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Author: Ramachandran Balakrishna Publisher: VDM Publishing ISBN: 9783836420891 Category : Education Languages : en Pages : 208
Book Description
Dynamic Traffic Assignment (DTA) models estimate and predict the evolution of congestion through detailed models and algorithms that capture travel demand, network supply and their complex interactions. The availability of rich time-varying traffic data spanning multiple days, collected by automatic surveillance technology, provides the opportunity to calibrate such a DTA model's many inputs and parameters so that its outputs reflect field conditions. DTA models are generally calibrated sequentially: supply model calibration (assuming known demand inputs) is followed by demand calibration with fixed supply parameters. This book develops an off-line DTA model calibration methodology for the simultaneous estimation of all demand and supply inputs and parameters, using sensor data. A complex, non-linear, stochastic optimization problem is solved, using any general traffic data. Case studies with DynaMIT, a DTA model with traffic estimation and prediction capabilities, indicate that the simultaneous approach significantly outperforms the sequential state of the art. This book is addressed to professionals and researchers who apply large-scale transportation models.
Author: Ramachandran Balakrishna Publisher: VDM Publishing ISBN: 9783836420891 Category : Education Languages : en Pages : 208
Book Description
Dynamic Traffic Assignment (DTA) models estimate and predict the evolution of congestion through detailed models and algorithms that capture travel demand, network supply and their complex interactions. The availability of rich time-varying traffic data spanning multiple days, collected by automatic surveillance technology, provides the opportunity to calibrate such a DTA model's many inputs and parameters so that its outputs reflect field conditions. DTA models are generally calibrated sequentially: supply model calibration (assuming known demand inputs) is followed by demand calibration with fixed supply parameters. This book develops an off-line DTA model calibration methodology for the simultaneous estimation of all demand and supply inputs and parameters, using sensor data. A complex, non-linear, stochastic optimization problem is solved, using any general traffic data. Case studies with DynaMIT, a DTA model with traffic estimation and prediction capabilities, indicate that the simultaneous approach significantly outperforms the sequential state of the art. This book is addressed to professionals and researchers who apply large-scale transportation models.
Author: Constantinos Antoniou Publisher: VDM Publishing ISBN: Category : Computers Languages : en Pages : 164
Book Description
Traffic estimation and prediction (or dynamic traffic assignment) models are expected to contribute to the reduction of travel time delays. In this book, an on-line calibration approach that jointly estimates all model parameters is presented. The methodology imposes no restrictions on the models, the parameters or the data that can be handled, and emerging or future data can be easily incorporated. The modeling approach is applicable to any simulation model and is not restricted to the application domain covered in this book. Several modified, non-linear Kalman Filter methodologies are presented, e.g. Extended Kalman Filter (EKF), Iterated EKF, Limiting EKF, and Unscented Kalman Filter. Extensive case studies on freeway networks in Europe and the US are used to demonstrate the approach, to verify the importance of on-line calibration, and to test the presented algorithms. The main target audience of this book comprises Intelligent Transportation Systems researchers and graduate students, as well as practitioners, including Metropolitan Planning Organization engineers and Traffic Management Center operators, and any reader with an interest in dynamic state and parameter estimation.
Author: Constantinos Antoniou Publisher: ISBN: Category : Languages : en Pages : 153
Book Description
(Cont.) application, the EKF has more desirable properties than the UKF. Furthermore, the Limiting EKF provides accuracy comparable to that of the best algorithm (EKF), but with computational complexity which is order(s) of magnitude lower than the other algorithms.
Author: Ramachandran Balakrishna Publisher: ISBN: Category : Languages : en Pages : 212
Book Description
(Cont.) Case studies with DynaMIT, a DTA model with traffic estimation and prediction capabilities, are used to demonstrate and validate the proposed methodology. A synthetic traffic network with known demand parameters and simulated sensor data is used to illustrate the improvement over the sequential approach, the ability to accurately recover underlying model parameters, and robustness in a variety of demand and supply situations. Archived sensor data and a network from Los Angeles, CA are then used to demonstrate scalability. The benefit of the proposed methodology is validated through a real-time test of the calibrated DynaMIT's estimation and prediction accuracy, based on sensor data not used for calibration. Results indicate that the simultaneous approach significantly outperforms the sequential state of the art.
