Phd Dissertation

Developing Decision-Making Process for Prioritizing Potential Alternatives of Truck Management Strategies

Publication Date

June 14, 2008

Author(s)

Choong Heon Yang

Areas of Expertise

Freight, Logistics, & Supply Chain

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Abstract

The objective of this dissertation is to develop a decision-making framework for prioritizing potential alternatives of truck management strategies using Multi-Criteria Decision-Making (MCDM) method. The motivation is drawn from the need for investigating and evaluating all likely impacts, resulting from the implementation of alternative truck strategies. The conventional evaluation methods such as the cost-benefit analysis can be addressed impacts involving monetary costs, but we believe these are insufficient to investigate all likely impacts. Our decision-making framework is developed to deal with all impacts that can transformable and non-transformable into monetary costs as well as to reflect decision-makers judgments. Two main objectives of this study are accomplished. The first is to explore all likely impacts, resulting from the implementation of alternatives truck management strategies, by performing a specific case study of before and after cases using traffic simulation models. A key feature of this part is to analyze various performance measures. They include both measures that can transformable and non-transformable into monetary costs as well as can reflect the standpoints of the public and the private sectors. Secondly, our framework is developed based on the Analytical Hierarchy Process (AHP), one of popular multi-criteria decision-making (MCDM) methods. This method enables the judgments and preferences of decision-maker to be quantified based on the relative importance of their own criteria, and to allow a quantitative interpretation from others. Another important contribution is to develop a 100-score conversion formula, a standard normalization technique. Since quantitative measurements have different scales, we need to incorporate these measurements into a single value. The formulas allow decision-makers to facilitate comparisons across potential alternatives. Final decision scores can be produced by multiplying the sum of scores of sub-criteria by estimated weight of the criteria. We believe that these final scores provide the argument to prioritize potential alternatives.

Phd Dissertation

Essays on urban transportation and transportation energy policy

Publication Date

June 14, 2008

Associated Project

Transportation Energy Policy and Urban Transportation: Policy Impacts on Travel Demand and Transportation Safety

Author(s)

Chun Kon (Cecilia) Kim

Areas of Expertise

Transportation Economics, Funding, & Finance

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Abstract

This dissertation outlines three topics on urban transportation energy, emphasizing the role of transportation energy policy, and aims to provide a single comprehensive framework to evaluate and compare different pricing and regulatory policy options for reducing transportation fuel consumption in the United States. In the first chapter, I examine the effect of population density on motor fuel (i.e., highway gasoline) consumption, controlling for other variables such as gas price, income, vehicle stock and so on, using state level aggregate cross-sectional time series data from 1966 to 2004. By estimating the impact of density on fuel consumption, I improve the understanding of the conventional logic that there is a negative correlation between population density and transportation energy use due to reduced average travel distance and availability of alternative modes in denser area. In the second part, I examine various transportation energy policy instruments such as a fuel tax, a mileage based VMT tax, Corporate Average Fuel Economy (CAFE) standards, a Pay-at-the-pump (PATP), and a Pay-as-you-drive (PAYD) insurance premium to measure policy impacts through computerized policy simulations. By fully integrating three interrelated economic demand decisions-size of vehicle stock, use of the vehicle stock, and energy efficiency-it can predict short-run, long-run, and dynamic effects of a policy change. The impacts are measured in terms of vehicle miles traveled, fuel consumption, greenhouse gas emissions, and cost savings. I also examine the impact of transportation energy policies on traffic safety in terms of the number of traffic accidents, traffic fatalities, and total accident costs. The outcome of this research provides a set of specific results comparing policy scenarios in a consistent manner. The results will provide guidance concerning whether the policy option would reduce energy dependency as well as undesirable side effects such as environmental problems and safety problems of motor-vehicle travel.

working paper

Developing Calibration Tools for Microscopic Traffic Simulation Final Report Part III: Global Calibration - O-D Estimation, Traffic Signal Enhancements and a Case Study

Publication Date

May 31, 2008

Author(s)

Michael Zhang, Lianyu Chu

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Intelligent Transportation Systems, Emerging Technologies, & Big Data

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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.

working paper

Development of A Path Flow Estimator for Inferring Steady-State and Time-Dependent Origin-Destination Trip Matrices

Publication Date

May 31, 2008

Author(s)

Michael Zhang, Ming-Sheng Lee, Anthony Chen, Will Recker

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Intelligent Transportation Systems, Emerging Technologies, & Big Data

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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.

