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density estimation</title> <meta name="description" content="Search results for: traffic density estimation"> <meta name="keywords" content="traffic density estimation"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="traffic density estimation"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 6310</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: traffic density estimation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6310</span> A Packet Loss Probability Estimation Filter Using Most Recent Finite Traffic Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pyung%20Soo%20Kim">Pyung Soo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Eung%20Hyuk%20Lee"> Eung Hyuk Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Mun%20Suck%20Jang"> Mun Suck Jang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A packet loss probability (PLP) estimation filter with finite memory structure is proposed to estimate the packet rate mean and variance of the input traffic process in real-time while removing undesired system and measurement noises. The proposed PLP estimation filter is developed under a weighted least square criterion using only the finite traffic measurements on the most recent window. The proposed PLP estimation filter is shown to have several inherent properties such as unbiasedness, deadbeat, robustness. A guideline for choosing appropriate window length is described since it can affect significantly the estimation performance. Using computer simulations, the proposed PLP estimation filter is shown to be superior to the Kalman filter for the temporarily uncertain system. One possible explanation for this is that the proposed PLP estimation filter can have greater convergence time of a filtered estimate as the window length M decreases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=packet%20loss%20probability%20estimation" title="packet loss probability estimation">packet loss probability estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20memory%20filter" title=" finite memory filter"> finite memory filter</a>, <a href="https://publications.waset.org/abstracts/search?q=infinite%20memory%20filter" title=" infinite memory filter"> infinite memory filter</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a> </p> <a href="https://publications.waset.org/abstracts/9519/a-packet-loss-probability-estimation-filter-using-most-recent-finite-traffic-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9519.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">674</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6309</span> Heavy Vehicle Traffic Estimation Using Automatic Traffic Recorders/Weigh-In-Motion Data: Current Practice and Proposed Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Faizan%20Rehman%20Qureshi">Muhammad Faizan Rehman Qureshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Al-Kaisy"> Ahmed Al-Kaisy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate estimation of traffic loads is critical for pavement and bridge design, among other transportation applications. Given the disproportional impact of heavier axle loads on pavement and bridge structures, truck and heavy vehicle traffic is expected to be a major determinant of traffic load estimation. Further, heavy vehicle traffic is also a major input in transportation planning and economic studies. The traditional method for estimating heavy vehicle traffic primarily relies on AADT estimation using Monthly Day of the Week (MDOW) adjustment factors as well as the percent heavy vehicles observed using statewide data collection programs. The MDOW factors are developed using daily and seasonal (or monthly) variation patterns for total traffic, consisting predominantly of passenger cars and other smaller vehicles. Therefore, while using these factors may yield reasonable estimates for total traffic (AADT), such estimates may involve a great deal of approximation when applied to heavy vehicle traffic. This research aims at assessing the approximation involved in estimating heavy vehicle traffic using MDOW adjustment factors for total traffic (conventional approach) along with three other methods of using MDOW adjustment factors for total trucks (class 5-13), combination-unit trucks (class 8-13), as well as adjustment factors for each vehicle class separately. Results clearly indicate that the conventional method was outperformed by the other three methods by a large margin. Further, using the most detailed and data intensive method (class-specific adjustment factors) does not necessarily yield a more accurate estimation of heavy vehicle traffic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20loads" title="traffic loads">traffic loads</a>, <a href="https://publications.waset.org/abstracts/search?q=heavy%20vehicles" title=" heavy vehicles"> heavy vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=truck%20traffic" title=" truck traffic"> truck traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=adjustment%20factors" title=" adjustment factors"> adjustment factors</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20data%20collection" title=" traffic data collection"> traffic data collection</a> </p> <a href="https://publications.waset.org/abstracts/192471/heavy-vehicle-traffic-estimation-using-automatic-traffic-recordersweigh-in-motion-data-current-practice-and-proposed-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192471.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">23</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6308</span> Density Based Traffic System Using Pic Microcontroller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tatipamula%20Samiksha%20Goud">Tatipamula Samiksha Goud</a>, <a href="https://publications.waset.org/abstracts/search?q=.A.Naveena">.A.Naveena</a>, <a href="https://publications.waset.org/abstracts/search?q=M.sresta"> M.sresta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic congestion is a major issue in many cities throughout the world, particularly in urban areas, and it is past time to switch from a fixed timer mode to an automated system. The current traffic signalling system is a fixed-time system that is inefficient if one lane is more functional than the others. A structure for an intelligent traffic control system is being designed to address this issue. When traffic density is higher on one side of a junction, the signal's green time is extended in comparison to the regular time. This study suggests a technique in which the signal's time duration is assigned based on the amount of traffic present at the time. Infrared sensors can be used to do this. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=infrared%20sensors" title="infrared sensors">infrared sensors</a>, <a href="https://publications.waset.org/abstracts/search?q=micro-controllers" title=" micro-controllers"> micro-controllers</a>, <a href="https://publications.waset.org/abstracts/search?q=LEDs" title=" LEDs"> LEDs</a>, <a href="https://publications.waset.org/abstracts/search?q=oscillators" title=" oscillators"> oscillators</a> </p> <a href="https://publications.waset.org/abstracts/152588/density-based-traffic-system-using-pic-microcontroller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152588.