1,564 research outputs found
Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events
Optimal traffic control under incident-driven congestion is crucial for road safety and maintaining network performance. Over the last decade, prediction and simulation of road traffic play important roles in network operation. This dissertation focuses on development of a machine learning-based prediction model, a stochastic cell transmission model (CTM), and an optimisation model. Numerical studies were performed to evaluate the proposed models. The results indicate that proposed models are helpful for road management during road incidents
Real-time Traffic Flow Detection and Prediction Algorithm: Data-Driven Analyses on Spatio-Temporal Traffic Dynamics
Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized
Optimization of path based sensor spacing on a freeway segment for travel time prediction during incidents
Congestion on freeways is increasing and a key source of it is non-recurring incidents. Accurate vehicle travel time predictions are needed during these incidents in order for roadway users to make informed trip decisions. Path based sensors are becoming a leading technology in gathering real-time travel time data. The data is used to make travel time predictions that are then provided through various means, such as dynamic message signs, to roadway users. These types of sensor are located at stationary points along a roadway and collect individual vehicle travel time data from vehicles as they drive pass the sensors.
The accuracy of the predictions, in terms of representing future travel times, is dependent on many factors including the sensor spacing along the roadway, the duration and location of a traffic incident, and the uncongested and congested traffic speeds and traffic flows. Understanding the relationship between the travel time prediction accuracy and the different variables is necessary to optimize sensor spacing. In addition, because incidents occur at different times of the day, have varying durations, occur at different locations, and cause different capacity reductions depending on the severity of the incident, the sensor spacing cannot be based on one incident scenario. Instead, multiple incident scenarios, along with the probability of each occurring, needs to be taken into account.
Path based sensor spacing during incidents on a freeway segment is optimized in this dissertation. In addition, the marginal benefit of additional sensors is calculated. A mathematical model and a solution methodology are developed. The mathematical model applies macroscopic traffic principles and shock wave theory. It calculates the travel time prediction error by sensor spacing during an incident on a freeway segment.
The solution algorithm consists of four main steps. First, historical incident data for the roadway are gathered. Second, the mathematical model is applied to determine the average travel time prediction error by sensor spacing for each of the historical incidents. Third, the weighted average travel time prediction error by sensor spacing is calculated, which considers all the possible incidents and the frequency of each occurring. Fourth, the optimal spacing is chosen which minimizes the weighted average error.
The applicability of the model and solution methodology is demonstrated through a case study of a ten mile freeway segment in Northern New Jersey
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
Fusing Loop and GPS Probe Measurements to Estimate Freeway Density
In an age of ever-increasing penetration of GPS-enabled mobile devices, the
potential of real-time "probe" location information for estimating the state of
transportation networks is receiving increasing attention. Much work has been
done on using probe data to estimate the current speed of vehicle traffic (or
equivalently, trip travel time). While travel times are useful to individual
drivers, the state variable for a large class of traffic models and control
algorithms is vehicle density. Our goal is to use probe data to supplement
traditional, fixed-location loop detector data for density estimation. To this
end, we derive a method based on Rao-Blackwellized particle filters, a
sequential Monte Carlo scheme. We present a simulation where we obtain a 30\%
reduction in density mean absolute percentage error from fusing loop and probe
data, vs. using loop data alone. We also present results using real data from a
19-mile freeway section in Los Angeles, California, where we obtain a 31\%
reduction. In addition, our method's estimate when using only the real-world
probe data, and no loop data, outperformed the estimate produced when only loop
data were used (an 18\% reduction). These results demonstrate that probe data
can be used for traffic density estimation
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