Digital Roadway Interactive Visualization and Evaluation Network Applications to WSDOT Operational Data Usage

DRIVE Net is a region-wide, Web-based transportation decision support system that adopts digital roadway maps as
the base, and provides data layers for integrating and analyzing a variety of data sources (e.g., traffic sensors, incident
records). Moreover, DRIVE Net offers a platform for streamlining transportation analysis and decision making, and it
serves as a practical tool for visualizing historical observations spatially and temporally. In its current implementation,
DRIVE Net demonstrates the potential to be used as a standard tool for incorporating multiple data sets from different
fields and as a platform for real-time decision making. In comparison with the previous version, the new DRIVE Net
system is now able to handle more complex computational tasks, perform large-scale spatial processing, and support
data sharing services to provide a stable and interoperable platform to process, analyze, visualize, and share
transportation data.

DRIVE Net’s capabilities include generating statistics for WSDOT’s Gray Notebook (GNB), including travel times,
throughput productivity, and traffic delay calculations for both general purpose and HOV lanes, each of which are
important performance indicators in the WSDOT congestion report. The DRIVE Net system includes robust loop
detector data processing and quality control methods to address the data quality issues impacting loop detectors
throughout the state. The capabilities of the DRIVE Net system have been expanded to include safety modeling,
hotspot identification, and incident induced delay estimation. Specifically, the Safety Performance module includes
functions that can be used to obtain traffic incident frequency, apply predictive models to estimate the safety
performance of road segments, and visualize and compare observed incident counts and different predictive models.
Additionally, a module providing multi-modal data analysis and visualization capabilities was developed as a pilot
experiment for integration of heterogeneous data. This module includes pedestrian and bicycle, public transit, park and
ride, Car2Go, and ferry data downloading and visualization. DRIVE Net now offers role-based access control, such
that access privileges to different functions and data resources can be assigned on a group or individual basis.

The new system is able to support more complex analytics and decision support features on a large-scale transportation
network, and is expected to be of great practical use for both traffic engineers and researchers. With a modular
structure and mature data integration and management framework, DRIVE Net can be expanded in the future to include
a variety of additional data resources and analytical capabilities.

Publication Date: 
Thursday, December 1, 2016
Publication Number: 
WA-RD 854.1
Last modified: 
05/01/2017 - 15:23
Yinhai Wang, Weibin Zhang, Kristian Henrickson, Ruimin Ke, Zhiyong Cui.
Smart Transportation Applications and Research Laboratory. University of Washington.
Number of Pages: 
Web applications, Decision support system, Traffic data, Digital maps, Geographic information systems, Data displays, Visualization, Data analysis, Data fusion, Real time information.