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Description
The environmental and economic assessment of neighborhood-scale transit-oriented urban form changes should include initial construction impacts through long-term use to fully understand the benefits and costs of smart growth policies. The long-term impacts of moving people closer to transit require the coupling of behavioral forecasting with environmental assessment. Using new

The environmental and economic assessment of neighborhood-scale transit-oriented urban form changes should include initial construction impacts through long-term use to fully understand the benefits and costs of smart growth policies. The long-term impacts of moving people closer to transit require the coupling of behavioral forecasting with environmental assessment. Using new light rail and bus rapid transit in Los Angeles, California as a case study, a life-cycle environmental and economic assessment is developed to assess the potential range of impacts resulting from mixed-use infill development. An integrated transportation and land use life-cycle assessment framework is developed to estimate energy consumption, air emissions, and economic (public, developer, and user) costs. Residential and commercial buildings, automobile travel, and transit operation changes are included and a 60-year forecast is developed that compares transit-oriented growth against growth in areas without close access to high-capacity transit service. The results show that commercial developments create the greatest potential for impact reductions followed by residential commute shifts to transit, both of which may be effected by access to high-capacity transit, reduced parking requirements, and developer incentives. Greenhouse gas emission reductions up to 470 Gg CO2-equivalents per year can be achieved with potential costs savings for TOD users. The potential for respiratory impacts (PM10-equivalents) and smog formation can be reduced by 28-35%. The shift from business-as-usual growth to transit-oriented development can decrease user costs by $3,100 per household per year over the building lifetime, despite higher rental costs within the mixed-use development.
ContributorsNahlik, Matthew (Author) / Chester, Mikhail V (Thesis advisor) / Pendyala, Ram (Committee member) / Fraser, Matthew (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Traffic congestion is a major externality in modern transportation systems with negative economic, environmental and social impacts. Freeway bottlenecks are one of the key elements besides the demand for travel by automobiles that determine the extent of congestion. The primary objective of this research is to provide a better understanding

Traffic congestion is a major externality in modern transportation systems with negative economic, environmental and social impacts. Freeway bottlenecks are one of the key elements besides the demand for travel by automobiles that determine the extent of congestion. The primary objective of this research is to provide a better understanding of factors for variations in bottleneck discharge rates. Specifically this research seeks to (i) develop a methodology comparable to the rigorous methods to identify bottlenecks and measure capacity drop and its temporal (day to day) variations in a region, (ii) understand the variations in discharge rate of a freeway weaving bottleneck with a HOV lane and (iii) understand the relationship between lane flow distribution and discharge rate on a weaving bottleneck resulted from a lane drop and a busy off-ramp. In this research, a methodology has been developed to de-noise raw data using Discrete Wavelet Transforms (DWT). The de-noised data is then used to precisely identify bottleneck activation and deactivation times, and measure pre-congestion and congestion flows using Continuous Wavelet Transforms (CWT). To this end a methodology which could be used efficiently to identify and analyze freeway bottlenecks in a region in a consistent, reproducible manner was developed. Using this methodology, 23 bottlenecks have been identified in the Phoenix metropolitan region, some of which result in long queues and large delays during rush-hour periods. A study of variations in discharge rate of a freeway weaving bottleneck with a HOV lane showed that the bottleneck discharge rate diminished by 3-25% upon queue formations, however, the discharge rate recovered shortly thereafter upon high-occupancy-vehicle (HOV) lane activation and HOV lane flow distribution (LFD) has a significant effect on the bottleneck discharge rate: the higher the HOV LFD, the lower the bottleneck discharge rate. The effect of lane flow distribution and its relationship with bottleneck discharge rate on a weaving bottleneck formed by a lane drop and a busy off-ramp was studied. The results showed that the bottleneck discharge rate and lane flow distribution are linearly related and higher utilization of the median lane results in higher bottleneck discharge rate.
ContributorsKandala, Srinivasa Srivatsav (Author) / Ahn, Soyoung (Thesis advisor) / Pendyala, Ram (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Public-Private Partnerships (P3) in North America have become a trend in the past two decades and are gaining attention in the transportation industry with some large scale projects being delivered by this approach. This is due to the need for alternative funding sources for public projects and for improved efficiency