Phd Dissertation

Essays in applied econometrics

Publication Date

May 31, 2008

Author(s)

Hao (Audry) Fang

Areas of Expertise

Transportation Economics, Funding, & Finance Travel Behavior, Land Use, & the Built Environment

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Abstract

This dissertation explores the estimation of non-linear multivariate systems in their reduced forms. The first essay develops a new method to solve multivariate discrete-continuous problems and applies the model to measure the influence of residential density on households’ vehicle holdings choices and vehicle usage. Traditional discrete-continuous modeling of vehicle holdings choice and vehicle usage becomes unwieldy with large numbers of vehicles and vehicle categories. I propose a more flexible method of modeling vehicle holdings in terms of number of vehicles in each category, with a Bayesian multivariate ordinal response system. I also combine the multivariate ordered equations with Tobit equations to jointly estimate vehicle type/usage demand in a reduced form, offering a simpler alternative to the traditional discrete/continuous analysis. Using the 2001 National Household Travel Survey data, I find that increasing residential density reduces households’ truck holdings and utilization in a statistically significant but economically insignificant way. The method developed above can be applied to other discrete-continuous problems. The second essay (with Ivan Jeliazkov) quantifies the interaction between political governance and macroeconomic performance in the United States by estimating a dynamic system: a vector autoregression (VAR) model involving macroeconomic variables and a presidential partisan dummy, and a regression equation of the presidential election outcome on the economic outcomes. The joint analysis of these components allows us to explore the dynamics of political business cycles and the impact of the economy on electoral uncertainty, and permits us to study their interaction. Our estimates of the short-run economic effects of elections are broadly consistent with the established view that short-run upturns in growth and employment follow the election of Democratic governments, while the opposite is true for Republicans. However, we show that the long-run outcomes are opposite to the short-run effects, which is in contrast to results in the existing literature where the long-run outcomes, although smaller in size, are found to be similar to those in the short-run. Our results from the electoral part of the model show that the incumbency effect in the U.S. is minimal, and that output growth has a noticeable and largely symmetric effect on the election outcomes for both parties. The last chapter (with David Brownstone) explores the influence of residential density on households’ vehicle fuel efficiency and usage choices with a sample of a national scale. A Bayesian approach that corrects for the endogeneity of the residential density is used to mitigate the problem of sample selectivity. The results show that an increase in residential density has a negligible effect on car choice and utilization, but reduces truck choice and utilization with a modest scale marked by statistical significance. The effects are larger than, but qualitatively consistent with, those obtained in Chapter 1, in which a California sample was used and the endogeneity of the density variable left uncorrected. Out-of-sample forecasting accuracy results are also reported to test the robustness of the model.

working paper

Integrated Construction Zone Traffic Management

Publication Date

May 31, 2008

Author(s)

Michael Zhang

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Intelligent Transportation Systems, Emerging Technologies, & Big Data Safety, Public Health, & Mobility Justice

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Abstract

Nonrecurrent traffic congestion caused by construction work constitutes a large proportion of the traffic congestion on highways. In TO 5300, we developed a comprehensive work zone traffic impact assessment procedure using a series of state-of-the-art dynamic network analysis tools as building blocks. This procedure is then implemented into a work zone traffic impact assessment software package called NetZone. This software package is capable of estimating time-dependent travel demand based on link counts, estimating demand diversion in response to work zone delay and various traffic management measures, showing traffic congestion level in the network over time, and providing network wide traffic performance measures with and without traffic congestion mitigation measures. The traffic performance measures provided in NetZone include average and longest delays, average and longest queue lengths, as well as the total delay in the network, before and during construction. Moreover, a friendly graphical userinterface makes NetZone easy to learn and use, and a preliminary case study shows that one canuse it to study a reasonably large network in a fraction of time that a micro-simulation package takes for the same network.

