5,524 research outputs found
A Bayesian Programming Approach to Car-following Model Calibration and Validation using Limited Data
Traffic simulation software is used by transportation researchers and
engineers to design and evaluate changes to roadways. These simulators are
driven by models of microscopic driver behavior from which macroscopic measures
like flow and congestion can be derived. Many models are designed for a subset
of possible traffic scenarios and roadway configurations, while others have no
explicit constraints on their application. Work zones (WZs) are one scenario
for which no model to date has reproduced realistic driving behavior. This
makes it difficult to optimize for safety and other metrics when designing a
WZ. The Federal Highway Administration commissioned the USDOT Volpe Center to
develop a car-following (CF) model for use in microscopic simulators that can
capture and reproduce driver behavior accurately within and outside of WZs.
Volpe also performed a naturalistic driving study to collect telematics data
from vehicles driven on roads with WZs for use in model calibration. During
model development, Volpe researchers observed difficulties in calibrating their
model, leaving them to question whether there existed flaws in their model, in
the data, or in the procedure used to calibrate the model using the data. In
this thesis, I use Bayesian methods for data analysis and parameter estimation
to explore and, where possible, address these questions. First, I use Bayesian
inference to measure the sufficiency of the size of the data set. Second, I
compare the procedure and results of the genetic algorithm based calibration
performed by the Volpe researchers with those of Bayesian calibration. Third, I
explore the benefits of modeling CF hierarchically. Finally, I apply what was
learned in the first three phases using an established CF model, Wiedemann 99,
to the probabilistic modeling of the Volpe model. Validation is performed using
information criteria as an estimate of predictive accuracy.Comment: Master's thesis, 64 pages, 10 tables, 9 figure
A Bayesian Programming Approach to Car-following Model Calibration and Validation using Limited Data
Traffic simulation software is used by transportation researchers and engineers to design and evaluate changes to roadway networks. Underlying these simulators are mathematical models of microscopic driver behavior from which macroscopic measures of flow and congestion can be recovered. Many models are intended to apply to only a subset of possible traffic scenarios and roadway configurations, while others do not have any explicit constraint on their applicability. Work zones on highways are one scenario for which no model invented to date has been shown to accurately reproduce realistic driving behavior. This makes it difficult to optimize for safety and other metrics when designing a work zone.
The Federal Highway Administration (FHWA) has commissioned the Volpe National Transportation Systems Center (Volpe) to develop a new car-following model, the Work Zone Driver Model (WZDM), for use in microscopic simulators that captures and reproduces driver behavior equally well within and outside of work zones. Volpe also performed a naturalistic driving study (NDS) to collect telematics data from vehicles driven on highways and urban roads that included work zones for use in model calibration. The data variables are relevant to the car-following model’s prediction task.
During model development, Volpe researchers observed difficulties in calibrating their model, leaving them to question whether there existed flaws in their model, in the data, or in the procedure used to calibrate the model using the data. In this thesis, I use Bayesian methods for data analysis and parameter estimation to explore and, where possible, address these questions.
First, I use Bayesian inference to measure the sufficiency of the size of the NDS data set. Second, I compare the procedure and results of the genetic algorithm-based calibration performed by the Volpe researchers with those of Bayesian calibration. Third, I explore the benefits of modeling car-following hierarchically. Finally, I apply what was learned in the first three phases using an established car-following model to the probabilistic modeling of WZDM. Validation is performed using information criteria as an estimate of predictive accuracy. A third model used for comparison with WZDM in the simulator, Wiedemann ’99, is also modeled probabilistically
A Simplified Cellular Automaton Model for City Traffic
We systematically investigate the effect of blockage sites in a cellular
automaton model for traffic flow. Different scheduling schemes for the blockage
sites are considered. None of them returns a linear relationship between the
fraction of ``green'' time and the throughput. We use this information for a
fast implementation of traffic in Dallas.Comment: 12 pages, 18 figures. submitted to Phys Rev
Imitating Driver Behavior with Generative Adversarial Networks
The ability to accurately predict and simulate human driving behavior is
critical for the development of intelligent transportation systems. Traditional
modeling methods have employed simple parametric models and behavioral cloning.
This paper adopts a method for overcoming the problem of cascading errors
inherent in prior approaches, resulting in realistic behavior that is robust to
trajectory perturbations. We extend Generative Adversarial Imitation Learning
to the training of recurrent policies, and we demonstrate that our model
outperforms rule-based controllers and maximum likelihood models in realistic
highway simulations. Our model both reproduces emergent behavior of human
drivers, such as lane change rate, while maintaining realistic control over
long time horizons.Comment: 8 pages, 6 figure
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
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
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