This dissertation proposes a set of coherent cognition-based paradigms to allow greater sensitivity and adaptability to emerging technologies and behavioral policies. These paradigms are derived from a cognition-based framework that explicates information source, medium, sensation, perception, and learning. The feasibility is demonstrated on an analytical example of multi-stakeholder decision processes and human-machine systems where the two types of entities can be incorporated with the same modeling scheme. This reduces the challenges in object-oriented programming in agent-based modeling practice, such as information intractability and data redundancy.
The first paradigm strictly models information as changes of uncertainty, which is applied in quantifying traveler information for the evaluation of dynamic message boards with various contents at various candidate locations near Downtown Los Angeles.
The second paradigm proposes to capture the prevailing and systemic bias in travel survey studies with a quantum logic. In a sense, this is a utilitarian extension to the quantum probability theory to resolve issues challenging for the classical probability theory. The paradigm is applied to quantify the inconsistency between the stated intentions and the reported preferences.
The third paradigm is developed for a utility-based decision model under risk around the proposed concept of Elastic Surprise. This concept makes feasible the differentiation between probability misperception and perceived uncertainty. It is shown that conventional methods on decision under risk such as Expected Utility Theory and Cumulative Prospect Theory are special cases. In addition, a specific form of Elastic Surprise under certain assumption on human’s cognition leads to Shannon’s information entropy and, hence, connects with the first paradigm. The method is tested in conjunction of the Cumulative Prospect Theory on travel time equivalency under risk in a survey study. The results show improvement in data fitting and cognition-wise result interpretability.
Finally, guided by the framework and the specificity of the paradigms are tested on a case study of multi-class multi-criteria dynamic traffic assignment where heterogeneous travelers’ risk preference on travel time is explicitly modeled.
Among all the freight modeling approaches, commodity-based model provides concerns on all travel modes and can capture the economic mechanisms driving freight movements. However, there are still challenges on how to effectively use public freight data and reflect the supply chain relations of various commodities. In this research, a commodity-based framework for freight demand forecasting using Structural Regression Model (SRM) is proposed and applied in California Statewide Freight Forecasting Model (CSFFM) using Freight Analysis Framework 4 data.
The framework developed in this study contains four innovative components: (1) mathematical approach for determining freight economic centroids; (2) aggregation of commodities using Fuzzy C-means clustering algorithm; (3) employing average travel distance by commodity group instead of highway skim to accurately represent real condition; (4) forecasting freight demand using Structural Regression Modeling method to comprehensively consider the direct effect, indirect effect and latent variable. The SRM is adopted in both total generation model and domestic direct demand model which combines the traditional generation and distribution steps. The application results are further compared with old CSFFM 1.0’s forecasts in 2012 to illustrate the advantages of proposed framework.
Transportation systems have been traditionally operated on a First-Come-First-Served (FCFS) fashion. FCFS consumption of supply naturally arises from supply being centralized by an infrastructure operator, without considering any individual-specific information from users. Thus, FCFS behaves as a status quo policy, generally considered as fair, since it is acknowledged that all users are treated equally. We know though, that there exists heterogeneity in users’ value of time and delay savings. Taking advantage of smartphones and connected vehicle environments, it is now possible to include this user heterogeneity into operations in order to increase overall system efficiency and fairness. I call this novel operational paradigm, collaborative consumption of transportation supply.
This seminar explores the idea of violating FCFS by allowing users to trade in real-time the part of supply they “own” while they participate into a transportation system. This de facto ownership emanates from the space-time region which each agent lawfully controls. This topic led me to introduce by the first time in transportation literature, the fundamental economic concept of envy-freeness as a behavioral paradigm. I also have expanded this concept to the domain of dynamic problems, which I call dynamic envy-freeness, and created a new envy-minimizing criteria family, which strongly fits into the existing axiomatic body of Welfare Economics.
Several applications of collaborative consumption are explored in this dissertation. First, I create PEXIC, Priced EXchanges in Intersection Control, in which users can pay other vehicles to reduce their delays in a fair manner. Second, Peer-to-peer (P2P) ride exchange in ridesharing systems, where trip property rights are transferred to users in such a way that they can trade their rides between each other. Third, I have studied a new operational policy in highway control: queue-jumping operations, where vehicles can jump a queue by paying the overtaken vehicles such that the resulting queue is fair and stable. Fourth, I have modelled a P2P ridesharing system as a truthful dual-role market exchange economy, which guarantees a ride-back. Finally, I present how collaborative consumption will be extended to urban systems.
More frequent vehicle movements are required for moving containers in a local area due to low unit volume that a single vehicle can handle compared with vessels and rails involved in the container supply chain. For this reason, truck operations for moving containers significantly affect not only transportation cost itself but also product price. They have inherent operational inefficiencies associated with empty container movements and container processes at facilities such as warehouses, distribution centers and intermodal terminals. One critical issue facing the trucking industry is the pressing need for truck routing plans that reduce such inefficiencies. Hence, this dissertation proposes to apply the concept of sharing resources, which is an emerging economic model, to container truck operations in order to resolve this issue. Two shareable resources – vehicles and containers – are considered.
