Yazar "Mudigonda, Sandeep" seçeneğine göre listele

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    ASSIST-ME postprocessing tool for transportation planning model output
    (Sage Publications Inc, 2013) Özbay, Kaan; Bartın, Bekir; Mudigonda, Sandeep; Iyer, Shrisan
    This paper presents Advanced Software for Statewide Integrated Sustainable Transportation System Monitoring and Evaluation (ASSIST-ME), an application for visualizing and analyzing the output of transportation planning models in a geographic information system environment. ASSIST-WEE was developed on a customized version of the AreGIS 9.2 Developer Engine in the Microsoft .NET Framework. The tool is built on a flexible framework that allows for adoption of any traditional transportation planning model, as demonstrated with the output of two major transportation planning models on different software platforms: the New York Best Practice Model, running in TransCAD, and the North Jersey Regional Transportation Model-Enhanced, running in CUBE. ASSIST-ME allows agencies and planners to easily work with transportation planning model output, analysis of which is often time-consuming and requires extensive training. It offers four key functionalities: data visualization, demand analysis, path analysis, and benefit-cost analysis. Data visualization and demand analysis enable the user to work easily with direct model output; the custom path and cost analysis tools support analyses beyond those possible with other software packages. The benefit-cost analysis functions utilize the latest quantification-monetization approaches employed in research and by government agencies and require no external applications or procedures. This process can be used for any planning scenario, but ASSIST-ME also allows for customization to modify input data or analysis procedures according to the user's needs. ASSIST-ME incorporates data visualization, data analysis, and output reporting functionalities in a single user-friendly setting that requires minimal training or knowledge of the models themselves.
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    Evaluating the resilience and recovery of public transit system using big data: Case study from New Jersey
    (Taylor & Francis Inc, 2019) Mudigonda, Sandeep; Özbay, Kaan; Bartın, Bekir
    Analyzing resilience and vulnerability of public transit networks is extremely important in the context of natural disasters as they are essential for evacuation. In this study, the public transit systems in New Jersey based on their vulnerability, resilience, and efficiency during the recovery period following Hurricane Sandy were analyzed. Diverse traffic, infrastructure, events, and web-based sources of Big Data are applied. Due to the sparsity of public transit performance measures for vulnerability, recovery, and resilience, various measures from existing literature were adapted for public transit. Following Hurricane Sandy, the bus transit network of NJ Transit (NJT) recovered much faster than its rail network. This was observed because the road infrastructure recovered much faster as compared to rail and subway networks. Additionally, the most critical link for the NJT buses remained intact during the hurricane whereas rail and subway systems suffered loss of power for driving and signaling. Performance measures such as critical links identification, change in travel time, friability, and resilience triangles for specific bus routes on the NJT bus network were estimated. Transit agencies can use these measures and methodologies in planning and preparing for disasters to study route vulnerability and transit network resilience and standardize performance measures.
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    Interactive lane closure and traffic information tool based on a geographic information system
    (Natl Acad Sciences, 2012) Bartın, Bekir; Özbay, Kaan; Mudigonda, Sandeep
    This paper describes the development of the Rutgers Interactive Lane Closure Application (RILCA), an interactive computer tool to plan lane closures for work zones. This tool provides traffic engineers with a computerized and easy-to-use lane closure application, along with other useful features. RILCA was developed with the Arc View geographic information system (GIS) software package as the main development environment. Arc View displays the interactive GIS map of the New Jersey Turnpike, Garden State Parkway, and other major freeways in New Jersey and its surrounding network. RILCA provides various analysis and visualization options to plan lane closures interactively, obtain traffic volume information, and conduct accident queries. RILCA was tested successfully, and New Jersey Department of Transportation (DOT) and New Jersey Turnpike Authority engineers now use it with all of the features presented in this paper. RILCA received an annual implementation award from the New Jersey DOT in 2009. The award recognized RILCA's usefulness and potential to improve the efficiency of traffic operations as a practical decision-support tool for lane closure decisions.