Matching Items (47)
Description
The objective of this dissertation is to empirically analyze the results of the retail location decision making process and how chain networks evolve given their value platform. It employs one of the largest cross-sectional databases of retailers ever assembled, including 50 US retail chains and over 70,000 store locations. Three closely related articles, which develop new theory explaining location deployment and behaviors of retailers, are presented. The first article, "Regionalism in US Retailing," presents a comprehensive spatial analysis of the domestic patterns of retailers. Geographic Information Systems (GIS) and statistics examine the degree to which the chains are deployed regionally versus nationally. Regional bias is found to be associated with store counts, small market deployment, and the location of the founding store, but not the age of the chain. Chains that started in smaller markets deploy more stores in other small markets and vice versa for chains that started in larger markets. The second article, "The Location Types of US Retailers," is an inductive analysis of the types of locations chosen by the retailers. Retail locations are classified into types using cluster analysis on situational and trade area data at the geographical scale of the individual stores. A total of twelve distinct location types were identified. A second cluster analysis groups together the chains with the most similar location profiles. Retailers within the same retail business often chose similar types of locations and were placed in the same clusters. Retailers generally restrict their deployment to one of three overall strategies including metropolitan, large retail areas, or market size variety. The third article, "Modeling Retail Chain Expansion and Maturity through Wave Analysis: Theory and Application to Walmart and Target," presents a theory of retail chain expansion and maturity whereby retailers expand in waves with alternating periods of faster and slower growth. Walmart diffused gradually from Arkansas and Target grew from the coasts inward. They were similar, however, in that after expanding into an area they reached a point of saturation and opened fewer stores, then moved on to other areas, only to revisit the earlier areas for new stores.
ContributorsJoseph, Lawrence (Author) / Kuby, Michael (Thesis advisor) / Matthews, Richard (Committee member) / Ó Huallacháin, Breandán (Committee member) / Kumar, Ajith (Committee member) / Arizona State University (Publisher)
Created2013
Description
It has been identified in the literature that there exists a "spatial mismatch" between geographical concentrations of lower-income or minority people who have relatively lower rates of car ownership, lower skills or educational attainment and who mainly rely on public transit for their travel, and low-skilled jobs for which they more easily qualify. Given this situation, various types of transportation projects have been constructed to improve public transit services and, alongside other goals, improve the connection between low-skilled workers and jobs. As indicators of performance, measures of job accessibility are commonly used in to gauge how such improvements have facilitated job access. Following this approach, this study investigates the impact of the Phoenix Metro Light Rail on job accessibility for the transit users, by calculating job accessibility before and after the opening of the system. Moreover, it also investigates the demographic profile of those who have benefited from improvements in job accessibility----both by income and by ethnicity. Job accessibility is measured using the cumulative opportunity approach which quantifies the job accessibility within different travel time limits, such as 30 and 45 minutes. ArcGIS is used for data processing and results visualization. Results show that the Phoenix light rail has improved job accessibility of the traffic analysis zones that are along the light rail line and Hispanic and lower-income groups have benefited more than their counterparts.
ContributorsLiu, Liyuan (Author) / Golub, Aaron (Thesis advisor) / Wentz, Elizabeth (Committee member) / Kuby, Michael (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation research contributes to the advancement of activity-based travel forecasting models along two lines of inquiry. First, the dissertation aims to introduce a continuous-time representation of activity participation in tour-based model systems in practice. Activity-based travel demand forecasting model systems in practice today are largely tour-based model systems that simulate individual daily activity-travel patterns through the prediction of day-level and tour-level activity agendas. These tour level activity-based models adopt a discrete time representation of activities and sequence the activities within tours using rule-based heuristics. An alternate stream of activity-based model systems mostly confined to the research arena are activity scheduling systems that adopt an evolutionary continuous-time approach to model activity participation subject to time-space prism constraints. In this research, a tour characterization framework capable of simulating and sequencing activities in tours along the continuous time dimension is developed and implemented using readily available travel survey data. The proposed framework includes components for modeling the multitude of secondary activities (stops) undertaken as part of the tour, the time allocated to various activities in a tour, and the sequence in which the activities are pursued.
