Event Type: PhD Defense

Freight operations are critical to our prosperity, but they also generate substantial external costs in the form of additional congestion, air pollution, and health impacts. Unfortunately these external costs are not well understood. In this dissertation, I focus on the drayage trucks that serve the San Pedro Bay Ports (or SPBP, i.e. the Ports of Los Angeles and Long Beach in Southern California), which is the largest port complex in the country. Freight routes providing access to the SPBP comprise a major rail-line (the Alameda Corridor) flanked by the I-110 and I-710 freeways, which both carry thousands of trucks per day. A number of policies have been implemented to reduce emissions on the ocean-side (e.g., limiting ship speeds and managing their queues) and in the Ports (e.g., providing power to docked ships so they do not have to run their engines). On the land-side, two policies were implemented: the Clean Trucks Program, which regulates drayage truck emissions and provides funds for their upgrade, and the PierPASS program (the focus of my dissertation), which shifts drayage trucks traffic from mid-day and peak hours to the evening and night hours. However, external costs from drayage trucks remain a major concern for communities adjacent to the ports because they bear a disproportionate fraction of the health impacts (respiratory and cardiovascular illness, cancer, and premature death) associated with the pollution generated by ports operations. In this context, my dissertation analyzes some of the benefits of shifting freight traffic to off-peak periods with an emphasis on congestion, air pollution (NOx, and PM) and related health impacts, using an innovative approach that expands microscopic traffic simulation model. My results will inform policy makers concerned with crafting cleaner logistics policies.

This dissertation assesses costs and benefits of two recent public rail transit systems in Kaohsiung, Taiwan’s second largest city: Kaohsiung’s mass rapid transit (MRT) system, which was completed and inaugurated in 2008 and Kaohsiung light rail transit (LRT) loop line, which is now under construction. I first focus on the benefits of the opening of Kaohsiung’s MRT system as reflected in the price of apartments with elevators. I combine two stage least squares with geographically weighted regression to analyze transactions of apartments with elevators in 2007 and 2009. This approach allows accounting for the joint determination of time-on-market information (TOM) and price while allowing hedonic parameters to vary spatially. Results show that the opening of the MRT had a statistically significant and positive impact on the value of apartments with elevators. However, accounting for TOM has a negligible impact on my results.

Second, I apply the theory of real options to capture uncertainty in operating revenues and costs in the context of build-operate-transfer (BOT) and operate-transfer (OT) contracts for Kaohsiung’s LRT loop line project. Unlike the traditional net present value (NPV) approach, real options analysis includes option values embedded in a project. Here, I rely on the binomial pricing approach to explore the value of the options to abandon and to expand the project. My findings show that the options to abandon or expand the LRT system are not sufficient to make a BOT contract attractive to a private firm, even under the best case scenario; however, accounting for the value of these options makes an OT contract at least 10% more attractive. These results show that accounting for uncertainty in large urban transportation projects can be important although the value of flexibility may not be sufficient to offset large construction costs.

A new framework for analyzing choice set formation for route choice models is presented and an algorithm is proposed. The algorithm is tested against a sample of GPS data for heavy trucks for the State of California. The results are presented in detail along with an analysis of both their qualitative and quantitative merits. A new algorithm for the route choice problem is also presented and its results analyzed against the state of the practice and state of the art. This new algorithm, ReMULAA, is also the first known closed solution algorithm for the route choice problem using the Multinomial Logit Model (MNL) for an entire class of networks (Directed Acyclic Networks) without explicit route enumeration. A correction for the MNL model to account for route overlapping is also presented and the results compared with other state-of-the-art route choice algorithms. The results of the application of ReMULAA in areal world model are also presented and its advantages discussed.

Freight forecasting models are data intensive and may require many explanatory variables to achieve prediction accuracy. One problem, particularly in the United States, is that public data sources are usually available only at highly aggregate geographic levels, while models with more disaggregate geographic levels are required for regional freight transportation planning. A second problem is that supply chain effects are often ignored or modeled with economic input-output models which lack explanatory power. This study addresses these challenges by considering a Structural Equation Modeling approach, that is not confined to a specific spatial structure as spatial regression models would be, and allows for correlations between industries. The goal of the proposed methodology is to design a reliable and policy sensitive modeling framework for long term commodity flow forecasting that makes the best use of public available data sources. Practicality and improvement over previously available freight generation and distribution models are the highlights of this approach.

