Browsing by Subject "Microsimulation"
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Item A method for estimating the inputs necessary to construct a microsimulation model using only publicly available data(2016-12) Van Hout, Alexander Joseph; Machemehl, Randy B.Standard traffic engineering methodologies rely heavily on traffic data collected in the field for the design and planning of roadways and intersections. This data can be used to build microsimulation models, which are versatile and realistic tools for analyzing traffic scenarios. Sometimes, however, time and budget do not allow for the collection of high quality data in the field, but answers to questions about traffic scenarios are still needed. This thesis provides a review of data that is typically available to the public online as well as existing traffic engineering methodologies that will be useful in manipulating that data. It presents an empirically derived method for estimating left turn, thru, and right turn counts at intersections based on tube counts on surrounding roadways and the characteristics of the intersection. It then presents an exploration of the distribution of directionality of traffic throughout the day. Finally, it presents a method for converting tube counts on an approach to an intersection to equivalent lane volumes so that signal timings can be estimated.Item Applications of microsimulation traffic data in infrastructure construction projects using 3D/4D CAD models(2013-05) Mandali, Yoganand; O'Brien, William J.Transportation projects often involve communication of project information between diverse parties and have been a challenge with increasing complexity. Communication, review and feedback are very important for planners, builders/developers and traffic engineers for successful project execution. Past research was successful in finding effective ways to communicate to stakeholders and improve project performance. 3D/4D CAD modeling has been one among them which offers potential benefits from planning to construction phase owing to its wide range of capabilities. However, there is no single tool to analyze traffic conditions and changing geometry during construction for reviewing and better decision-making. A methodology to use DTA models as a source for traffic information and development of traffic visualization during construction with microsimulation output is discussed in this thesis. The benefits of adding traffic information to 3D/4D CAD models and some potential areas of application are explored. Two case studies on TxDOT transportation construction projects are considered to explain the modeling and analysis for better understanding of different phases of the projects. Also, a small construction scenario was analyzed to validate the traffic data generated from DTA models for their use as an input to microsimulation models.Item Changes in freeway level of service with the introduction of autonomous and connected vehicles(2018-12) Espinoza, Edoardo; Machemehl, Randy B.Connected and Autonomous vehicles (CAVs) have risen in popularity in recent years and are expected to bring with them many changes including driver safety, expansion of ridership from people currently unable to drive, and more travel miles from long trip commuters. From an engineering standpoint, CAVs are expected to bring with them an increase in highway capacities because of their ability to react faster than human drivers and produce shorter time headways between successive vehicles. CAVs are not anticipated to dominate the traffic stream until another 20 to 40 years and are expected to be introduced gradually into the transportation system. Shorter time headways suggest that freeways may be positively affected by the new technology and new procedures will need to be established in order to analyze highway capacities in the future. The 6th Edition Highway Capacity Manual is one of the main sources that is used by the engineering community to estimate capacities of freeway segments. This study documents a new simulation tool to discover the capacity implications for a basic freeway segment of different CAV market penetrations and reduced time headwaysItem Evolution of the household vehicle fleet : anticipating fleet compostion, plug-in hybrid electric vehicle (PHEV) adoption and greenhouse gas (GHG) emissions in Austin, Texas(2009-12) Musti, Sashank; Kockelman, Kara; Machemehl, Randy B.In today’s world of volatile fuel prices and climate concerns, there is little study on the relation between vehicle ownership patterns and attitudes toward potential policies and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s household-fleet evolution. Results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are vehicle purchase price, type/class, and fuel economy (with 30%, 21% and 19% of respondents placing these in their top three). Most (56%) respondents also indicated that they would seriously consider purchasing a Plug-In Hybrid Electric Vehicle (PHEV) if it were to cost $6,000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings. 25-year simulations suggest that 19% of Austin’s vehicle fleet could be comprised of Hybrid Electric Vehicles (HEVs) and PHEVs under adoption of a feebate policy (along with PHEV availability in Year 1 of the simulation, and current gas prices throughout). Under all scenarios vehicle usage levels (in total vehicle miles traveled [VMT]) are predicted to increase overall, along with average vehicle ownership levels (per household, and per capita); and a feebate policy is predicted to raise total regional VMT slightly (just 4.43 percent, by simulation year 25), relative to the trend scenario, while reducing CO2 emissions only slightly (by 3.8 percent, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 17% and CO2 emissions by 22% (relative to trend). Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. And HEVs, PHEVs and Smart Cars are estimated to represent a major share of the fleet’s VMT (25%) by year 25 under the feebate scenario. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet), yet feebate-policy receipts exceed rebates in each simulation year. A 15% reduction in the usage levels of SUVs, CUVs and minivans is observed in the $5/gallon scenario (relative to trend). Mean use levels per vehicle of HEVs and PHEVs are simulated to have a variation of 753 and 495 across scenarios. In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have even more significant effects on energy dependence and greenhouse gas emissions.Item Modular autonomous intersection management simulation for stochastic and priority auction paradigms(2021-12-03) Liao, Carlin; Boyles, Stephen David, 1982-; Claudel, Christian; Kumar, Krishna; Stone, PeterAutomated intersections, when combined with the proliferation of autonomous vehicles (AVs), allow for more precise and innovative methods to control traffic at these integral choke points in the road system. In this dissertation, I develop a refined, modular framework for autonomous intersection management (AIM) simulation and implement it as a software library with robust documentation and testing to support present and future research in this field. Demonstrating this framework's efficacy, I apply it to study two topic areas in the AIM space: stochastic movement and priority auctions. Stochastic AIM is introduced as an extension of traditional AIM that permits probabilistic reservations of space and time in an intersection. Its use case is motivated by the integration of human-driven vehicles into AIM using augmented reality guidance to behave more accurately to AV movement, while still making some stochastic deviations from AV-identical trajectories. These deviations are quantified using experimental data from human drivers in a driving simulator merged into a stochastic vehicle movement model. Experimental results suggest that, with this paradigm, AIM can decrease delay significantly, even at low AV penetration levels (less than 20%). Finally, I conceptualize intersection priority auctions into the newly developed AIM framework as itself a modular framework that supports the dispatch of multiple vehicles simultaneously from either separate lanes or a single lane without relying on preset signal phases. This auction framework further supports three payment formulas for the winner of the priority auction: first-price, second-price, or a novel externality payment mechanism. Using experiments implemented in the novel AIM simulator, my results demonstrate significant reduction in value-weighted delay using the multiple dispatch configuration and novel payment mechanism compared to other configurations, with the novel formula incentivizing truthful reporting of valuations more than its alternatives.Item On integrating models of household vehicle ownership, composition, and evolution with activity based travel models(2012-12) Paleti Ravi Venkata Durga, Rajesh; Bhat, Chandra R. (Chandrasekhar R.), 1964-; Abrevaya, Jason; Pendyala, Ram; Machemehl, Randy; Boyles, StephenActivity-based travel demand model systems are increasingly being deployed to microsimulate daily activity-travel patterns of individuals. However, a critical dimension that is often missed in these models is that of vehicle type choice. The current dissertation addresses this issue head-on and contributes to the field of transportation planning in three major ways. First, this research develops a comprehensive vehicle micro-simulation framework that incorporates state-of-the-art household vehicle type choice, usage, and evolution models. The novelty of the framework developed is that it accommodates all the dimensions characterizing vehicle fleet/usage decisions, as well as accommodates all dimensions of vehicle transactions (i.e., fleet evolution) over time. The models estimated are multiple discrete-continuous models (vehicle type being the discrete component and vehicle mileage being the continuous component) and spatial discrete choice models that explicitly accommodate for multiple vehicle ownership and spatial interactions among households. More importantly, the vehicle fleet simulator developed in this study can be easily integrated within an activity-based microsimulation framework. Second, the vehicle fleet evolution and composition models developed in this dissertation are used to predict the vehicle fleet characteristics, annual mileage, and the associated fuel consumption and green-house gas (GHG) emissions for future years as a function of the built