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6307</span> Indian Road Traffic Flow Analysis Using Blob Tracking from Video Sequences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Balaji%20Ganesh%20Rajagopal">Balaji Ganesh Rajagopal</a>, <a href="https://publications.waset.org/abstracts/search?q=Subramanian%20Appavu%20alias%20Balamurugan">Subramanian Appavu alias Balamurugan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayyalraj%20Midhun%20Kumar"> Ayyalraj Midhun Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Krishnan%20Nallaperumal"> Krishnan Nallaperumal </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intelligent Transportation System is an Emerging area to solve multiple transportation problems. Several forms of inputs are needed in order to solve ITS problems. Advanced Traveler Information System (ATIS) is a core and important ITS area of this modern era. This involves travel time forecasting, efficient road map analysis and cost based path selection, Detection of the vehicle in the dynamic conditions and Traffic congestion state forecasting. This Article designs and provides an algorithm for traffic data generation which can be used for the above said ATIS application. By inputting the real world traffic situation in the form of video sequences, the algorithm determines the Traffic density in terms of congestion, number of vehicles in a given path which can be fed for various ATIS applications. The Algorithm deduces the key frame from the video sequences and follows the Blob detection, Identification and Tracking using connected components algorithm to determine the correlation between the vehicles moving in the real road scene. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20transportation" title="traffic transportation">traffic transportation</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20density%20estimation" title=" traffic density estimation"> traffic density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=blob%20identification%20and%20tracking" title=" blob identification and tracking"> blob identification and tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=relative%20velocity%20of%20vehicles" title=" relative velocity of vehicles"> relative velocity of vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation%20between%20vehicles" title=" correlation between vehicles"> correlation between vehicles</a> </p> <a href="https://publications.waset.org/abstracts/12455/indian-road-traffic-flow-analysis-using-blob-tracking-from-video-sequences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12455.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">510</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6306</span> Density-based Denoising of Point Cloud</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faisal%20Zaman">Faisal Zaman</a>, <a href="https://publications.waset.org/abstracts/search?q=Ya%20Ping%20Wong"> Ya Ping Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=Boon%20Yian%20Ng"> Boon Yian Ng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this, we present a novel approach using modified kernel density estimation (KDE) technique with bilateral filtering to remove noisy points and outliers. First we present a method for estimating optimal bandwidth of multivariate KDE using particle swarm optimization technique which ensures the robust performance of density estimation. Then we use mean-shift algorithm to find the local maxima of the density estimation which gives the centroid of the clusters. Then we compute the distance of a certain point from the centroid. Points belong to outliers then removed by automatic thresholding scheme which yields an accurate and economical point surface. The experimental results show that our approach comparably robust and efficient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=point%20preprocessing" title="point preprocessing">point preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier%20removal" title=" outlier removal"> outlier removal</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20reconstruction" title=" surface reconstruction"> surface reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20density%20estimation" title=" kernel density estimation "> kernel density estimation </a> </p> <a href="https://publications.waset.org/abstracts/37614/density-based-denoising-of-point-cloud" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37614.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">344</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6305</span> Polynomially Adjusted Bivariate Density Estimates Based on the Saddlepoint Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20B.%20Provost">S. B. Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Susan%20Sheng"> Susan Sheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An alternative bivariate density estimation methodology is introduced in this presentation. The proposed approach involves estimating the density function associated with the marginal distribution of each of the two variables by means of the saddlepoint approximation technique and applying a bivariate polynomial adjustment to the product of these density estimates. Since the saddlepoint approximation is utilized in the context of density estimation, such estimates are determined from empirical cumulant-generating functions. In the univariate case, the saddlepoint density estimate is itself adjusted by a polynomial. Given a set of observations, the coefficients of the polynomial adjustments are obtained from the sample moments. Several illustrative applications of the proposed methodology shall be presented. Since this approach relies essentially on a determinate number of sample moments, it is particularly well suited for modeling massive data sets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=density%20estimation" title="density estimation">density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20cumulant-generating%20function" title=" empirical cumulant-generating function"> empirical cumulant-generating function</a>, <a href="https://publications.waset.org/abstracts/search?q=moments" title=" moments"> moments</a>, <a href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation" title=" saddlepoint approximation"> saddlepoint approximation</a> </p> <a href="https://publications.waset.org/abstracts/72664/polynomially-adjusted-bivariate-density-estimates-based-on-the-saddlepoint-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72664.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">280</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6304</span> Mathematical Study for Traffic Flow and Traffic Density in Kigali Roads</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kayijuka%20Idrissa">Kayijuka Idrissa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work investigates a mathematical study for traffic flow and traffic density in Kigali city roads and the data collected from the national police of Rwanda in 2012. While working on this topic, some mathematical models were used in order to analyze and compare traffic variables. This work has been carried out on Kigali roads specifically at roundabouts from Kigali Business Center (KBC) to Prince House as our study sites. In this project, we used some mathematical tools to analyze the data collected and to understand the relationship between traffic variables. We applied the Poisson distribution method to analyze and to know the number of accidents occurred in this section of the road which is from KBC to Prince House. The results show that the accidents that occurred in 2012 were at very high rates due to the fact that this section has a very narrow single lane on each side which leads to high congestion of vehicles, and consequently, accidents occur very frequently. Using the data of speeds and densities collected from this section of road, we found that the increment of the density results in a decrement of the speed of the vehicle. At the point where the density is equal to the jam density the speed becomes zero. The approach is promising in capturing sudden changes on flow patterns and is open to be utilized in a series of intelligent management strategies and especially in noncurrent congestion effect detection and control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20methods" title="statistical methods">statistical methods</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20flow" title=" traffic flow"> traffic flow</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20distribution" title=" Poisson distribution"> Poisson distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=car%20moving%20technics" title=" car moving technics"> car moving technics</a> </p> <a href="https://publications.waset.org/abstracts/65222/mathematical-study-for-traffic-flow-and-traffic-density-in-kigali-roads" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65222.