Commercial Operations

The objectives of this paper are to present models of travelers’ mode choice, drivers’ diversion
from normal route, and drivers’ adherence to pre-trip advised route under different levels of
information using data from the same experiment. A travel simulator was used as a dynamic data
collection tool. The simulator uses realistic network, two modes of travel, real historical
volumes, and different weather conditions. In this study, sixty-five subjects have completed ten
trial days each. Generalized Estimating Equations (GEEs) with binomial probit link function
were used to account for correlation between choices made by the same subject in the three
models. The modeling results show that the travel time and familiarity with the device that
provides the information had significant effects in the three models. It is shown that developing
in-vehicle information devices may lead to less transit usage in some cases, this flags a drawback
of this technology. As the level of information increases (pre-trip to pre-trip and en-route;
advice-free information to information with advice), drivers become more likely to divert from
normal route. Number of traffic signals on the normal route and advised route affect diversion
from normal route and compliance with pre-trip advised route.

University of Central Florida

Presented at the 12th World Congress on Intelligent Transport Systems, November 6-10, 2005, San Francisco, California

This paper presents a methodology for developing a model for estimating and predicting
incident duration and identifying variables influencing the incident duration in the state of
Maryland. The incident information from years 2003 to 2005 from the Maryland State
Highway (MDSHA) database is used for model development, and year 2006 for the model
validation. Classification Trees (CT) were used for a preliminary analysis to understand the
influence of the variables associated with an incident. Based on the findings from CT, this
study employed the Rule-Based Tree Model (RBTM) to develop the primary prediction
model. The overall confidence for the estimated model was over 80% with several
remarkable findings regarding the associations between factors and incident duration.
Although the estimated results from RBTM are quite acceptable, supplemental models along
with better quality database are required to improve the prediction accuracy for the duration
of a detected incident.

University of Maryland at College Park

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

Added Value Services (AVS) for road based freight transportation have gained the attention of public authorities, industry and the private sector.  It is accentuated by systems developed or being under development for road user charging, which have the potential to support additional services through sharing of functionalities (e.g. communication, positioning and payment). To design telematic systems with many services, the relationships underlying such services and necessary functionalities is challenging to analyze. The aim of this paper is to develop a map or guide between important AVS and their functionalities that can be used as a foundation for analyzing potential system architectures for achieving such services. The paper attempts to address differing interest between commercial stakeholders and public authorities in deploying telematic systems for AVS. An analytical method for studying synergy behaviour between road-based telematic services, and required functionalities is proposed. The method is based on a modified hierarchical cluster agglomeration and is inspired by multiuse component based software design. Results of applying the method on AVS in the Swedish Road User Charing system shows that the choice of functionalities play an important role in estimating the synergy for a group of AVS, and hence in the architecture. 

Blekinge Institute of Technology

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

The Research and Innovative Technology Administration (RITA) has made the application of
technology and the improvement of freight flow movement priorities in its 2030 Plan. With the
expected increase in the number of trucks on our highways, coupled with modern logistic
practices and concern for highway safety and homeland security, enhancing freight flow and
mitigating congestion will require the application of new and innovative technologies.
The Center for Advanced Transportation Systems Simulation (CATSS) at the University of
Central Florida in conjunction with The Florida Department of Transportation Commercial
Vehicle Operations Program began evaluating technology to support commercial truck
compliance and enforcement. Under this project, researchers evaluated off-the-shelf technology, integrating remote sensing and spatial information hardware with communications software into what would eventually become a Remotely Operated Compliance Station or ROCS. The station is designed to function so that it can generate and analyze data to support identification of freight flow congestion and test the suitability of the system as a model for regulatory compliance, enforcement and mitigating congestion bottlenecks at a major port and freight gateway. This new technology-reliant architecture will lead to improved enforcement, better security, and a more efficient utilization of enforcement personnel who can plan their activities around areas where violations occur. Efficient enforcement will remove unsafe vehicles and operators off our roadways ensuring safe and secure travel and freight movement.

University of Central Florida

Paper submitted for publication and presentation at the ITS America’s 2009 Annual Meeting and
Exposition

The New York State Department of Transportation Region 11 (New York City Region) has
one of the busiest and most recognized public transportation systems in the nation. The
construction of a state-of-the-art Joint Transportation Management Center (JTMC) facility in
New York City has been a multi-year effort and will be key to safely reducing congestion an
efficiently moving people, goods, services and information vital to the economy of both the
State and City of New York. It will provide the necessary correlation and investigative
procedures that are unique to this environment. The ability to manage incidents or situations
while monitoring events in real time will allow partner agencies to better handle movement i
high business urban management centers such as New York City.

This paper will introduce a strategy for a large, multi-stakeholder program for improved
transportation safety and traffic management and the critical software platform to implement
that strategy. It specifically addresses the need to integrate existing field devices along with
emerging technologies including wide ranging data sources and automated situation
management to improve the performance of the overall traffic network.

NYC Department of Transportation

USDOT, FHWA

VidSys, Inc.

NYC Police Department

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