-utilities for decelerations (i.e. loss in travel
time) with a perceived probability of being involved in rear-end collision
crashes. Following the testing of the model general structure, the authors
translate the corresponding behavioral psychology theory - prospect theory -
into an efficientmicroscopic traffic modeling with more elaborate stochastic
characteristics considered in a risk-taking environment. The formulated model
offers a better understanding of drivers behavior, particularly under
extreme/incident conditions.Comment: Submitted to Transp. Res. B: Methodologica
Learning Traffic Flow Dynamics using Random Fields
This paper presents a mesoscopic traffic flow model that explicitly describes
the spatio-temporal evolution of the probability distributions of vehicle
trajectories. The dynamics are represented by a sequence of factor graphs,
which enable learning of traffic dynamics from limited Lagrangian measurements
using an efficient message passing technique. The approach ensures that
estimated speeds and traffic densities are non-negative with probability one.
The estimation technique is tested using vehicle trajectory datasets generated
using an independent microscopic traffic simulator and is shown to efficiently
reproduce traffic conditions with probe vehicle penetration levels as little as
10\%. The proposed algorithm is also compared with state-of-the-art traffic
state estimation techniques developed for the same purpose and it is shown that
the proposed approach can outperform the state-of-the-art techniques in terms
reconstruction accuracy
Improved 2D Intelligent Driver Model simulating synchronized flow and evolution concavity in traffic flow
This paper firstly show that 2 Dimensional Intelligent Driver Model (Jiang et
al., PloS one, 9(4), e94351, 2014) is not able to replicate the synchronized
traffic flow. Then we propose an improved model by considering the difference
between the driving behaviors at high speeds and that at low speeds.
Simulations show that the improved model can reproduce the phase transition
from synchronized flow to wide moving jams, the spatiotemporal patterns of
traffic flow induced by traffic bottleneck, and the evolution concavity of
traffic oscillations (i.e. the standard deviation of the velocities of vehicles
increases in a concave/linear way along the platoon). Validating results show
that the empirical time series of traffic speed obtained from Floating Car Data
can be well simulated as well.Comment: arXiv admin note: text overlap with arXiv:1507.0405
Cellular automaton model with dynamical 2D speed-gap relation reproduces empirical and experimental features of traffic flow
This paper proposes an improved cellular automaton traffic flow model based
on the brake light model, which takes into account that the desired time gap of
vehicles is remarkably larger than one second. Although the hypothetical steady
state of vehicles in the deterministic limit corresponds to a unique
relationship between speeds and gaps in the proposed model, the traffic states
of vehicles dynamically span a two-dimensional region in the plane of speed
versus gap, due to the various randomizations. It is shown that the model is
able to well reproduce (i) the free flow, synchronized flow, jam as well as the
transitions among the three phases; (ii) the evolution features of disturbances
and the spatiotemporal patterns in a car-following platoon; (iii) the empirical
time series of traffic speed obtained from NGSIM data. Therefore, we argue that
a model can potentially reproduce the empirical and experimental features of
traffic flow, provided that the traffic states are able to dynamically span a
2D speed-gap region
Cellular automaton model simulating spatiotemporal patterns, phase transitions and evolution concavity in traffic flow
This paper firstly show that a recent model (Tian et al., Transpn. Res. B 71,
138-157, 2015) is not able to well replicate the evolution concavity in traffic
flow, i.e. the standard deviation of vehicles increases in a concave/linear way
along the platoon. Then we propose an improved model by introducing the safe
speed, the logistic function of the randomization probability, and small
randomization deceleration for low-speed vehicles into the model. Simulations
show that the improved model can well reproduce the metastable states, the
spatiotemporal patterns, the phase transition behaviors of traffic flow, and
the evolution concavity of traffic oscillations. Validating results show that
the empirical time series of traffic speed obtained from Floating Car Data can
be well simulated as well
Modelling traffic flow fluctuations
By analyzing empirical time headway distributions of traffic flow, a
hypothesis about the underlying stochastic process can be drawn. The results
found lead to the assumption that the headways of individual vehicles
follow a linear stochastic process with multiplicative noise, . The resulting stationary distribution has a
power-law tail, especially for densities where cars are interacting strongly.
Analyzing additionally the headways for accelerating and decelerating cars, the
slow-to-start effect proposed as a mechanism for traffic jam stability can be
demonstrated explicitly. Finally, the standard deviation of the speed
differences between following cars can be used to get a clear characterization
of (at least) three different regimes of traffic flow that can be identified in
the data. Using the empirical results to enhance a microscopic traffic flow
model, it can be demonstrated that such a model describes the fluctuations of
traffic flow quite satisfactorily.Comment: 11 pages, 12 figure
Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning
This study proposes a framework for human-like autonomous car-following
planning based on deep reinforcement learning (deep RL). Historical driving
data are fed into a simulation environment where an RL agent learns from trial
and error interactions based on a reward function that signals how much the
agent deviates from the empirical data. Through these interactions, an optimal
policy, or car-following model that maps in a human-like way from speed,
relative speed between a lead and following vehicle, and inter-vehicle spacing
to acceleration of a following vehicle is finally obtained. The model can be
continuously updated when more data are fed in. Two thousand car-following
periods extracted from the 2015 Shanghai Naturalistic Driving Study were used
to train the model and compare its performance with that of traditional and
recent data-driven car-following models. As shown by this study results, a deep
deterministic policy gradient car-following model that uses disparity between
simulated and observed speed as the reward function and considers a reaction
delay of 1s, denoted as DDPGvRT, can reproduce human-like car-following
behavior with higher accuracy than traditional and recent data-driven
car-following models. Specifically, the DDPGvRT model has a spacing validation
error of 18% and speed validation error of 5%, which are less than those of
other models, including the intelligent driver model, models based on locally
weighted regression, and conventional neural network-based models. Moreover,
the DDPGvRT demonstrates good capability of generalization to various driving
situations and can adapt to different drivers by continuously learning. This
study demonstrates that reinforcement learning methodology can offer insight
into driver behavior and can contribute to the development of human-like
autonomous driving algorithms and traffic-flow models
Transportation Planning and Traffic Flow Models
In this paper, we focus on the different traffic flow models that exist in
literature. Due to our frequently encountered confusion among traffic engineers
and policy makers, this paper goes into more detail about transportation
planning models on the one hand, and traffic flow models on the other hand. The
former deal with households that make certain decisions which lead to
transportation and the use of infrastructure, as opposed to the latter which
explicitly describe the physical propagation of traffic flows in a road
network. Our goal is not to give a full account (as that would be a
dissertation of its own, given the broadness of the field), but rather to
impose upon the reader a thorough feeling for the differences between
transportation planning and traffic flow models. Because of the high course of
progress over the last decade (or even during the last five years), this paper
tries to chronicle both past models, as well as some of the latest developments
in this area
Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network
This study proposes a deep learning methodology to predict the propagation of
traffic shockwaves. The input to the deep neural network is time-space diagram
of the study segment, and the output of the network is the predicted (future)
propagation of the shockwave on the study segment in the form of time-space
diagram. The main feature of the proposed methodology is the ability to extract
the features embedded in the time-space diagram to predict the propagation of
traffic shockwaves
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