Second, the dissertation focuses on the implementation of a vehicle fleet composition model component that can be used not only to simulate the mix of vehicle types owned by households but also to identify the specific vehicle that will be used for a specific tour. Virtually all of the activity-based models in practice only model the choice of mode without due consideration of the type of vehicle used on a tour. In this research effort, a comprehensive vehicle fleet composition model system is developed and implemented. In addition, a primary driver allocation model and a tour-level vehicle type choice model are developed and estimated with a view to advancing the ability to track household vehicle usage through the course of a day within activity-based travel model systems. It is envisioned that these advances will enhance the fidelity of activity-based travel model systems in practice.
Second, the dissertation focuses on the implementation of a vehicle fleet composition model component that can be used not only to simulate the mix of vehicle types owned by households but also to identify the specific vehicle that will be used for a specific tour. Virtually all of the activity-based models in practice only model the choice of mode without due consideration of the type of vehicle used on a tour. In this research effort, a comprehensive vehicle fleet composition model system is developed and implemented. In addition, a primary driver allocation model and a tour-level vehicle type choice model are developed and estimated with a view to advancing the ability to track household vehicle usage through the course of a day within activity-based travel model systems. It is envisioned that these advances will enhance the fidelity of activity-based travel model systems in practice.
ContributorsGarikapati, Venu Madhav (Author) / Pendyala, Ram M. (Thesis advisor) / Zhou, Xuesong (Committee member) / Lou, Yingyan (Committee member) / Arizona State University (Publisher)
Created2014
Description
In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and behavior of mobile objects is an important issue for extracting useful information and knowledge about mobile phenomena. Potential applications across a wide range of fields include urban and transportation planning, Location-Based Services, and logistics. This research is designed to contribute to the existing state-of-the-art in tracking and modeling mobile objects, specifically targeting three challenges in investigating spatio-temporal patterns and processes; 1) a lack of space-time analysis tools; 2) a lack of studies about empirical data analysis and context awareness of mobile objects; and 3) a lack of studies about how to evaluate and test agent-based models of complex mobile phenomena. Three studies are proposed to investigate these challenges; the first study develops an integrated data analysis toolkit for exploration of spatio-temporal patterns and processes of mobile objects; the second study investigates two movement behaviors, 1) theoretical random walks and 2) human movements in urban space collected by GPS; and, the third study contributes to the research challenge of evaluating the form and fit of Agent-Based Models of human movement in urban space. The main contribution of this work is the conceptualization and implementation of a Geographic Knowledge Discovery approach for extracting high-level knowledge from low-level datasets about mobile objects. This allows better understanding of space-time patterns and processes of mobile objects by revealing their complex movement behaviors, interactions, and collective behaviors. In detail, this research proposes a novel analytical framework that integrates time geography, trajectory data mining, and 3D volume visualization. In addition, a toolkit that utilizes the framework is developed and used for investigating theoretical and empirical datasets about mobile objects. The results showed that the framework and the toolkit demonstrate a great capability to identify and visualize clusters of various movement behaviors in space and time.
