A spectrum of traffic engineering and modern transportation planning problems requires the knowledge of the underlying trip pattern, commonly represented by dynamic Origin-Destination (OD) trip tables. In view of the fact that direct survey of trip pattern is technically problematic and economically infeasible, there have been a great number of methods proposed in the literature for updating the existing OD tables from traffic counts and/or other data sources. Unfortunately, there remain several common theoretical and practical aspects which impact the estimation accuracy and limit the use of these methods from most real-world applications. This dissertation itemizes and examines these critical issues. Then, the dissertation presents the developments, evaluations, and applications of two new frameworks intended to be used with the current and near-future data, respectively.
The first framework offers a systematic and practical procedure for preparing dynamic demand inputs for microscopic traffic simulation under planning applications with an estimation module based solely on traffic counts. Under this framework, the traditional planning model is augmented with a filter traffic simulation step, which captures important spatial-temporal characteristics of route and traffic patterns within a large surrounding network, to improve the flow estimates entering and leaving the final microscopic simulation network. A new bounded dynamic OD estimation model and a solution algorithm for solving a large problem are also proposed.
The second framework utilizes additional information from small probe samples collected over multiple days. There are two steps under this framework. The first step includes a suite of empirical and hierarchical Bayesian models used in estimating time-dependent travel time distributions, destination fractions, and route fractions from probe data. These models provide multi-level posterior parameters and tend to moderate extreme estimates toward the overall mean with the magnitude depending on their precision, thus overcoming several problems due to non-uniform (over time and space) small sampling rates. The second step involves a construction of initial OD tables, an estimation of route-link fractions via a Monte Carlo simulation, and an updating procedure using a new dynamic OD estimation formulation which can also take into account the stochastic properties of the assignment matrix.
Uncertainties in transportation capacity and cost pose a significant
challenge for both shippers and carriers in the trucking industry. In
the practice of adopting lean and demand-responsive logistics systems,
orders are required to be delivered rapidly, accurately and reliably,
even under demand uncertainty. These tougher demands on the industry
motivate the need to introduce new instruments to manage transportation
service contracts. One way to hedge these uncertainties is to use
concepts from the theory of Real Options to craft derivative contracts,
which we call truckload options in this dissertation. In its simplest
form, a truckload call (put) option gives its holder the right to buy
(sell) truckload services on a specific route, at a predetermined price
on a predetermined date. The holder decides if a truckload option should
be exercised depending on information available when the option expires.
Truckload options are not yet available, however, so the purpose of
this dissertation is to develop a truckload options pricing model and to
show the usefulness of truckload options to both shippers and carriers.
Since the price of a truckload option depends on the spot price of a
truckload, we first model the dynamics of spot rates using a common
stochastic process. Unlike financial markets where high frequency data
are available, spot prices for trucking services are not public and we
can only observe some monthly statistics. This complicates slightly the
estimation of necessary parameters, which we obtain via two independent
methods (variogram analysis and maximum likelihood), before developing a
truckload options pricing formula. A numerical illustration based on
real data shows that truckload options would be quite valuable to the
trucking industry.
This dissertation develops a method to create value through more
flexible procurement contracts, which could benefit the trucking
industry as a whole – particularly in an uncertain business environment.
Truckload rate and truckload options price are solidly investigated
and modeled. In addition, parameter estimation for a continuous
stochastic model is practically explored using discrete statistics.
Finally, numerical trading examples are illustrated and a picture of
truckload option trading becoming reality presented. The complicated
results indicate that truckload options have the potential of
stimulating the entire trucking and logistics industries.
Because of the growing importance of hub-and-spoke operations in the
trucking industry, crossdocking has become an important and effective
tool to transfer freight. Companies like Wal-Mart, Costco and Home Depot
are using this kind of facility in their logistics operations.
Efficiently operating crossdocks, thereby reducing unnecessary waiting
and staging congestion for freight and workers are important issues for
managers.
To deal with the above issues, this dissertation uses real-time
information about the contents of inbound and outbound pallets and the
locations of pallets to schedule unloading for waiting trailers and
assign destinations for pallets. We show how to incorporate the
information of waiting freight in trailers to benefit trailer
scheduling; we also present how to use the information on freight
staging to mitigate congestion. Two dynamic trailer scheduling and four
alternate destination strategies are proposed accordingly and compared
with baseline scenarios in this research.
