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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 flow prediction"> <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> 7726</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: traffic flow prediction</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7726</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">7725</span> A Conv-Long Short-term Memory Deep Learning Model for Traffic Flow Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Reza%20Sattarzadeh">Ali Reza Sattarzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Ronny%20J.%20Kutadinata"> Ronny J. Kutadinata</a>, <a href="https://publications.waset.org/abstracts/search?q=Pubudu%20N.%20Pathirana"> Pubudu N. Pathirana</a>, <a href="https://publications.waset.org/abstracts/search?q=Van%20Thanh%20Huynh"> Van Thanh Huynh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20algorithms" title="deep learning algorithms">deep learning algorithms</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=spatiotemporal%20features" title=" spatiotemporal features"> spatiotemporal features</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20flow%20prediction" title=" traffic flow prediction"> traffic flow prediction</a> </p> <a href="https://publications.waset.org/abstracts/153966/a-conv-long-short-term-memory-deep-learning-model-for-traffic-flow-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153966.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 class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7724</span> The Design of a Vehicle Traffic Flow Prediction Model for a Gauteng Freeway Based on an Ensemble of Multi-Layer Perceptron</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tebogo%20Emma%20Makaba">Tebogo Emma Makaba</a>, <a href="https://publications.waset.org/abstracts/search?q=Barnabas%20Ndlovu%20Gatsheni"> Barnabas Ndlovu Gatsheni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cities of Johannesburg and Pretoria both located in the Gauteng province are separated by a distance of 58 km. The traffic queues on the Ben Schoeman freeway which connects these two cities can stretch for almost 1.5 km. Vehicle traffic congestion impacts negatively on the business and the commuter’s quality of life. The goal of this paper is to identify variables that influence the flow of traffic and to design a vehicle traffic prediction model, which will predict the traffic flow pattern in advance. The model will unable motorist to be able to make appropriate travel decisions ahead of time. The data used was collected by Mikro’s Traffic Monitoring (MTM). Multi-Layer perceptron (MLP) was used individually to construct the model and the MLP was also combined with Bagging ensemble method to training the data. The cross—validation method was used for evaluating the models. The results obtained from the techniques were compared using predictive and prediction costs. The cost was computed using combination of the loss matrix and the confusion matrix. The predicted models designed shows that the status of the traffic flow on the freeway can be predicted using the following parameters travel time, average speed, traffic volume and day of month. The implications of this work is that commuters will be able to spend less time travelling on the route and spend time with their families. The logistics industry will save more than twice what they are currently spending. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bagging%20ensemble%20methods" title="bagging ensemble methods">bagging ensemble methods</a>, <a href="https://publications.waset.org/abstracts/search?q=confusion%20matrix" title=" confusion matrix"> confusion matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20traffic%20flow" title=" vehicle traffic flow"> vehicle traffic flow</a> </p> <a href="https://publications.waset.org/abstracts/36966/the-design-of-a-vehicle-traffic-flow-prediction-model-for-a-gauteng-freeway-based-on-an-ensemble-of-multi-layer-perceptron" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36966.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">7723</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">7722</span> Cellular Traffic Prediction through Multi-Layer Hybrid Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Supriya%20H.%20S.">Supriya H. S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandrakala%20B.%20M."> Chandrakala B. M.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep learning based models have been recently successful adoption for network traffic prediction. However, training a deep learning model for various prediction tasks is considered one of the critical tasks due to various reasons. This research work develops Multi-Layer Hybrid Network (MLHN) for network traffic prediction and analysis; MLHN comprises the three distinctive networks for handling the different inputs for custom feature extraction. Furthermore, an optimized and efficient parameter-tuning algorithm is introduced to enhance parameter learning. MLHN is evaluated considering the “Big Data Challenge” dataset considering the Mean Absolute Error, Root Mean Square Error and R^2as metrics; furthermore, MLHN efficiency is proved through comparison with a state-of-art approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MLHN" title="MLHN">MLHN</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20traffic%20prediction" title=" network traffic prediction"> network traffic prediction</a> </p> <a href="https://publications.waset.org/abstracts/154887/cellular-traffic-prediction-through-multi-layer-hybrid-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154887.