Abstract

Reliable origin/destination (O-D) data are critical to many applications in transportation planning, design and operations. Because of the high costs of and challenges in obtaining reliable O-D trip matrices from surveys or other direct sampling methods, estimating O-D trip tables from a readily available data source, traffic counts, provides an attractive, economical alternative. This project investigates one such an estimation method and implements it in a user-friendly software tool called Visual PFE TD. The developed O-D estimation tool can be used to obtain both static and dynamic O-D trip tables for traffic simulation studies, project evaluations, and transportation planning in a more streamlined and less time-consuming manner. For example, it has been used to obtain an initial seed matrix for Paramics’ O-D estimator to speed up the latter’s O-D estimation process.

A logit path flow estimator (LPFE) originally proposed by Michael Bell (1995) is adopted in this research for inferring both steady and time-dependent O-D trip tables. LPFE is chosen because: 1) it incorporates the logit-based route choice model while avoiding several difficulties encountered in the conventional bi-level formulation; 2) it avoids the difficult dynamic traffic assignment problem through decomposes the dynamic O-D estimation problem into a sequence of static problems, yet takes into account of queuing by linking the static problems across time with residual queues which can be carried over from one period to subsequent periods; and finally, 3) it has been validated in a number of scenarios as a potential tool to determine O-D flows and path travel times in various transportation networks.

In this research, we extended the original LPFE formulation and improved the efficiency of solution algorithms, implemented both steady-state and time-dependent LPFE in an object-oriented programming (OOP) framework, tested the performance of LPFE using synthetic data and quantify the accuracy and reliability of its O-D trip table estimates. We also developed Visual PFE and Visual PFE-TD, the graphic user interfaces (GUI) for both static and time-dependent LPFE.

Our test case studies show that LPFE is able to produce path flows and O-D travel demands that accurately match traffic counts under the logit traffic assignment assumption. We also found that information reflecting the spatial structure of travel demands (e.g., a historical O-D table) is of great value to the improvement of the quality of O-D trip estimates, and that LPFE can still produce satisfying estimates even when traffic counts are only available on a small portion of links, as long as such structural information is maintained in the base O-D table.

Abstract

The central goal of this research is to develop a systematic framework and the support tools to ease, streamline and speed up the calibration of micro simulation projects. Part III of the final report documents the accomplishments achieved in the second phase of the research project. They include the following.

First, to overcome the lengthy time it takes for GA to obtain local and global driving behavior modeling parameters, we implemented a faster heuristic optimization technique, the simultaneous perturbation stochastic approximation (SPSA) and compared its performance with other heuristic optimization methods. Results indicate that SPSA can achieve comparable calibration accuracy with much less computational time than the often used Genetic Algorithm (GA) method.

Second, we developed a much faster O-D estimation tool to obtain an initial time-dependent O-D trip table. This O-D trip table can be used as a seed table in Paramics’ own O-D estimator for further refinement, or directly used in a micro simulation. In either case, the estimation time of O-D trip tables can be considerably shortened. Since our O-D estimation tool makes use of a macroscopic traffic model (logit path flow estimator, or LPFE), a network conversion tool is therefore developed to convert Paramics’s detailed network settings to those of LPFE and vice versa.

Third, we enhanced the vehicle actuated signal control APIs in Paramics, making it more versatile to implement and simulate various types of actuated traffic control strategies found in practice. We also developed a set of guidelines to help micro simulation users to set up and check signal settings in a micro simulation project.

Finally, we developed a summary statistics tools to track, diagnose and report on the calibration as it progresses or after it terminates, and carried out a case study using the SR-41 network in Fresno to demonstrate the use of the developed tools, identify potential problems and summarizing our calibration experiences with large scale networks.

Our case study indicates that the developed calibration tools can indeed ease, streamline and speed up the calibration of micro simulation, particularly when the network concerned is large. It also reveals that the calibration of a micro simulation is a complex task that involves numerous engineering judgments and cannot be fully automated. In a micro simulation, every modeling detail matters and each must be treated properly to ensure a good simulation outcome.