Public-Private Partnerships (P3) in North America have become a trend in the past two decades and are gaining attention in the transportation industry with some large scale projects being delivered by this approach. This is due to the need for alternative funding sources for public projects and for improved efficiency of these projects in order to save time and money. Several research studies have been done, including mature markets in Europe and Australia, on the cost and schedule performance of transportation projects but no similar study has been conducted in North America. This study focuses on cost and schedule performance of twelve P3 transportation projects during their construction phase, costing over $100 million each, consisting of roads and bridges only with no signature tunnels. The P3 approach applied in this study is the Design-Build-Finance-Operate-Maintain (DBFOM) model and the results obtained are compared with similar research studies on North American Design-Build (DB) and Design-Bid-Build (DBB) projects. The schedule performance for P3 projects in this study was found to be -0.23 percent versus estimated as compared to the 4.34 percent for the DBB projects and 11.04 percent for the DB projects in the Shrestha study, indicating P3 projects are completed in less time than other methods. The cost performance in this study was 0.81 percent for the P3 projects while in the Shrestha study the average cost increase for the four DB projects was found to be 1.49 percent while for the DBB projects it was 12.71 percent, again indicating P3 projects reduce cost compared to other delivery approaches. The limited number of projects available for this study does not allow us to draw an explicit conclusion on the performance of P3s in North America but paves the way for future studies to explore more data as it becomes available. However, the results in this study show that P3 projects have good cost and schedule adherence to the contract requirements. This study gives us an initial comparison of P3 performance with the more traditional approach and shows us the empirical benefits and limitations of the P3 approach in the highway construction industry.
ContributorsBansal, Ankita (Author) / Chasey, Allan (Thesis advisor) / Gibson, Edd (Committee member) / Pendyala, Ram (Committee member) / Arizona State University (Publisher)
Created2012
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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

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.
ContributorsLiu, Jiangtao (Author) / Zhou, Xuesong (Thesis advisor) / Pendyala, Ram (Committee member) / Mirchandani, Pitu (Committee member) / Lou, Yingyan (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Arizona's transportation infrastructure is in need of an update. The American Society of Civil Engineers (ASCE) State Infrastructure 2017 Report Card scores Arizona's roads at a D+ and Arizona's bridges at a B. These grades are indicative that the serviceability levels of the roads and bridges are less than adequate.

Arizona's transportation infrastructure is in need of an update. The American Society of Civil Engineers (ASCE) State Infrastructure 2017 Report Card scores Arizona's roads at a D+ and Arizona's bridges at a B. These grades are indicative that the serviceability levels of the roads and bridges are less than adequate. These grades may seem tolerable in light of a national bridge C+ grade and a national road D grade, but the real problem lies in Arizona's existing funding gap that is in danger of exponentially increasing in the future. With an influx of vehicles on Arizona's roads and bridges, the cost of building, repairing, and maintaining them will grow and cause a problematic funding shortage. This report explores the current state of Arizona's roads and bridges as well as the policy and funding sources behind them, using statistics from the ASCE infrastructure report card and the Federal Highway Administration. Additionally, it discusses how regular, preventative maintenance for transportation infrastructure is the economically responsible choice for the state because it decreases delays and fuel expenses, prevents possible catastrophes, and increases human safety. To prioritize preventative transportation infrastructure maintenance, the common mentality that allows it to be sidelined for more newsworthy projects needs to be changed. Along with gaining preventative maintenance revenues through increasing vehicular taxes and fees, encouraging transportation policymakers and politicians to make economic decisions in favor of maintenance rather than waiting until failure is a reliable way to encourage regular, preventative maintenance.
ContributorsBurdett, Courtney (Author) / Hjelmstad, Keith (Thesis director) / Pendyala, Ram (Committee member) / Civil, Environmental and Sustainable Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
This dissertation research is concerned with the study of two important traffic phenomena; merging and lane-specific traffic behavior. First, this research investigates merging traffic behavior through empirical analysis and evaluation of freeway merge ratios. Merges are important components of freeways and traffic behavior around them have a significant impact in

This dissertation research is concerned with the study of two important traffic phenomena; merging and lane-specific traffic behavior. First, this research investigates merging traffic behavior through empirical analysis and evaluation of freeway merge ratios. Merges are important components of freeways and traffic behavior around them have a significant impact in the evolution and stability of congested traffic. At merges, drivers from conflicting traffic branches take turns to merge into a single stream at a rate referred to as the “merge ratio”. In this research, data from several freeway merges was used to evaluate existing macroscopic merge models and theoretical principles of merging behavior. Findings suggest that current merge ratio estimation methods can be insufficient to represent site-specific merge ratios, due to observed within-site variations and unaccounted effects of downstream merge geometry. To overcome these limitations, merge ratios were formulated based on their site-specific lane flow distribution (LFD), the proportion of flow in each freeway lane, for two types of merge geometries. Results demonstrate that the proposed methods are able to improve merge ratio estimates, reproduce within-site variations of merge ratio, and represent more effectively disproportionate redistribution of merging flow for merges where vehicles compete directly to merge due a downstream lane reduction.