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">89</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">7721</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">7720</span> Field Saturation Flow Measurement Using Dynamic Passenger Car Unit 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=Ramesh%20Chandra%20Majhi">Ramesh Chandra Majhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Saturation flow is a very important input variable for the design of signalized intersections. Saturation flow measurement is well established for homogeneous traffic. However, saturation flow measurement and modeling is a challenging task in heterogeneous characterized by multiple vehicle types and non-lane based movement. Present study focuses on proposing a field procedure for Saturation flow measurement and the effect of typical mixed traffic behavior at the signal as far as non-lane based traffic movement is concerned. Data collected during peak and off-peak hour from five intersections with varying approach width is used for validating the saturation flow model. The insights from the study can be used for modeling saturation flow and delay at signalized intersection in heterogeneous traffic conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=passenger%20car%20unit" title=" passenger car unit"> passenger car unit</a>, <a href="https://publications.waset.org/abstracts/search?q=saturation%20flow" title=" saturation flow"> saturation flow</a>, <a href="https://publications.waset.org/abstracts/search?q=signalized%20intersection" title=" signalized intersection"> signalized intersection</a> </p> <a href="https://publications.waset.org/abstracts/63968/field-saturation-flow-measurement-using-dynamic-passenger-car-unit-under-mixed-traffic-condition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63968.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">327</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">7719</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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7718</span> Traffic Prediction with Raw Data Utilization and Context Building</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhou%20Yang">Zhou Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Heli%20Sun"> Heli Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianbin%20Huang"> Jianbin Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jizhong%20Zhao"> Jizhong Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaojie%20Qiao"> Shaojie Qiao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic prediction is essential in a multitude of ways in modern urban life. The researchers of earlier work in this domain carry out the investigation chiefly with two major focuses: (1) the accurate forecast of future values in multiple time series and (2) knowledge extraction from spatial-temporal correlations. However, two key considerations for traffic prediction are often missed: the completeness of raw data and the full context of the prediction timestamp. Concentrating on the two drawbacks of earlier work, we devise an approach that can address these issues in a two-phase framework. First, we utilize the raw trajectories to a greater extent through building a VLA table and data compression. We obtain the intra-trajectory features with graph-based encoding and the intertrajectory ones with a grid-based model and the technique of back projection that restore their surrounding high-resolution spatial-temporal environment. To the best of our knowledge, we are the first to study direct feature extraction from raw trajectories for traffic prediction and attempt the use of raw data with the least degree of reduction. In the prediction phase, we provide a broader context for the prediction timestamp by taking into account the information that are around it in the training dataset. Extensive experiments on several well-known datasets have verified the effectiveness of our solution that combines the strength of raw trajectory data and prediction context. In terms of performance, our approach surpasses several state-of-the-art methods for traffic prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20prediction" title="traffic prediction">traffic prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=raw%20data%20utilization" title=" raw data utilization"> raw data utilization</a>, <a href="https://publications.waset.org/abstracts/search?q=context%20building" title=" context building"> context building</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20reduction" title=" data reduction"> data reduction</a> </p> <a href="https://publications.waset.org/abstracts/150300/traffic-prediction-with-raw-data-utilization-and-context-building" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150300.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">127</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">7717</span> A Deep Learning Approach to Real Time and Robust Vehicular Traffic Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bikis%20Muhammed">Bikis Muhammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Sehra%20Sedigh%20Sarvestani"> Sehra Sedigh Sarvestani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20R.%20Hurson"> Ali R. Hurson</a>, <a href="https://publications.waset.org/abstracts/search?q=Lasanthi%20Gamage"> Lasanthi Gamage</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time%20prediction" title=" real time prediction"> real time prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=GAT" title=" GAT"> GAT</a>, <a href="https://publications.waset.org/abstracts/search?q=Bi-LSTM" title=" Bi-LSTM"> Bi-LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=attention" title=" attention"> attention</a> </p> <a href="https://publications.waset.org/abstracts/170750/a-deep-learning-approach-to-real-time-and-robust-vehicular-traffic-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170750.