working paper

The influence of emissions specific characteristics on vehicle operation: A micro-simulation analysis

Publication Date

May 31, 2011

Author(s)

Nesamani Kalandiyur, Jean-Daniel Saphores, Michael McNally, R. (Jay) Jayakrishnan

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment Zero-Emission Vehicles & Low-Carbon Fuels

View working paper

Abstract

The goal of this paper is to predict the fraction of time vehicles spend in different operating conditions from readily observable emission specific characteristics (ESC), which include geometric design, roadway environment, traffic characteristics, and driver behavior. We rely on a calibrated micro-simulation model to generate second-by-second vehicle trajectory data and use structural equation modeling to understand the influence of observed link ESC on vehicle operation. Our results reveal that 67 percent of link speed variance is explained by emission specific characteristics. At the aggregate level, geometric design elements exert a greater influence on link speed than traffic characteristics, the roadside environment, and driving style. Moreover, the speed limit has the strongest influence on vehicle operation, followed by facility type and driving style. This promising approach can be used to predict vehicle operation for models like MOVES, which was recently released by the Environmental Protection Agency.

Phd Dissertation

Essays in Industrial Organization

Publication Date

June 14, 2011

Author(s)

Ana Pesic

Areas of Expertise

Transportation Economics, Funding, & Finance

View Phd Dissertation

Abstract

Three research papers, all broadly focused on industrial organization, comprise the chapters of this dissertation. Although all these papers address market inefficiencies that arise in various industry setting, the first paper differs in theme and scope from the rest of the dissertation. The first chapter, “Switching costs and entry in the mortgage industry”, investigates the impact of switching costs and entry on the interest rate spread in the mortgage industry. I use enactment of anti-predatory lending laws across the U.S. to measure a reduction in borrowers’ switching costs. The empirical findings show that both entry and interest rate spread rise with the advent of these laws because these laws enable low-quality applicants to obtain financing more easily than before. The results suggest that lower switching costs exacerbate the adverse selection problem, so policies that reduce these costs may not produce clear benefits. The second chapter, “Information leakage and stability of research joint ventures in a differentiated product market”, analyzes the impact of information leakage on research Joint venture’s (RJV) stability and R&D expenditure in a heterogeneous product market. Firms solve their problem in three stages: in the first stage firms decide whether to join a RJV, in the second stage firms decide on the level of information sharing, and in the final stage firms engage in Cournot competition. Main results indicate a U-shaped curve between venture’s size and product differentiation when leakage is high. This research can offer a better understanding of RJVs and provide insight into policies and actions necessary to promote R&D and better flow of information in the innovation sector. The third chapter, “The impact of information leakage and product differentiation on the research joint venture’s size and R&D”, empirically examines theory set forth in the second chapter. The study uses data on RJVs from the NIST’s ATP program. As a measure of information leakage, we use a percentage of all patent litigation cases within a RJV that are valid. The main results reveal that information leakage is not a determining factor in ventures choice to include other firms and engage in cooperative R&D.

Phd Dissertation

Probabilistic Learning for Analysis of Sensor-Based Human Activity Data

Publication Date

May 31, 2011

Author(s)

Jon Hutchins

Areas of Expertise

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

View Phd Dissertation

Abstract

As sensors that measure daily human activity become increasingly affordable and ubiquitous, there is a corresponding need for algorithms that unearth useful information from the resulting sensor observations. Many of these sensors record a time series of counts reflecting two behaviors: 1) the underlying hourly, daily, and weekly rhythms of natural human activity, and 2) bursty periods of unusual behavior. This dissertation explores a probabilistic framework for human-generated count data that (a) models the underlying recurrent patterns and (b) simultaneously separates and characterizes unusual activity via a Poisson-Markov model. The problems of event detection and characterization using real world, noisy sensor data with significant portions of data missing and corrupted measurements due to sensor failure are investigated. The framework is extended in order to perform higher level inferences, such as linking event models in a multi-sensor building occupancy model, and incorporating the occupancy measurement from loop detectors (in addition to the count measurement) to apply the model to problems in transportation research.

Phd Dissertation

The Interplay of Urban Traffic Route Guidance, Network Control and Driver Response: A Convergent Algorithmic and Model-based Framework

Publication Date

May 31, 2011

Associated Project

Research Grant – Inchul Yang

Author(s)