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">282</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6303</span> A Theorem Related to Sample Moments and Two Types of Moment-Based Density Estimates</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serge%20B.%20Provost">Serge B. Provost</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Numerous statistical inference and modeling methodologies are based on sample moments rather than the actual observations. A result justifying the validity of this approach is introduced. More specifically, it will be established that given the first n moments of a sample of size n, one can recover the original n sample points. This implies that a sample of size n and its first associated n moments contain precisely the same amount of information. However, it is efficient to make use of a limited number of initial moments as most of the relevant distributional information is included in them. Two types of density estimation techniques that rely on such moments will be discussed. The first one expresses a density estimate as the product of a suitable base density and a polynomial adjustment whose coefficients are determined by equating the moments of the density estimate to the sample moments. The second one assumes that the derivative of the logarithm of a density function can be represented as a rational function. This gives rise to a system of linear equations involving sample moments, the density estimate is then obtained by solving a differential equation. Unlike kernel density estimation, these methodologies are ideally suited to model ‘big data’ as they only require a limited number of moments, irrespective of the sample size. What is more, they produce simple closed form expressions that are amenable to algebraic manipulations. They also turn out to be more accurate as will be shown in several illustrative examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=density%20estimation" title="density estimation">density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=log-density" title=" log-density"> log-density</a>, <a href="https://publications.waset.org/abstracts/search?q=polynomial%20adjustments" title=" polynomial adjustments"> polynomial adjustments</a>, <a href="https://publications.waset.org/abstracts/search?q=sample%20moments" title=" sample moments"> sample moments</a> </p> <a href="https://publications.waset.org/abstracts/107130/a-theorem-related-to-sample-moments-and-two-types-of-moment-based-density-estimates" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107130.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">165</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6302</span> Estimation and Comparison of Delay at Signalized Intersections Based on Existing Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arpita%20Saha">Arpita Saha</a>, <a href="https://publications.waset.org/abstracts/search?q=Satish%20Chandra"> Satish Chandra</a>, <a href="https://publications.waset.org/abstracts/search?q=Indrajit%20Ghosh"> Indrajit Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Delay implicates the time loss of a traveler while crossing an intersection. Efficiency of traffic operation at signalized intersections is assessed in terms of delay caused to an individual vehicle. Highway Capacity Manual (HCM) method and Webster’s method are the most widely used in India for delay estimation purpose. However, in India, traffic is highly heterogeneous in nature with extremely poor lane discipline. Therefore, to explore best delay estimation technique for Indian condition, a comparison was made. In this study, seven signalized intersections from three different cities where chosen. Data was collected for both during morning and evening peak hours. Only under saturated cycles were considered for this study. Delay was estimated based on the field data. With the help of Simpson’s 1/3 rd rule, delay of under saturated cycles was estimated by measuring the area under the curve of queue length and cycle time. Moreover, the field observed delay was compared with the delay estimated using HCM, Webster, Probabilistic, Taylor’s expansion and Regression methods. The drawbacks of the existing delay estimation methods to be use in Indian heterogeneous traffic conditions were figured out, and best method was proposed. It was observed that direct estimation of delay using field measured data is more accurate than existing conventional and modified methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=delay%20estimation%20technique" title="delay estimation technique">delay estimation technique</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20delay" title=" field delay"> field delay</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20traffic" title=" heterogeneous traffic"> heterogeneous traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=signalised%20intersection" title=" signalised intersection"> signalised intersection</a> </p> <a href="https://publications.waset.org/abstracts/62348/estimation-and-comparison-of-delay-at-signalized-intersections-based-on-existing-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62348.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">301</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6301</span> Predictive Analytics in Traffic Flow Management: Integrating Temporal Dynamics and Traffic Characteristics to Estimate Travel Time</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20Ezziani">Maria Ezziani</a>, <a href="https://publications.waset.org/abstracts/search?q=Rabie%20Zine"> Rabie Zine</a>, <a href="https://publications.waset.org/abstracts/search?q=Amine%20Amar"> Amine Amar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilhame%20Kissani"> Ilhame Kissani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a predictive model for urban transportation engineering, which is vital for efficient traffic management. Utilizing comprehensive datasets and advanced statistical techniques, the model accurately forecasts travel times by considering temporal variations and traffic dynamics. Machine learning algorithms, including regression trees and neural networks, are employed to capture sequential dependencies. Results indicate significant improvements in predictive accuracy, particularly during peak hours and holidays, with the incorporation of traffic flow and speed variables. Future enhancements may integrate weather conditions and traffic incidents. The model's applications range from adaptive traffic management systems to route optimization algorithms, facilitating congestion reduction and enhancing journey reliability. Overall, this research extends beyond travel time estimation, offering insights into broader transportation planning and policy-making realms, empowering stakeholders to optimize infrastructure utilization and improve network efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20analytics" title="predictive analytics">predictive analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20flow" title=" traffic flow"> traffic flow</a>, <a href="https://publications.waset.org/abstracts/search?q=travel%20time%20estimation" title=" travel time estimation"> travel time estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20transportation" title=" urban transportation"> urban transportation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20management" title=" traffic management"> traffic management</a> </p> <a href="https://publications.waset.org/abstracts/183326/predictive-analytics-in-traffic-flow-management-integrating-temporal-dynamics-and-traffic-characteristics-to-estimate-travel-time" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183326.