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">72</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">7716</span> Machine Learning Techniques to Develop Traffic Accident Frequency Prediction Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20Aguiar">Rodrigo Aguiar</a>, <a href="https://publications.waset.org/abstracts/search?q=Adelino%20Ferreira"> Adelino Ferreira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Road traffic accidents are the leading cause of unnatural death and injuries worldwide, representing a significant problem of road safety. In this context, the use of artificial intelligence with advanced machine learning techniques has gained prominence as a promising approach to predict traffic accidents. This article investigates the application of machine learning algorithms to develop traffic accident frequency prediction models. Models are evaluated based on performance metrics, making it possible to do a comparative analysis with traditional prediction approaches. The results suggest that machine learning can provide a powerful tool for accident prediction, which will contribute to making more informed decisions regarding road safety. <p class="card-text"><strong>Keywords:</strong> <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=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20of%20accidents" title=" frequency of accidents"> frequency of accidents</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20safety" title=" road safety"> road safety</a> </p> <a href="https://publications.waset.org/abstracts/178875/machine-learning-techniques-to-develop-traffic-accident-frequency-prediction-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178875.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">89</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">7715</span> The Kidney-Spine Traffic System: Future Cities, Ensuring World Class Civic Amenities in Urban India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abhishek%20Srivastava">Abhishek Srivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeevesh%20Nandan"> Jeevesh Nandan</a>, <a href="https://publications.waset.org/abstracts/search?q=Manish%20Kumar"> Manish Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study was taken to analyse the alternative source of traffic system for effective and more convenient traffic flow by reducing points of conflicts as well as angle of conflict and keeping in view to minimize the problem of unnecessarily long waiting time, delays, congestion, traffic jam and geometric delays due to intersection between circular and straight lanes. It is a twin kidney-spine type structure system with special allowance for Highway users for quicker passes. Thus reduction in number and intensity of accidents, significance reduction in traffic jam, conservation of valuable time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20system" title="traffic system">traffic system</a>, <a href="https://publications.waset.org/abstracts/search?q=collision%20reduction%20of%20vehicles" title=" collision reduction of vehicles"> collision reduction of vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=smooth%20flow%20of%20vehicles" title=" smooth flow of vehicles"> smooth flow of vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20jam" title=" traffic jam"> traffic jam</a> </p> <a href="https://publications.waset.org/abstracts/15808/the-kidney-spine-traffic-system-future-cities-ensuring-world-class-civic-amenities-in-urban-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15808.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">426</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">7714</span> Monthly River Flow Prediction Using a Nonlinear Prediction Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20H.%20Adenan">N. H. Adenan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20M.%20Noorani"> M. S. M. Noorani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=river%20flow" title="river flow">river flow</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20prediction%20method" title=" nonlinear prediction method"> nonlinear prediction method</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20space" title=" phase space"> phase space</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20linear%20approximation" title=" local linear approximation"> local linear approximation</a> </p> <a href="https://publications.waset.org/abstracts/2867/monthly-river-flow-prediction-using-a-nonlinear-prediction-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2867.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">412</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">7713</span> A Spatial Information Network Traffic Prediction Method Based on Hybrid Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jingling%20Li">Jingling Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi%20Zhang"> Yi Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Liang"> Wei Liang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao%20Cui"> Tao Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20Li"> Jun Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20information%20network" title="spatial information network">spatial information network</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20prediction" title=" traffic prediction"> traffic prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20decomposition" title=" wavelet decomposition"> wavelet decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20model" title=" time series model"> time series model</a> </p> <a href="https://publications.waset.org/abstracts/106062/a-spatial-information-network-traffic-prediction-method-based-on-hybrid-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106062.