Safety

A  car access indication  system  allows traffic moving in both directions to flow smoothly
because it warns drivers of the existence of oncoming cars. This system should ideally be
installed  on  mountainous  roads  when  it  becomes  affordable.  The  system  has  received  an
initial favorable reception from road users in the areas in which it is installed; however, only
limited effects of the system have been investigated to date. Therefore, this study measured
the effect of the system quantitatively by researching changes in vehicle behavior and local
road users’ perception before and after the system installation. The result of this study shows
the advantages and technical considerations of this system on mountainous roads.

Tokyo University of Science, Japan

Kochi University of Technology, Japan

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

This  paper  proposes  a  real-time  adaptive  control  model  for  signalized  intersections  that
decides optimal control parameters commonly found in modern actuated controllers, aiming
to exploit the adaptive functionality of traffic-actuated control and to improve the performance
of  traffic-actuated  signal  system.  This  model  incorporates  a  flow  prediction  process  that
estimates the future arrival rates and turning proportions at target intersections based on the
available signal timing plan and detector information. Signal control parameters are optimized
dynamically cycle-by-cycle to satisfy these estimated demands. The proposed adaptive control
strategy is tested on a network consisting of thirty-eight actuated signals using microscopic
simulation. Simulation results show that the proposed adaptive model is able to improve the
performance of the study network, especially under off-peak traffic conditions.

University of California, Irvine

University of California, Berkeley

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

This study attempts to develop an arterial incident detection model by applying an
Artificial Neural Network (ANN) with simulation data. A section of the US-1 corridor in Miami-
Dade County, Florida was selected as the study area and coded in the CORSIM microscopic
simulation model. Two data sets were generated via CORSIM simulation for model
development and assessment. Multiple ANN models were designed for various scenarios. The
model performances were evaluated using the selected measures of effectiveness (MOE),
including detection rate (DR) and false alarm rate (FAR). The results showed that the ANN
models in general could detect arterial incidents with a high DR of 90-95% and an acceptable
FAR of lower than 4%. The study also identified some preferred features in the design of ANN
incident detection models for this application. These include the detector configuration scheme,
the selection of model input features, and the employment of data from previous cycles.

DMJM Harris

Florida International University

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

The overall objective of the study is to understand the occurrence of
primary and secondary incidents and relevant incident management strategies, as
well as to understand how primary incident duration and secondary incident
occurrence are related. Specifically, secondary incidents are more likely to occur
if the primary incident lasts long; at the same time, the durations of primary
incidents are expected to be longer if secondary incidents occur. The work will
allow State Departments of Transportation to estimate the chances of a secondary
incident based on the characteristics of the primary incident, evaluate associated
delays, and aid in identifying incident management strategies to mitigate the
impacts of both primary and secondary incidents. Freeway incident and roadway
inventory data from the Hampton Roads area in Virginia were used in this study.
Modeling and simulation techniques were applied to develop primary incident
duration and secondary incident occurrence/duration prediction models. Models
for primary incident durations and whether or not a secondary incident occurs are
estimated. The interdependence is modeled by using the incident duration as
endogenous variable in secondary incident occurrence models. The results show
statistical evidence for interdependence, but when it is taken into account, no
substantial differences in the magnitudes and statistical significance for the
estimated independent variables are found (compared to when the
interdependence is not accounted for). Statistically significant correlations are
found between secondary incident occurrence and other variables, allowing us to
recommend aggressive incident clearance procedures on qualifying high-volume
roadways to avoid secondary incidents.

Old Dominion University

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

A driver fatigue detection system has been developed under test-driving conditions that include 24 hours of driving on a test track at Nihon University and 12 hours of driving on a national highway in Japan. The level of driver fatigue is shown as a Driver Fatigue Index (DFI). After the detection system was installed in the heavy trucks used in this study, various experiments aimed at establishing practical usage parameters for the system were conducted under actual operating conditions. Truck positioning data was verified by GPS. Three-dimensional acceleration levels, the vehicle’s operating speed, the driver’s heart rate, body surface temperature, and DFI were monitored, with the collected data transmitted automatically to the researchers by a mobile packet transmission system. The results of the study were displayed on a web page used to manage driver fatigue levels for road safety purposes.

Nihon University

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