Inchul Yang

Areas of Expertise

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

View Phd Dissertation

Abstract

Much effort has been made in the past on the supply side to relieve road traffic congestion which undermines the mobility in urban networks and brings heavy social costs, but building additional roadway capacity is no longer considered a viable option. A better alternative is the efficient management of existing networks, for which we can envisage new possibilities that emerge in light of the recent increase in the use of private providers’ digital map and traffic information systems. These systems have evolved mostly without much public sector influence, but some paradigm shift is needed for thinking about the directions of future developments that will show societal benefits also open up private-sector opportunities. In this context, we develop a multi-agent advanced traffic management and information systems (ATMIS) framework with day-to-day dynamics where private agencies are included as traffic information service providers (ISPs) together with public agencies handling the traffic control and the users (drivers) as the decision-makers. One important paradigm shift is that the emergence of private ISPs makes it possible to obtain path-based data via retrieval of individual trajectory diaries and current position information from their subscribers. The availability of such path-based data can bring about the development of new path-based ATMIS algorithms. Such new algorithms can be capable of taking into account the routing effects of advanced traveler information systems (ATIS). Under the assumption that the traffic management center (TMC) has some (even approximate) knowledge of the ISPs’ optimal strategies, it is possible to design optimal route guidance and control strategies (ORGCS) that takes into account the anticipated ISP reactions in terms of route-level flows. In light of these issues, we develop a routing-based real-time cycle-free network-wide signal control scheme (R2CFNet) that uses path-based data. The scheme also allows the avoidance of day-to-day games between ISPs and signal control through the use of weights on the queue delays in the control objective function. The weights are essentially operator parameters designed to incorporate ORGCS and day-to-day behavior. The proposed control scheme, of course, responds to detected traffic (demand) rates on a real-time basis in response to the control delays on network routes. Another theoretical advance in the research is in the development of a modeling scheme that uses a new optimization algorithm for a convergent simulation-based dynamic traffic assignment (DTA) model. This model incorporates a Gradient Projection (GP) algorithm, as opposed to the traditionally-used Method of Successive Averages (MSA), and it displays significantly better convergence characteristics. A consistent day-to-day dynamic framework is also developed, incorporating an elaborate microscopic simulation model to capture traffic network performance, to study network dynamics under multiple private ISPs and the new signal control scheme. The results of parametric simulations have shown that the proposed framework is capable of effectively capturing the effects of the interplay of urban traffic route guidance, network control and user response. It is seen that an appropriate combination of ATIS market penetration rate and the special-purpose signal control settings could divert some portion of travel demand to different routes. This is achieved by constraining the signal settings to conform to certain longer-term strategies. The performance and efficiency of the components of the proposed framework such as the DTA model, the day-to-day dynamics model and the R2CFNet control scheme have been investigated through various numerical experiments that show promising results. Lastly, several future topics of relevance to the framework are discussed.

published journal article

Network-based real option models

Publication Date

April 30, 2011

Associated Project

Large Scale Real Options Models for Network Investment Planning & Operational Risk Hedging

Author(s)

Joseph Chow, Amelia Regan

Areas of Expertise

Freight, Logistics, & Supply Chain

View published journal article

Abstract

Building on earlier work to incorporate real option methodologies into network modeling, two models are proposed. The first is the network option design problem, which maximizes the expanded net present value of a network investment as a function of network design variables with the option to defer the committed design investment. The problem is shown to be a generalized version of the network design problem and the multi-period network design problem. A heuristic based on radial basis functions is used to solve the problem for continuous link expansion with congestion effects. The second model is a link investment deferral option set, which decomposes the network investment deferral option into individual, interacting link or project investments. This model is a project selection problem under uncertainty, where each link or project can be deferred such that the expanded net present value is maximized. The option is defined in such a way that a lower bound can be solved using an exact method based on multi-option least squares Monte Carlo simulation. Numerical tests are conducted with the classical Sioux Falls network and compared to earlier published results.

Related Publications

Phd Dissertation | February 03, 2025

Flexible Management of Transportation Networks under Uncertainty

working paper

Estimating Emissions Using an Integrated Traffic Model

Publication Date

January 31, 2011

Author(s)

Qi-Jian Gan, Jielin Sun, Wenlong Jin, Jean-Daniel Saphores

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment Zero-Emission Vehicles & Low-Carbon Fuels

View working paper

Abstract

Regulators concerned with traffic related emissions on large networks should consider allowing modelers to use mesoscopic traffic models (such as the MCDKW model) that can adequately represent congestion along with appropriate emissions models. This would simplify regulatory analyses, reduce errors, and cut costs.

working paper

Incorporating Vehicular Emissions into an Efficient Mesoscopic Traffic Model: An Application to the Alameda Corridor, CA

Publication Date

January 31, 2011

Author(s)

Qi-Jian Gan, Jielin Sun, Wenlong Jin, Jean-Daniel Saphores

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment Zero-Emission Vehicles & Low-Carbon Fuels

View working paper

Abstract

We couple EMFAC with a dynamic mesoscopic traffic model to create an efficient tool for generating information about traffic dynamics and emissions of various pollutants (CO2, PM10, NOX, and TOG) on large scale networks. Our traffic flow model is the multi-commodity discrete kinematic wave (MCDKW) model, which is rooted in the cell transmission model but allows variable cell sizes for more efficient computations. This approach allows us to estimate traffic emissions and characteristics with a precision similar to microscopic simulation but much faster. To assess the performance of this tool, we analyze traffic and emissions on a large freeway network located between the ports of Los Angeles/Long Beach and downtown Los Angeles. Comparisons of our mesoscopic simulation results with microscopic simulations generated by TransModeler under both congested and free flow conditions show that hourly emission estimates of our mesoscopic model are within 4 to 15 percent of microscopic results with a computation time divided by a factor of 6 or more. Our approach provides policymakers with a tool more efficient than microsimulation for analyzing the effectiveness of regional policies designed to reduce air pollution from motor vehicles.

published journal article

High-Coverage Point-to-Point Transit: Study of Path-Based Vehicle Routing Through Multiple Hubs

Publication Date

December 31, 2010

Associated Project

High Coverage Point to Point Transit: A Public-Private Incrementally Implementable Mass Transit Strategy for Sustainability With Real-time Shared-ride Vehicles.