Author: Haizheng Zhang (Ph. D.) Publisher: ISBN: Category : Languages : en Pages : 152
Book Description
The severity of traffic congestion is increasing each year in the US, resulting in higher travel times, and increased energy consumption and emissions. They have led to an increasing emphasis on the development of tools for trac management, which intends to alleviate congestion by more eciently utilizing the existing infrastructure. Eective trac management necessitates the generation of accurate short-term predictions of trac states and in this context, simulation-based Dynamic Trac Assignment (DTA) systems have gained prominence over the years. However, a key challenge that remains to be addressed with real-time DTA systems is their scalability and accuracy for applications to large-scale urban networks. A key component of real-time DTA systems that impacts scalability and accuracy is online calibration which attempts to adjust simulation parameters in real-time to match as closely as possible simulated measurements with real-time surveillance data. This thesis contributes to the existing literature on online calibration of DTA systems in three respects: (1) modeling explicitly the stochasticity in simulators and thereby improving accuracy; (2) augmenting the State Space Model (SSM) to capture the delayed measurements on large-scale and congested networks; (3) presenting a gradient estimation procedure called partitioned simultaneous perturbation (PSP) that utilizes an assumed sparse gradient structure to facilitate real-time performance. The results demonstrate that, first, the proposed approach to address stochasticity improves the accuracy of supply calibration on a synthetic network. Second, the augmented SSM improves both estimation and prediction accuracy on a congested synthetic network and the large-scale Singapore expressway network. Finally, compared with the traditional finite difference method, the PSP reduces the number of computations by 90% and achieves the same calibration accuracy on the Singapore expressway network. The proposed methodologies have important applications in the deployment of real-time DTA systems for large scale urban networks.
Author: Enyang Huang Publisher: ISBN: Category : Languages : en Pages : 114
Book Description
This thesis compares alternative and proposes new candidate algorithms for the online calibration of Dynamic Traffic Assignment (DTA). The thesis presents two formulations to on-line calibration: 1) The classical statespace formulation and 2) The direct optimization formulation. Extended Kalman Filter (EKF) is presented and validated under the state-space formulation. Pattern Search (PS), Conjugate Gradient Method (CG) and Gradient Descent (GD) are presented and validated under the direct optimization formulation. The feasibility of the approach is demonstrated by showing superior accuracy performance over alternative DTA model with limited calibration capabilities. Although numerically promising, the computational complexity of these base-line algorithms remain high and their application to large networks is still questionable. To address the issue of scalability, this thesis proposes novel extensions of the aforementioned GD and EKF algorithms. On the side of algorithmic advancement, the Partitioned Simultaneous Perturbation (PSP) method is proposed to overcome the computational burden associated with the Jacobian approximation within GD and EKF algorithms. PSP-GD and PSP-EKF prove to be capable of producing prediction results that are comparable to that of the GD and EKF, despite achieving speed performance that are orders of magnitude faster. On the side of algorithmic implementation, the computational burden of EKF and GD are distributed onto multiple processors. The feasibility and effectiveness of the Para-GD and Para-EKF algorithms are demonstrated and it is concluded that that distributed computing significantly increases the overall calibration speed.