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">84</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6300</span> On the Cluster of the Families of Hybrid Polynomial Kernels in Kernel Density Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benson%20Ade%20Eniola%20Afere">Benson Ade Eniola Afere</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the years, kernel density estimation has been extensively studied within the context of nonparametric density estimation. The fundamental components of kernel density estimation are the kernel function and the bandwidth. While the mathematical exploration of the kernel component has been relatively limited, its selection and development remain crucial. The Mean Integrated Squared Error (MISE), serving as a measure of discrepancy, provides a robust framework for assessing the effectiveness of any kernel function. A kernel function with a lower MISE is generally considered to perform better than one with a higher MISE. Hence, the primary aim of this article is to create kernels that exhibit significantly reduced MISE when compared to existing classical kernels. Consequently, this article introduces a cluster of hybrid polynomial kernel families. The construction of these proposed kernel functions is carried out heuristically by combining two kernels from the classical polynomial kernel family using probability axioms. We delve into the analysis of error propagation within these kernels. To assess their performance, simulation experiments, and real-life datasets are employed. The obtained results demonstrate that the proposed hybrid kernels surpass their classical kernel counterparts in terms of performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classical%20polynomial%20kernels" title="classical polynomial kernels">classical polynomial kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20of%20families" title=" cluster of families"> cluster of families</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20error" title=" global error"> global error</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20Kernels" title=" hybrid Kernels"> hybrid Kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20density%20estimation" title=" Kernel density estimation"> Kernel density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a> </p> <a href="https://publications.waset.org/abstracts/171468/on-the-cluster-of-the-families-of-hybrid-polynomial-kernels-in-kernel-density-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171468.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">93</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6299</span> Parametric Estimation of U-Turn Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yonas%20Masresha%20Aymeku">Yonas Masresha Aymeku</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of capacity modelling at U-turns is to develop a relationship between capacity and its geometric characteristics. In fact, the few models available for the estimation of capacity at different transportation facilities do not provide specific guidelines for median openings. For this reason, an effort is made to estimate the capacity by collecting the data sets from median openings at different lane roads in Hyderabad City, India. Wide difference (43% -59%) among the capacity values estimated by the existing models shows the limitation to consider for mixed traffic situations. Thus, a distinct model is proposed for the estimation of the capacity of U-turn vehicles at median openings considering mixed traffic conditions, which would further prompt to investigate the effect of different factors that might affect the capacity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geometric" title="geometric">geometric</a>, <a href="https://publications.waset.org/abstracts/search?q=guiddelines" title=" guiddelines"> guiddelines</a>, <a href="https://publications.waset.org/abstracts/search?q=median" title=" median"> median</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicles" title=" vehicles"> vehicles</a> </p> <a href="https://publications.waset.org/abstracts/184454/parametric-estimation-of-u-turn-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184454.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">68</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6298</span> Robust and Real-Time Traffic Counting System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hossam%20M.%20Moftah">Hossam M. Moftah</a>, <a href="https://publications.waset.org/abstracts/search?q=Aboul%20Ella%20Hassanien"> Aboul Ella Hassanien</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the recent years the importance of automatic traffic control has increased due to the traffic jams problem especially in big cities for signal control and efficient traffic management. Traffic counting as a kind of traffic control is important to know the road traffic density in real time. This paper presents a fast and robust traffic counting system using different image processing techniques. The proposed system is composed of the following four fundamental building phases: image acquisition, pre-processing, object detection, and finally counting the connected objects. The object detection phase is comprised of the following five steps: subtracting the background, converting the image to binary, closing gaps and connecting nearby blobs, image smoothing to remove noises and very small objects, and detecting the connected objects. Experimental results show the great success of the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20counting" title="traffic counting">traffic counting</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20management" title=" traffic management"> traffic management</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/43835/robust-and-real-time-traffic-counting-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43835.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">294</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6297</span> Urban Traffic: Understanding the Traffic Flow Factor Through Fluid Dynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sathish%20Kumar%20Jayaraj">Sathish Kumar Jayaraj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of urban traffic dynamics, underpinned by the principles of fluid dynamics, offers a distinct perspective to comprehend and enhance the efficiency of traffic flow within bustling cityscapes. Leveraging the concept of the Traffic Flow Factor (TFF) as an analog to the Reynolds number, this research delves into the intricate interplay between traffic density, velocity, and road category, drawing compelling parallels to fluid dynamics phenomena. By introducing the notion of Vehicle Shearing Resistance (VSR) as an analogy to dynamic viscosity, the study sheds light on the multifaceted influence of traffic regulations, lane management, and road infrastructure on the smoothness and resilience of traffic flow. The TFF equation serves as a comprehensive metric for quantifying traffic dynamics, enabling the identification of congestion hotspots, the optimization of traffic signal timings, and the formulation of data-driven traffic management strategies. The study underscores the critical significance of integrating fluid dynamics principles into the domain of urban traffic management, fostering sustainable transportation practices, and paving the way for a more seamless and resilient urban mobility ecosystem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20flow%20factor%20%28TFF%29" title="traffic flow factor (TFF)">traffic flow factor (TFF)</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20traffic%20dynamics" title=" urban traffic dynamics"> urban traffic dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=fluid%20dynamics%20principles" title=" fluid dynamics principles"> fluid dynamics principles</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20shearing%20resistance%20%28VSR%29" title=" vehicle shearing resistance (VSR)"> vehicle shearing resistance (VSR)</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20congestion%20management" title=" traffic congestion management"> traffic congestion management</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainable%20urban%20mobility" title=" sustainable urban mobility"> sustainable urban mobility</a> </p> <a href="https://publications.waset.org/abstracts/182540/urban-traffic-understanding-the-traffic-flow-factor-through-fluid-dynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182540.