Our simulation results suggest that:
1. Our strategies are effective. The two time-based trailer scheduling
algorithms can save cycle times as high as 64%, 57% and 30% in the
4-to-4, 4-to-8 and 8-to-8 crossdock scenarios, respectively; the four
alternate destination strategies can save cycle times as high as 34% in
the 8-to-8 staging crossdock scenarios. In addition, these strategies
can raise throughputs for crossdocks. These effects should result in a
noticeable influence on supply chain networks, including shorter
transportation lead-time, more reliable on-time deliveries and less
inventory costs.
2. In our alternate destination strategies, even if a destination change
results in extra time for value-added services for freight, the
strategies are still worthy to adopt.
3. The combination models of our trailer scheduling algorithms and
alternate destination strategies work better than solely implementing an
alternate destination strategy when trailer arrivals are dense.
4. A higher flexibility on choosing alternate destinations can bring
higher performance for crossdocks.
Efficient freight transportation is an essential for a strong economic
system. A rapid growth of freight demand, however, lessens the
efficiency of provided infrastructure. In order to alleviate this
problem effectively, evaluation studies have to be performed in order to
invest the limited budget for the best of social benefits. In addition
to many difficulties on making a decision for each project investment,
it is made harder by the complimentary and substitution effects that
happen when considering transportation project together. Current
practices, however, limit number of project combinations in order to
avoid numerous tests. The best project combination may have never been
realized.
This dissertation proposes network design models which can automatically
create project combinations and searching for the best. The network
design models have been studied for the passenger movements and focus on
highway expansions. In this dissertation, the focus is shifted to the
freight movements which involve multimodal transportation improvements.
Our freight network design model is developed based on the bi-level
optimization model. The development then involves two components. The
first task is to set the freight investment problems within the bi-level
format. This includes finding a suitable freight flow prediction model
which can work well with the bi-level model. The second task is to
provide a solution algorithm to solve the problem.
The dissertation sets the framework of the freight flow network design
model, identifies expecting model issues, and provides alternatives that
alleviate them. Through a series of developments, the final model uses
the shipper-carrier freight equilibrium model to represent freight
behaviors. Capacity constraints are used as a mean to emphasize limited
services since the reliability issues, an important factor for freight
movements, cannot be captured by steady state traffic assignment. A case
study is implemented to allocate a budget for improvements on the
California highway network. The transportation modes are selected by the
shipper model which can be trucks, rails, or the multimodal
transportation. The results shown that the proposed network design model
provided better solutions compared with traditional ranking methods. The
solution algorithm can manage the problem with reasonable project
alternatives. However, the computation expense increases rapidly with
increasing number of project alternatives.
Commercial vehicles typically represent a small fraction of vehicular
traffic on most roadways. However, their influence on the economy,
environment, traffic performance, infrastructure, and safety are much
more significant than their diminutive numerical presence suggests.
This dissertation describes the development and prototype implementation
of a new high-fidelity inductive loop sensor and a ground-breaking
commercial vehicle classification system based on the vehicle inductive
signatures obtained from this sensor technology. This new sensor
technology is relatively easy to install and has the potential to yield
reliable and highly detailed vehicle inductive signatures for advanced
traffic surveillance applications.
The Speed PRofile INterpolation Temporal-Spatial (SPRINTS)
transformation model developed in this dissertation improves vehicle
signature data quality under adverse traffic conditions where
acceleration and deceleration effects can distort inductive vehicle
signatures. The axle classification model enables commercial vehicles
to be classified accurately by their axle configuration. The body
classification models reveal the function and unique impacts of the
drive and trailer units of each commercial vehicle.
Together, the results reveal the significant potential of this inductive
sensor technology in providing a more comprehensive commercial vehicle
data profile based on a unique ability to extract both axle
configuration information as well as high fidelity undercarriage
profiles within a single sensor technology to provide richer insight on
commercial vehicle travel statistics.