Author(s)

Jae Young Jung, R. (Jay) Jayakrishnan

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

View published journal article

Abstract

This study focuses on the optimization and simulation modeling associated with the design of alternative transportation, the high-coverage point-to-point transit (HCPPT), which involves a sufficient number of deployed small vehicles with advanced-information supply schemes. This paper identifies the inefficiency of the existing heuristic rules for vehicle routing and proposes a new optimization approach for an HCPPT solution. A path-based model for routing through multiple hubs as opposed to a single pair of hubs is formulated to improve HCPPT operational schemes. This study also develops a simulation framework for the application of the proposed algorithm. To illustrate the system and computational performances of the proposed model, simulations are conducted with different sets of scenarios and model parameters. The path-based model shows reasonable performance over the various demand patterns in level of service and ride time index. It is also shown that, with the use of constraint-driven schemes and model parameters, the scale of the problem is reduced. The computational times are shown to be quite small, and demonstrate the viability in real-time operations.

Related Publications

Phd Dissertation | February 03, 2025

Shared-ride Passenger Transportation Systems with Real-time Routing

Phd Dissertation

Epitaxis: A System for Deductive and Constructive Program Queries

Publication Date

December 30, 2010

Author(s)

James Benvenuto

Areas of Expertise

Other

View Phd Dissertation

Abstract

Modern computer hardware (multi-core, multi gigahertz processors with gigabytes of RAM and terabytes of disk) along with IDEs allows programmers to build computer programs which are bigger and more complex than they can understand or keep in their working memories. Additionally, the problems these programs are designed to model are ever more complicated. Consequently, programs are full of inconsistencies, mistakes, and incompleteness’s. These problems are difficult to detect, difficult to locate, and difficult to correct. Often a change is made by a programmer to fix a problem for which understanding all the repercussions of the change is difficult. Consequently, further bugs are introduced into the code base. Because of the pervasiveness of software in society and the potential severity of the consequences of bugs, software developers need ever better tools to help them understand, navigate, and follow the consequences of their development and maintenance activities. This dissertation presents a novel framework based on tree/graph searching and parsing, deductive retrieval, dynamic analysis, symbolic execution, aspect oriented programming, and an open interpreter to allow a software developer to navigate, locate features, find bugs, and abstract information in software. The system is designed to have a fast modify-test cycle such that the programmer can search and test the software as it is being edited without time consuming recompilation, reinstrumenting, or database repopulating each time an edit is made to the code base. The system is language independent, requiring only files to specify the language grammar, control flow graph transformation, and execution semantics. In addition, because of the flexibility and programmability of the system it is an excellent environment to perform further research on program analysis techniques such as dynamic analysis, symbolic execution and abstract interpretation. A prototype system has been built along with data files for the C programming language which demonstrates the feasibility of the system and its ability to scale to “modern-sized” programs.

Phd Dissertation

Broadcasting in Vehicular Ad Hoc Networks

Publication Date

November 29, 2010

Author(s)

Rex Chen

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data

View Phd Dissertation

Abstract

Traffic congestion and accidents continue to take a toll on our society with congestion causing billions of dollars in economic costs and millions of traffic accidents annually worldwide. For many years now, transportation planners have been pursuing an aggressive agenda to increase road safety through Intelligent Transportation System initiatives. Vehicular Ad Hoc Network (VANET) based information systems have considerable promise for improving traffic safety, reducing congestion and increasing environmental efficiency of transportation systems. To achieve the future road safety vision, time-sensitive, safety-critical applications in vehicular communication networks are necessary. However, there are numerous technical hurdles for deploying VANET on the road network and its full potential will not be realized until the issues related to communication reliability, delay and security are solved. VANET is a specific type of mobile ad hoc network (MANET) with unique characteristics that are different from a general MANET. These attributes include the traffic conditions (network density), mobility model (vehicle movements) and the network topology (road layout) imposed by the underlying transportation system. When disseminating data in VANET the communication system is faced with the scalability problem in dense networks and the connectivity issue in sparse ones. In this dissertation, we study broadcasting for VANET that are applicable to traffic safety applications. We investigate ways to improve reliability and reduce delay under numerous traffic conditions (free flow and congested flow traffic scenarios). Further, we incorporate vehicular traffic information to increase the communication efficiency in dynamic vehicular networks while mitigating the broadcast storm problem. We believe that the contributions in this dissertation will be of interest for both the computer networking and transportation research communities.