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<div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28102</span> A Study of Cloud Computing Solution for Transportation Big Data Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ilgin%20G%C3%B6ka%C5%9Far">Ilgin Gökaşar</a>, <a href="https://publications.waset.org/abstracts/search?q=Saman%20Ghaffarian"> Saman Ghaffarian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The need for fast processed big data of transportation ridership (eg., smartcard data) and traffic operation (e.g., traffic detectors data) which requires a lot of computational power is incontrovertible in Intelligent Transportation Systems. Nowadays cloud computing is one of the important subjects and popular information technology solution for data processing. It enables users to process enormous measure of data without having their own particular computing power. Thus, it can also be a good selection for transportation big data processing as well. This paper intends to examine how the cloud computing can enhance transportation big data process with contrasting its advantages and disadvantages, and discussing cloud computing features. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</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=ITS" title=" ITS"> ITS</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20data%20processing" title=" traffic data processing"> traffic data processing</a> </p> <a href="https://publications.waset.org/abstracts/32308/a-study-of-cloud-computing-solution-for-transportation-big-data-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32308.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">468</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">28101</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">28100</span> GIS for Simulating Air Traffic by Applying Different Multi-radar Positioning Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amara%20Rafik">Amara Rafik</a>, <a href="https://publications.waset.org/abstracts/search?q=Bougherara%20Maamar"> Bougherara Maamar</a>, <a href="https://publications.waset.org/abstracts/search?q=Belhadj%20Aissa%20Mostefa"> Belhadj Aissa Mostefa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Radar data is one of the many data sources used by ATM Air Traffic Management systems. These data come from air navigation radar antennas. These radars intercept signals emitted by the various aircraft crossing the controlled airspace and calculate the position of these aircraft and retransmit their positions to the Air Traffic Management System. For greater reliability, these radars are positioned in such a way as to allow their coverage areas to overlap. An aircraft will therefore be detected by at least one of these radars. However, the position coordinates of the same aircraft and sent by these different radars are not necessarily identical. Therefore, the ATM system must calculate a single position (radar track) which will ultimately be sent to the control position and displayed on the air traffic controller's monitor. There are several techniques for calculating the radar track. Furthermore, the geographical nature of the problem requires the use of a Geographic Information System (GIS), i.e. a geographical database on the one hand and geographical processing. The objective of this work is to propose a GIS for traffic simulation which reconstructs the evolution over time of aircraft positions from a multi-source radar data set and by applying these different techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ATM" title="ATM">ATM</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=radar%20data" title=" radar data"> radar data</a>, <a href="https://publications.waset.org/abstracts/search?q=air%20traffic%20simulation" title=" air traffic simulation"> air traffic simulation</a> </p> <a href="https://publications.waset.org/abstracts/168613/gis-for-simulating-air-traffic-by-applying-different-multi-radar-positioning-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168613.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">85</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">28099</span> Traffic Light Detection Using Image Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vaishnavi%20Shivde">Vaishnavi Shivde</a>, <a href="https://publications.waset.org/abstracts/search?q=Shrishti%20Sinha"> Shrishti Sinha</a>, <a href="https://publications.waset.org/abstracts/search?q=Trapti%20Mishra"> Trapti Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based trafﬁc light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20light%20detection" title="traffic light detection">traffic light detection</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</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=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/137254/traffic-light-detection-using-image-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137254.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">173</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">28098</span> Design of Traffic Counting Android Application with Database Management System and Its Comparative Analysis with Traditional Counting Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Nouman">Muhammad Nouman</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Tiwana"> Fahad Tiwana</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Irfan"> Muhammad Irfan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsin%20Tiwana"> Mohsin Tiwana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic congestion has been increasing significantly in major metropolitan areas as a result of increased motorization, urbanization, population growth and changes in the urban density. Traffic congestion compromises efficiency of transport infrastructure and causes multiple traffic concerns; including but not limited to increase of travel time, safety hazards, air pollution, and fuel consumption. Traffic management has become a serious challenge for federal and provincial governments, as well as exasperated commuters. Effective, flexible, efficient and user-friendly traffic information/database