Personal Mobility

In Taiwan, freeway traffic is often congested during peak hour or during seasonal holiday periods. For deploying Advance Traveler Information System, to predict the travel time is essential. There are many various methods had been created to predict the travel time in the past. However, most of them are too complex. In this paper, a model using the Grey forecasting theory is adopted in views to its few data needed for getting prediction result within short term. The predicted result is good, especially for the travel time changed in short term. Furthermore, the improvement for overcoming the shortcomings by using simple Grey model is also conducted to reduce travel time fluctuation impact and then to enhance the prediction accuracy. The results implicate that these methods can do help to improve the prediction accuracy. A revised method of Grey travel time prediction model is then developed.

Institute of Civil Engineering, National Taiwan University

Presented at the ITS America Annual Conference and Exposition,November 16-20, 2008, New York, New York

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

Intelligent Transportation Systems (ITS) are targeted at the study of (1) how
information can be used to improve the safety and mobility of the driver, and (2) the potential
impact of information to reduce fuel consumption and greenhouse gas emissions. This paper
focuses on the latter, i.e. the economical and environmental impact of ITS.  We examine the
following three questions: (1) What are the incentives and disincentives that may change
automobile consumption and commute choice?  In particular, we look at historical data on
gasoline prices and price as a disincentive to discourage fuel consumption; (2) What do
education and information campaigns do to help promote fuel consumption reduction?  After
surveying various education programs to contrast their effectiveness and scalability, we
propose a real time fuel consumption information feedback mechanism to study its impact on
changing driving behavior and patterns.  A pilot study is under way to quantify its
effectiveness and we report our preliminary findings here; (3) How does privileged traffic
information impact traffic flow and benefit subscribers of such information?  We provide a
simple network example to examine the value of information and the impact of it on
commuter travel time.  A more complete mathematical model is being developed to provide
insight on the value of real time traffic and incident information. Collectively, they seek to
address the following questions: (1) What ITS-related incentive mechanisms (and others) are
available to promote efficient driving? (2) How will users behave under situations of
information asymmetry? (3) What is the value of information to a user as such information is
disseminated more broadly?

Stanford University

Volkswagen Group of America, Inc. Electronics Research Laboratory

Presented at the 15th World Congress on Intelligent Transport Systems, November 16-20, 2008, New York, New York

This paper describes the ongoing evaluation study of anonymous mobile call sampling for transportation applications underway in the Washington, D.C. metropolitan area. The Universities of Maryland and Virginia, with support from their respective State Departments of Transportation, are investigating the potential of the RadioCamera technology invented by U.S. Wireless Corporation to provide critical data for traffic management applications. This method of data collection is contrasted with other probe and point detection mechanisms. Descriptions of prototype transportation management applications employing this technology are offered. Potential technical limitations are described, as well as means by which they might be obviated. The planned economic evaluation is outlined, including the economic measures to be quantified, the range over which valuation will be conducted, and the means by which benefits will be enumerated. Finally, some conclusions are offered.

University of Virginia

University of Maryland

Presented at the 11th ITS Annual Conference and Exposition, June 4-7, 2001 Miami Beach, Florida

Transportation engineers and planners worldwide are faced with the challenge of improving transit services in urban areas using low cost means. Transit signal priority is considered to be an effective way to improve transit service reliability and efficiency. In light of the interest in testing and deploying transit signal priority on a major arterial in Northern Virginia, this research focuses on the impacts of transit signal priority along the U.S.1 corridor in Fairfax County, in terms of benefits to transit and impacts on other traffic. Using a simulation tool, VISSIM, these impacts were assessed considering a ten second green extension priority strategy. Also, this assessment is being supported by a field study giving researchers an opportunity to compare simulation and field study results. The results of the simulation analysis indicated that the Fairfax Connector buses benefit from the green extension strategy with little to no impact on the other non-transit traffic. Overall, improvements of 3.61% were found for bus service reliability and 2.64% for bus efficiency, while negative impacts were found in the form of increases in queue lengths on side streets by a maximum value of approximately one vehicle.Preliminary field results show a comparable improvement in bus efficiency of 2.38%. While evaluating the impacts of provision of priority to transit buses this research emphasizes that ‘green extension’ strategy provides constant benefits with little to no disbenefit to other traffic. This research tries to provide a comparative depiction of results consistent with past studies showing ‘green extension’ as aviable option, even in most congested corridors without disrupting non-transit traffic.

Wilbur Smith Associates

Virginia Polytechnic Institute and State University

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