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">147</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">7712</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">7711</span> A Machine Learning Approach for Intelligent Transportation System Management on Urban Roads</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Dhamaniya">Ashish Dhamaniya</a>, <a href="https://publications.waset.org/abstracts/search?q=Vineet%20Jain"> Vineet Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Chouhan"> Rajesh Chouhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic management is one of the gigantic issue in most of the urban roads in al-most all metropolitan cities in India. Speed is one of the critical traffic parameters for effective Intelligent Transportation System (ITS) implementation as it decides the arrival rate of vehicles on an intersection which are majorly the point of con-gestions. The study aimed to leverage Machine Learning (ML) models to produce precise predictions of speed on urban roadway links. The research objective was to assess how categorized traffic volume and road width, serving as variables, in-fluence speed prediction. Four tree-based regression models namely: Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Extreme Gradient Boost (XGB)are employed for this purpose. The models' performances were validated using test data, and the results demonstrate that Random Forest surpasses other machine learning techniques and a conventional utility theory-based model in speed prediction. The study is useful for managing the urban roadway network performance under mixed traffic conditions and effective implementation of ITS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stream%20speed" title="stream speed">stream speed</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20roads" title=" urban roads"> urban roads</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%20flow" title=" traffic flow"> traffic flow</a> </p> <a href="https://publications.waset.org/abstracts/183645/a-machine-learning-approach-for-intelligent-transportation-system-management-on-urban-roads" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183645.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">70</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">7710</span> On Flow Consolidation Modelling in Urban Congested Areas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serban%20Stere">Serban Stere</a>, <a href="https://publications.waset.org/abstracts/search?q=Stefan%20Burciu"> Stefan Burciu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The challenging and continuously growing competition in the urban freight transport market emphasizes the need for optimal planning of transportation processes in terms of identifying the solution of consolidating traffic flows in congested urban areas. The aim of the present paper is to present the mathematical framework and propose a methodology of combining urban traffic flows between the distribution centers located at the boundary of a congested urban area. The three scenarios regarding traffic flow between consolidation centers that are taken into consideration in the paper are based on the same characteristics of traffic flows. The scenarios differ in terms of the accessibility of the four consolidation centers given by the infrastructure, the connections between them, and the possibility of consolidating traffic flows for one or multiple destinations. Also, synthetical indicators will allow us to compare the scenarios considered and chose the indicated for our distribution system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distribution%20system" title="distribution system">distribution system</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20and%20multiple%20destinations" title=" single and multiple destinations"> single and multiple destinations</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20consolidation%20centers" title=" urban consolidation centers"> urban consolidation centers</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20flow%20consolidation%20schemes" title=" traffic flow consolidation schemes"> traffic flow consolidation schemes</a> </p> <a href="https://publications.waset.org/abstracts/136236/on-flow-consolidation-modelling-in-urban-congested-areas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136236.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">156</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">7709</span> Traffic Analysis and Prediction Using Closed-Circuit Television Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aragorn%20Joaquin%20Pineda%20Dela%20Cruz">Aragorn Joaquin Pineda Dela Cruz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20transportation%20system" title="intelligent transportation system">intelligent transportation system</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=traffic%20analysis" title=" traffic analysis"> traffic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</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=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20prediction" title=" traffic prediction"> traffic prediction</a> </p> <a href="https://publications.waset.org/abstracts/158196/traffic-analysis-and-prediction-using-closed-circuit-television-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158196.