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Search results for: Zoi Christoforou
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class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" 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="Zoi Christoforou"> <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> 2</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Zoi Christoforou</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Long Short-Time Memory Neural Networks for Human Driving Behavior Modelling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lu%20Zhao">Lu Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadir%20Farhi"> Nadir Farhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yeltsin%20Valero"> Yeltsin Valero</a>, <a href="https://publications.waset.org/abstracts/search?q=Zoi%20Christoforou"> Zoi Christoforou</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Haddadou"> Nadia Haddadou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a long short-term memory (LSTM) neural network model is proposed to replicate simultaneously car-following and lane-changing behaviors in road networks. By combining two kinds of LSTM layers and three input designs of the neural network, six variants of the LSTM model have been created. These models were trained and tested on the NGSIM 101 dataset, and the results were evaluated in terms of longitudinal speed and lateral position, respectively. Then, we compared the LSTM model with a classical car-following model (the intelligent driving model (IDM)) in the part of speed decision. In addition, the LSTM model is compared with a model using classical neural networks. After the comparison, the LSTM model demonstrates higher accuracy than the physical model IDM in terms of car-following behavior and displays better performance with regard to both car-following and lane-changing behavior compared to the classical neural network model. <p class="card-text"><strong>Keywords:</strong> <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=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=car-following" title=" car-following"> car-following</a>, <a href="https://publications.waset.org/abstracts/search?q=lane-change" title=" lane-change"> lane-change</a> </p> <a href="https://publications.waset.org/abstracts/139730/long-short-time-memory-neural-networks-for-human-driving-behavior-modelling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139730.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">261</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">1</span> Driver Behavior Analysis and Inter-Vehicular Collision Simulation Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lu%20Zhao">Lu Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadir%20Farhi"> Nadir Farhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Zoi%20Christoforou"> Zoi Christoforou</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Haddadou"> Nadia Haddadou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The safety test of deploying intelligent connected vehicles (ICVs) on the road network is a critical challenge. Road traffic network simulation can be used to test the functionality of ICVs, which is not only time-saving and less energy-consuming but also can create scenarios with car collisions. However, the relationship between different human driver behaviors and the car-collision occurrences has been not understood clearly; meanwhile, the procedure of car-collisions generation in the traffic numerical simulators is not fully integrated. In this paper, we propose an approach to identify specific driver profiles from real driven data; then, we replicate them in numerical traffic simulations with the purpose of generating inter-vehicular collisions. We proposed three profiles: (i) 'aggressive': short time-headway, (ii) 'inattentive': long reaction time, and (iii) 'normal' with intermediate values of reaction time and time-headway. These three driver profiles are extracted from the NGSIM dataset and simulated using the intelligent driver model (IDM), with an extension of reaction time. At last, the generation of inter-vehicular collisions is performed by varying the percentages of different profiles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vehicular%20collisions" title="vehicular collisions">vehicular collisions</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20driving%20behavior" title=" human driving behavior"> human driving behavior</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=car-following%20models" title=" car-following models"> car-following models</a>, <a href="https://publications.waset.org/abstracts/search?q=microscopic%20traffic%20simulation" title=" microscopic traffic simulation"> microscopic traffic simulation</a> </p> <a href="https://publications.waset.org/abstracts/139723/driver-behavior-analysis-and-inter-vehicular-collision-simulation-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139723.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">171</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div 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