Author: Haizheng Zhang (S.M.) Publisher: ISBN: Category : Languages : en Pages : 86
Book Description
The calibration (estimation of inputs and parameters) for dynamic traffic assignment (DTA) systems is a crucial process for traffic prediction accuracy, and thus critical to global traffic management applications to reduce traffic congestion. In support of the real-time traffic management, the DTA calibration algorithm should also be online, in terms of: 1) estimating inputs and parameters in a time interval only based on data up to that time; 2) performing calibration faster than real-time data generation. Generalized least squares (GLS) methods and Kalman filter-based methods are proved useful in online calibration. However, in literature, the road networks selected to test online calibration algorithms are usually simple and have small number of parameters. Thus their effectiveness when applied to high dimensions and large networks is not well proved. In this thesis, we implemented the extended Kalman filter (EKF) and tested it on the Singapore expressway network with synthetic data that replicate real world demand level. The EKF is diverging and the DTA system is even worse than when no calibration is applied. The problem lies in the truncation process in DTA systems. When estimated demand values are negative, they are truncated to 0 and the overall demand is overestimated. To overcome this problem, this thesis presents a modified EKF method, called constrained EKF. Constrained EKF solves the problem of over-estimating the overall demand by imposing constraints on the posterior distribution of the state estimators and obtain the maximum a posteriori (MAP) estimates within the feasible region. An algorithm of iteratively adding equality constraints followed by the coordinate descent method is applied to obtain the MAP estimates. In our case study, this constrained EKF implementation added less than 10 seconds of computation time and improved EKF significantly. Results show that it also outperforms GLS, probably because its inherent covariance update procedure has an advantage of adapting changes compared to fixed covariance matrix setting in GLS. The contributions of this thesis include: 1) conducting online calibration algorithms on a large network with relatively high dimensional parameters, 2) identifying drawbacks of a widely applied solution for online DTA calibration in a large network, 3) improving an existing algorithm from non-convergence to great performance, 4) proposing an efficient and simple method for the improved algorithm, 5) attaining better performance than an existing benchmark algorithm.
Author: Vikrant Suhas Vaze Publisher: ISBN: Category : Languages : en Pages : 180
Book Description
(Cont.) The estimation results are tested using a calibrated Microscopic Traffic Simulator (MITSIMLab). The results are compared to the base case of calibration using only the conventional point sensor data. The results indicate that the utilization of AVI data significantly improves the calibration accuracy.
Author: Lu Lu (S.M.) Publisher: ISBN: Category : Languages : en Pages : 111
Book Description
The off-line calibration is a crucial step for the successful application of Dynamic Traffic Assignment (DTA) models in transportation planning and real time traffic management. While traditional approaches focus on the separate or sequential estimation of demand and supply in a DTA system, a recently proposed framework calibrates the demand and supply models simultaneously by formulating the off-line calibration as a constrained optimization problem. Simultaneous Perturbation Stochastic Approximation (SPSA) has been reported in the literature to be the most suitable solution algorithm for this problem due to its highly efficient gradient estimation approach. However, it turns out that the performance of SPSA in terms of convergence rate and long run accuracy can deteriorate significantly when the physical network size and the number of considered time intervals increase. To overcome this problem, this thesis proposes a new algorithm, called Weighted SPSA, or W-SPSA. W-SPSA improves SPSA's gradient estimation process by effectively reducing the noise generated by irrelevant measurements. Synthetic tests are performed to systematically compare the performance of SPSA and W-SPSA. W-SPSA shows scalability and robustness in the tests and outperforms SPSA under different problem scales and characteristics. The application of W-SPSA in real world large-scale DTA systems is demonstrated with a case study of the entire Singapore expressway network. Results show that WSPSA is a more suitable algorithm than SPSA for the off-line calibration of large-scale DTA models. The contributions of the thesis include: 1) identifying limitations of a state-of-the- art solution algorithm for the DTA off-line calibration problem, 2) presenting rigorous definitions of an enhanced algorithm and proposing approaches to estimate the required algorithm parameters, 3) systematically comparing the performance of the new algorithm against the state-of-the-art, 4) demonstrating the characteristics of the new algorithm through experiments, and 5) discussing the general steps and empirical technical considerations when tackling real world DTA off-line calibration problems.
Author: Ramachandran Balakrishna
Author: Ramachandran Balakrishna
Author: Constantinos Antoniou
Author: Constantinos Antoniou
Author: Ramachandran Balakrishna
Author: Haizheng Zhang (Ph. D.)
Author: Enyang Huang
Author: Haizheng Zhang (S.M.)
Author: Vikrant Suhas Vaze
Author: Lu Lu (S.M.)
Author: Kunal Kamlakar Kundé