This study extends the literature on routing and scheduling problems that arise from container movements, and examines the possible benefits of sharing resources across customers. A series of truck container routing and scheduling problems were developed by assuming different levels of resource sharing among; (1) customers of one trucking operator, (2) customers across collaborations of multiple operators, and (3) customers over multi-day operations. To enable a trucking company to operate its fleet under a shared resource environment, two operational strategies – street turning and decoupling operations – together with temporal precedence constraints – in addition to the time constraints that are typically included in the vehicle routing problem with time windows (VRPTW) – were adopted to address the proposed problems.
Two meta-heuristic algorithms based on a variable neighborhood search (VNS) scheme were developed to solve the proposed problems, including temporal precedence constraints – which are computationally more expensive – for real-world applications. To address flexible time windows resulting from temporal precedence constraints, a novel feasibility check algorithm was developed.
Results from a series of numerical experiments confirm that the proposed approach leverages the advantages of resource sharing, and the meta-heuristic algorithms are efficient solution approaches for each problem with the targeted resource sharing. Consequently, this dissertation offers a platform for the development of a decision-support tool for drayage companies by applying three different levels of resource sharing into their operations.
Researchers have been concerned that suburban sprawl could reinforce gendered mobility patterns and lead to gendered differences in mobility. Previous studies also argued that the effectiveness of land use policy could be influenced by men and women’s different mobility patterns in response to built environments. To address these concerns, this dissertation uses the 2010-2012 California Household Travel Survey data and directly compares the within-household gendered travel and spatial behaviors for households with paired heads living in Southern California. The study examines whether built environments, including destination accessibility, design and walkability have different impacts on male and female heads’ daily travel and activity space behaviors and whether potential urban design can help improve gendered inequality in daily mobility.
Based on negative binomial, Tobit, and generalized least squares regressions, the results show that that male and female heads respond to built environments with different travel and spatial behaviors. Living in walkable and accessible areas is likely to encourage male heads to walk, reduce their dependence on driving, locate activity center close to home, and have spatially concentrated activities. Female heads tend to respond to walkable and accessible living environments with reducing automobile travel and with centering and confining their activities near residential neighborhoods. The negative binomial, Tobit, and binary logit regression analyses that investigate the influences of built environments on gendered inequality indicate that high walkability and regional accessibility are likely to reduce the gendered inequality in motorized travel distance and relax female heads’ spatial (and temporal) constraints relative to their husbands.
This dissertation contributes to the policy debates by informing planners and feminist geographers that the effects of built environments can be heterogeneous even for men and women from similar backgrounds and compact design can be the key to gendered equity. Given that compact developments are being rapidly implemented in Southern California, this dissertation study is expected to help shape effective and efficient land use policies in the future.
An efficient transportation system requires adequate and well-maintained infrastructure to relieve congestion, reduce accidents, and promote economic competitiveness. However, there is a growing gap between public financial commitments and the cost of maintaining, let alone expanding, the U.S. road transportation infrastructure. Moreover, the tools used to evaluate transportation infrastructure investments are typically deterministic and rely on present value calculations, even though it is well-known that this approach is likely to result in sub-optimal decisions in the presence of uncertainty, which is pervasive in transportation infrastructure decisions. In this context, the purpose of this dissertation is to propose a framework based on real options and advanced numerical methods to make better road infrastructure decisions in the presence of demand uncertainty.
I first develop a real option framework to find the optimal investment timing, endogenous toll rate, and road capacity of a private inter-city highway under demand uncertainty. Traffic congestion is represented by a BPR function, competition with an existing road is captured by user equilibrium, and travel demand between the two cities follows a geometric Brownian motion with a reflecting upper barrier. I derive semi-analytical solutions for the investment threshold, the dynamic toll rates and the optimum capacity. Result shows the importance of modeling congestion and an upper demand barrier – features that are missing from previous studies.
I then extend this real options framework to study two additional ways of funding an inter-city highway project: with public funds or via a Public-Private Partnership (PPP). Using Monte Carlo simulation, I investigate the value of a non-compete clause for both a local government and for private firms involved in the PPP.
Since road infrastructure investments are rarely made in isolation, I also extend my real options framework to the Multi-period Continuous Network Design Problem (CNDP), to analyze the investment timing and capacity of multiple links under demand uncertainty. No algorithm is currently available to solve the multi-period CNDP under uncertainty in a reasonable time. I propose and test a new algorithm called “Approximate Least Square Monte Carlo simulation” that dramatically reduces the computing time to solve the CNDP while generating accurate solutions.
Trip chaining is a common phenomenon generally known as linking multiple activities and trips in one travel process. A good understanding about trip chaining complexity is important for travel demand model development and for transportation policy design. However, most of the existing studies on trip chaining limit the complexity classification scheme on number of trips chained and neglect other dimensions that also elevate the degree of complexity. The purpose of this study is to develop a new approach, Tour Complexity Index (TCI), that integrates the multi-dimensional nature of trip chaining into the complexity assessment.