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">102</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">7708</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">7707</span> Sustainable Traffic Flow: The Case Study of Un-Signalized Pedestrian Crossing at Stationary Bottleneck and Its Impact on Traffic Flow</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imran%20Badshah">Imran Badshah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper study the impact of Un-signalized pedestrian on traffic flow at Stationary Bottleneck. The Highway Capacity Manual (HCM) analyze the methodology of level of service for Urban street segment but it does not include the impact of un-signalized pedestrian crossing at stationary bottleneck. The un-signalized pedestrian crossing in urban road segment causes conflict between vehicles and pedestrians. As a result, the average time taken by vehicle to travel along a road segment increased. The speed of vehicle and the level of service decreases as the running time of a segment increased. To analyze the delay, we need to determine the pedestrian speed while crossing the road at a stationary bottleneck. The objective of this research is to determine the speed of pedestrian and its impact on traffic flow at stationary bottleneck. In addition, the result of this study should be incorporated in the Urban Street Analysis Chapter of HCM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stationary%20bottleneck" title="stationary bottleneck">stationary bottleneck</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=pedestrian%20speed" title=" pedestrian speed"> pedestrian speed</a>, <a href="https://publications.waset.org/abstracts/search?q=HCM" title=" HCM"> HCM</a> </p> <a href="https://publications.waset.org/abstracts/159043/sustainable-traffic-flow-the-case-study-of-un-signalized-pedestrian-crossing-at-stationary-bottleneck-and-its-impact-on-traffic-flow" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159043.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">91</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">7706</span> Empirical Investigations on Speed Differentiations of Traffic Flow: A Case Study on a Basic Freeway Segment of O-2 in Istanbul</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Rashid%20Sarand">Hamed Rashid Sarand</a>, <a href="https://publications.waset.org/abstracts/search?q=Kemal%20Sel%C3%A7uk%20%C3%96%C4%9F%C3%BCt"> Kemal Selçuk Öğüt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speed is one of the fundamental variables of road traffic flow that stands as an important evaluation criterion for traffic analyses in several aspects. In particular, varieties of speed variable, such as average speed, free flow speed, optimum speed (capacity speed), acceleration/deceleration speed and so on, have been explicitly considered in the analysis of not only road safety but also road capacity. In the purpose of realizing 'road speed – maximum speed difference across lanes' and 'road flow rate – maximum speed difference across lanes' relations on freeway traffic, this study presents a case study conducted on a basic freeway segment of O-2 in Istanbul. The traffic data employed in this study have been obtained from 5 remote traffic microwave sensors operated by Istanbul Metropolitan Municipality. The study stretch is located between two successive freeway interchanges: Ümraniye and Kavacık. Daily traffic data of 4 years (2011-2014) summer months, July and August are used. The speed data are analyzed into two main flow areas such as uncongested and congested flows. In this study, the regression analyses were carried out in order to examine the relationship between maximum speed difference across lanes and road speed. These investigations were implemented at uncongested and congested flows, separately. Moreover, the relationship between maximum speed difference across lanes and road flow rate were evaluated by applying regression analyses for both uncongested and congested flows separately. It is concluded that there is the moderate relationship between maximum speed difference across lanes and road speed in 50% cases. Additionally, it is indicated that there is the moderate relationship between maximum speed difference across lanes and road flow rate in 30% cases. The maximum speed difference across lanes decreases as the road flow rate increases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=maximum%20speed%20difference" title="maximum speed difference">maximum speed difference</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=remote%20traffic%20microwave%20sensor" title=" remote traffic microwave sensor"> remote traffic microwave sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=speed%20differentiation" title=" speed differentiation"> speed differentiation</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20flow" title=" traffic flow "> traffic flow </a> </p> <a href="https://publications.waset.org/abstracts/36794/empirical-investigations-on-speed-differentiations-of-traffic-flow-a-case-study-on-a-basic-freeway-segment-of-o-2-in-istanbul" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36794.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">367</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">7705</span> The Prediction of Effective Equation on Drivers' Behavioral Characteristics of Lane Changing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khashayar%20Kazemzadeh">Khashayar Kazemzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hanif%20Dasoomi"> Mohammad Hanif Dasoomi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=effective%20equation" title="effective equation">effective equation</a>, <a href="https://publications.waset.org/abstracts/search?q=lane%20changing" title=" lane changing"> lane changing</a>, <a href="https://publications.waset.org/abstracts/search?q=drivers%E2%80%99%20behavioral%20characteristics" title=" drivers’ behavioral characteristics"> drivers’ behavioral characteristics</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20models" title=" regression models"> regression models</a> </p> <a href="https://publications.waset.org/abstracts/36201/the-prediction-of-effective-equation-on-drivers-behavioral-characteristics-of-lane-changing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36201.