There are two primary developed in this study. The first one is a structural commodity generation model. The second model is the Structural Equations for Multi-Commodity OD Distribution (SEMCOD) model. The models are specified and estimated based on FAF3 data. It is shown that the proposed modeling framework provides a better fit to the data than independent regression models for each commodity. The three components of the models are: direct and indirect effects, supply chain elasticities at zone level and at origin-destination level, and intra-zonal supply-demand interactions. A validation of the geographic scalability of the model is conducted using a zoning system consisting of 97 county or sub-county zones in California

Transportation has been a significant contributor to greenhouse gas and criteria air pollutant emissions. Emission mitigation strategies are essential in reducing transportation’s impacts on our environment. In order to effectively develop and evaluate on-road emissions reduction strategies, accurate quantification of emissions is the critical first step. The accuracy and resolution of the traffic measures needed by the emission models will directly affect the emission estimation results. This dissertation investigates the application of traffic detection technologies to deriving the traffic measures needed for accurate on-road emissions estimation.

The inductive vehicle signature (IVS) system is identified as the most promising technology to couple with EPA’s latest MOVES emission model for estimating emissions accurately. Models and algorithms based on the IVS detection system are developed to generate the two most important traffic measures for emission estimation: vehicle mix and average speed. The performances of the models are verified using real-world field data.

Although average speed has been the most common input into emission models, the MOVES model is capable of using second-by-second vehicle speed trajectories to estimate emissions more accurately. Crowd sourced GPS data can also be used by emission models like MOVES to estimate emissions. In this study, we aim to answer two most fundamental questions: 1) how to use the GPS data, and 2) how the penetration rate of the GPS probes affects the emission results. It is found that emissions can be estimated with high accuracy and reliability with even a very small penetration rate of GPS probes.

We conclude that the IVS detection system and GPS probe data can be successfully applied to estimate accurate and reliable on-road emissions estimation. Discussions on the application of the models developed in this study to various scenarios are included.

This dissertation focuses on the integration of the Household Activity Pattern Problem (HAPP) with various locational decisions considering both supply and demand sides. We present several methods to merge these two distinct areas—transportation infrastructure and travel demand procedures—into an integrated framework that has been previously exogenously linked by feedback or equilibrium processes.

From the demand side, the Location Selection Problem for the Household Activity Pattern Problem (LSP-HAPP) is developed. LSP-HAPP extends the HAPP by adding the capability to make destination choices simultaneously with other travel decisions of household activity allocation, activity sequence, and departure time. From the supply side, the network decisions are determined as an integral function of travel demand rather than a given fixed OD matrix. The Location – Household Activity Pattern Problem (Location-HAPP), a facility location problem with full-day scheduling and routing considerations is developed. This is in the category of Location-Routing Problems (LRPs), where the decisions of facility location models are influenced by possible vehicle routings. Location-HAPP takes the set covering model as a location strategy, and HAPP as the scheduling and routing tool. The Network Design Problem is integrated with the Household Activity Pattern Problem (NDP-HAPP) as a bilevel optimization problem. The bilevel structure includes an upper level network design while the lower level includes a set of disaggregate household itinerary optimization problems, posed as HAPP or LSP-HAPP.

Utilizing the aforementioned models, two transportation sustainability studies are then conducted for the adoption of Alternative Fuel Vehicles (AFVs). From the demand, we measure the household inconvenience level of operating AFVs. From the supply side of the AFV infrastructure, Location-HAPP is applied to the incubation of the minimum refueling infrastructure required to support early adoption of Hydrogen Fuel Cell Vehicles (HFCVs).

The growth of urban vehicle traffic generates serious transportation and environmental problems in most countries of the world. Intelligent transportation systems (ITS) are effective means to solve basic traffic problems, such as driving safety, road congestion, disaster supplies, emissions, etc. Inter-vehicle communication (IVC) system is one of the most important components of ITS. In recent years, the rapid development of information technologies leads a revolution in IVC, enabling IVC be a powerful multifunctional system. However, there exist numerous challenges for IVC studies. This dissertation is aimed to address three urgent and critical issues in IVC: efficiency of information exchanging among connected vehicles, simulation methods, and IVC applications.