ContributorsNara, Atsushi (Author) / Torrens, Paul M. (Thesis advisor) / Myint, Soe W (Committee member) / Kuby, Michael (Committee member) / Griffin, William A. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Regional differences of inventive activity and economic growth are important in economic geography. These differences are generally explained by the theory of localized knowledge spillovers, which argues that geographical proximity among economic actors fosters invention and innovation. However, knowledge production involves an increasing number of actors connecting to non-local partners. The space of knowledge flows is not tightly bounded in a given territory, but functions as a network-based system where knowledge flows circulate around alignments of actors in different and distant places. The purpose of this dissertation is to understand the dynamics of network aspects of knowledge flows in American biotechnology. The first research task assesses both spatial and network-based dependencies of biotechnology co-invention across 150 large U.S. metropolitan areas over four decades (1979, 1989, 1999, and 2009). An integrated methodology including both spatial and social network analyses are explicitly applied and compared. Results show that the network-based proximity better defines the U.S. biotechnology co-invention urban system in recent years. Co-patenting relationships of major biotechnology centers has demonstrated national and regional association since the 1990s. Associations retain features of spatial proximity especially in some Midwestern and Northeastern cities, but these are no longer the strongest features affecting co-inventive links. The second research task examines how biotechnology knowledge flows circulate over space by focusing on the structural properties of intermetropolitan co-invention networks. All analyses in this task are conducted using social network analysis. Evidence shows that the architecture of the U.S. co-invention networks reveals a trend toward more organized structures and less fragmentation over the four years of analysis. Metropolitan areas are increasingly interconnected into a large web of networked environment. Knowledge flows are less likely to be controlled by a small number of intermediaries. San Francisco, New York, Boston, and San Diego monopolize the central positions of the intermetropolitan co-invention network as major American biotechnology concentrations. The overall network-based system comes close to a relational core/periphery structure where core metropolitan areas are strongly connected to one another and to some peripheral areas. Peripheral metropolitan areas are loosely connected or even disconnected with each other. This dissertation provides empirical evidence to support the argument that technological collaboration reveals a network-based system associated with different or even distant geographical places, which is somewhat different from the conventional theory of localized knowledge spillovers that once dominated understanding of the role of geography in technological advance.
ContributorsLee, Der-Shiuan (Author) / Ó Huallacháin, Breandán (Thesis advisor) / Anselin, Luc (Committee member) / Kuby, Michael (Committee member) / Lobo, Jose (Committee member) / Arizona State University (Publisher)
Created2011
Description
The development of microsimulation approaches to urban systems modeling has occurred largely in three parallel streams of research, namely, land use, travel demand and traffic assignment. However, there are important dependencies and inter-relationships between the model systems which need to be accounted to accurately and comprehensively model the urban system. Location choices affect household activity-travel behavior, household activity-travel behavior affects network level of service (performance), and network level of service, in turn, affects land use and activity-travel behavior. The development of conceptual designs and operational frameworks that represent such complex inter-relationships in a consistent fashion across behavioral units, geographical entities, and temporal scales has proven to be a formidable challenge. In this research, an integrated microsimulation modeling framework called SimTRAVEL (Simulator of Transport, Routes, Activities, Vehicles, Emissions, and Land) that integrates the component model systems in a behaviorally consistent fashion, is presented. The model system is designed such that the activity-travel behavior model and the dynamic traffic assignment model are able to communicate with one another along continuous time with a view to simulate emergent activity-travel patterns in response to dynamically changing network conditions. The dissertation describes the operational framework, presents the modeling methodologies, and offers an extensive discussion on the advantages that such a framework may provide for analyzing the impacts of severe network disruptions on activity-travel choices. A prototype of the model system is developed and implemented for a portion of the Greater Phoenix metropolitan area in Arizona to demonstrate the capabilities of the model system.