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">450</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">7704</span> Traffic Congestions Modeling and Predictions by Social Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bojan%20Najdenov">Bojan Najdenov</a>, <a href="https://publications.waset.org/abstracts/search?q=Danco%20Davcev"> Danco Davcev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reduction of traffic congestions and the effects of pollution and waste of resources that come with them has been a big challenge in the past decades. Having reliable systems to facilitate the process of modeling and prediction of traffic conditions would not only reduce the environmental pollution, but will also save people time and money. Social networks play big role of people’s lives nowadays providing them means of communicating and sharing thoughts and ideas, that way generating huge knowledge bases by crowdsourcing. In addition to that, crowdsourcing as a concept provides mechanisms for fast and relatively reliable data generation and also many services are being used on regular basis because they are mainly powered by the public as main content providers. In this paper we present the Social-NETS-Traffic-Control System (SNTCS) that should serve as a facilitator in the process of modeling and prediction of traffic congestions. The main contribution of our system is to integrate data from social networks as Twitter and also implements a custom created crowdsourcing subsystem with which users report traffic conditions using an android application. Our first experience of the usage of the system confirms that the integrated approach allows easy extension of the system with other social networks and represents a very useful tool for traffic control. <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=congestion%20reduction" title=" congestion reduction"> congestion reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=crowdsource" title=" crowdsource"> crowdsource</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20networks" title=" social networks"> social networks</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=android" title=" android"> android</a> </p> <a href="https://publications.waset.org/abstracts/25742/traffic-congestions-modeling-and-predictions-by-social-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25742.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">482</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">7703</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">7702</span> Leveraging the Power of Dual Spatial-Temporal Data Scheme for Traffic Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang%20Zhou">Yang Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Heli%20Sun"> Heli Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianbin%20Huang"> Jianbin Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jizhong%20Zhao"> Jizhong Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaojie%20Qiao"> Shaojie Qiao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traffic prediction is a fundamental problem in urban environment, facilitating the smart management of various businesses, such as taxi dispatching, bike relocation, and stampede alert. Most earlier methods rely on identifying the intrinsic spatial-temporal correlation to forecast. However, the complex nature of this problem entails a more sophisticated solution that can simultaneously capture the mutual influence of both adjacent and far-flung areas, with the information of time-dimension also incorporated seamlessly. To tackle this difficulty, we propose a new multi-phase architecture, DSTDS (Dual Spatial-Temporal Data Scheme for traffic prediction), that aims to reveal the underlying relationship that determines future traffic trend. First, a graph-based neural network with an attention mechanism is devised to obtain the static features of the road network. Then, a multi-granularity recurrent neural network is built in conjunction with the knowledge from a grid-based model. Subsequently, the preceding output is fed into a spatial-temporal super-resolution module. With this 3-phase structure, we carry out extensive experiments on several real-world datasets to demonstrate the effectiveness of our approach, which surpasses several state-of-the-art methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20prediction" title="traffic prediction">traffic prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial-temporal" title=" spatial-temporal"> spatial-temporal</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neural%20network" title=" recurrent neural network"> recurrent neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=dual%20data%20scheme" title=" dual data scheme"> dual data scheme</a> </p> <a href="https://publications.waset.org/abstracts/150299/leveraging-the-power-of-dual-spatial-temporal-data-scheme-for-traffic-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150299.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">117</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">7701</span> Passenger Flow Characteristics of Seoul Metropolitan Subway Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kang%20Won%20Lee">Kang Won Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Jung%20Won%20Lee"> Jung Won Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Characterizing the network flow is of fundamental importance to understand the complex dynamics of networks. And passenger flow characteristics of the subway network are very relevant for an effective transportation management in urban cities. In this study, passenger flow of Seoul metropolitan subway network is investigated and characterized through statistical analysis. Traditional betweenness centrality measure considers only topological structure of the network and ignores the