management systems characterize traffic conditions by making use of traffic counts for storage, processing, and visualization. While, the emerging data collection technologies continue to proliferate, its accuracy can be guaranteed through the comparison of observed data with the manual handheld counters. This paper presents the design of tablet based manual traffic counting application and framework for development of traffic database management system for Pakistan. The database management system comprises of three components including traffic counting android application; establishing online database and its visualization using Google maps. Oracle relational database was chosen to develop the data structure whereas structured query language (SQL) was adopted to program the system architecture. The GIS application links the data from the database and projects it onto a dynamic map for traffic conditions visualization. The traffic counting device and example of a database application in the real-world problem provided a creative outlet to visualize the uses and advantages of a database management system in real time. Also, traffic data counts by means of handheld tablet/ mobile application can be used for transportation planning and forecasting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=manual%20count" title="manual count">manual count</a>, <a href="https://publications.waset.org/abstracts/search?q=emerging%20data%20sources" title=" emerging data sources"> emerging data sources</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20information%20quality" title=" traffic information quality"> traffic information quality</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20surveillance" title=" traffic surveillance"> traffic surveillance</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20counting%20device" title=" traffic counting device"> traffic counting device</a>, <a href="https://publications.waset.org/abstracts/search?q=android%3B%20data%20visualization" title=" android; data visualization"> android; data visualization</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/101612/design-of-traffic-counting-android-application-with-database-management-system-and-its-comparative-analysis-with-traditional-counting-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101612.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">193</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">28097</span> Geographic Information System for Simulating Air Traffic By Applying Different Multi-Radar Positioning Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amara%20Rafik">Amara Rafik</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostefa%20Belhadj%20Aissa"> Mostefa Belhadj Aissa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Radar data is one of the many data sources used by ATM Air Traffic Management systems. These data come from air navigation radar antennas. These radars intercept signals emitted by the various aircraft crossing the controlled airspace and calculate the position of these aircraft and retransmit their positions to the Air Traffic Management System. For greater reliability, these radars are positioned in such a way as to allow their coverage areas to overlap. An aircraft will therefore be detected by at least one of these radars. However, the position coordinates of the same aircraft and sent by these different radars are not necessarily identical. Therefore, the ATM system must calculate a single position (radar track) which will ultimately be sent to the control position and displayed on the air traffic controller's monitor. There are several techniques for calculating the radar track. Furthermore, the geographical nature of the problem requires the use of a Geographic Information System (GIS), i.e. a geographical database on the one hand and geographical processing. The objective of this work is to propose a GIS for traffic simulation which reconstructs the evolution over time of aircraft positions from a multi-source radar data set and by applying these different techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ATM" title="ATM">ATM</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=radar%20data" title=" radar data"> radar data</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/156884/geographic-information-system-for-simulating-air-traffic-by-applying-different-multi-radar-positioning-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156884.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">118</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">28096</span> Measured versus Default Interstate Traffic Data in New Mexico, USA</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Hasan">M. A. Hasan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Islam"> M. R. Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20A.%20Tarefder"> R. A. Tarefder</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates how the site specific traffic data differs from the Mechanistic Empirical Pavement Design Software default values. Two Weigh-in-Motion (WIM) stations were installed in Interstate-40 (I-40) and Interstate-25 (I-25) to developed site specific data. A computer program named WIM Data Analysis Software (WIMDAS) was developed using Microsoft C-Sharp (.Net) for quality checking and processing of raw WIM data. A complete year data from November 2013 to October 2014 was analyzed using the developed WIM Data Analysis Program. After that, the vehicle class distribution, directional distribution, lane distribution, monthly adjustment factor, hourly distribution, axle load spectra, average number of axle per vehicle, axle spacing, lateral wander distribution, and wheelbase distribution were calculated. Then a comparative study was done between measured data and AASHTOWare default values. It was found that the measured general traffic inputs for I-40 and I-25 significantly differ from the default values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AASHTOWare" title="AASHTOWare">AASHTOWare</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic" title=" traffic"> traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=weigh-in-motion" title=" weigh-in-motion"> weigh-in-motion</a>, <a href="https://publications.waset.org/abstracts/search?q=axle%20load%20distribution" title=" axle load distribution"> axle load distribution</a> </p> <a href="https://publications.waset.org/abstracts/42451/measured-versus-default-interstate-traffic-data-in-new-mexico-usa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42451.