ContributorsKonduri, Karthik Charan (Author) / Pendyala, Ram M. (Thesis advisor) / Ahn, Soyoung (Committee member) / Kuby, Michael (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2012
Description
Measuring the success of a transportation project as it is envisioned in the Regional Transportation Plan (RTP) and is detailed in an Environmental Impact Statement (EIS) is not part of any current planning process, for a post construction analysis may have political consequences for the project participants, would incur additional costs, and may be difficult to define in terms of scope. With local, state and federal budgets shrinking, funding sources are demanding that the performance of a project be evaluated and project stakeholders be held accountable. The Transportation Research Board (TRB) developed a framework that allows transportation agencies to customize their reporting so that a project's performance can be measured. In the case of the Red Mountain Freeway, the selected performance measure allows for comparing the population forecasts, the traffic volumes, and the project costs defined in the final EIS to actual population growth, actual average annual daily traffic (ADT), and actual project costs obtained from census data, the City of Mesa, and contractor bids, respectively. The results show that population projections for both Maricopa County and the City of Mesa are within less than half a percent of the actual annual population growth. The traffic analysis proved more difficult due to inconsistencies within the EIS documents, variations in the local arterials used to produce traffic volume, and in the projection time-spans. The comparison for the total increase in traffic volume generated a difference of 11.34 percent and 89.30 percent. An adjusted traffic volume equal to all local arterials and US 60 resulted in a difference of 40 percent between the projected and actual ADT values. As for the project cost comparison, not only were the costs within the individual documents inconsistent, but they were underestimated by as much as 75 percent. Evaluating the goals as described in an EIS document using the performance measure guidelines provided by the TRB may provide the tool that can help promote conflict resolution for political issues that arise, streamline the planning process, and measure the performance of the transportation system, so that lessons learned can be applied to future projects.
ContributorsKizior, Angelika (Author) / Golub, Aaron (Thesis advisor) / Kuby, Michael (Committee member) / Wentz, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2012
Description
The accurate prediction of pavement network condition and performance is important for efficient management of the transportation infrastructure system. By reducing the error of the pavement deterioration prediction, agencies can save budgets significantly through timely intervention and accurate planning. The objective of this research study was to develop a methodology for calculating a pavement condition index (PCI) based on historical distress data collected in the databases from Long-Term Pavement Performance (LTPP) program and Minnesota Road Research (Mn/ROAD) project. Excel™ templates were developed and successfully used to import distress data from both databases and directly calculate PCIs for test sections. Pavement performance master curve construction and verification based on the PCIs were also developed as part of this research effort. The analysis and results of LTPP data for several case studies indicated that the study approach is rational and yielded good to excellent statistical measures of accuracy.
It is believed that the InfoPaveTM LTPP and Mn/ROAD database can benefit from the PCI templates developed in this study, by making them available for users to compute PCIs for specific road sections of interest. In addition, the PCI-based performance model development can be also incorporated in future versions of InfoPaveTM. This study explored and analyzed asphalt pavement sections. However, the process can be also extended to Portland cement concrete test sections. State agencies are encouraged to implement similar analysis and modeling approach for their specific road distress data to validate the findings.
ContributorsWu, Gan (Author) / Kaloush, Kamil (Thesis advisor) / Zhou, Xuesong (Committee member) / Underwood, Benjamin Shane (Committee member) / Arizona State University (Publisher)
Created2015
Description
This research develops heuristics to manage both mandatory and optional network capacity reductions to better serve the network flows. The main application discussed relates to transportation networks, and flow cost relates to travel cost of users of the network. Temporary mandatory capacity reductions are required by maintenance activities. The objective of managing maintenance activities and the attendant temporary network capacity reductions is to schedule the required segment closures so that all maintenance work can be completed on time, and the total flow cost over the maintenance period is minimized for different types of flows. The goal of optional network capacity reduction is to selectively reduce the capacity of some links to improve the overall efficiency of user-optimized flows, where each traveler takes the route that minimizes the traveler’s trip cost. In this dissertation, both managing mandatory and optional network capacity reductions are addressed with the consideration of network-wide flow diversions due to changed link capacities.
This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule.
Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.
This research first investigates the maintenance scheduling in transportation networks with service vehicles (e.g., truck fleets and passenger transport fleets), where these vehicles are assumed to take the system-optimized routes that minimize the total travel cost of the fleet. This problem is solved with the randomized fixed-and-optimize heuristic developed. This research also investigates the maintenance scheduling in networks with multi-modal traffic that consists of (1) regular human-driven cars with user-optimized routing and (2) self-driving vehicles with system-optimized routing. An iterative mixed flow assignment algorithm is developed to obtain the multi-modal traffic assignment resulting from a maintenance schedule. The genetic algorithm with multi-point crossover is applied to obtain a good schedule.
