Personal Mobility

A “Virtual DMS” method has been created for presenting travel time information to callers of
the North Carolina 511 traveler information telephone service (NC 511). When travel time
information is presented to drivers on Dynamic Message Signs (DMS), the travel times
reported are measured from the physical DMS to a downstream location, such as a cross
street or interchange. As such, the traveler’s location and direction of travel are fixed. This
presentation format is much different than traditional 511 travel time dissemination where the
caller’s location and direction of travel is not only variable but the caller could be calling
from, or could be interested in, any roadway. North Carolina combined the best of both DMS
and traditional 511 travel time presentation, providing callers their travel times to
downstream exits based on their current location and travel direction; termed “Virtual DMS”.

PBS&J

Presented at the ITS America Annual Conference and Exposition, May 3-5, 2010, Houston, Texas

Travel time data and its dissemination to the traveling public has been implemented in the latest software release for Maryland’s CHART ATMS in December 2009.  A particular challenge for the CHART system was that the existing speed detector system lacked sufficient density to reliably calculate travel times along most of the state’s highway network. The solution was to utilize travel time data from a subscription service (INRIX[RM] being the chosen vendor).  This paper provides an overview on how the INRIX speed data is collected into the CHART system; how routes are defined; how the travel times are calculated per defined route; how administrators and operators manage the data and design messages; and how the travel times are displayed to the travelling public.

Turnkey Technology Corp.

CSC

Presented at the ITS America Annual Conference and Exposition, May 3-5, 2010, Houston, Texas

Users of real-time traffic information want to know how long their trips are going to take in order to choose between alternate routes or modes, determine when to leave, or adjust their schedules if necessary. This has spurred interest among traffic managers to estimate point-to-point travel times as part of Advanced Traveler Information Systems (ATIS). Of course, it is impossible to always predict point-to-point travel times with perfect accuracy. There are numerous sources of potential error including the reliability of sensors, the calculation of travel time from sensor measurements, and the inability to accurately forecast how conditions will change over the course of a pending trip.

In this paper we underscore the importance of measuring the accuracy of ATIS travel time estimates, discuss the pros and cons of different data collection techniques and provide cost estimates for sufficient studies. This may be as simple as driving a moderately-equipped probe vehicle to measure “ground truth” travel times. Probe vehicle techniques are the best approach to assess the accuracy of an ATIS that covers a number of segments in a metropolitan network. We estimate that 100 probe vehicle runs would comprise a sufficient study for an average sized metropolitan area. Collecting this much data would cost approximately $21,000.

Day-to-day travel time variability is a key indicator for how accurate ATIS travel time estimates need to be. An error of 20% is a suitable initial target, though this value may vary significantly by metropolitan area. Under ideal circumstances, one could calculate network-wide variability using archived ATIS travel time estimates. However, if these estimates are shown to be inaccurate based on the ground truth data obtained from the probe vehicle study, this would lead to an inaccurate estimate of variability. Therefore, if travel time estimation error is 20% or worse, additional field data using license plate matching techniques should be taken for the purpose of accurately characterizing day-to- day variability. For a single study, we estimate this would cost approximately $48,000.

Mitretek Systems, Inc.

Presented at the ITS America Annual Conference and Exposition, April 26 - 28, 2004 San Antonio, Texas

The purpose of this paper is to provide an overview of the types of information transit agencies can share as part of an integrated regional ITS solution and discuss technical keys to successful participation by transit agencies in a regional ITS solution.

Integration of intelligent transportation systems is still largely defined and implemented on a region-by-region basis. While much good work has been done to define a national ITS architecture and associated standards, ultimately the work of a region’s planners and agencies leads to a successful implementation of integrated intelligent transportation systems that will result in increased mobility, safety, and security for the people using a region’s transportation network. It takes a shared regional vision to implement a successful integrated regional ITS solution. Once a shared vision has been established and embraced, then a thorough understanding of the current state of ITS standards, technologies, and architectures is needed to make the vision a reality. This paper discusses the importance of a shared vision in the successful realization of an integrated ITS solution. It then turns it focus to transit’s role in an integrated ITS solution discussing the types of information a transit agency produces that is off value to a regional ITS solution and the types of information from other regional entities which a transit agency may wish to be a consumer. The author then addresses the issues of ITS standards, technologies, and architectures as they relate to transit. The paper concludes with a summary of keys to a successful regional ITS solution.

Siemens Transportation Systems, Inc.

Presented at the ITS America Annual Conference and Exposition, April 26 - 28, 2004 San Antonio, Texas

An innovative method is presented in this paper to derive the transportation mode shares on

urban freeways using mobile-phone trajectory information. It consists of two major parts:

offline learning and online inference. The offline learning first extracts the temporal feature

from the mobile-phone trajectories. By comparing to the existed link volumes, the inference

parameters are calibrated through the offline learning process. The online inference determines

the transportation modes for each individual mobile phone users in a real-time manner. The

methodology was tested via a case study designed for both the offline learning and online

inference parts. The results show the great potential of using mobile-phone trajectory

information as a means to estimating the transportation mode shares.

University of Wisconsin-Madison

Southeast University

South Dakota State University

Presented at the 18th World Congress on ITS, October 2011, Orlando, Florida