The study contains three analysis components. The first component introduces the TCI approach as a trip chaining complexity measure that not only considers number of trips chained but also includes the spatial relationship across destinations, the route arrangement, and the urban environment of the destinations. By comparing descriptive statistics and generalized linear model results from TCI approach with those from traditional approach, we find that the TCI approach offers more information regarding trip chaining and mode choice. The application of TCI is further demonstrated in the following components. The second component investigates the intrapersonal daily and weekly travel variability with travel characterized by TCI and mode choice. The result reinforces an argument in current literature that the common single-day travel survey may produce biased estimation due to the day-to-day variance in travel behavior. Result also finds that proximity to a new transit service from place of residence is connected with a decline in variability. The third component explores a framework for travel pattern recognition where pattern is characterized by TCI as well. The discrepancy analysis which is a generalized analysis of variance (ANOVA) method is applied to associate individual characteristics with travel pattern. In addition, both components use Sequential Alignment Method (SAM) for travel pattern representation. The TCI approach and proposed analysis frameworks are validated using the longitudinal GPS trajectory data collected between 2011 and 2013 at west Los Angeles area for Expo Study.
Trucks contribute disproportionally to traffic congestion, emissions, road safety issues, and infrastructure and maintenance costs.
In addition, truck flow patterns are known to vary by season and time-of-day as trucks serve different industries and facilities.
Therefore, truck flow data are critical for transportation planning, freight modeling, and highway infrastructure design and
operations. However, the current data sources only provide partial truck flow or point observations. This dissertation developed a
framework for estimating path flows of trucks by tracking individual vehicles as they traverse detector stations over long distances.
Truck physical attributes and inductive waveform signatures were collected from advanced point detector systems and used to match
vehicles between detector locations by a Selective Weighted Bayesian Model (SWBM). The key feature variables that were the
most influential in distinguishing vehicles were identified and emphasized in the SWBM to efficiently and successfully track
vehicles across road networks.
The initial results showed that the Bayesian approach with the full integration of two complementary detector data types – advanced
inductive loop detectors and Weigh-in-Motion (WIM) sensors – could successfully track trucks over long distances (i.e., 26 miles)
by minimizing the impacts of measurement variations and errors from the detection systems. The network implementation of the
model demonstrated high coverage and accuracy, which affirmed the capability of the tracking approach to provide comprehensive
truck travel patterns in a complex network. Specifically, the model was able to successfully match 90 percent of multi-unit trucks
where only 67 percent of trucks observed at a downstream site passed an upstream detection site.
A strategic plan to identify optimal sensor locations to maximize benefits from the truck tracking model was also proposed. A
decision model that optimally locates sensors to capture the maximum truck OD and route flow was investigated using a goal
programming approach. This approach suggested optimal locations for tracking implementation in a large truck network considering
a limited budget. Results showed that sensor locations from a maximum-flow-capturing approach were more advantageous to
observe truck flow than a conventional sensor location approach that focuses on OD and route identifiability.
Disasters, specifically earthquakes, result in worldwide catastrophic losses annually. The first seventy-two hours are the most critical and so any reduction in response time is a much-needed contribution. This is especially true in cases where parts of the communication infrastructure are severely damaged. Traditional disaster relief logistics models tend to rely on the assumption that information flow is continuous throughout the system following the onset of a natural disaster. A new integrated framework for disaster relief logistics that optimizes the movement of critical information along with physical movements is proposed in order to alleviate post-disaster conditions in a more accurate and timely manner. The framework consists of an information network and a transportation network with interrelationships. The framework was applied to the Irvine Golden Triangle Network and the Knoxville Network for up to three different cases. The DYNASMART-P simulation program performance was compared against the Time Dependent Network Simplex paths approach combined with the information updating feedback loop. The average total travel times of vehicles travelling to the trauma center in the study areas were compared in order to quantify the improvements of the integrated solution framework. The results show a significant reduction of average total travel times for vehicles transporting injured patients to the trauma center.
Recent advances in communication technology coupled with increasing environmental concerns, road congestion, and the high cost of vehicle ownership have directed more attention to the opportunity cost of empty seats traveling throughout the transportation networks every day. Peer-to-peer (P2P) ridesharing is a good way of using the existing passenger-movement capacity on the vehicles, thereby addressing the concerns about the increasing demand for transportation that is too costly to ad-dress via infrastructural expansion.
This dissertation is dedicated to the optimization of the matching process between the partici-pants in a ridesharing system. More specifically, focus of this book is on multi-hop matching, in which riders have the possibility of transferring between vehicles. Different algorithms have been presented for various implementation strategies of ridesharing systems. Multiple case studies assess the important role ridesharing can play as a separate mode, or in conjunc-tion with other modes of transportation, in multi-modal settings.