Information transmission efficiency, which can be measured by communication throughput or capacity, is a fundamental property of vehicular ad hoc networks. This dissertation theoretically analyzes communication throughputs, including broadcast and unicast communications, under discrete and continuous vehicular ad hoc networks (VANETs). We also examine influence of transmission range, influence ratio, market penetration rate of IVC-equipped vehicles, percentage of senders and traffic waves on throughputs. Furthermore, we derive a theoretical formulation to calculate communication capacities under uniform traffic streams. And, an integer programming (IP) model is improved to explore capacities in general traffic, and a genetic algorithm is constructed to search the solutions efficiently.

The second contribution of this dissertation is the development of a hybrid traffic simulation model to evaluate transportation systems incorporated with IVC technologies. As IVC-equipped vehicles are able to obtain more road information and they are controlled to pursue some objectives, they will behave differently from others, and transportation systems will become heterogeneous. This dissertation presents a hybrid traffic simulation model coupling microscopic and macroscopic models to address heterogeneity in transportation systems. In the model, equipped vehicles are regulated by a car-following model, while the other vehicles are described as continuous media with the Lighthill-Whitham-Richard (LWR) model. We analytically study the model on a single-lane road using a modified Godunov method. The hybrid model shows its potential of accurate wave propagation from individual vehicles to continuous traffic streams, and reversely; i.e., the model is capable of analyzing heterogeneous traffic. Moreover, consistency, stability and convergence of the hybrid model are carefully investigated. The model also shows the advancement of computational efficiency and control flexibility on traffic simulations.

Finally, for IVC applications in environment, we propose a green driving strategy to smooth traffic flow and lower pollutant emissions and fuel consumption. In this dissertation, we study constant and dynamic green driving strategies based on inter-vehicle communications. Generally, speed limit control in successful strategies guarantee a vehicle’s speed profile be smooth while still following its leader during a relative long time period. A theoretical analysis of constant strategies demonstrates that optimal smoothing effects can be achieved when a speed limit is set to be close to but not smaller than average speed of traffic. We consider a dynamic strategy in which controlled vehicles share location and speed information based on a feedback control system. The influence of market penetration rate of equipped vehicles and communication delay on the strategy is also analyzed. Besides the development of the green driving strategy, we construct a green driving APP for smartphones on the Google Android platform and design a field experiment to check the feasibility of the strategy. The results are promising and support the advancements of IVC on reducing emissions and fuel consumption.

Freight transportation demand is a highly variable process over time and
space. Two challenges in current regional freight forecasting are the
lack of consideration of the space-time trade-offs and the lack of
behaviorally-based models for temporally assigning annual commodity
flows to daily flows. State-of-the-practice models typically use fixed
factors for temporal assignment and do not address the tradeoffs between
transport costs and inventory costs, which can aid in quantifying the
impact of different land uses on monthly truck distributions or the
impact of rising fuel costs on shipment frequency and warehousing needs.
This dissertation work makes the first step toward explicitly modeling
the freight temporal distributions and proposes a novel approach that
adopts the concept of Network Economics and Economic Order Quantity
(EOQ) inventory in an agent-based freight demand modeling framework.

Unlike other agent-based models that seek to replace the whole freight
forecasting process, the proposed model relies on other aggregate models
to generate annual distribution channels (commodity OD matrix) and
monthly demand distributions by commodity type. This frees the model to
focus on trade-offs between transport and inventory without having to
bear the burden of limited disaggregate data for other choices.

The modeling framework is composed of two main components: (1) a
supplier selection module to indicate the supply chain interactions and
determine the order quantity from one firm to another firm while meeting
the zone level flow constraints; (2) an EOQ-based inventory operation
module to indicate the goods movement daily pattern and determine the
daily firm-firm flows by modeling firms’ inventory replenishment
decisions. By aggregating the daily firm-firm flows back up to the zone
level, we get the average zone-zone daily flows by commodity types as
the final output.

The whole framework has been fully examined using the California data. A
union of 6 datasets is utilized as inputs to model the daily flows of
503 firm groups in California during the 261 weekdays in year 2007. As
one parameter of the normative model, the unit inventory holding cost
has been calibrated through matching with the given inventory data. A
simple comparison of the model outputs with the fixed factor approach is
conducted. Four use cases are presented to demonstrate the effectiveness
of such a new model for freight transport analysis.

