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.
Strategies, models, and algorithms facilitating such models are explored
to provide transportation network managers and planners with more
flexibility under uncertainty. Network design problems with
non-stationary stochastic OD demand are formulated as real option
investment problems and dynamic programming solution methodologies are
used to obtain the value of flexibility to defer and re-design a
network. The design premium is shown to reflect the opportunity cost of
committing to a “preferred alternative” in transportation planning.
Both network option and link option design problems are proposed with
solution algorithms and tested on the classical Sioux Falls, SD network.
Results indicate that allowing individual links to be deferred can
have significant option value.
A resource relocation model using non-stationary stochastic variables as
chance constraints is proposed. The model is applied to air tanker
relocation for initial attack of wildfires in California, and results
show that the flexibility to switch locations with non-stationary
stochastic variables providing 3-day or 7-day forecasts is more
cost-effective than relocations without forecasting.
Due to the computational costs of these more complex network models, a
faster converging heuristic based on radial basis functions is evaluated
for continuous network design problems for the Anaheim, CA network with
a 31-dimensional decision variable. The algorithm is further modified
and then proven to converge for multi-objective problems. Compared to
other popular multi-objective solution algorithms in the literature such
as the genetic algorithm, the proposed multi-objective radial basis
function algorithm is shown to be most effective.
The algorithm is applied to a flexible robust toll pricing problem,
where toll pricing is proposed as a strategy to manage network
robustness over multiple regimes of link capacity uncertainty. A link
degradation simulation model is proposed that uses multivariate
Bernoulli random variables to simulate correlated link failures. The
solution to a multi-objective mean-variance toll pricing problem is
obtained for the Sioux Falls network under low and high probability
seasons, showing that the flexibility to adapt the Pareto set of toll
solutions to changes in regime – e.g. hurricane seasons, security threat
levels, etc – can increase value in terms of an epsilon indicator.
With soaring oil prices and growing concerns for global warming, there
is increasing interest in the environmental performance of
transportation systems. This dissertation contributes to this growing
literature through three independent yet related projects essays that
deal with transportation technology, infrastructure, and policy.
My first essay analyze the increasing interest for hybrid cars by
Californians based on a statewide phone survey conducted in July of 2004
by Public Policy Institute of California (PPIC) using discrete choice
models. Results suggest that the possibility for single drivers to use
hybrid vehicles in HOV lanes is more important than short term concerns
for air pollution, support for energy efficiency policies, long term
concerns for global warming, education, and income. This suggests that
programs designed to improve the environmental performance of individual
vehicles need to rely on tangible benefits for drivers; to make a
difference, they cannot rely on environmental beliefs alone.
The second essay is concerned with assessments of Travel Demand
management (TDM) policies, which have been used to deal with congestion,
air pollution, and now global warming. I compare two TDM programs: Rule
2202 (The on-road motor vehicle mitigation options in southern
California) and the Commute Trip Reduction Program (CTR) in Washington
State. My results reveal that after 2002, the impacts of Rule 2202 are
mixed. Commuters’ modal choices are affected by worksite
characteristics but only two (out of six) basic strategies effect the
change in average vehicle ridership (AVR). Moreover, the level of
subsidies appears to play an important role in commuting behavior. In
Washington State, location has an impact on AVR and combinations of
location and employee duties influence the single occupant vehicle
index. Details of the CTR and its relative success suggest that there
is room for improving Rule 2202 to make it friendlier to businesses and
more effective.
Finally, I examine the health impacts of NOx (nitrogen oxides) and PM
(particulate matter) generated by trains moving freight through the
Alameda Corridor to and from the Ports of Los Angeles and Long Beach.
After estimating baseline emissions for 2005, I examine two scenarios:
in the first one, I assume that all long-haul and switching locomotives
are upgraded to Tier 2 (from Tier 1); in the second scenario, all Tier 2
locomotives operating in the study area are replaced with cleaner, Tier
3 locomotives. I find that mortality from PM exposure accounts for the
largest component of health impacts, with 2005 annual costs from excess
mortality in excess of $40 million. A shift to Tier 2 locomotives would
save approximately half of these costs while the benefits of shifting
from Tier 2 to Tier 3 locomotives would be much smaller. To my
knowledge, this is the first comprehensive assessment of the health
impacts of freight train transportation in a busy freight corridor.