The activity-based travel demand model recognizes that travel is derived
from the demand for activity participation distributed in space. The
focus on intra-household interactions and linkages between people’s
behavior and social and physical environment has been identified as
emerging features of the activity-based approach that would be important
to travel behavior research. The dissertation is dedicated to an
in-depth exploration of the within-household interactions by theoretical
specification and empirical development of the household activity time
allocation models based on a utility maximization framework with the
household as the unit of analysis. Furthermore, the dissertation also
aims to propose a model of the household activity scheduling process
primarily focusing on task allocation mechanisms on the basis of the
human agents adjusting themselves to the built social and physical
environment.
Development of the activity time allocation model in this dissertation
includes two types of structural time allocation models. First, the
collective models based on two assumptions that household heads have
their own utility functions and that decisions by them reach
Pareto-efficient outcomes are introduced to develop intra-household
activity time allocation models for leisure demand and housework
activity. Secondly, intra-household time allocation to housework
activity is further examined through the estimation of time allocation
to the different types of activities by the different types of household
members along with extensive exploration of various theories and
identification of related interactions.
This dissertation proposes a household activity scheduling process a
model design based on a weekly pattern system, which is expected to keep
various advantages compared to a deterministic daily model system. Along
with learning and adaptation procedures, the human being as a learning
agent is designed to prepare strategic plans of behavior to achieve
individual goals through interactive environments, and operationalize
those plans via activity execution requiring the participation of other
agents. At the household level, the household and its members as
decision agents are also designed to optimize the allocation of the
available household labor resource under the presence of the
uncertainties of the physical and social environments. After describing
the mathematical framework and solution procedure, a simulation
experiment is conducted within a hypothetical environment to demonstrate
how the proposed model works.
The objective of this dissertation is to develop a decision-making
method framework for prioritizing various potential alternatives of
truck management strategies using Multi-Criteria Decision-Making (MCDM)
method. The motivation of this research is derived from the need of
investigating and evaluating all likely impacts resulting from the
implementation of truck strategies. Since the conventional evaluation
methods such as the cost-benefit analysis can only be considered impacts
involving monetary scales, we believe these are insufficient to
investigate the all likely impacts. Our method is developed in order to
address all measures that can transformable and non-transformable as
well as to reflect decision-makers’ priorities of the problem. As a
result, two main objectives are accomplished in our study. The first is
to investigate the all likely impacts resulting from the implementation
of truck management strategies by performing a specific case study of
before and after cases using traffic simulation models. A key feature of
this part is to analyze various performance measures. They include both
measures that can transformable and non-transformable into monetary
costs as well as can reflect the standpoints of the public and the
private sectors. Secondly, a decision-making method is developed using
the Analytical Hierarchy Process (AHP) method which is one of popular
multi-criteria decision-making (MCDM) methods. This method enables the
judgments and preferences of decision-makers to be quantified based on
the relative importance of their own criteria, and to allow a
quantitative interpretation from others. Another important contribution
of our work is to suggest a “score-allocation” method which is a
normalization technique. Since quantitative measurements have different
scales, we need to incorporate these measurements into a single value.
This method allows decision-makers easily to facilitate comparisons
among potential alternatives. We believe that scores across alternatives
provide the argument to prioritize potential alternatives of truck
strategies.
Estimating and forecasting travel demand have been a popular study topic
among transportation researchers; however the research needs to pursue
new direction with the advent of data from the potential availability of
newer types of data previously not envisaged. In this dissertation, the
author develops approaches for two aspects of travel demand analysis in
the transportation network: A newer OD estimation method, and a
household activity-based demand modeling framework.
First, a trip-based dynamic OD estimation model is developed. Several
previous studies on OD trip table estimation focused on a static problem
and many recent dynamic OD estimation methods also have not sufficiently
proved their practical applicability. In order to overcome the
shortcomings, this dissertation introduces supplementary information
(i.e., vehicle trajectory data) to a dynamic OD estimation model.
However, the trip-based approach has certain well-known limitations. OD
estimation results can not give satisfactory solutions for forecasting
purposes, and the estimated OD table only contains materialized trips,
which implies that no latent travel demand is included in the table. To
overcome these drawbacks, the second item of focus in the dissertation
is in developing a dynamic agent-based household activity and travel
demand simulation model framework named DYNAHAP. The framework
calculates a demand pattern in terms of activity chains generated by
synthetic families. A traffic simulator then executes the activity
chains, and finally an aggregated dynamic traffic pattern is generated.
