Using Real-Time Citywide Datasets to Generate Artificial Intelligent (AI) Solutions: A City of Henderson, Nevada Case Study

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LINDA LIM, Ludian

PRESENTATION DESCRIPTION

Data visualization may be plentiful; matching an area of need with the right data resources within an organization is essential. Aligning operational decisions to the systematic and automatic interpretation of data requires a vision for advancing the digital space and treating data as an asset. As society’s ability to collect, store, and consume data has grown over time, so has the need to extract and explain that information. Visualizations can often do so more succinctly, engagingly, and quickly than spreadsheets and narratives alone. However, the challenge can arise when government entities do not have complete data visibility, leading to an underutilization of the available data resources. From a transportation perspective, this can mean resolving issues based on customer complaints as opposed to taking a data-driven approach. Similar to the analogy of applying band-aids to wounds, this reactive approach to traffic management becomes unsustainable and can lead to even higher costs for future investments.
This paper aims to provide both state and local transportation agencies with strategies they can use to help them visualize Big Data to extract business intelligence. It presents a case study of the Data Evaluation and Visualization project for the City of Henderson, Nevada. The methodology for this case study involves undertaking a data dive to understand all the data the city has access to, how their data is processed, and what data sources to use to enhance the performance and safety of their roadway infrastructure. In addition, this data dive involves defining the city’s regional data goals, conducting interviews, collecting field data, performing systems audits, building key performance indicators (KPIs), conducting gap analysis, and providing recommendations and a specifications document to build the data visualization application. Results from this paper execute the data visibility pipeline (shown in Figure 1) and the visualization methods to help the City of Henderson conceptualize the performance of the traffic network.
This paper presents an innovative approach to data visualization by building a platform that integrates data from all the systems to generate AI solutions for the traffic network. It fits in with the theme of this conference, Innovative Visualization Frontiers, and is applicable in two of the topic areas: 1) Cross-Cutting Applications of Visualization for Transportation, and 2) Performance Visualization and Performance Management for Transportation. For the first topic, the data visualization platform is an innovative application that can address a broad range of transportation contexts such as transportation design, construction, planning, operations, and performance management. For the second topic, the data visualization platform can incorporate built-in KPIs to actively monitor the performance of the traffic network in real-time. An example of this shown in Figure 2.
This project was recently awarded the Institute of Transportation Engineers (ITE) Mountain District’s Transportation Achievement Award in May 2022, under the Transportation Systems Management and Operations (TSMO) category.

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