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">62</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6296</span> Quantitative Analysis Of Traffic Dynamics And Violation Patterns Triggered By Cruise Ship Tourism In Victoria, British Columbia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Qasim">Muhammad Qasim</a>, <a href="https://publications.waset.org/abstracts/search?q=Laura%20Minet"> Laura Minet</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Victoria (BC), Canada, is a major cruise ship destination, attracting over 600,000 tourists annually. Residents of the James Bay neighborhood, home to the Ogden Point cruise terminal, have expressed concerns about the impacts of cruise ship activity on local traffic, air pollution, and safety compliance. This study evaluates the effects of cruise ship-induced traffic in James Bay, focusing on traffic flow intensification, density surges, changes in traffic mix, and speeding violations. To achieve these objectives, traffic data was collected in James Bay during two key periods: May, before the peak cruise season, and August, during full cruise operations. Three Miovision cameras captured the vehicular traffic mix at strategic entry points, while nine traffic counters monitored traffic distribution and speeding violations across the network. Traffic data indicated an average volume of 308 vehicles per hour during peak cruise times in May, compared to 116 vehicles per hour when no ships were in port. Preliminary analyses revealed a significant intensification of traffic flow during cruise ship "hoteling hours," with a volume increase of approximately 10% per cruise ship arrival. A notable 86% surge in taxi presence was observed on days with three cruise ships in port, indicating a substantial shift in traffic composition, particularly near the cruise terminal. The number of tourist buses escalated from zero in May to 32 in August, significantly altering traffic dynamics within the neighborhood. The period between 8 pm and 11 pm saw the most significant increases in traffic volume, especially when three ships were docked. Higher vehicle volumes were associated with a rise in speed violations, although this pattern was inconsistent across all areas. Speeding violations were more frequent on roads with lower traffic density, while roads with higher traffic density experienced fewer violations, due to reduced opportunities for speeding in congested conditions. PTV VISUM software was utilized for fuzzy distribution analysis and to visualize traffic distribution across the study area, including an assessment of the Level of Service on major roads during periods before and during the cruise ship season. This analysis identified the areas most affected by cruise ship-induced traffic, providing a detailed understanding of the impact on specific parts of the transportation network. These findings underscore the significant influence of cruise ship activity on traffic dynamics in Victoria, BC, particularly during peak periods when multiple ships are in port. The study highlights the need for targeted traffic management strategies to mitigate the adverse effects of increased traffic flow, changes in traffic mix, and speed violations, thereby enhancing road safety in the James Bay neighborhood. Further research will focus on detailed emissions estimation to fully understand the environmental impacts of cruise ship activity in Victoria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cruise%20ship%20tourism" title="cruise ship tourism">cruise ship tourism</a>, <a href="https://publications.waset.org/abstracts/search?q=air%20quality" title=" air quality"> air quality</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20violations" title=" traffic violations"> traffic violations</a>, <a href="https://publications.waset.org/abstracts/search?q=transport%20dynamics" title=" transport dynamics"> transport dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=pollution" title=" pollution"> pollution</a> </p> <a href="https://publications.waset.org/abstracts/190464/quantitative-analysis-of-traffic-dynamics-and-violation-patterns-triggered-by-cruise-ship-tourism-in-victoria-british-columbia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190464.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">22</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6295</span> An Approach to Apply Kernel Density Estimation Tool for Crash Prone Location Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kazi%20Md.%20Shifun%20Newaz">Kazi Md. Shifun Newaz</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Miaji"> S. Miaji</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahnewaz%20Hazanat-E-Rabbi"> Shahnewaz Hazanat-E-Rabbi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the kernel density estimation tool has been used to identify most crash prone locations in a national highway of Bangladesh. Like other developing countries, in Bangladesh road traffic crashes (RTC) have now become a great social alarm and the situation is deteriorating day by day. Today’s black spot identification process is not based on modern technical tools and most of the cases provide wrong output. In this situation, characteristic analysis and black spot identification by spatial analysis would be an effective and low cost approach in ensuring road safety. The methodology of this study incorporates a framework on the basis of spatial-temporal study to identify most RTC occurrence locations. In this study, a very important and economic corridor like Dhaka to Sylhet highway has been chosen to apply the method. This research proposes that KDE method for identification of Hazardous Road Location (HRL) could be used for all other National highways in Bangladesh and also for other developing countries. Some recommendations have been suggested for policy maker to reduce RTC in Dhaka-Sylhet especially in black spots. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hazardous%20road%20location%20%28HRL%29" title="hazardous road location (HRL)">hazardous road location (HRL)</a>, <a href="https://publications.waset.org/abstracts/search?q=crash" title=" crash"> crash</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20density" title=" kernel density"> kernel density</a> </p> <a href="https://publications.waset.org/abstracts/43744/an-approach-to-apply-kernel-density-estimation-tool-for-crash-prone-location-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43744.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">314</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6294</span> Urban Design via Estimation Model for Traffic Index of Cities Based on an Artificial Intelligence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Sobhan%20Alvani">Seyed Sobhan Alvani</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Gohari"> Mohammad Gohari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> By developing cities and increasing the population, traffic congestion has become a vital problem. Due to this crisis, urban designers try to present solutions to decrease this difficulty. On the other hand, predicting the model with perfect accuracy is essential for solution-providing. The current study presents a model based on artificial intelligence which can predict traffic index based on city population, growth rate, and area. The accuracy of the model was evaluated, which is acceptable and it is around 90%. Thus, urban designers and planners can employ it for predicting traffic index in the future to provide strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20index" title="traffic index">traffic index</a>, <a href="https://publications.waset.org/abstracts/search?q=population%20growth%20rate" title=" population growth rate"> population growth rate</a>, <a href="https://publications.waset.org/abstracts/search?q=cities%20wideness" title=" cities wideness"> cities wideness</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a> </p> <a href="https://publications.waset.org/abstracts/187941/urban-design-via-estimation-model-for-traffic-index-of-cities-based-on-an-artificial-intelligence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187941.