In recent years the Clean Trucks Program (CTP) has been enacted at
California’s San Pedro Bay Ports (SPBPs) of Long Beach and Los Angeles
to help address major environmental issues associated with port
operations. “Clean trucks” that utilized public funds to replace older
polluting drayage trucks were required to be fitted with GPS units for
compliance monitoring. Such GPS data collected by the clean drayage
trucks provide a significant opportunity to investigate drayage truck
tour behaviors distinct from general commercial vehicles.

With the background, this dissertation consists of three topics: 1) Tour
Behavior of Clean Drayage Trucks; 2) Tour-Based Entropy Maximization
Model of Drayage Trucks; and 3) Drayage Truck Tour Modeling Using the
Inverse Selective Vehicle Routing Problem (InvSSVRP) in Southern
California. As expected, the first step is to analyze GPS data for
interpreting the drayage trucks’ characteristics. In the second and
third steps, tour-based models are developed using aggregate and
disaggregate level approaches.

An analytical framework is introduce for processing GPS data to both
interpret the trip chaining of the clean drayage trucks, and to prepare
sufficient tour data for clean truck modeling at the SPBPs. After
analyzing data using the toolkit, one of the significant findings from
the clean drayage truck behaviors is that the tours could be classified
under four types, three of which contain repetitive trip patterns in a
tour while the remainder tends to travel in circulative patterns to
avoid visiting the same location multiple times. This provides both the
answer that the current tour-based model cannot address drayage truck
behavior and why tour-based modeling of the drayage trucks is developed
separately.

Two other theoretical advances in the research are the development of
tour-based models using an Entropy Maximization Algorithm and a
Selective Vehicle Routing Problem.

For the aggregate level, the revised tour-based entropy maximization
model upgrades the tour-based entropy maximization model by Wang and
Holguín-Veras (2009) which mostly focuses on other commercial vehicles.
After introducing new constraints regarding sequential visits to nodes,
the clean drayage truck tour behavior can be well addressed.

At the disaggregate level, the SSVRP provides a utility-maximizing
decision-making optimization framework under spatial-temporal
constraints to explain observed truck patterns as activity participation
analogous to household activity patterns. This would be impossible
without the capability of the InvSSVRP to calibrate the objective
coefficients and arrival time constraints such that observed patterns
are optimal values. The nodes are sequence-expanded to allowing multiple
visits at each node and divided into two arrival states (from depot or
not from depot) in the SSVRP provide much more realism in capturing the
drayage truck behavior.

To make better use of the two proposed models, the framework of each
tour-based model estimation and forecasting process is illustrated.
Lastly, several future topics of relevance to improving the tour-based
models are discussed.

With concerns about environmental issues, transportation studies have
extensively evaluated emissions impacts associated with traffic
operational strategies and transportation policies. However, the impact
studies mainly relied on emissions impacts with a demand forecasting
model. The planning model cannot capture individual vehicles’
interactions (i.e., lane changes or stop-and-go situation) or detailed
traffic operations such as traffic signals. These limitations lead to
under- or over-estimated emissions while evaluating several policies.
Even though many studies utilized microscopic traffic models to better
estimate emissions, the studies have not considered further steps such
as air quality estimation and health impact studies.

This research develops an integrated framework for evaluating air
quality and health impacts of transportation corridors using microscopic
traffic model, micro-scale emissions model, non-steady state dispersion
model, and health impact model. The main advantage of this approach is
to better estimate air quality and health impacts from vehicle
interactions and detailed traffic management strategies.

As a case study, we evaluate air quality and health impacts of several
scenarios associated with major transportation corridors accessing the
San Pedro Bay Ports (SPBP) complex, California. The corridors consist of
20 miles-long major freight freeways and arterials, as well as line-haul
rail along the Alameda corridor and several rail yards associated with
the SPBP complex. For the scenarios, we consider a clean truck program,
cleaner locomotives, and modal shifts compared to the 2005 baseline. All
scenarios performed with the integrated framework have provided larger
improvements of air quality and health impacts associated with
transportation corridors than conventional frameworks using
transportation planning models. However, the difference in air quality
and health impacts from modal shift scenarios between clean trucks and
locomotives are minor.

As explanatory research, pollution response surface models are
developed. The main feature of the pollution response surface model is
to avoid the high computational cost of the microscopic traffic model,
which makes it difficult to estimate traffic for multiple days needed
for evaluating emissions and health impacts. A conceptual framework for
estimating pollution response surface models is proposed. Using a toy
network, response surfaces of NOX and PM are estimated.