The developed methods and tools can help better plan and operate construction activities on highways, and more effectively manage traffic to reduce travel delays, both are consistent with Caltrans’s goals of increasing productivity and safety.

Phd Dissertation

Realistic models for scheduling tasks on network nodes

Publication Date

May 31, 2008

Author(s)

Jie Hu

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data

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Abstract

Parallel distributed computing has been widely studied and utilized to enable grids (or clusters) to meet the increasing demand of computation, especially in the field of scientific computation and modeling. The goal of distributed computing platforms is to provide the necessary infrastructure so that applications and their users can aggregate resources dynamically and shorten the processing time of the applications. With the rapid development of the Internet, the current distributed computing platforms are becoming more complicated. A typical type of modern distributed computing platforms is grid. Because the modern distributed computing platforms may contain numerous computational network nodes, one important challenge is how to schedule the load of tasks to these network nodes efficiently [1]. In addition, the environments of current computational platforms are becoming more complicated due to the availability of high performance network nodes and interconnects. Therefore, more advanced scheduling algorithms should be utilized to handle these problems. It is therefore necessary to create a new generation of schedulers that provides more comprehensive support for addressing the modern distributed computing platform requirements, so that the network nodes can be utilized effectively By analyzing and identifying the limitations of applying conventional scheduler technologies for distributed parallel applications, this dissertation presents a new design and its associated algorithms for enhancing conventional schedulers to provide better performance with considering more realistic factors. This dissertation also presents both mathematical and empirical analysis of three different proposed models. This dissertation provides three contributions to the field of task scheduling in distributed computing. First, current published algorithms are analyzed and weakness are exposed when real-world factors are considered, such as startup-costs, arbitrary processor times. Second, it contributes to the design of the scheduling algorithms by considering more realistic factors, which extend the usages of schedulers. Finally, it presents empirical and analytical results to demonstrate the effectiveness and the advantage of the proposed algorithms. The work in this dissertation has a broader impact beyond the algorithms in which they were developed, as it provides deeper understanding of scheduling tasks in the more realistic models, which will allow us to design more efficient algorithms.

working paper

Real-World CO2 Impacts of Traffic Congestion

Publication Date

February 29, 2008

Author(s)

Matthew Barth, Kanok Boriboonsomsin

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Intelligent Transportation Systems, Emerging Technologies, & Big Data Transportation Economics, Funding, & Finance

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Abstract

Transportation plays a significant role in carbon dioxide (CO2) emissions, accounting for approximately a third of the United States’ inventory. In order to reduce CO2 emissions in the future, transportation policy makers are looking to make vehicles more efficient and increasing the use of carbon-neutral alternative fuels. In addition, CO2 emissions can be lowered by improving traffic operations, specifically through the reduction of traffic congestion. This paper examines traffic congestion and its impact on CO2 emissions using detailed energy and emission models and linking them to real-world driving patterns and traffic conditions. Using a typical traffic condition in Southern California as example, it has been found that CO2 emissions can be reduced by up to almost 20% through three different strategies: 1) congestion mitigation strategies that reduce severe congestion, allowing traffic to flow at better speeds; 2) speed management techniques that reduce excessively high free-flow speeds to more moderate conditions; and 3) shock wave suppression techniques that eliminate the acceleration/deceleration events associated with stop-and-go traffic that exists during congested conditions.

Phd Dissertation

Activity-based Travel Demand Model with Time-use and Microsimulation Incorporating Intra-Household Interactions

Publication Date

February 4, 2008

Author(s)

Hee-Hyung Kim

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

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Abstract

The activity-based travel demand model recognizes that travel is derived from the demand for activity participation distributed in space and time. The focus on intrahousehold interactions and linkages between people’s behavior and social and physical environment has been identified as emerging features of the activity-based approach that would be important to travel behavior research. The dissertation is dedicated to an indepth exploration of the within-household interactions by theoretical specification and empirical development of the household activity time allocation models based on a utility maximization framework with the household as the unit of analysis. Furthermore, the dissertation also aims to propose a model of the household activity scheduling process primarily focusing on task allocation mechanisms on the basis of the human agents adjusting themselves to the built social and physical environment.