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">40</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6293</span> On Modeling Data Sets by Means of a Modified Saddlepoint Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serge%20B.%20Provost">Serge B. Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Yishan%20Zhang"> Yishan Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A moment-based adjustment to the saddlepoint approximation is introduced in the context of density estimation. First applied to univariate distributions, this methodology is extended to the bivariate case. It then entails estimating the density function associated with each marginal distribution by means of the saddlepoint approximation and applying a bivariate adjustment to the product of the resulting density estimates. The connection to the distribution of empirical copulas will be pointed out. As well, a novel approach is proposed for estimating the support of distribution. As these results solely rely on sample moments and empirical cumulant-generating functions, they are particularly well suited for modeling massive data sets. Several illustrative applications will be presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=empirical%20cumulant-generating%20function" title="empirical cumulant-generating function">empirical cumulant-generating function</a>, <a href="https://publications.waset.org/abstracts/search?q=endpoints%20identification" title=" endpoints identification"> endpoints identification</a>, <a href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation" title=" saddlepoint approximation"> saddlepoint approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=sample%20moments" title=" sample moments"> sample moments</a>, <a href="https://publications.waset.org/abstracts/search?q=density%20estimation" title=" density estimation"> density estimation</a> </p> <a href="https://publications.waset.org/abstracts/144553/on-modeling-data-sets-by-means-of-a-modified-saddlepoint-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144553.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6292</span> A Generalisation of Pearson's Curve System and Explicit Representation of the Associated Density Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20B.%20Provost">S. B. Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Zareamoghaddam"> Hossein Zareamoghaddam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A univariate density approximation technique whereby the derivative of the logarithm of a density function is assumed to be expressible as a rational function is introduced. This approach which extends Pearson’s curve system is solely based on the moments of a distribution up to a determinable order. Upon solving a system of linear equations, the coefficients of the polynomial ratio can readily be identified. An explicit solution to the integral representation of the resulting density approximant is then obtained. It will be explained that when utilised in conjunction with sample moments, this methodology lends itself to the modelling of ‘big data’. Applications to sets of univariate and bivariate observations will be presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=density%20estimation" title="density estimation">density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=log-density" title=" log-density"> log-density</a>, <a href="https://publications.waset.org/abstracts/search?q=moments" title=" moments"> moments</a>, <a href="https://publications.waset.org/abstracts/search?q=Pearson%27s%20curve%20system" title=" Pearson's curve system"> Pearson's curve system</a> </p> <a href="https://publications.waset.org/abstracts/89345/a-generalisation-of-pearsons-curve-system-and-explicit-representation-of-the-associated-density-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89345.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">281</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6291</span> Estimating Destinations of Bus Passengers Using Smart Card Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hasik%20Lee">Hasik Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Seung-Young%20Kho"> Seung-Young Kho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=destination%20estimation" title="destination estimation">destination estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20density%20estimation" title=" Kernel density estimation"> Kernel density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20card%20data" title=" smart card data"> smart card data</a>, <a href="https://publications.waset.org/abstracts/search?q=validation" title=" validation"> validation</a> </p> <a href="https://publications.waset.org/abstracts/80452/estimating-destinations-of-bus-passengers-using-smart-card-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80452.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6290</span> Vehicle Speed Estimation Using Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prodipta%20Bhowmik">Prodipta Bhowmik</a>, <a href="https://publications.waset.org/abstracts/search?q=Poulami%20Saha"> Poulami Saha</a>, <a href="https://publications.waset.org/abstracts/search?q=Preety%20Mehra"> Preety Mehra</a>, <a href="https://publications.waset.org/abstracts/search?q=Yogesh%20Soni"> Yogesh Soni</a>, <a href="https://publications.waset.org/abstracts/search?q=Triloki%20Nath%20Jha"> Triloki Nath Jha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OpenCV" title="OpenCV">OpenCV</a>, <a href="https://publications.waset.org/abstracts/search?q=Haar%20Cascade%20classifier" title=" Haar Cascade classifier"> Haar Cascade classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=DLIB" title=" DLIB"> DLIB</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOV3" title=" YOLOV3"> YOLOV3</a>, <a href="https://publications.waset.org/abstracts/search?q=centroid%20tracker" title=" centroid tracker"> centroid tracker</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20detection" title=" vehicle detection"> vehicle detection</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20tracking" title=" vehicle tracking"> vehicle tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20speed%20estimation" title=" vehicle speed estimation"> vehicle speed estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/153549/vehicle-speed-estimation-using-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153549.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">84</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6289</span> A Semiparametric Approach to Estimate the Mode of Continuous Multivariate Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tiee-Jian%20Wu">Tiee-Jian Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chih-Yuan%20Hsu"> Chih-Yuan Hsu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mode estimation is an important task, because it has applications to data from a wide variety of sources. We propose a semi-parametric approach to estimate the mode of an unknown continuous multivariate density function. Our approach is based on a weighted average of a parametric density estimate using the Box-Cox transform and a non-parametric kernel density estimate. Our semi-parametric mode estimate improves both the parametric- and non-parametric- mode estimates. Specifically, our mode estimate solves the non-consistency problem of parametric mode estimates (at large sample sizes) and reduces the variability of non-parametric mode estimates (at small sample sizes). The performance of our method at practical sample sizes is demonstrated by simulation examples and two real examples from the fields of climatology and image recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Box-Cox%20transform" title="Box-Cox transform">Box-Cox transform</a>, <a href="https://publications.waset.org/abstracts/search?q=density%20estimation" title=" density estimation"> density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=mode%20seeking" title=" mode seeking"> mode seeking</a>, <a href="https://publications.waset.org/abstracts/search?q=semiparametric%20method" title=" semiparametric method"> semiparametric method</a> </p> <a href="https://publications.waset.org/abstracts/53756/a-semiparametric-approach-to-estimate-the-mode-of-continuous-multivariate-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53756.