Validating Dynamic Message Sign Freeway Travel Time Messages with Ground Truth Geospatial Data

Abstract

U.S. transportation agencies have invested more than $300 million in dynamic message sign (DMS) systems for communicating important messages to travelers, including weather conditions, incidents, construction, and homeland security and AMBER alerts. Recent FHWA policy encourages states to use their DMS infrastructure more effectively by displaying reliable travel-time information along freeway corridors. The Oregon Department of Transportation (ODOT), which maintains a freeway surveillance system in the Portland metropolitan area, calculates and reports travel-time estimates derived from inductive loop detector data. These estimates can be reported via 18 DMS and other traveler information systems but are displayed on a limited basis at key junction points during peak periods. Results are presented of an evaluation of ODOT's travel-time reporting capabilities by comparing the ODOT travel-time estimates with probe vehicle (ground truth) travel times. The freeway network was divided into 17 key corridors, and 87 probe vehicle runs were collected under various traffic conditions with handheld Global Positioning System devices. This evaluation used the regional transportation data archive, Portland Regional Transportation Archive Listing, to generate the ODOT travel-time estimates. Probe estimates were compared to travel estimates on the corridors with a paired t-test. The evaluation found that travel times on a majority of links were reasonably accurate under most traffic conditions but were affected by the presence of an incident or by poor detector placement.

Travel Information: Time to Drop the Labels?

“…Some studies (e.g., [50] and [51]) simultaneously refer to both; it is not directly clear how this would work in practice. Other studies discuss information services that convey only current travel conditions (e.g., [52]). Of those studies that consider dynamic information services that predict traffic conditions, it often remains unclear how these predictions are being made.…”