transportation factors. This paper proposes a weighted betweenness centrality measure that incorporates monthly passenger flow volume. We apply the proposed measure on the Seoul metropolitan subway network involving 493 stations and 16 lines. Several interesting insights about the network are derived from the new measures. Using Kolmogorov-Smirnov test, we also find out that monthly passenger flow between any two stations follows a power-law distribution and other traffic characteristics such as congestion level and throughflow traffic follow exponential distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=betweenness%20centrality" title="betweenness centrality">betweenness centrality</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation%20coefficient" title=" correlation coefficient"> correlation coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=power-law%20distribution" title=" power-law distribution"> power-law distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Korea%20traffic%20DB" title=" Korea traffic DB"> Korea traffic DB</a> </p> <a href="https://publications.waset.org/abstracts/84936/passenger-flow-characteristics-of-seoul-metropolitan-subway-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84936.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">289</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">7700</span> A New Car-Following Model with Consideration of the Brake Light</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhiyuan%20Tang">Zhiyuan Tang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ju%20Zhang"> Ju Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenyuan%20Wu"> Wenyuan Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this research, a car-following model with consideration of the status of the brake light is proposed. The numerical results show that the stability of the traffic flow is improved. The ability of the brake light to reduce car accident is also showed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brake%20light" title="brake light">brake light</a>, <a href="https://publications.waset.org/abstracts/search?q=car-following%20model" title=" car-following model"> car-following model</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=regional%20planning" title=" regional planning"> regional planning</a>, <a href="https://publications.waset.org/abstracts/search?q=transportation" title=" transportation"> transportation</a> </p> <a href="https://publications.waset.org/abstracts/28404/a-new-car-following-model-with-consideration-of-the-brake-light" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28404.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">579</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">7699</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">7698</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">7697</span> Variability in Saturation Flow and Traffic Performance at Urban Signalized Intersection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20N.%20Salini">P. N. Salini</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Anish%20Kini"> B. Anish Kini</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Ashalatha"> R. Ashalatha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> At signalized intersections with heterogeneous traffic, the percentage share of different vehicle categories have a bearing on the inter-vehicle space utilization, which eventually impacts the saturation flow. This paper analyzed the impact of the percentage share of various vehicle categories in the traffic stream on the saturation flow at signalized intersections by video graphing major intersections with varying geometry in Kerala, India. It was found that as the percentage share of two-wheelers increases, the saturation flow at signalized intersections increases and vice-versa for the percentage share of cars. The effect of bus blockage and parking maneuvers on the saturation flow were also studied. As the distance of bus blockage increases from the stop line, the effect on the saturation flow decreases, while with more buses stopping at the same bus stop, the saturation flow reduces further. The study revealed that with higher kerbside parking maneuvers on the upstream, the saturation flow reduces, and with an increase in the distance of the parking maneuver from the stop line, the effect on the saturation flow decreases. The adjustment factors for bus blockage due to bus stops within 75m downstream and parking maneuvers within 75m upstream of the intersection have been established for mixed traffic conditions. These adjustment factors could empower the urban planners, enforcement personnel and decision-makers to estimate the reduction in the capacity of signalized intersections for suggesting improvements in the form of parking restrictions/ bus stop relocation for existing intersections or make design changes for planned intersections. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=signalized%20intersection" title="signalized intersection">signalized intersection</a>, <a href="https://publications.waset.org/abstracts/search?q=saturation%20flow" title=" saturation flow"> saturation flow</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=capacity" title=" capacity"> capacity</a> </p> <a href="https://publications.waset.org/abstracts/131466/variability-in-saturation-flow-and-traffic-performance-at-urban-signalized-intersection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131466.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">125</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%20flow%20prediction&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=traffic%20flow%20prediction&page=3">3</a></li> <li class="page-item"><a class="page-link" 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