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">343</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">28095</span> Relationship between Driving under the Influence and Traffic Safety</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eun%20Hak%20Lee">Eun Hak Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Young-Hyun%20Seo"> Young-Hyun Seo</a>, <a href="https://publications.waset.org/abstracts/search?q=Hosuk%20Shin"> Hosuk Shin</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> Among traffic crashes, driving under the influence (DUI) of alcohol is the most dangerous behavior in Seoul, South Korea. In 2016 alone 40 deaths occurred on of 2,857 cases of DUI. Since DUI is one of the major factors in increasing the severity of crashes, the intensive management of DUI required to reduce traffic crash deaths and the crash damages. This study aims to investigate the relationship between DUI and traffic safety in order to establish countermeasures for traffic safety improvement. The analysis was conducted on the habitual drivers who drove under the influence. Information of habitual drivers is matched to crash data and fine data. The descriptive statistics on data used in this study, which consists of driver license acquisition, traffic fine, and crash data provided by the Korean National Police Agency, are described. The drivers under the influence are classified by statistically significant criteria, such as driver’s age, license type, driving experience, and crash reasons. With the results of the analysis, we propose some countermeasures to enhance traffic safety. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=driving%20under%20influence" title="driving under influence">driving under influence</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20safety" title=" traffic safety"> traffic safety</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20crash" title=" traffic crash"> traffic crash</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20fine" title=" traffic fine"> traffic fine</a> </p> <a href="https://publications.waset.org/abstracts/85925/relationship-between-driving-under-the-influence-and-traffic-safety" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85925.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">28094</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">28093</span> Intelligent Ambulance with Advance Features of Traffic Management and Telecommunication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mamatha%20M.%20N.">Mamatha M. N.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic problems, congested traffic, and flow management were recognized as major problems mostly in all the areas, which have caused a problem for the ambulance which carries the emergency patient. The proposed paper aims in the development of ambulance which reaches the nearby hospital faster even in heavy traffic scenario. This process is activated by implementing hardware in an ambulance as well as in traffic post thus allowing a smooth flow to the ambulance to reach the hospital in time. 1) The design of the vehicle to have a communication between ambulance and traffic post. 2)Electronic Health Record with Data-acquisition system 3)Telemetry of acquired biological parameters to the nearest hospital. Thus interfacing all these three different modules and integrating them on the ambulance could reach the hospital earlier than the present ambulance. The system is accurate and efficient of 99.8%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-telemetry" title="bio-telemetry">bio-telemetry</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition" title=" data acquisition"> data acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=patient%20database" title=" patient database"> patient database</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20traffic%20control" title=" automatic traffic control"> automatic traffic control</a> </p> <a href="https://publications.waset.org/abstracts/51414/intelligent-ambulance-with-advance-features-of-traffic-management-and-telecommunication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51414.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">315</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">28092</span> Development of K-Factor for Road Geometric Design: A Case Study of North Coast Road in Java </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edwin%20Hidayat">Edwin Hidayat</a>, <a href="https://publications.waset.org/abstracts/search?q=Redi%20Yulianto"> Redi Yulianto</a>, <a href="https://publications.waset.org/abstracts/search?q=Disi%20Hanafiah"> Disi Hanafiah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> On the one hand, parameters which are used for determining the number of lane on the new road construction are average annual average daily traffic (AADT) and peak hour factor (K-factor). On the other hand, the value of K-factor listed in the guidelines and manual for road planning in Indonesia is a value of adoption or adaptation from foreign guidelines or manuals. Thus, the value is less suitable for Indonesian condition due to differences in road conditions, vehicle type, and driving behavior. The purpose of this study is to provide an example on how to determine k-factor values at a road segment with particular conditions in north coast road, West Java. The methodology is started with collecting traffic volume data for 24 hours over 365 days using PLATO (Automated Traffic Counter) with the approach of video image processing. Then, the traffic volume data is divided into per hour and analyzed by comparing the peak traffic volume in the 30th hour (or other) with the AADT in the same year. The analysis has resulted that for the 30th peak hour the K-factor is 0.97. This value can be used for planning road geometry or evaluating the road capacity performance for the 4/2D interurban road. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=road%20geometry" title="road geometry">road geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=K-factor" title=" K-factor"> K-factor</a>, <a href="https://publications.waset.org/abstracts/search?q=annual%20average%20daily%20traffic" title=" annual average daily traffic"> annual average daily traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=north%20coast%20road" title=" north coast road"> north coast road</a> </p> <a href="https://publications.waset.org/abstracts/95926/development-of-k-factor-for-road-geometric-design-a-case-study-of-north-coast-road-in-java" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95926.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">161</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">28091</span> Geographic Information System-Based Identification of Road Traffic Crash Hotspots on Rural Roads in Oman</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Bakhit%20Kashoob">Mohammed Bakhit Kashoob</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Salim%20Al-Maashani"> Mohammed Salim Al-Maashani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Abdullah%20Al-Marhoon"> Ahmed Abdullah Al-Marhoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of Geographic Information System (GIS) tools in the analysis of traffic crash data can help to identify locations or hotspots with high instances or risk of traffic crashes. The identification of traffic crash hotspots can effectively improve road safety measures. Mapping of road traffic crash hotspots can help the concerned authorities to give priority and take targeted measures and improvements to the road structure at these locations to reduce traffic crashes and fatalities. In Oman, there are countless rural roads that have more risks for traveling vehicles compared to urban roads. The likelihood of traffic crashes as well as fatality rate may increase with the presence of risks that are associated with the rural type of community. In this paper, the traffic crash hotspots on rural roads in Oman are specified using spatial analysis methods in GIS and traffic crash data. These hotspots are ranked based on the frequency of traffic crash occurrence (i.e., number of traffic crashes) and the rate of fatalities. The result of this study presents a map visualization of locations on rural roads with high traffic crashes and high fatalities rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=road%20safety" title="road safety">road safety</a>, <a href="https://publications.waset.org/abstracts/search?q=rural%20roads" title=" rural roads"> rural roads</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20crash" title=" traffic crash"> traffic crash</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS%20tools" title=" GIS tools"> GIS tools</a> </p> <a href="https://publications.waset.org/abstracts/153334/geographic-information-system-based-identification-of-road-traffic-crash-hotspots-on-rural-roads-in-oman" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153334.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">149</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">28090</span> The Effect of User Comments on Traffic Application Usage</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20Gokasar">I. Gokasar</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Bakioglu"> G. Bakioglu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the unprecedented rates of technological improvements, people start to solve their problems with the help of technological tools. According to application stores and websites in which people evaluate and comment on the traffic apps, there are more than 100 traffic applications which have different features with respect to their purpose of usage ranging from the features of traffic apps for public transit modes to the features of traffic apps for private cars. This study focuses on the top 30 traffic applications which were chosen with respect to their download counts. All data about the traffic applications were obtained from related websites. The purpose of this study is to analyze traffic applications in terms of their categorical attributes with the help of developing a regression model. The analysis results suggest that negative interpretations (e.g., being deficient) does not lead to lower star ratings of the applications. However, those negative interpretations result in a smaller increase in star rate. In addition, women use higher star rates than men for the evaluation of traffic applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20app" title="traffic app">traffic app</a>, <a href="https://publications.waset.org/abstracts/search?q=real%E2%80%93time%20information" title=" real–time information"> real–time information</a>, <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=regression%20analysis" title=" regression analysis"> regression analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=dummy%20variables" title=" dummy variables"> dummy variables</a> </p> <a href="https://publications.waset.org/abstracts/52331/the-effect-of-user-comments-on-traffic-application-usage" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52331.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">429</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">28089</span> Comparison of Different k-NN Models for Speed Prediction in an Urban Traffic Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyoung%20Kim">Seyoung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeongmin%20Kim"> Jeongmin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang%20Ryel%20Ryu"> Kwang Ryel Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (<em>k</em>-NN) as predictive models is that it does not require any explicit model building. Instead, <em>k</em>-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up <em>k</em>-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different <em>k</em>-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=k-NN" title=" k-NN"> k-NN</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%20speed%20prediction" title=" traffic speed prediction"> traffic speed prediction</a> </p> <a href="https://publications.waset.org/abstracts/43415/comparison-of-different-k-nn-models-for-speed-prediction-in-an-urban-traffic-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43415.