environment, demographics, fuel and related technology, and policy scenarios. This exercise contributes in substantial ways to the identification of promising strategies to increase the penetration of alternative-fuel vehicles and fuel-efficient vehicles, reduce energy consumption, and reduce greenhouse gas emissions. Lastly, this research captures several complex interactions between vehicle ownership, location, and activity-travel decisions of individuals by estimating 1) a joint tour-based model of tour complexity, passenger accompaniment, vehicle type choice, and tour length, and 2) an integrated model of residential location, work location, vehicle ownership, and commute tour characteristics. The methodology used for estimating these models allows the specification and estimation of multi-dimensional choice model systems covering a wide spectrum of dependent variable types (including multinomial, ordinal, count, and continuous) and may be viewed as a major advance with the potential to lead to redefine the way activity-based travel model systems are structured and implemented.Item On the use of traffic assignment and microsimulation to predict work zone effects(2022-05-06) Smith, Harry Vincent, II; Machemehl, Randy B.The field of transportation modeling exists at a crossroads. Practice lags behind advancements in theory, software, and computer power. During this transitional phase, it becomes difficult to understand which estimations are reliable. Several popular programs work toward similar purposes, but inputs and outputs from one program might not translate to another. This thesis analyzes the cross-compatibility of static traffic assignment and microsimulation for the San Antonio, Texas area. The methodology uses two models already built and in use for the area: one in TransCAD for static traffic assignment and another in VISSIM for microsimulation. In Chapter 2, the reader is presented an overview of the literature on several topics. First, the basics of work zone demand management and traffic control plans are covered. Then, this section introduces the differences between static and dynamic traffic assignment. The discussion also includes reasons why advancements have not been universally implemented yet. Next, the text of Chapter 3 and 4 explains the methodology and results from traffic assignment and microsimulation. Challenges and successes of the entire process are thoroughly explored. A conclusion in Chapter 5 assesses the feasibility of combining the two methods to obtain useful results of user delay and traffic volume for planners.Item Spatial prediction of AADT in unmeasured locations by universal kriging and microsimulation of vehicle holdings and car-market pricing dynamics(2011-05) Selby, Brent Frederick; Kockelman, Kara; Wang, XiaokunChapters 1 through 5 of this thesis explore the application of kriging and geographically weighted regression (GWR) methods for prediction of average daily traffic counts across the Texas network. Accurate measurements of traffic are essential for proper planning and management of pavements, roadway upgrades, congestion mitigation, and other aspects of ground-based transport. Results based on Euclidean distances are compared to those using network distances, and both allow for strategic spatial interpolation of count values while controlling for each location’s roadway functional classification, lane count, speed limit, employment density, and population access. Both universal kriging and GWR are found to reduce errors (in practically and statistically significant ways) over non-spatial regression techniques, though errors remain quite high at some sites, particularly those with low counts and/or in less measurement-dense areas. Nearly all tests indicated that the predictive capabilities of kriging exceed those of GWR by average absolute errors of 3 to 8 percent. Interestingly, the estimation of kriging parameters by network distances showed no enhanced performance over that with Euclidean distances, which require less data and are much more easily computed. Chapters 6 through 10 explore vehicle purchase and use decisions, which can be central to estimates of crash outcomes, emissions, gas-tax revenues, and national energy security. An auction-style microsimulation of fluctuating vehicle prices is combined with a random-utility-maximizing choice model to produce a model for the evolution of personal-vehicle fleets, recognizing both used- and new-vehicle markets. All buyers and available vehicles are enter the auction process for vehicle selection, with demand, supply and price signals of used cars endogenous to the model. The thesis describes the modeling framework in detail, along with its implementation using Austin, Texas data (for behavioral parameters and a synthetic population). The fleet dynamics are simulated over a 20-year period, highlighting the model’s flexibility and reasonable response to multiple inputs and contextual scenarios. A simulation of doubled gas prices showed a large increase (10%) in the share of the sub-compacts, with smaller decreases in pickup trucks, vans and large cars. A high scrappage rate, sometimes employed to increase turnover, resulted in used-vehicle sales falling by 12%, and new-vehicle sales growing by 3%.