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Time filter

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Bangkok, Thailand

Guo J.,Nanjing Southeast University | Huang W.,Nanjing Southeast University | Wei Y.,Nanjing Southeast University | Zhang L.,Transportation Engineer
KSCE Journal of Civil Engineering | Year: 2013

Considering the importance of estimating speed from single loop detector measurements for many Intelligent Transportation System (ITS) related applications, a variety of algorithms have been developed in the literature for specific time intervals. However, the performance of these algorithms over a spectrum of time intervals has not yet received appropriate attention in the transportation research community. In this study, a Kalman filter based algorithm is selected as a typical representative and investigated over a spectrum of 30 time intervals starting from 1-minute to 30-minute with one minute increment. Empirical results using real world data show that the selected algorithm has workable performances for most of the time intervals under investigation. Specifically, the performances of the selected approach improve for intervals from 1-minute to 5-minute, stay stable for intervals between 5-minute and 15-minute, and decrease slightly for longer time intervals greater than 15-minute. The results demonstrate the ability of the selected algorithm to be readily implemented in a variety of transportation applications with specific time interval needs. Future work is recommended to develop a framework of coupling speed estimating algorithms and real world transportation applications through investigating other single loop speed estimation approaches and ITS application related time interval needs. © 2013 Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg. Source


Izadpanah P.,Transportation Engineer | Nichol S.,Ontario Ministry of Transportation | Hadayeghi A.,Transportation Engineering | Malone B.,Transportation
Institute of Transportation Engineers Annual Meeting and Exhibit 2012 | Year: 2012

Many road authorities recognize the challenges associated with a reactive approach to road safety and have adopted a proactive and systematic approach in their road safety initiatives. However, automation is a key challenge road authorities face in implementing an efficient and effective traffic safety management program. In the summer of 2009, the American Association of State Highway and Transportation Officials released the first version of SafetyAnalyst software, and in 2010 published the Highway Safety Manual. The Highway Safety Manual provides road safety knowledge and tools in a practical form to facilitate improved decision-making based on safety performance. The focus of the Highway Safety Manual is to provide quantitative information for decision-making. SafetyAnalyst software incorporates methodologies set forth in the Highway Safety Manual for road safety management in computerized analytical tools. These tools support the identification of safety improvement needs and the decisionmaking process for developing a system-wide program of safety improvement projects. The Ministry of Transportation of Ontario has initiated a project to configure SafetyAnalyst to meet their needs in managing the road safety analysis of their highway network. In this initiative, all four SafetyAnalyst analysis modules, which include the Network Screening Tool, Diagnosis Tool, Countermeasure Selection Tool, Economic Appraisal Tool, Priority Ranking Tool, and Countermeasure Evaluation Tool, are configured for the road network under the jurisdiction of the Ministry of Transportation of Ontario. As part of this initiative, the Ministry's Safety Performance Functions are being updated for road sections, interchanges, ramps, ramp terminals, and intersections. The functional forms of these safety performance functions are compatible with those of SafetyAnalyst. Moreover, the SafetyAnalyst modules are configured with Ontario's customized values. The main goal of this paper is to summarize the lessons learned in this initiative to assist other road authorities in their prospective undertakings related to SafetyAnalyst. This paper highlights challenges associated with compiling infrastructure data, traffic volume data, collision data, importing data into SafetyAnalyst while keeping the road authority's databases unchanged, Safety Performance Functions in SafetyAnalyst, and customized values for various modules in SafetyAnalyst. This paper provides solutions to address these challenges. It also recommends the necessary steps for road authorities before starting an initiative to configure SafetyAnalyst for their network. Source


Kattan L.,University of Calgary | Tay R.,La Trobe University | Acharjee S.,Transportation Engineer
Accident Analysis and Prevention | Year: 2011

Since speeding is one of the major causes of frequent and severe traffic accidents around school and playground areas, many jurisdictions have reduced the speed limits in these areas to protect children who may be at risk. This paper investigated the speed compliance, mean speed and 85th percentile speed at selected school and playground zones in the City of Calgary in Alberta. Our results showed that the mean speed was lower and the rate of compliance was higher in the school zone compared to the playground zone, 2 lane roads relative to 4 lane roads, roads with fencing, traffic control devices and the presence of speed display device or children, and zones that were longer (>200 m). Accordingly, this study provided recommendations to improve the effectiveness of school and playground zone speed limits. © 2011 Elsevier Ltd. All rights reserved. Source


Selby B.,Transportation Engineer | Kockelman K.M.,University of Texas at Austin
Journal of Transport Geography | Year: 2013

This work explores the application of two distinctive spatial methods for prediction of average daily traffic counts across the Texas network. Results based on Euclidean distances are compared to those using network distances, and both allow for strategic spatial interpolation of count values while controlling for each roadway's functional classification, lane count, speed limit, and other site attributes. Both universal kriging and geographically weighted regression (GWR) are found to reduce errors (in practically and statistically significant ways) over non-spatial regression techniques, though errors remain quite high at some sites, particularly those with low counts and/or in less measurement-dense areas. Nearly all tests indicated that the predictive capabilities of kriging exceed those of GWR by average absolute errors of 3-8%. Interestingly, the estimation of kriging parameters by network distances show no enhanced performance over Euclidean distances, which require less data and are much more easily computed. © 2012 Elsevier Ltd. Source


Schultz G.G.,Brigham Young University | Black C.W.,Transportation Engineer | Saito M.,Brigham Young University
T and DI Congress 2014: Planes, Trains, and Automobiles - Proceedings of the 2nd Transportation and Development Institute Congress | Year: 2014

A number of steps have been taken over the past several years to advance the Utah Department of Transportation safety initiative. Previous research began the development of a hierarchical Bayesian model to analyze crashes on Utah roadways. The model analyzes roadway segments and determines a posterior predictive distribution of crashes. The actual numbers of crashes for each segment are compared to the predictive distribution and a percentile is calculated. A high percentile indicates more crashes than would be expected and a low percentile indicates less. A Geographic Information System (GIS) framework was developed to facilitate the analysis. The GIS framework has the capability to format the raw data such that it can be read into the statistical model. The GIS framework also displays the numerical data output by the statistical model spatially, allowing for an easy and intuitive analysis of 'hot spots' or 'black spots.' The purpose of this paper is to outline the GIS framework for crash data analysis, the results of which can be used to further evaluate those segments classified as hot spots. © 2014 American Society of Civil Engineers. Source

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