Calibration of Traffic Simulation Models using SPSA

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Γεωπληροφορική

Calibration of Traffic Simulation Models using SPSA

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Γεωπληροφορική

Computational framework for the estimation of dynamic OD trip matrices

Origin-Destination (OD) trip matrices describe traffic behavior patterns across the network and play a key role as primary data input to many traffic models. OD matrices are a critical requirement, in traffic assignment models, static or dynamic. However, OD matrices are not yet directly observable; thus, the current practice consists of adjusting an initial a priori matrix from link flow counts, speeds, travel times and other aggregate demand data, supplied by a layout of traffic counting stations. The availability of new traffic measurements from ICT applications offers the possibility to formulate and develop more efficient algorithms, especially suited for real-time applications. This work proposes an integrated computational framework in which an off-line procedure generates the time-sliced OD matrices, which are the input to an on-line estimator, whose sensitivity with respect to the available traffic measurements is analyzed.Origin-Destination (OD) trip matrices describe traffic behavior patterns across the network and play a key role as primary data input to many traffic models. OD matrices are a critical requirement, in traffic assignment models, static or dynamic. However, OD matrices are not yet directly observable; thus, the current practice consists of adjusting an initial a priori matrix from link flow counts, speeds, travel times and other aggregate demand data, supplied by a layout of traffic counting stations. The availability of new traffic measurements from ICT applications offers the possibility to formulate and develop more efficient algorithms, especially suited for real-time applications; whose efficiency depends, among other factors, on the quality of the seed matrix. This paper proposes an integrated computational framework in which an off-line procedure generates the time-sliced OD matrices, which are the input to an on-line estimator, whose sensitivity with respect to the available traffic measurements is analyzed.Preprin

Online calibration for simulation-based dynamic traffic assignment : towards large-scale and real-time performance

Thesis: Ph. D. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 149-152).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.by Haizheng Zhang.Ph. D. in Transportatio

Improved Calibration Method for Dynamic Traffic Assignment Models

The calibration of dynamic traffic assignment (DTA) models involves the estimation of model parameters to best replicate real-world measurements. Good calibration is essential to estimate and predict accurately traffic states, which are crucial for traffic management applications to alleviate congestion. A widely used approach to calibrate simulation-based DTA models is the extended Kalman filter (EKF). The EKF assumes that the DTA model parameters are unconstrained, although they are in fact constrained; for instance, origin–destination (O-D) flows are nonnegative. This assumption is typically not problematic for small- and medium-scale networks in which the EKF has been successfully applied. However, in large-scale networks (which typically contain numbers of O-D pairs with small magnitudes of flow), the estimates may severely violate constraints. In consequence, simply truncating the infeasible estimates may result in the divergence of EKF, leading to extremely poor state estimations and predictions. To address this issue, a constrained EKF (CEKF) approach is presented; it imposes constraints on the posterior distribution of the state estimators to obtain the maximum a posteriori (MAP) estimates that are feasible. The MAP estimates are obtained with a heuristic followed by the coordinate descent method. The procedure determines the optimum and are computationally faster by 31.5% over coordinate descent and by 94.9% over the interior point method. Experiments on the Singapore expressway network indicated that the CEKF significantly improved model accuracy and outperformed the traditional EKF (up to 78.17%) and generalized least squares (up to 17.13%) approaches in state estimation and prediction

Algorithmic and implementation aspects of on-line calibration of Dynamic Traffic Assignment

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Cataloged from PDF version of thesis. Page 114 blank.Includes bibliographical references (p. 109-113).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.by Enyang Huang.S.M

Calibration of dynamic traffic assignment models with point-to-point traffic surveillance

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2007.Includes bibliographical references (p. 173-180).Accurate calibration of demand and supply simulators within a Dynamic Traffic Assignment (DTA) system is critical for the provision of consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as Automatic Vehicle Identification (AVI) technology provide a rich source of disaggregate traffic data. This thesis presents a methodology for calibration of demand and supply model parameters using travel time measurements obtained from these emerging traffic sensing technologies. The calibration problem has been formulated in two different frameworks, viz. in a state-space framework and in a stochastic optimization framework. Three different algorithms are used for solving the calibration problem, a gradient approximation based path search method (SPSA), a random search meta-heuristic (GA) and a Monte-Carlo simulation based technique (Particle Filter). The methodology is first tested using a small synthetic study network to illustrate its effectiveness. Later the methodology is applied to a real traffic network in the Lower Westchester County region in New York to demonstrate its scalability.(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.by Vikrant Vaze.S.M

PC-SPSA: Employing Dimensionality Reduction to Limit SPSA Search Noise in DTA Model Calibration

Calibration and validation have long been a significant topic in traffic model development. In fact, when moving to dynamic traffic assignment (DTA) models, the need to dynamically update the demand and supply components creates a considerable burden on the existing calibration algorithms, often rendering them impractical. These calibration approaches are mostly restricted either due to non-linearity or increasing problem dimensionality. Simultaneous perturbation stochastic approximation (SPSA) has been proposed for the DTA model calibration, with encouraging results, for more than a decade. However, it often fails to converge reasonably with the increase in problem size and complexity. In this paper, we combine SPSA with principal components analysis (PCA) to form a new algorithm, we call, PC-SPSA. The PCA limits the search area of SPSA within the structural relationships captured from historical estimates in lower dimensions, reducing the problem size and complexity. We formulate the algorithm, demonstrate its operation, and explore its performance using an urban network of Vitoria, Spain. The practical issues that emerge from the scale of different variables and bounding their values are also analyzed through a sensitivity analysis using a non-linear synthetic function