Continuous Emulation and Multiscale Visualization of Traffic Flow Using Stationary Roadside Sensor Data

Xu, Haowen; Berres, Anne; Tennille, Sarah A.; Ravulaparthy, Srinath K.; Wang, Chieh; Sanyal, Jibonananda

doi:10.1109/tits.2021.3094808

Continuous Emulation and Multiscale Visualization of Traffic Flow Using Stationary Roadside Sensor Data

Journal Article · Mon Aug 08 00:00:00 EDT 2022 · IEEE Transactions on Intelligent Transportation Systems

DOI:https://doi.org/10.1109/tits.2021.3094808· OSTI ID:1818683

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

With the advent of the next-generation traffic monitoring systems, there has been a significant increase in the spatial-temporal resolution of vehicle mobility data in many cities. Effective analysis and visualization of such data can provide transportation planners with data-driven insights, which can facilitate the understanding of multiscale traffic dynamics. In this paper, we present a web-based traffic emulator for emulating and visualizing near-real-time and historical traffic flows on highways using data from road-side sensors. To construct a continuous traffic flow, the emulator adopts an analytical pipeline that can (a) integrate traffic data collected from discrete road-side radar detection sensors, (b) interpolate traffic conditions (vehicle speed and volume) on unmeasured road segments based on traffic flow theory, and (c) generate lane-specific vehicle trajectories and movements using a mathematically optimized representation of the road network. Our app also provides an integrated visual workflow that allows users to explore the interconnected traffic dynamics using an appropriate traffic flow visualization selected based on the level of detail. We devise two innovative geo-visualization techniques that utilize an animated strips-network representation and a lane usage matrix to visualize lane performances. To ensure a smooth emulation of large-scale traffic flow in an easy-to-access web environment, we implement the emulator using client-side GPU-accelerated techniques. Lastly, we close with a case study that visualizes traffic dynamics of two scenarios - an afternoon peak hour and a traffic accident - in Chattanooga, Tennessee. Our app visualizes the responses of traffic dynamics during different traffic conditions, and to the presence of the traffic accident at different spatial scales.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1818683

Journal Information:: IEEE Transactions on Intelligent Transportation Systems, Journal Name: IEEE Transactions on Intelligent Transportation Systems Journal Issue: 8 Vol. 23; ISSN 1524-9050

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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