Development of the activity time allocation model in this dissertation includes two types of structural time allocation models. First, the collective models based on two assumptions that household heads have their own utility functions and that decisions by them reach Pareto-efficient outcomes are introduced to develop intra-household activity time allocation models for leisure demand and housework activity. Secondly, intrahousehold time allocation to housework activity is further examined through the estimation of time allocation to the different types of activities by the different types of household members along with extensive exploration of various theories and identification of related interactions.

This dissertation proposes a household activity scheduling process with a model design based on a weekly pattern system, which is expected to keep various advantages compared to a deterministic daily model system. Along with learning and adaptation procedures, the human being as a learning agent is designed to prepare strategic schedules of behavior to achieve individual goals through interactive environments, and implement those plans via activity execution. At the household level, the household and its members as decision agents are also designed to optimize the allocation of the available household labor resource under the presence of the uncertainties of the physical and social environments. After describing the mathematical framework and solution procedure, a simulation experiment is conducted within a hypothetical environment to demonstrate how the proposed model works.

Phd Dissertation

New dynamic travel demand modeling methods in advanced data collecting environments.

Publication Date

January 21, 2008

Author(s)

Hyunmyung Kim

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data Travel Behavior, Land Use, & the Built Environment

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Abstract

Estimating and forecasting travel demand have been a popular study topic among transportation researchers; however the research needs to pursue new direction with the advent of data from the potential availability of newer types of data previously not envisaged. In this dissertation, the author reviews previous studies on this topic and develops approaches for two aspects of travel demand analysis in the transportation network: A newer OD estimation method and a household activity-based demand modeling framework. First, a trip-based dynamic OD estimation model is developed. Several previous studies on OD trip table estimation focused on a static problem and many recent dynamic OD estimation methods also have not sufficiently proved their practical applicability. In order to overcome the shortcomings, this dissertation introduces supplementary information (i.e., vehicle trajectory data) to a dynamic OD estimation model. However, the trip-based approach has certain well-known shortcomings. OD estimation results can not give satisfactory solutions for forecasting purposes, and the estimated OD table only contains materialized trips, which implies that no latent travel demand is included in the table. Therefore, the estimated OD table does not have sufficient information for identifying the real travel demand pattern and it is not so useful for transportation planning works. Contrarily, a standard four-step model has a better capability for explaining a travel demand pattern. However, when we load the OD trip table calculated by the four-step model, we might see some discrepancies between simulated traffic patterns and the ground truth. The discrepancies can come from various factors such as insufficient network capacities and unexplained influencing factors. When the discrepancy is caused by insufficient network capacities, then it can be solved by an iterative adjusting procedure. Using the ground truth such as link traffic counts, it might be updated correctly. However, if the discrepancies come from incapability of the four-step model, then we should look for a new approach. The capability of the four-step model already has been criticized continuously by numerous activity researchers because a trip-based approach does not correctly consider the real motivation of travel. To overcome these drawbacks, the second item of fucud in the dissertation is in developing a dynamic agent-based household activity and travel demand simulation model framework named DYNAHAP. The framework calculates a demand pattern in terms of activity chains generated by synthetic families. A traffic simulator then executes the activity chains, and finally an aggregated dynamic traffic pattern is generated. In order to calibrate DYNAHAP, huge activity data should be gathered. Such tasks had been regarded very difficult or even nearly impossible before, but with the development of data collecting technologies, currently we have several ways for collecting the activity chains of individuals. Like vehicle trajectory data, sample activity chains collected from personal communication devices such as PDA (Personal digital assistant) could be used for DYNAHAP calibration. Some numerical test results also will be given for proving the performance of the developed models. In last chapter, some important issues for future study are also discussed.