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RISS 인기검색어
- 검색키워드
- 저자 : Rilett, Laurence R. (검색결과 4 건)
- 무료
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조한선,Laurence R. Rilett,박동주 대한토목학회 2011 KSCE JOURNAL OF CIVIL ENGINEERING Vol.15 No.3
Because the prime objective of current preemption methods at signalized intersections near highway-railroad grade crossings is to clear the crossing, secondary objectives such as safe pedestrian crossing and vehicle delay are given less consideration or are ignored completely during the preemption. Under certain circumstances state-of-the-practice traffic signal preemption strategies may cause serious pedestrian safety and efficiency problems at signalized intersections near highway-railroad grade crossings. Recently, an Improved Transition Preemption Strategy (ITPS) that is specifically designed to improve intersection performance and pedestrian safety was developed by Cho and Rilett. Even if the ITPS algorithm improved both the safety and efficiency of signalized intersections near highway-railroad grade crossings, it is impossible to measure the exact benefit of ITPS because the ITPS algorithm was tested only for the worst case scenario at the development stage. For this paper, the ITPS algorithm was tested under normal operating condition, that is, trains were designed to arrive at the crossing randomly during the cycle. Also, the effect of pedestrians was analyzed using a VISSIM simulation model which was calibrated to field conditions. Finally, a benefit/cost analysis was performed. It was concluded that the ITPS algorithm improves both the safety and efficiency of signalized intersections near highway-railroad grade crossings for normal operating condition.
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William L. Eisele,Bhaven Naik,Laurence R. Rilett 서울시립대학교 도시과학연구원 2015 도시과학국제저널 Vol.19 No.3
Route travel time variability estimates provide performance information related to the reliability of a trip and allow for confidence bands to be placed around the mean travel time estimates. The naïve (and sometimes used) method to estimate route travel time variance (reliability) is to assume independence of link travel times and consequently sum the individual link variances along the route. In this approach, correlation between links is assumed as zero and the approach is straightforward, but assuming independence is not realistic. This paper describes a post-processing procedure for providing improved route travel time mean and variance estimates, while taking into consideration the correlation existent between individual link travel times. Using automatic vehicle identification (AVI) and inductance loop detector (ILD) data from two separate routes in Texas, a practical application of the theory established by Fu and Rilett (1998. Expected shortest paths in dynamic and stochastic traffic networks. Transportation Research Part B: Methodological, 32(7), 499–516) is used to quantify link travel time correlation. The paper also provides an investigation on the usefulness of the loess statistical method and a polynomial regression model to estimate the distributional properties of link and route travel times. Another significant finding is that route reliability estimated from link ILD speeds (extrapolated to travel times) was not correlated to actual route travel time reliability measured by simultaneously operated probe vehicles. This work is unique in that instrumented probe vehicles were operated at exactly the same times as the roadway sensor data were collected, allowing direct comparison of the travel time estimates from all empirical data sources. The research presents valuable insight on how confidence intervals may be placed on travel time mean estimates for all traffic conditions. With the increased use of travel time data sources such as smartphones, connected vehicles, and private-sector data sources, the methods presented in this paper are invaluable for effective transportation system performance monitoring of both persons and freight movement.
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New methodologies for predicting corridor travel time mean and reliability
Zifeng Wu,Laurence R. Rilett,Weijun Ren 서울시립대학교 도시과학연구원 2022 도시과학국제저널 Vol.26 No.3
Accurate travel time prediction is very important for real-time traveller information systems. Many existing traveller information systems provide point estimates of forecast travel times. Often the forecast corridor travel time is estimated as a direct summation of the forecast link travel times on the route. This approach neglects the correlation between link travel times and may lead to inaccurate route travel time forecasts. This paper improves upon the simple addition method by accounting for the dependency of link travel times on the arrival time at that specific link which further correlates to its preceding links. In addition, this paper also explores the potential of using the nonlinear autoregressive with exogenous inputs (NARX) model and feedforward neural network model to forecast the corridor travel time mean and reliability metrics. To the authors knowledge this is the first time, short-term travel time reliability is measured by a reliability interval which is based on the forecasts of corridor travel time mean and standard deviation. The prediction methodologies developed in this paper are tested on an urban arterial that has been instrumented with Bluetooth readers so empirical travel times are available. It was found that the proposed NARX model outperforms the other models that were studied with respect to mean corridor travel time prediction. In terms of the reliability interval prediction, the performance of various models is presented as a Pareto Optimal Frontier trading off accuracy and usability. The proposed NARX model and three other tested models are all on the Pareto Optimal Frontier.
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