Characteristics of the built environment, such as the mixture of land uses, transportation infrastructure, and neighborhood design, have often been associated with reduced automobile use and increased walking and transit use. However, a significant gap remains in our understanding of travel behavior, especially with respect with social environmental and attitudinal factors influencing travel, such as crime rates and the perceptions of walking. This dissertation, comprised of four empirical essays, explores the complex relationships between the built and social environment and neighborhood travel by focusing on non-work travel for individuals sampled from eight communities in the South Bay area of Los Angeles County.
In the first essay, I examine claims made by proponents of New Urbanism that traditional neighborhood designs promote walking and discourage driving by comparing automobile and walking trip rates for mixed-use centers and auto-oriented corridors. The results showed no discernable differences in individual driving trips between these two types of neighborhoods while more walking trips were reported in mixed-use centers. Therefore, the results both support and challenge New Urbanist claims.
The second essay examines the interactions between race/ethnicity, demographic change, and travel behavior by comparing driving and walking trips across racial and ethnic groups. The results showed that African-Americans took fewer driving trips and Asians walked less compared to non-Hispanic whites, and that Hispanics who walk are more sensitive to demographic changes in their neighborhood than other groups.
The third essay focuses on crime and perceptions of safety and how they impact walking behavior. After taking sociodemographic and built environment factors into account, violent crime rates had a strong deterrent effect on walking across race, income, and gender groups, while perceptions of neighborhood safety varied.
In the fourth essay, I focus on whether the built environment encourages walking above and beyond individuals’ attitudes toward walking. By comparing individuals with positive attitudes toward walking with those with neutral or negative attitudes, the results showed that individuals with positive attitudes were more responsive to built environment characteristics than those held negative attitudes. These findings suggest differences in walking behavior are more strongly shaped by personal attitudes than the built environment.
Use of advanced traffic control systems ranks as one of the most
cost-effective actions for urban transportation improvements to mitigate
total delay and alleviate fuel consumption and air pollution.
Nonetheless, Adaptive Signal System, the most advanced type of traffic
control designed for real-time traffic responsive operations, is not
widely accepted in field implementation. Benefits of such systems are
not fully realized yet, mainly because of the large cost for installment
and maintenance of required sensor systems for traffic forecast.
Moreover, even with the sensor systems, the performance still suffers
due to inaccurate prediction caused by the limitation of data sources
and deficiencies in the control algorithms.
Based on these observations, this study developed the applications of
emerging data sources in traffic control system. Traffic parameters are
collected under the new traffic information system such as a Persistent
Traffic Cookies (PTC) system conceptually proposed at UC Irvine using
wireless communication between a vehicle and a roadside hardware. With
the preliminary study results under the system, this study develops
traffic control schemes with the traffic forecast resulting from the PTC
system. Initially, general methods are presented to generate required
input, that is path-based traffic variables such as the turning flows
and travel time from PTC data. The inputs were implemented in two
different traffic control schemes; subnetwork definition for
area-control and signal optimization scheme in network-level. The
relevant spatial boundary for area-control is determined by a systematic
approach on the basis of traffic dynamics estimated by the PTC data.
Basically, the approach is to group multiple interconnected
intersections with strong control dependencies on each other, which can
be measured by the path flow among the intersections. Another
application is a signal optimization scheme at the network-level under
the assumption of fully decentralized control embedded with indirect
signal coordination consideration. Local optimization was accomplished
by a Dynamic Programming approach incorporating with a modified Rolling
Horizon Scheme and network-wide coordination was indirectly achieved by
iterative approach with repeated local optimizations.
For an evaluation of proposed control scheme, a simulation study was
presented using Irvine Triangular Network constructed in microscopic
simulation software. Results show that the proposed scheme is capable
of reducing total delays in a network, in comparison to Actuated Signal
Control already installed in the study network. It is also shown that
the scheme that incorporates certain modified rolling horizon methods
performs better than that with a more conventional rolling horizon method.