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">363</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">28088</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">28087</span> Sourcing and Compiling a Maltese Traffic Dataset MalTra</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriele%20Borg">Gabriele Borg</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexei%20De%20Bono"> Alexei De Bono</a>, <a href="https://publications.waset.org/abstracts/search?q=Charlie%20Abela"> Charlie Abela</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Big%20Data" title="Big Data">Big Data</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicular%20traffic" title=" vehicular traffic"> vehicular traffic</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=mobile%20data%20patterns" title=" mobile data patterns"> mobile data patterns</a> </p> <a href="https://publications.waset.org/abstracts/153117/sourcing-and-compiling-a-maltese-traffic-dataset-maltra" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153117.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">109</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">28086</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">28085</span> Artificial Neural Network and Statistical Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomas%20Berhanu%20Bekele">Tomas Berhanu Bekele</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea of avoiding traffic instabilities and homogenizing traffic flow in such a way that the risk of accidents is minimized and traffic flow is maximized. Lately, Intelligent Transport Systems (ITS) has become an important area of research to solve such road traffic-related issues for making smart decisions. It links people, roads and vehicles together using communication technologies to increase safety and mobility. Moreover, accurate prediction of road traffic is important to manage traffic congestion. The aim of this study is to develop an ANN model for the prediction of traffic flow and to compare the ANN model with the linear regression model of traffic flow predictions. Data extraction was carried out in intervals of 15 minutes from the video player. Video of mixed traffic flow was taken and then counted during office work in order to determine the traffic volume. Vehicles were classified into six categories, namely Car, Motorcycle, Minibus, mid-bus, Bus, and Truck vehicles. The average time taken by each vehicle type to travel the trap length was measured by time displayed on a video screen. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20transport%20system%20%28ITS%29" title="intelligent transport system (ITS)">intelligent transport system (ITS)</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20flow%20prediction" title=" traffic flow prediction"> traffic flow prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network%20%28ANN%29" title=" artificial neural network (ANN)"> artificial neural network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a> </p> <a href="https://publications.waset.org/abstracts/183194/artificial-neural-network-and-statistical-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183194.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">67</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">28084</span> Synthetic Data-Driven Prediction Using GANs and LSTMs for Smart Traffic Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20Peri">Srinivas Peri</a>, <a href="https://publications.waset.org/abstracts/search?q=Siva%20Abhishek%20Sirivella"> Siva Abhishek Sirivella</a>, <a href="https://publications.waset.org/abstracts/search?q=Tejaswini%20Kallakuri"> Tejaswini Kallakuri</a>, <a href="https://publications.waset.org/abstracts/search?q=Uzair%20Ahmad"> Uzair Ahmad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GAN" title="GAN">GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory%20%28LSTM%29" title=" long short-term memory (LSTM)"> long short-term memory (LSTM)</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20%20data%20generation" title=" synthetic data generation"> synthetic data generation</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/192173/synthetic-data-driven-prediction-using-gans-and-lstms-for-smart-traffic-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192173.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">14</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">28083</span> Analysis of Operating Speed on Four-Lane Divided Highways under Mixed Traffic Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chaitanya%20Varma">Chaitanya Varma</a>, <a href="https://publications.waset.org/abstracts/search?q=Arpan%20Mehar"> Arpan Mehar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study demonstrates the procedure to analyse speed data collected on various four-lane divided sections in India. Field data for the study was collected at different straight and curved sections on rural highways with the help of radar speed gun and video camera. The data collected at the sections were analysed and parameters pertain to speed distributions were estimated. The different statistical distribution was analysed on vehicle type speed data and for mixed traffic speed data. It was found that vehicle type speed data was either follows the normal distribution or Log-normal distribution, whereas the mixed traffic speed data follows more than one type of statistical distribution. The most common fit observed on mixed traffic speed data were Beta distribution and Weibull distribution. The separate operating speed model based on traffic and roadway geometric parameters were proposed in the present study. The operating speed model with traffic parameters and curve geometry parameters were established. Two different operating speed models were proposed with variables 1/R and Ln(R) and were found to be realistic with a different range of curve radius. The models developed in the present study are simple and realistic and can be used for forecasting operating speed on four-lane highways. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=highway" title="highway">highway</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20traffic%20flow" title=" mixed traffic flow"> mixed traffic flow</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=operating%20speed" title=" operating speed"> operating speed</a> </p> <a href="https://publications.waset.org/abstracts/33813/analysis-of-operating-speed-on-four-lane-divided-highways-under-mixed-traffic-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33813.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">460</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">28082</span> Form of Distribution of Traffic Accident and Environment Factors of Road Affecting of Traffic Accident in Dusit District, Only Area Responsible of Samsen Police Station</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Musthaya%20Patchanee">Musthaya Patchanee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aimed to study form of traffic distribution and environmental factors of road that affect traffic accidents in Dusit District, only areas responsible of Samsen Police Station. Data used in this analysis is the secondary data of traffic accident case from year 2011. Observed area units are 15 traffic lines that are under responsible of Samsen Police Station. Technique and method used are the Cartographic Method, the Correlation Analysis, and the Multiple Regression Analysis. The results of form of traffic accidents show that, the Samsen Road area had most traffic accidents (24.29%), second was Rachvithi Road (18.10%), third was Sukhothai Road (15.71%), fourth was Rachasrima Road (12.38%), and fifth was Amnuaysongkram Road (7.62%). The result from Dusit District, only areas responsible of Samsen police station, has suggested that the scale of accidents have high positive correlation with statistic significant at level 0.05 and the frequency of travel (r=0.857). Traffic intersection point (r=0.763)and traffic control equipments (r=0.713) are relevant factors respectively. By using the Multiple Regression Analysis, travel frequency is the only one that has considerable influences on traffic accidents in Dusit district only Samsen Police Station area. Also, a factor in frequency of travel can explain the change in traffic accidents scale to 73.40 (R2 = 0.734). By using the Multiple regression summation from analysis was Y ̂=-7.977+0.044X6. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=form%20of%20traffic%20distribution" title="form of traffic distribution">form of traffic distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20factors%20of%20road" title=" environmental factors of road"> environmental factors of road</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20accidents" title=" traffic accidents"> traffic accidents</a>, <a href="https://publications.waset.org/abstracts/search?q=Dusit%20district" title=" Dusit district"> Dusit district</a> </p> <a href="https://publications.waset.org/abstracts/9487/form-of-distribution-of-traffic-accident-and-environment-factors-of-road-affecting-of-traffic-accident-in-dusit-district-only-area-responsible-of-samsen-police-station" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9487.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">391</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">28081</span> Graph Based Traffic Analysis and Delay Prediction Using a Custom Built Dataset</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriele%20Borg">Gabriele Borg</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexei%20Debono"> Alexei Debono</a>, <a href="https://publications.waset.org/abstracts/search?q=Charlie%20Abela"> Charlie Abela</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale. Furthermore, a series of traffic prediction graph neural network models are conducted to compare MalTra to large-scale traffic datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20neural%20networks" title="graph neural networks">graph neural networks</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=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20data%20patterns" title=" mobile data patterns"> mobile data patterns</a> </p> <a href="https://publications.waset.org/abstracts/152972/graph-based-traffic-analysis-and-delay-prediction-using-a-custom-built-dataset" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152972.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">131</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">28080</span> A Study of Traffic Assignment Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelfetah%20Laouzai">Abdelfetah Laouzai</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachid%20Ouafi"> Rachid Ouafi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a traffic network, users usually choose their way so that it reduces their travel time between pairs origin-destination. This behavior might seem selfish as it produces congestions in different parts of the network. The traffic assignment problem (TAP) models the interactions between congestion and user travel decisions to obtain vehicles flows over each axis of the traffic network. The resolution methods of TAP serve as a tool allows predicting users’ distribution, identifying congesting points and affecting the travelers’ behavior in the choice of their route in the network following dynamic data. In this article, we will present a review about specific resolution approach of TAP. A comparative analysis is carried out on those approaches so that it highlights the characteristics, advantages and disadvantages of each. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20traffic" title="network traffic">network traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=travel%20decisions" title=" travel decisions"> travel decisions</a>, <a href="https://publications.waset.org/abstracts/search?q=approaches" title=" approaches"> approaches</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20assignment" title=" traffic assignment"> traffic assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=flows" title=" flows"> flows</a> </p> <a href="https://publications.waset.org/abstracts/37867/a-study-of-traffic-assignment-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37867.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">474</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">28079</span> Information Processing and Visual Attention: An Eye Tracking Study on Nutrition Labels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rosa%20Hendijani">Rosa Hendijani</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Ghadimi%20Herfeh"> Amir Ghadimi Herfeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nutrition labels are diet-related health policies. They help individuals improve food-choice decisions and reduce intake of calories and unhealthy food elements, like cholesterol. However, many individuals do not pay attention to nutrition labels or fail to appropriately understand them. According to the literature, thinking and cognitive styles can have significant effects on attention to nutrition labels. According to the author's knowledge, the effect of global/local processing on attention to nutrition labels have not been previously studied. Global/local processing encourages individuals to attend to the whole/specific parts of an object and can have a significant impact on people's visual attention. In this study, this effect was examined with an experimental design using the eye-tracking technique. The research hypothesis was that individuals with local processing would pay more attention to nutrition labels, including nutrition tables and traffic lights. An experiment was designed with two conditions: global and local information processing. Forty participants were randomly assigned to either global or local conditions, and their processing style was manipulated accordingly. Results supported the hypothesis for nutrition tables but not for traffic lights. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=eye-tracking" title="eye-tracking">eye-tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=nutrition%20labelling" title=" nutrition labelling"> nutrition labelling</a>, <a href="https://publications.waset.org/abstracts/search?q=global%2Flocal%20information%20processing" title=" global/local information processing"> global/local information processing</a>, <a href="https://publications.waset.org/abstracts/search?q=individual%20differences" title=" individual differences"> individual differences</a> </p> <a href="https://publications.waset.org/abstracts/132051/information-processing-and-visual-attention-an-eye-tracking-study-on-nutrition-labels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132051.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">159</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">28078</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">28077</span> Effect of Traffic Volume and Its Composition on Vehicular Speed under Mixed Traffic Conditions: A Kriging Based Approach</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> Use of speed prediction models sometimes appears as a feasible alternative to laborious field measurement particularly, in case when field data cannot fulfill designer’s requirements. However, developing speed models is a challenging task specifically in the context of developing countries like India where vehicles with diverse static and dynamic characteristics use the same right of way without any segregation. Here the traffic composition plays a significant role in determining the vehicular speed. The present research was carried out to examine the effects of traffic volume and its composition on vehicular speed under mixed traffic conditions. Classified traffic volume and speed data were collected from different geometrically identical six lane divided arterials in New Delhi. Based on these field data, speed prediction models were developed for individual vehicle category adopting Kriging approximation technique, an alternative for commonly used regression. These models are validated with the data set kept aside earlier for validation purpose. The predicted speeds showed a great deal of agreement with the observed values and also the model outperforms all other existing speed models. Finally, the proposed models were utilized to evaluate the effect of traffic volume and its composition on speed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speed" title="speed">speed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kriging" title=" Kriging"> Kriging</a>, <a href="https://publications.waset.org/abstracts/search?q=arterial" title=" arterial"> arterial</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20volume" title=" traffic volume"> traffic volume</a> </p> <a href="https://publications.waset.org/abstracts/62347/effect-of-traffic-volume-and-its-composition-on-vehicular-speed-under-mixed-traffic-conditions-a-kriging-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62347.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">353</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">28076</span> Mapping of Traffic Noise in Riyadh City-Saudi Arabia </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20A.%20Alsaif">Khaled A. Alsaif</a>, <a href="https://publications.waset.org/abstracts/search?q=Mosaad%20A.%20Foda"> Mosaad A. Foda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present work aims at development of traffic noise maps for Riyadh City using the software Lima. Road traffic data were estimated or measured as accurate as possible in order to obtain consistent noise maps. The predicted noise levels at some selected sites are validated by actual field measurements, which are obtained by a system that consists of a sound level meter, a GPS receiver and a database to manage the measured data. The maps show that noise levels remain over 50 dBA and can exceed 70 dBA at the nearside of major roads and highways. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=noise%20pollution" title="noise pollution">noise pollution</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20traffic%20noise" title=" road traffic noise"> road traffic noise</a>, <a href="https://publications.waset.org/abstracts/search?q=LimA%20predictor" title=" LimA predictor"> LimA predictor</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS" title=" GPS"> GPS</a> </p> <a href="https://publications.waset.org/abstracts/36791/mapping-of-traffic-noise-in-riyadh-city-saudi-arabia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36791.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">384</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">28075</span> Learning Traffic Anomalies from Generative Models on Real-Time Observations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fotis%20I.%20Giasemis">Fotis I. Giasemis</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexandros%20Sopasakis"> Alexandros Sopasakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study focuses on detecting traffic anomalies using generative models applied to real-time observations. By integrating a Graph Neural Network with an attention-based mechanism within the Spatiotemporal Generative Adversarial Network framework, we enhance the capture of both spatial and temporal dependencies in traffic data. Leveraging minute-by-minute observations from cameras distributed across Gothenburg, our approach provides a more detailed and precise anomaly detection system, effectively capturing the complex topology and dynamics of urban traffic networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic" title="traffic">traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=GNN" title=" GNN"> GNN</a>, <a href="https://publications.waset.org/abstracts/search?q=GAN" title=" GAN"> GAN</a> </p> <a href="https://publications.waset.org/abstracts/193544/learning-traffic-anomalies-from-generative-models-on-real-time-observations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193544.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">8</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">28074</span> Statistically Accurate Synthetic Data Generation for Enhanced Traffic Predictive Modeling Using Generative Adversarial Networks and Long Short-Term Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20Peri">Srinivas Peri</a>, <a href="https://publications.waset.org/abstracts/search?q=Siva%20Abhishek%20Sirivella"> Siva Abhishek Sirivella</a>, <a href="https://publications.waset.org/abstracts/search?q=Tejaswini%20Kallakuri"> Tejaswini Kallakuri</a>, <a href="https://publications.waset.org/abstracts/search?q=Uzair%20Ahmad"> Uzair Ahmad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effective traffic management and infrastructure planning are crucial for the development of smart cities and intelligent transportation systems. This study addresses the challenge of data scarcity by generating realistic synthetic traffic data using the PeMS-Bay dataset, improving the accuracy and reliability of predictive modeling. Advanced synthetic data generation techniques, including TimeGAN, GaussianCopula, and PAR Synthesizer, are employed to produce synthetic data that replicates the statistical and structural characteristics of real-world traffic. Future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is planned to capture both spatial and temporal correlations, further improving data quality and realism. The performance of each synthetic data generation model is evaluated against real-world data to identify the best models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are utilized to model and predict complex temporal dependencies within traffic patterns. This comprehensive approach aims to pinpoint areas with low vehicle counts, uncover underlying traffic issues, and inform targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study supports data-driven decision-making that enhances urban mobility, safety, and the overall efficiency of city planning initiatives. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GAN" title="GAN">GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory" title=" long short-term memory"> long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20data%20generation" title=" synthetic data generation"> synthetic data generation</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/191235/statistically-accurate-synthetic-data-generation-for-enhanced-traffic-predictive-modeling-using-generative-adversarial-networks-and-long-short-term-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191235.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">26</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">28073</span> Reactive Analysis of Different Protocol in Mobile Ad Hoc Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manoj%20Kumar">Manoj Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Routing protocols have a central role in any mobile ad hoc network (MANET). There are many routing protocols that exhibit different performance levels in different scenarios. In this paper, we compare AODV, DSDV, DSR, and ZRP routing protocol in mobile ad hoc networks to determine the best operational conditions for each protocol. We analyze these routing protocols by extensive simulations in OPNET simulator and show how to pause time and the number of nodes affect their performance. In this study, performance is measured in terms of control traffic received, control traffic sent, data traffic received, sent data traffic, throughput, retransmission attempts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AODV" title="AODV">AODV</a>, <a href="https://publications.waset.org/abstracts/search?q=DSDV" title=" DSDV"> DSDV</a>, <a href="https://publications.waset.org/abstracts/search?q=DSR" title=" DSR"> DSR</a>, <a href="https://publications.waset.org/abstracts/search?q=ZRP" title=" ZRP "> ZRP </a> </p> <a href="https://publications.waset.org/abstracts/16618/reactive-analysis-of-different-protocol-in-mobile-ad-hoc-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16618.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">518</span> </span> </div> 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