Based on the Braess’ paradox that removing some links may alleviate the congestion of user-optimized flows, this research generalizes the Braess’ paradox to reduce the capacity of selected links to improve the efficiency of the resultant user-optimized flows. A heuristic is developed to identify links to reduce capacity, and the corresponding capacity reduction amounts, to get more efficient total flows. Experiments on real networks demonstrate the generalized Braess’ paradox exists in reality, and the heuristic developed solves real-world test cases even when commercial solvers fail.
ContributorsPeng, Dening (Author) / Mirchandani, Pitu B. (Thesis advisor) / Sefair, Jorge (Committee member) / Wu, Teresa (Committee member) / Zhou, Xuesong (Committee member) / Arizona State University (Publisher)
Created2017
Description
Recently, automation, shared use, and electrification are proposed and viewed as the “three revolutions” in the future transportation sector to significantly relieve traffic congestion, reduce pollutant emissions, and increase transportation system sustainability. Motivated by the three revolutions, this research targets on the passenger-focused scheduled transportation systems, where (1) the public transit systems provide high-quality ridesharing schedules/services and (2) the upcoming optimal activity planning systems offer the best vehicle routing and assignment for household daily scheduled activities.
The high quality of system observability is the fundamental guarantee for accurately predicting and controlling the system. The rich information from the emerging heterogeneous data sources is making it possible. This research proposes a modeling framework to systemically account for the multi-source sensor information in urban transit systems to quantify the estimated state uncertainty. A system of linear equations and inequalities is proposed to generate the information space. Also, the observation errors are further considered by a least square model. Then, a number of projection functions are introduced to match the relation between the unique information space and different system states, and its corresponding state estimate uncertainties are further quantified by calculating its maximum state range.
In addition to optimizing daily operations, the continuing advances in information technology provide precious individual travel behavior data and trip information for operational planning in transit systems. This research also proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. An agent-based single-level integer linear formulation is proposed and can be effectively by the Lagrangian decomposition.
The recently emerging trend of self-driving vehicles and information sharing technologies starts creating a revolutionary paradigm shift for traveler mobility applications. By considering a deterministic traveler decision making framework, this research addresses the challenges of how to optimally schedule household members’ daily scheduled activities under the complex household-level activity constraints by proposing a set of integer linear programming models. Meanwhile, in the microscopic car-following level, the trajectory optimization of autonomous vehicles is also studied by proposing a binary integer programming model.
The high quality of system observability is the fundamental guarantee for accurately predicting and controlling the system. The rich information from the emerging heterogeneous data sources is making it possible. This research proposes a modeling framework to systemically account for the multi-source sensor information in urban transit systems to quantify the estimated state uncertainty. A system of linear equations and inequalities is proposed to generate the information space. Also, the observation errors are further considered by a least square model. Then, a number of projection functions are introduced to match the relation between the unique information space and different system states, and its corresponding state estimate uncertainties are further quantified by calculating its maximum state range.
In addition to optimizing daily operations, the continuing advances in information technology provide precious individual travel behavior data and trip information for operational planning in transit systems. This research also proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. An agent-based single-level integer linear formulation is proposed and can be effectively by the Lagrangian decomposition.
The recently emerging trend of self-driving vehicles and information sharing technologies starts creating a revolutionary paradigm shift for traveler mobility applications. By considering a deterministic traveler decision making framework, this research addresses the challenges of how to optimally schedule household members’ daily scheduled activities under the complex household-level activity constraints by proposing a set of integer linear programming models. Meanwhile, in the microscopic car-following level, the trajectory optimization of autonomous vehicles is also studied by proposing a binary integer programming model.
ContributorsLiu, Jiangtao (Author) / Zhou, Xuesong (Thesis advisor) / Pendyala, Ram (Committee member) / Mirchandani, Pitu (Committee member) / Lou, Yingyan (Committee member) / Arizona State University (Publisher)
Created2018