Event Type: PhD Defense

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.

Accidents are the largest source of external costs related to transportation in the United States with annual costs estimated to exceed $200 billion per year. Incidents also create traffic backups and delays that can result in secondary incidents (i.e., collisions that occur as a result of other incidents). Although incident management has received a lot of attention from academics and practitioners alike, secondary incidents have so far been somewhat neglected.

The main purpose of this dissertation is to investigate empirically whether the implementation of changeable message signs (CMS), which are one Intelligent Transportation System tool, can reduce secondary collisions. After reviewing previously published methods for estimating secondary accidents, I implement a Binary Speed Contour Map approach to detect secondary incidents using PeMS data. I also estimate the extra time lost to congestion because of incidents. My study area is a portion of Interstate 5 that stretches 74 miles from the Mexico-US border to Orange County, CA. This freeway has an average annualized daily traffic volume of 230,000 vehicles and fifty-five miles of it are equipped with CMS. My unique dataset includes incident data for 2008 combined with detailed weather data, elements of freeway geometry, and information about CMS usage.

I identify a total of 10,172 incidents in my study area in 2008. Using the BSCM approach, I find that 4.6 percent ofcollisions were secondary incidents. Moreover, my statistical model shows that incidents occurring during evening peak hours on Fridays are more likely to result in secondary crashes as do more severe incidents, areas with a complex
geometry, wet pavement, and changeable message signs (CMS). The maximum effectiveness of a CMS is approximately 10.5 miles for a range of 21 miles. Finally, annual incident-related congestion is approximately 1.9 hours per freeway vehicle, which represents five percent of the 37 hours of annual traffic delay experienced by the average San Diego motorist.

The unifying theme of this dissertation’s three applications of economics to transportation is an attempt to make transportation more efficient, environmentally friendlier and fairer.

In my first essay, I apply game theory and the notion of Cournot equilibrium to transportation. I compare two networks, hub-and-spoke and a point-to-point network, which is served by two non-cooperative transportation firms. I find that the way in which two firms set their respective network, either direct indirect service, has an effect on their costs and profits.

In my second essay, I analyze the ownership of hybrid electric vehicles by U.S. households using the 2009 National Household Travel Survey to understand the impact of various government policies aimed at increasing hybrid vehicle ownership, such as granting access to high-occupancy vehicle lanes, tax credits, and parking incentives. I use a logit model; explanatory variables include socio-economic characteristics, along with urban form, as well as policy variables. Understanding which policies are most cost-effective at fostering HEV ownership would allow policy makers to make effective use of public resources.

In my third essay, I address equity in transportation by stratifying the NHTS into three income groups: low-income, middle-income and upper-income. The purpose is to determine whether income affects travel behavior. I analyze questions in the 2009 NHTS that were not available in previous NHTS surveys. These questions inquire about internet use, medical condition and physical activity. I also estimate a multinomial logit model and find that those living in poverty and who report having a medical condition are more likely to make medical trips. Upper-income individuals are more likely to report social and recreational trips, meal and trips labeled as “other.” Analyzing trips by income is important from an equity standpoint when allocating scarce public funds for transportation projects, since it tells us what income groups are likely to be affected by specific transportation projects.

There is recent increase in the use of private providers’ digital map
and traffic information systems that have evolved mostly without much
public sector influence. 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.

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. This can bring about the
development of new path-based ATMIS algorithms that are 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) taking 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.  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.

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.  An appropriate combination of ATIS market penetration rate
and 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.

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. In this dissertation, we studybroadcasting for VANETs that are applicable to many 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 communication efficiency in dynamic vehicular networks. We believe that the contributions in this dissertation will be of interest to both the computer networking and transportation research communities.

With advances in computation and sensing, real-time adaptive control has
become an increasingly attractive option for improving the operational
efficiency at signalized intersections. The great advantage of adaptive
signal controllers is that the cycle length, phase splits and even phase
sequence can be changed to satisfy current traffic demand patterns to a
maximum degree, not confined by preset limits. To some extent,
traffic-actuated controllers are themselves “adaptive” in view of their
ability to vary control outcomes in response to real-time vehicle
registrations at loop detectors, but this adaptability is restricted by
a set of predefined, fixed control parameters that are not adaptive to
current conditions. To achieve the functionality of truly adaptive
controllers, a set of online optimized phasing and timing parameters are
needed.

This dissertation proposes a real-time, on-line control algorithm that
aims to maintain the adaptive functionality of actuated controllers
while improving the performance of signalized networks under
traffic-actuated control. To facilitate deployment of the control, this
algorithm is developed based on the timing protocol of the standard NEMA
eight-phase full-actuated dual-ring controller. In formulating the
optimal control problem, a flow prediction model is developed to
estimate future vehicle arrivals at the target intersection, the traffic
condition at the target intersection is described as “over-saturated”
throughout the timing process, i.e., in the sense that a multi-server
queuing system is continually occupied, and the optimization objective
is specified as the minimization of total cumulative vehicle queue as an
equivalent to minimizing total intersection control delay. According to
the implicit timing features of actuated control, a modified rolling
horizon scheme is devised to optimize four basic control
parameters—phase sequence, minimum green, unit extension and maximum
green—based on the future flow estimations, and these optimized
parameters serve as available signal timing data for further
optimizations. This dynamically recursive optimization procedure
properly reflects the functionality of truly adaptive controllers.
Microscopic simulation is used to test and evaluate the proposed control
algorithm in a calibrated network consisting of thirty-eight actuated
signals. Simulation results indicate that the proposed algorithm has the
potential to improve the performance of the signalized network under the
condition of different traffic demand levels.

Comprehensive Assessment of Managed Lane Performance and Characteristics
Managed lanes that include high occupancy vehicle (HOV) and high
occupancy and toll (HOT) lanes have been conducted for decades. Although
being regarded as efficient and sustainable transport, managed lanes
face such undiscovered issues as their performance regarding speed
dispersion, equilibrium relationships between managed lanes and general
purpose (GP) lanes in terms of speed and level of service, and joint
evaluation of managed lane elements like eligibility, access control,
and pricing. The goal of this dissertation is to provide theoretical and
practical approaches to assessing managed lane operations under four
modules, namely speed dispersion analysis, speed equilibrium analysis,
lane management hot spot analysis, and optimal managed lane policy
assessment. The first module correlates speed dispersion with the
fundamental traffic flow parameters, and reveals that the coefficients
of variation of speed for HOV and GP lanes are exponential with
occupancy, negative exponential with space mean speed, and two-phase
linear to flow, while the standard deviations of speed for both lanes do
not display any simple regression form of either occupancy, space mean
speed, or flow. The second module proposes two HOV schemes respectively
under lane utilization and travel time savings for speed equilibrium
between HOV and GP lanes. The schemes present distinct speed pairs by
congestion level, but speed of HOV lanes is identically ensured no less
than GP lanes under both schemes. The second module also covers an HOT
scheme that adopts value of time and value of reliability to formulate
HOT tolls with respect to speed of GP lanes. The third module identifies
lane management and congestion hot spots by contrasting the level of
service of managed lanes and GP lanes in a deterministic or stochastic
way. The case study indicates that lane management hot spots are
spatially and temporally dynamic, and a non-hot spot less likely turns
to a congestion hot spot without being a lane management hot spot as
transition, or vise versa. The last module develops two macroscopic
approaches to screening the policy combination set of managed lanes, and
eliminates the combinations by 60% in the selected scenario. Finally,
the optimal/non-inferior policies for non-eliminated combinations are
verified by solving such a case as a multi-objective binary integer
linear programming problem.