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">285</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6288</span> ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keunhong%20Chae">Keunhong Chae</a>, <a href="https://publications.waset.org/abstracts/search?q=Seokho%20Yoon"> Seokho Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20offset" title="frequency offset">frequency offset</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclic%20prefix" title=" cyclic prefix"> cyclic prefix</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum-likelihood" title=" maximum-likelihood"> maximum-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%0D%0Anoise" title=" non-Gaussian noise"> non-Gaussian noise</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a> </p> <a href="https://publications.waset.org/abstracts/10266/ml-based-blind-frequency-offset-estimation-schemes-for-ofdm-systems-in-non-gaussian-noise-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10266.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">476</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6287</span> Artificial Neural Network Based Approach for Estimation of Individual Vehicle Speed under Mixed Traffic Condition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subhadip%20Biswas">Subhadip Biswas</a>, <a href="https://publications.waset.org/abstracts/search?q=Shivendra%20Maurya"> Shivendra Maurya</a>, <a href="https://publications.waset.org/abstracts/search?q=Satish%20Chandra"> Satish Chandra</a>, <a href="https://publications.waset.org/abstracts/search?q=Indrajit%20Ghosh"> Indrajit Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speed%20model" title="speed model">speed model</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=arterial" title=" arterial"> arterial</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20traffic" title=" mixed traffic"> mixed traffic</a> </p> <a href="https://publications.waset.org/abstracts/62366/artificial-neural-network-based-approach-for-estimation-of-individual-vehicle-speed-under-mixed-traffic-condition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62366.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">388</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6286</span> Reduction of the Number of Traffic Accidents by Function of Driver's Anger Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masahiro%20Miyaji">Masahiro Miyaji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When a driver happens to be involved in some traffic congestion or after traffic incidents, the driver may fall in a state of anger. State of anger may encounter decisive risk resulting in severer traffic accidents. Preventive safety function using driver’s psychosomatic state with regard to anger may be one of solutions which would avoid that kind of risks. Identifying driver’s anger state is important to create countermeasures to prevent the risk of traffic accidents. As a first step, this research figured out root cause of traffic incidents by means of using Internet survey. From statistical analysis of the survey, dominant psychosomatic states immediately before traffic incidents were haste, distraction, drowsiness and anger. Then, we replicated anger state of a driver while driving, and then, replicated it by means of using driving simulator on bench test basis. Six types of facial expressions including anger were introduced as alternative characteristics. Kohonen neural network was adopted to classify anger state. Then, we created a methodology to detect anger state of a driver in high accuracy. We presented a driving support safety function. The function adapts driver’s anger state in cooperation with an autonomous driving unit to reduce the number of traffic accidents. Consequently, e evaluated reduction rate of driver’s anger in the traffic accident. To validate the estimation results, we referred the reduction rate of Advanced Safety Vehicle (ASV) as well as Intelligent Transportation Systems (ITS). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kohonen%20neural%20network" title="Kohonen neural network">Kohonen neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=driver%E2%80%99s%20anger%20state" title=" driver’s anger state"> driver’s anger state</a>, <a href="https://publications.waset.org/abstracts/search?q=reduction%20of%20traffic%20accidents" title=" reduction of traffic accidents"> reduction of traffic accidents</a>, <a href="https://publications.waset.org/abstracts/search?q=driver%E2%80%99s%20state%20adaptive%20driving%20support%20safety" title=" driver’s state adaptive driving support safety"> driver’s state adaptive driving support safety</a> </p> <a href="https://publications.waset.org/abstracts/48011/reduction-of-the-number-of-traffic-accidents-by-function-of-drivers-anger-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48011.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6285</span> A Hybrid Traffic Model for Smoothing Traffic Near Merges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shiri%20Elisheva%20Decktor">Shiri Elisheva Decktor</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharon%20Hornstein"> Sharon Hornstein</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Highway merges and unmarked junctions are key components in any urban road network, which can act as bottlenecks and create traffic disruption. Inefficient highway merges may trigger traffic instabilities such as stop-and-go waves, pose safety conditions and lead to longer journey times. These phenomena occur spontaneously if the average vehicle density exceeds a certain critical value. This study focuses on modeling the traffic using a microscopic traffic flow model. A hybrid traffic model, which combines human-driven and controlled vehicles is assumed. The controlled vehicles obey different driving policies when approaching the merge, or in the vicinity of other vehicles. We developed a co-simulation model in SUMO (Simulation of Urban Mobility), in which the human-driven cars are modeled using the IDM model, and the controlled cars are modeled using a dedicated controller. The scenario chosen for this study is a closed track with one merge and one exit, which could be later implemented using a scaled infrastructure on our lab setup. This will enable us to benchmark the results of this study obtained in simulation, to comparable results in similar conditions in the lab. The metrics chosen for the comparison of the performance of our algorithm on the overall traffic conditions include the average speed, wait time near the merge, and throughput after the merge, measured under different travel demand conditions (low, medium, and heavy traffic). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=highway%20merges" title="highway merges">highway merges</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20modeling" title="traffic modeling">traffic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=SUMO" title=" SUMO"> SUMO</a>, <a href="https://publications.waset.org/abstracts/search?q=driving%20policy" title=" driving policy"> driving policy</a> </p> <a href="https://publications.waset.org/abstracts/154241/a-hybrid-traffic-model-for-smoothing-traffic-near-merges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154241.