Cross-cutting Issues

This paper presents a new approach for processing vehicle trajectories collected from the field. Unlike traditional approaches such as Finite Differencing or Locally Weighed Regression, the proposed approach combines bi-level optimization with spline interpolations, seeking to minimize not only measurement errors, but also internal inconsistency errors in positions, speeds and accelerations data. Real-life vehicle trajectories collected from I-94 WB were used to test the proposed approach. Results indicate the new approach is effective in eliminating both measurement and inconsistency errors. Moreover, the proposed approach is further compared to Locally Weighted Regression, an approach that has been commonly used in earlier studies, by conducting a sensitivity analysis where the magnitude of measurement errors is varied with different values. The comparison results show that the proposed approach is not only more robust with respect to varying measurement errors, but also more effective in removing data inconsistency from vehicle speed and acceleration profiles.

KLD Associates Inc.

Minnesota Traffic Observatory

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

A common method of Dilemma zone (DZ) protection at isolated intersections is to hold the green until the number of vehicles in DZ is lower than a threshold number. Since the threshold number is empirical and fixed, it cannot accommodate the dynamic and time-varying traffic patterns and therefore have to be adjusted regularly. This paper presents a new Markov-process-based DZ protection algorithm, which considers each time step, the algorithm first predicts the future states with the Markov state-transit matrix then compare them with the current state to determine whether to end the green or not. As a result, the new end-green criterion is determined not by the fixed threshold values but by the current state and the Markov state-transit matrix. Meanwhile, the Markov matrix is automatically updated when the new observed detected state transitions come in. The simulation results showed that the new algorithm maintains reliable and effective protection in a dynamic traffic environment. At last, we present an evaluation of the new algorithm performance using two methods of calculating the current state: low-fidelity prediction with advance detectors and high-fidelity prediction with future Connected Vehicles Technologies.

University of Kentucky

Virginia Polytechnic Institute and State University

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

A common method of Dilemma zone (DZ) protection at isolated intersections is to hold the green until the number of vehicles in DZ is lower than a threshold number. Since the threshold number is empirical and fixed, it cannot accommodate the dynamic and time-varying traffic patterns and therefore have to be adjusted regularly. This paper presents a new Markov-process-based DZ protection algorithm, which considers the number of vehicles in DZ (state) as a Markov process. At each time step, the algorithm first predicts the future states with the Markov state-transit matrix then compare them with the current state to determine whether to end the green or not. As a result, the new end-green criterion is determined not by the fixed threshold values but by the current state and the Markov state-transit matrix. Meanwhile, the Markov matrix is automatically updated when the new observed detected state transitions come in. The simulation results showed that the new algorithm maintains reliable and effective protection in a dynamic traffic environment. At last, we present an evaluation of the new algorithm performance using two methods of calculating the current state: low-fidelity prediction with advance detectors and high-fidelity prediction with future Connected Vehicles Technologies.

Authors: Pengfei Li Ph.D., Montasir M. Abbas, Ph.D., P.E., Raghu Pasupathy, Ph.D.

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

The aim of this study is to report on the development of the evaluation method using the travel time reliability and case studies for the particular transportation projects on Tokyo Metropolitan Expressway (MEX), including traffic safety measures and new routes. Each project may contribute to safer and smoother traffic, but most projects were assessed by different methods. It is difficult to assess various projects using the unified measurement. In this paper, the Buffer Time Index (BTI) and the statistics of travel time were used for the evaluation of travel time reliability, and those were applied to the actual transportation projects on MEX. Then, new reliability indexes which are suitable for analyses of various projects are proposed. Using these indexes, the effects of both the safety projects and the construction projects can be assessed. These assessments were allowed by plentiful traffic data, which were collected by vehicle detectors with an approximately 300m interval on MEX.

Metropolitan Expressway Company Limited

Pacific Consultants Company Limited

Institute of Industrial Science, the University of Tokyo

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

Floating car information systems (probe car information systems) are currently
being implemented. As a critical service, estimated link travel times are obtained from
collected link travel times for utilization in vehicle navigation systems. Such estimated link
travel times must be appropriate for the situations arising during navigation, since they are
affected by various factors, including time period, traffic volume, and weather. However, the
actual system does not consider the effect of weather for link travel time estimation. Besides,
it does not make an evaluation of the floating car data actually available. Furthermore, no
in-depth evaluation is made regarding the minimum amount of data necessary for estimation
of link travel time. This paper uses data actually obtained by floating car systems, and takes
into account the effect of weather (e.g., snowfall) for an estimation of link travel time, thereby
revealing the effect of weather differences on estimated link travel time. In addition, it
evaluates the amount of data required to obtain estimated link travel time, as well as estimated
values, while maintaining accuracy, thereby revealing the characteristics of such data from
various actual data.

Honda Motor Co.

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