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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: 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: 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: 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: 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: 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: Jaume Barceló Publisher: Springer Science & Business Media ISBN: 1441961429 Category : Business & Economics Languages : en Pages : 450
Book Description
The increasing power of computer technologies, the evolution of software en- neering and the advent of the intelligent transport systems has prompted traf c simulation to become one of the most used approaches for traf c analysis in s- port of the design and evaluation of traf c systems. The ability of traf c simulation to emulate the time variability of traf c phenomena makes it a unique tool for capturing the complexity of traf c systems. In recent years, traf c simulation – and namely microscopic traf c simulation – has moved from the academic to the professional world. A wide variety of traf- c simulation software is currently available on the market and it is utilized by thousands of users, consultants, researchers and public agencies. Microscopic traf c simulation based on the emulation of traf c ows from the dynamics of individual vehicles is becoming one the most attractive approaches. However, traf c simulation still lacks a uni ed treatment. Dozens of papers on theory and applications are published in scienti c journals every year. A search of simulation-related papers and workshops through the proceedings of the last annual TRB meetings would support this assertion, as would a review of the minutes from speci cally dedicated meetings such as the International Symposiums on Traf c Simulation (Yokohama, 2002; Lausanne, 2006; Brisbane, 2008) or the International Workshops on Traf c Modeling and Simulation (Tucson, 2001; Barcelona, 2003; Sedona, 2005; Graz 2008). Yet, the only comprehensive treatment of the subject to be found so far is in the user’s manuals of various software products.
Author: Dung-Ying Lin Publisher: ISBN: Category : Languages : en Pages : 340
Book Description
Dynamic Traffic Assignment (DTA) is gaining wider acceptance among agencies and practitioners because it serves as a more realistic representation of real-world traffic phenomena than static traffic assignment. Many metropolitan planning organizations and transportation departments are beginning to utilize DTA to predict traffic flows within their networks when conducting traffic analysis or evaluating management measures. To analyze DTA-based optimization applications, it is critical to obtain the dual (or gradient) information as dual information can typically be employed as a search direction in algorithmic design. However, very limited number of approaches can be used to estimate network-wide dual information while maintaining the potential to scale. This dissertation investigates the theoretical/practical aspects of DTA-based dual approximation techniques and explores DTA applications in the context of various transportation models, such as transportation network design, off-line DTA capacity calibration and dynamic congestion pricing. Each of the later entities is formulated as bi-level programs. Transportation Network Design Problem (NDP) aims to determine the optimal network expansion policy under a given budget constraint. NDP is bi-level by nature and can be considered a static case of a Stackelberg game, in which transportation planners (leaders) attempt to optimize the overall transportation system while road users (followers) attempt to achieve their own maximal benefit. The first part of this dissertation attempts to study NDP by combining a decomposition-based algorithmic structure with dual variable approximation techniques derived from linear programming theory. One of the critical elements in considering any real-time traffic management strategy requires assessing network traffic dynamics. Traffic is inherently dynamic, since it features congestion patterns that evolve over time and queues that form and dissipate over a planning horizon. It is therefore imperative to calibrate the DTA model such that it can accurately reproduce field observations and avoid erroneous flow predictions when evaluating traffic management strategies. Satisfactory calibration of the DTA model is an onerous task due to the large number of variables that can be modified and the intensive computational resources required. In this dissertation, the off-line DTA capacity calibration problem is studied in an attempt to devise a systematic approach for effective model calibration. Congestion pricing has increasingly been seen as a powerful tool for both managing congestion and generating revenue for infrastructure maintenance and sustainable development. By carefully levying tolls on roadways, a more efficient and optimal network flow pattern can be generated. Furthermore, congestion pricing acts as an effective travel demand management strategy that reduces peak period vehicle trips by encouraging people to shift to more efficient modes such as transit. Recently, with the increase in the number of highway Build-Operate-Transfer (B-O-T) projects, tolling has been interpreted as an effective way to generate revenue to offset the construction and maintenance costs of infrastructure. To maximize the benefits of congestion pricing, a careful analysis based on dynamic traffic conditions has to be conducted before determining tolls, since sub-optimal tolls can significantly worsen the system performance. Combining a network-wide time-varying toll analysis together with an efficient solution-building approach will be one of the main contributions of this dissertation. The problems mentioned above are typically framed as bi-level programs, which pose considerable challenges in theory and as well as in application. Due to the non-convex solution space and inherent NP-complete complexity, a majority of recent research efforts have focused on tackling bi-level programs using meta-heuristics. These approaches allow for the efficient exploration of complex solution spaces and the identification of potential global optima. Accordingly, this dissertation also attempts to present and compare several meta-heuristics through extensive numerical.
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: Ramachandran Balakrishna
Author: Ramachandran Balakrishna
Author: Ramachandran Balakrishna
Author: Constantinos Antoniou
Author: Lu Lu (S.M.)
Author: Constantinos Antoniou
Author: Enyang Huang
Author: Ashish Gupta (S.M.)
Author: Jaume Barceló
Author: Dung-Ying Lin
Author: Vikrant Suhas Vaze