Multiscale and Multivariate Transportation System Visualization for Shopping District Traffic and Regional Traffic

Abstract

In this paper, we present a suite of visualization techniques for sensor-based transportation system data at different scales to facilitate the exploration of interconnected traffic dynamics at intersections and highways. These techniques are designed for analyzing multivariate traffic data from radar-based highway sensors and camera-based intersection sensors recording turn movements and vehicle speed, in the Chattanooga Metropolitan Area, with the capability of (a) revealing multiscale mobility patterns using different levels of data aggregation (e.g., individual sensor for microscale, multiple sensors along a corridor for mesoscale, and a larger number of sensors across the region for macroscale visualization) at different intervals (e.g., 5-min intervals, time of day, full day, and day-of-the-week), and (b) exploring the spatial variation of multiple traffic-related variables (e.g., volumes, speeds, turn movements, and traffic light colors) provided by the sensors. We close with a case study to demonstrate the effectiveness of our multiscale and multivariate visualization techniques. At microscale, we focused on intersection data from a shopping district around Shallowford Road in East Chattanooga. For mesoscale visualization, we studied the Shallowford Road corridor and an adjacent stretch of I-75. At macroscale, we included highway data from the Chattanooga Metropolitan Area. All visualizations were integrated into a web-based situational awareness tool to promote user access and interaction. At a minimum, each visualization provides the option for selecting dates for real-time (depending on sensor availability) and historical data, and additional information on hovering, though most provide more detailed information, including different views of the selected data, or interactive highlights.

A New Method for Inverter Diagnosis of Electric Locomotive Using Adversarial Neural Networks

“…Get a diagnosis. Literature [14] comprehensively uses two methods of principal component analysis and support vector machine for fault diagnosis of power electronic rectifier. First, the principal component analysis method is used to extract the fault eigenvalues of the three-phase rectifier, and then the model established by the support vector machine is used and realizes fault diagnosis.…”

A top-down approach for a multi-scale identification of risk areas in infrastructures: particularization in a case study on road safety

“…If we focus on discussing our results compared to results obtained in previous works, we can say the topdown multi-scale methodology shows results at the three scales: location and prioritization of the hot spots at the network level, weighing of the safety factor at the system level and risk map creation at the object level. None of the previous works [14][15][16][17][18] related to this multiscale approach, showed results for the different scales and none considered together the hazards that are taken into account in the present work. Regarding the MCDM and validation of the result that is developed in parts 2 and 3 of our methodology, the works revised [19,[21][22][23][24]54] used some MCDM but they do not validate their results with real data.…”

“…Some works related to this multiscale approach are mentioned as follows. Berres et al [14] presented techniques for the exploration of interconnected traffic dynamics at intersections and highways. These techniques are based on sensors at different scales, which were named microscale, mesoscale, and macroscale.…”

“…Many of the previous works mentioned [14,15,17,18] only consider one hazard (such as road safety or landslide). The only one that considers several risks [16] does not have the same approach as this work because it does not treat the hazards in a geolocated way to identify risk areas.…”

A traffic accident dataset for Chattanooga, Tennessee

“…For example, the file 2020-11-03-1611-00I24W182.8.csv contains data for an accident which occurred at 4:11 p.m. on November 3, 2020, on I-24 Westbound near the radar sensor at mile marker 182.8. For more information on the radar data and its potential uses, please refer to [ [7] , [8] , [9] ].…”