In order to calibrate DYNAHAP, various activity data should be gathered.
Such tasks had been regarded very difficult or even nearly impossible
before, but with the development of data collecting technologies,
currently we have several ways for collecting the activity chains of
individuals. Like vehicle trajectory data, sample activity chains
collected from personal communication devices such as PDA (Personal
digital assistant) could be used for DYNAHAP calibration. Some numerical
test results also will be given for the purpose of proving the
performance of the developed models.
Variations in traffic volumes and changes in travel-related characteristics significantly contribute to the level of vehicular emissions. However, in current practice, travel forecasting models rely on steady state hourly averages and are thus incapable of accurately capturing the effects of network traffic variations accurately on emissions. Recent research has focused on the implementation of modal emission models to overcome some of these shortcomings in existing emission rate models. A primary input to modal emission models is the fraction of time spent in different driving patterns. The estimation accuracy, however, is hampered by the application of static travel demand models for predicting driving patterns. There is a real need to evolve alternate methods to accurately predict driving patterns.
This dissertation proposes an approach to predicting driving patterns more accurately by applying different models at the macroscopic and microscopic network levels. The proposed models more accurately estimate the driving pattern by considering a set of Emission Specific Characteristics (ESC) for each network link. Specific ESC considered in this research includes geometric design elements, traffic characteristics, roadside environment characteristics, and driver behavior.
Two different models have been developed in this study to capture the driving patterns at each network level. The first model is designed to capture macro-scale driving patterns (average speed) in a larger network and the second model is designed to capture micro-scale driving patterns. The two models have been developed using structural equations. They have been calibrated, evaluated, and validated using a microscopic traffic simulation model. Analysis of the models reveals that geometric design elements exert greater influence on driving patterns than traffic characteristics, roadway environment characteristics, and driver behavior in the estimation of emissions. This research has concluded that, for congested traffic conditions, the proposed models capture driving patterns more accurately than current practice and, consequently, these models estimate the range of emissions more accurately. Models that estimate time-dependent emissions in the presence of traffic sensor data were also successfully estimated.
Traffic operations field computational resources as well as the bandwidth of field communication links are often quite limited. Accordingly, for real-time implementation of Advanced Transportation Management and Information Systems (ATMIS) strategies, such as vehicle re-identification, there is strong interest in development of field-based techniques and models that can perform satisfactorily while minimizing field computational and communication requirements. The ILD (Inductive Loop Detector)-based Vehicle ReIDentification system (ILD-VReID) is an example of a currently applied approach. Although ILDs are not without limitations as a traffic sensor, they are widely used for historical reasons and the sunken investment in the large installed base makes their use in this research highly cost-effective. Therefore, this dissertation develops a new vehicle re-identification algorithm, RTREID-2, for real-time implementation by adopting a PSR (Piecewise Slope Rate) approach that extracts features from raw vehicle signature data. The results of cases studies indicate that RTREID-2 is capable of accurately providing individual vehicle tracking information and performance measurements such as travel time and speed. The potential contributions of RTREID-2 are: application to square and round single loop configurations, and reduced computational requirements associated with re-estimation or transferability of the speed models used in the previously developed approach. As a consequence RTREID-2 is obviated for site-specific calibration and transferability issues. A freeway corridor study also demonstrates that RTREID-2 has the potential to be implemented successfully in a congested freeway corridor, utilizing data obtained from both homogenous and heterogeneous loop detection systems. A real-time vehicle classification model, which is based on the PSR approach, was also developed on the part of RTREID-2. The classification model can successfully classify vehicles into 15 classes using single loop detector data without any axle explicit information. The initial results also suggest the potential for transferability of the vehicle classification approach and are very encouraging. To investigate real-time freeway performance measurement in a real-world setting, the design of RTPMS (Real-time Traffic Performance Measurement System) that is based on RTREID-2 is also presented in this dissertation. A simulation of RTPMS is conducted to evaluate its feasibility. The simulation results demonstrate the potential of implementing RTPMS in real world application.