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">106</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6284</span> Traffic Density Measurement by Automatic Detection of the Vehicles Using Gradient Vectors from Aerial Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saman%20Ghaffarian">Saman Ghaffarian</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilgin%20G%C3%B6ka%C5%9Far"> Ilgin Gökaşar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aerial%20images" title="aerial images">aerial images</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20transportation%20systems" title=" intelligent transportation systems"> intelligent transportation systems</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20density%20measurement" title=" traffic density measurement"> traffic density measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20detection" title=" vehicle detection"> vehicle detection</a> </p> <a href="https://publications.waset.org/abstracts/32312/traffic-density-measurement-by-automatic-detection-of-the-vehicles-using-gradient-vectors-from-aerial-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32312.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">379</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6283</span> Perception of Risk toward Traffic Violence among Road Users in Makassar, Indonesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sulasmi%20Sudirman">Sulasmi Sudirman</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachmadanty%20Mujah%20Hartika"> Rachmadanty Mujah Hartika</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic violence is currently a big issue in Indonesia. However, the road users perceived risk that is caused by traffic violence is low. The lack of safety driving awareness is one of the factors that road users committed to traffic violence. There are several lists of common traffic violence in Indonesia such as lack of physical fitness, not wearing helmet, unfasten seatbelt, breaking through the traffic light, not holding a driving license, and some more violence. This research sought to explore the perception of road users toward traffic violence. The participants were road users in Makassar, Indonesia who were using cars and motorbikes. The method of the research was a qualitative approach by using a personal interview to collect data. The research showed that there three main ideas of perceiving traffic violence which are motives, environment that supported traffic violence, and reinforcement. The road users committed traffic violence had particular motive, for example, rushing. The road users committed to traffic violence when other road users and significant other did the same. The road users committed traffic violence when the police were not there to give a ticket. It can be concluded that the perception of road users toward traffic violence determined by internal aspect, the social aspect, and regulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=perception" title="perception">perception</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20users" title=" road users"> road users</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic" title=" traffic"> traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=violence" title=" violence"> violence</a> </p> <a href="https://publications.waset.org/abstracts/105587/perception-of-risk-toward-traffic-violence-among-road-users-in-makassar-indonesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105587.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">222</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6282</span> Implementation of Traffic Engineering Using MPLS Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishal%20H.%20Shukla">Vishal H. Shukla</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjay%20B.%20Deshmukh"> Sanjay B. Deshmukh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic engineering, at its center, is the ability of moving traffic approximately so that traffic from a congested link is moved onto the unused capacity on another link. Traffic Engineering ensures the best possible use of the resources. Now to support traffic engineering in the today’s network, Multiprotocol Label Switching (MPLS) is being used which is very helpful for reliable packets delivery in an ongoing internet services. Here a topology is been implemented on GNS3 to focus on the analysis of the communication take place from one site to other through the ISP. The comparison is made between the IP network & MPLS network based on Bandwidth & Jitter which are one of the performance parameters using JPERF simulator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GNS3" title="GNS3">GNS3</a>, <a href="https://publications.waset.org/abstracts/search?q=JPERF" title=" JPERF"> JPERF</a>, <a href="https://publications.waset.org/abstracts/search?q=MPLS" title=" MPLS"> MPLS</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20engineering" title=" traffic engineering"> traffic engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=VMware" title=" VMware"> VMware</a> </p> <a href="https://publications.waset.org/abstracts/23898/implementation-of-traffic-engineering-using-mpls-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23898.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">487</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6281</span> Evaluating Traffic Congestion Using the Bayesian Dirichlet Process Mixture of Generalized Linear Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ren%20Moses">Ren Moses</a>, <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Kidando"> Emmanuel Kidando</a>, <a href="https://publications.waset.org/abstracts/search?q=Eren%20Ozguven"> Eren Ozguven</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassir%20Abdelrazig"> Yassir Abdelrazig</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study applied traffic speed and occupancy to develop clustering models that identify different traffic conditions. Particularly, these models are based on the Dirichlet Process Mixture of Generalized Linear regression (DML) and change-point regression (CR). The model frameworks were implemented using 2015 historical traffic data aggregated at a 15-minute interval from an Interstate 295 freeway in Jacksonville, Florida. Using the deviance information criterion (DIC) to identify the appropriate number of mixture components, three traffic states were identified as free-flow, transitional, and congested condition. Results of the DML revealed that traffic occupancy is statistically significant in influencing the reduction of traffic speed in each of the identified states. Influence on the free-flow and the congested state was estimated to be higher than the transitional flow condition in both evening and morning peak periods. Estimation of the critical speed threshold using CR revealed that 47 mph and 48 mph are speed thresholds for congested and transitional traffic condition during the morning peak hours and evening peak hours, respectively. Free-flow speed thresholds for morning and evening peak hours were estimated at 64 mph and 66 mph, respectively. The proposed approaches will facilitate accurate detection and prediction of traffic congestion for developing effective countermeasures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20congestion" title="traffic congestion">traffic congestion</a>, <a href="https://publications.waset.org/abstracts/search?q=multistate%20speed%20distribution" title=" multistate speed distribution"> multistate speed distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20occupancy" title=" traffic occupancy"> traffic occupancy</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixtures%20of%20generalized%20linear%20model" title=" Dirichlet process mixtures of generalized linear model"> Dirichlet process mixtures of generalized linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20change-point%20detection" title=" Bayesian change-point detection"> Bayesian change-point detection</a> </p> <a href="https://publications.waset.org/abstracts/67198/evaluating-traffic-congestion-using-the-bayesian-dirichlet-process-mixture-of-generalized-linear-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67198.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">294</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=traffic%20density%20estimation&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=traffic%20density%20estimation&page=3">3</a></li> <li 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