The objective of this research to develop quantitative measures that capture various aspects of underlying network structure, using aggregate level travel data from fifty metropolitan areas across the U.S. The influence of these measures on system performance is then tested using statistical regression models. The results corroborate that the quantitative measures of network structure affect the system performance. The results from this analysis can be used to develop network design guidelines that can be used to address current transportation problems.

This research analyzes the influence of network structure on household spatial patterns, as measured by activity spaces. The analysis uses street network and travel survey data from the Twin Cities and South Florida to compile measures of network structure. Statistical regression models test the relationship between network structure and travel. The results show that network design does influence travel, after controlling for other non-network based measures. Results from this analysis can be used to understand how changes in network can be used to bring about desired changes in travel behavior.

This paper develops a positive theory of network connectivity, seeking to explain the micro-foundations of alternative network topologies as the result of self-interested actors. By building roads, landowners hope to increase their parcels ’ accessibility and economic value. A simulation model is performed on a grid-like land use layer with a downtown in the center, whose structure resembles the early form of many Midwestern and Western (US) cities. The topological attributes for the networks are evaluated. This research posits that road networks experience an evolutionary process where a tree-like structure first emerges around the centered parcel before the network pushes outward to the periphery. In addition, road network topology undergoes clear phase changes as the economic values of parcels vary. The results demonstrate that even without a centralized authority, road networks have the property of self-organization and evolution, and, that in the absence of intervention, the tree-like or web-like nature of networks is a result of the underlying economics.

Network structure varies across cities. This variation may yield important knowledge about how the internal structure of the city affects its performance. This paper systematically compares a set of surface transportation network structure variables (connectivity, hierarchy, circuity, treeness, entropy, accessibility) across the 50 largest metropolitan areas in the United States. A set of scaling parameters are discovered to show how network size and structure vary with city size. These results suggest that larger cities are physically more inter-connected. Hypotheses are presented as to why this might obtain. This paper then consistently measures and ranks access to jobs across 50 US metropolitan areas. It uses that accessibility measure, along with network structure variables and city size to help explain journey-to-work time and auto mode share in those cities. A 1 percent increase in accessibility reduces average metropolitan commute times by about 90 seconds each way. A 1 percent increase in network connectivity reduces commute time by 0.1 percent. A 1 percent increase in accessibility results in a 0.0575 percent drop in auto mode share, while a 1 percent increase in treeness reduces auto mode share by 0.061 percent. Use of accessibility and network structure measures is important for planning and evaluating the performance of network investments and land use changes.

This paper examines the nature of first mover advantages on spatiallydifferentiated surface transportation networks. The literature on first mover advantages identifies a number of sources that explain their existence. However whether those sources exist on spatial networks, and how they play out with true capital immobility have been unanswered questions. By examining empirical examples including commuter rail and the Underground in London and roads in the Twin Cities of Minneapolis and St. Paul, first mover advantages were observed in rail stations but not in the road network. A simulation model was then constructed to replicate the growth of surface transportation networks incorporating idealized deployment decisions and to test whether the first network elements (links, nodes) remain strongest (or even strong) into the future. Simulation experiments were conducted and Spearman rank correlation tests revealed that first mover advantages exist in both nodes and links and become increasingly prominent as the network evolves due to the accumulated advantage of earlier established network elements. Simulation results also disclosed that network growth with a higher concentration of initial land uses results in stronger first mover advantages, and that the extent may vary as the topological attributes of the network change over time. The sensitivity of simulation results on model parameters are also discussed.

This dissertation explores transportation development and models the evolutionary growth of transportation networks including its determining factors. In particular, it examines the organization of ownership in the provision of transportation infrastructure as a pivotal driving factor. A series of stand-alone studies is dedicated to a comprehensive examination of network growth and ownership structure from different approaches analytically, empirically, and in simulation. The analytical model presents a game-theoretic analysis of centralized versus decentralized governance choice on a serial road network. It reveals that, depending on the tradeoff between the benefits and costs associated with alternative decision-making processes, governance choice reflects constituents’ collective spending preferences on infrastructure, and may spontaneously shift as the network improves over time. Empirical studies on Minneapolis skyways and Indiana Interurbans examine the expansion of transportation networks as a discrete sequence of link additions over time. Both studies suggest that the deployment of a network has to some extent followed

Research on travel behavior has traditionally focused on ways that infrastructure investments, namely, the urban form and built environment, can be used to influence travel. Proponents argue that overall travel can be reduced by bringing the trip origins and destination closer. Horning et al. (2008) point out that the inherent assumption underlying this argument is the

Pattern recognition is an important step in map generalization. Pattern recognition in street network is significant for street network generalization. A grid is characterized by a set of mostly parallel lines, which are crossed by a second set of parallel lines with roughly right angle. Inspired by object recognition in image processing, this paper presents an approach to the grid recognition in street network based on graph theory. Firstly, the bridges and isolated points of the network are identified and deleted repeatedly. Secondly, the similar orientation graph is created, in which the vertices represent street segments and the edges represent the similar orientation relation between streets. Thirdly, the candidates are extracted through graph operators such as finding connected component, finding maximal complete sub-graph, join and intersection. Finally, the candidate are evaluated by deleting bridges and isolated lines repeatedly, reorganizing them into stroke models, changing these stroke models into street intersection graphs in which vertices represent strokes and edges represent strokes intersecting each other, and then calculating the clustering coefficient of these graphs. Experimental result shows the proposed approach is valid in detecting the grid pattern in lower degradation situation. 1.

Accessibility, network structure, and consumers’ destination choice: a GIS analysis of GPS travel data

First and foremost, I would like to thank my advisor, Prof. David Levinson, for his guidance during my time at the University of Minnesota. I compare a Ph.D to running a marathon and Dr. Levinson provided me the right support at the right time to help me navigate the course. I am always amazed at his creativity and his ability to tackle a research problem in so many ways. I hope I can emulate him while I pursue my career. I would also like to acknowledge the members of my doctoral committee- Dr. Yingling Fan, Dr. Henry Liu and Dr. Julian Marshall for their support. Their insights helped me get a better understanding of my research. I would like to extend a special thanks to Dr. Nikolas Geroliminis, École Polytechnique Fédérale de Lausanne, Switzerland, for cheerfully stepping in as a substitute committee member, at the last minute, during my preliminary oral examination. This dissertation would not have been possible without the invaluable contribution of Dr. Hartwig Hochmair, University of Florida. His programming expertise made my life so much easier and I am extremely thankful to him for his assistance and interest in my dissertation. I would like to thank Mark Filipi, Metropolitan Council, for patiently responding to my numerous emails. A heartfelt thanks to all the undergraduate students that worked with me on this dissertation- Paul Anderson, Andrea Hamman and Anne Haws. Paul deserves a special thanks for his assistance with the data collection and clean up efforts. I would also like to acknowledge the organizations that provided the necessary financial