Second, this research investigates lane-specific traffic behavior through empirical analysis and statistical modeling of lane flow distribution. Lane-specific traffic behavior is also an important component in evaluating freeway performance and has a significant impact in the mechanism of queue evolution, particularly around merges, and bottleneck discharge rate. In this research, site-specific linear LFD trends of three-lane congested freeways were investigated and modeled. A large-scale data collection process was implemented to systematically characterize the effects of several traffic and geometric features of freeways in the occurrence of between-site LFD variations. Also, an innovative three-stage modeling framework was used to model LFD behavior using multiple logistic regression to describe between-site LFD variations and Dirichlet regression to model recurrent combinations of linear LFD trends. This novel approach is able to represent both between and within site variations of LFD trends better, while accounting for the unit-sum constraint and distribution assumptions inherent of proportions data. Results revealed that proximity to freeway merges, a site’s level of congestion, and the presence of HOV lanes are significant factors that influence site-specific recurrent LFD behavior.

Findings from this work significantly improve the state-of-the-art knowledge on merging and lane-specific traffic behavior, which can help to improve traffic operations and reduce traffic congestion in freeways.
ContributorsReina, Paulina (Author) / Ahn, Soyoung (Thesis advisor) / Pendyala, Ram (Committee member) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Travel time is the main transportation system performance measure used by the planning community to evaluate the impacts of traffic congestion on infrastructure investment projects and policy development plans. Planners rely on the travel demand model tool estimates for the selection and prioritization of critical and sensitive projects to meet

Travel time is the main transportation system performance measure used by the planning community to evaluate the impacts of traffic congestion on infrastructure investment projects and policy development plans. Planners rely on the travel demand model tool estimates for the selection and prioritization of critical and sensitive projects to meet the fiscally constraint requirements imposed by the Federal Highway Administration (FHWA) on their transportation improvement programs (TIP). While travel demand model estimates have been successfully implemented in the evaluation of project scenarios or alternatives, the application of the methods used in the travel demand model to generate these estimates continues to present a critical challenge, particularly to modelers who have to produce a validated model upon which traffic predictions can be made. The various volume-delay functions (VDFs) including the Bureau of Public Roads (BPR) function, used in the travel demand model to relate traffic volume to travel time, are developed based on system-wide attributes. BPR function in its polynomial form is computationally efficient and simple for implementation in a transport planning software. The planning community has long recognized that the BPR function cannot capture traffic flow dynamics and queue evolution processes. Besides, it has difficulties in using the average travel time measure to describe an oversaturated bottleneck with high density but low throughput. This dissertation aims to propose a simplified and yet effective point-queue based modeling approach built on the cumulative vehicle arrival concept, and the polynomial equation formula, based on Newell’s method, to estimate travel time at a corridor level using real-world speed and count measurements. A traffic state estimation (TSE) method is also proposed to characterize data into various states, such as congested state and uncongested state, using Markov Chain to capture current traffic pattern and Bayesian Classifier to infer congestion effects. As the testbed for the case study, the research selects the Phoenix freeway corridor with year-round traffic data collected from embedded traffic loop detectors. The results and effectiveness of the proposed methods are discussed to shed light on the calibration of link performance function, which is an analytical building block for system-wide performance evaluation.
ContributorsBelezamo, Baloka (Author) / Zhou, Xuesong (Thesis advisor) / Pendyala, Ram (Committee member) / Lou, Yingyan (Committee member) / Arizona State University (Publisher)
Created2020
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Description
Infrastructure systems are facing non-stationary challenges that stem from climate change and the increasingly complex interactions between the social, ecological, and technological systems (SETSs). It is crucial for transportation infrastructures—which enable residents to access opportunities and foster prosperity, quality of life, and social connections—to be resilient under these non-stationary challenges.

Infrastructure systems are facing non-stationary challenges that stem from climate change and the increasingly complex interactions between the social, ecological, and technological systems (SETSs). It is crucial for transportation infrastructures—which enable residents to access opportunities and foster prosperity, quality of life, and social connections—to be resilient under these non-stationary challenges. Vulnerability assessment (VA) examines the potential consequences a system is likely to experience due to exposure to perturbation or stressors and lack of the capacity to adapt. Post-fire debris flow and heat represent particularly challenging problems for infrastructure and users in the arid U.S. West. Post-fire debris flow, which is manifested with heat and drought, produces powerful runoff threatening physical transportation infrastructures. And heat waves have devastating health effects on transportation infrastructure users, including increased mortality rates. VA anticipates the potential consequences of these perturbations and enables infrastructure stakeholders to improve the system's resilience. The current transportation climate VA—which only considers a single direct climate stressor on the infrastructure—falls short of addressing the wildfire and heat challenges. This work proposes advanced transportation climate VA methods to address the complex and multiple climate stressors and the vulnerability of infrastructure users. Two specific regions were chosen to carry out the progressive transportation climate VA: 1) the California transportation networks’ vulnerability to post-fire debris flows, and 2) the transportation infrastructure user’s vulnerability to heat exposure in Phoenix.
ContributorsLi, Rui (Author) / Chester, Mikhail V. (Thesis advisor) / Middel, Ariane (Committee member) / Hondula, David M. (Committee member) / Pendyala, Ram (Committee member) / Arizona State University (Publisher)
Created2022