## What is/are Real World Traffic?

Real World Traffic - The case study on a real-world traffic network comprised of 0.^{[1]}Extensive experiments on a real-world traffic state dataset validate the effectiveness of our method by showing that GLT-GCRNN outperforms the state-of-the-art methods in terms of different metrics.

^{[2]}The model was built by adding complexity and the simulated results are analysed and compared for real-world traffic performance.

^{[3]}Our simulation results based on a real-world traffic data set demonstrate the advantages of the proposed approaches.

^{[4]}Despite the low-order system dynamics, the piecewise fuel/efficiency map, gear shifting, and real-world traffic/road situations bring system discontinuities/switchings and pure state constraints into the problem formulation, which make the problem highly nonlinear and nontrivial to solve.

^{[5]}To validate the proposed system, experiments are performed on the real-world traffic data provided by the Aliyun Tianchi platform.

^{[6]}The key problem in C-RAN optimization is to dynamically allocate BBUs and map them to RRHs under cost and quality constraints, since real-world traffic and mobility are difficult to predict, and there are enormous numbers of candidate RRH-BBU mapping schemes.

^{[7]}While the former is able to represent many details of traffic and model large-scale, real-world traffic situations with a co-evolutionary approach, the latter provides an environment for provable mathematical statements and results on exact user equilibria.

^{[8]}To evaluate the effectiveness of FlexSensing, we simulate the mobility of different vehicles involved in the scenario at different times of the day based on real-world traffic data collected from the city of Helsinki and select a real-time object detection application for a case study.

^{[9]}We evaluate T-MGCN on two real-world traffic datasets and observe improvement by approximately 3% to 6% as compared to the state-of-the-art baseline.

^{[10]}Research objectives To address this concern, further insights are needed in how drivers monitor automation in complex real-world traffic, and how their behaviour and performance change with long-term automated driving experience.

^{[11]}Imitation learning on real-world data has the potential to improve the simulation of real-world traffic.

^{[12]}The optimal speed control strategy is evaluated in both a simulated traffic scenario and a real-world traffic scenario.

^{[13]}Despite the low-order system dynamics, the piecewise fuel/efficiency map, gear shifting, and real-world traffic/road situations bring system discontinuities/switchings and pure state constraints into the problem formulation, which make the problem highly nonlinear and nontrivial to solve.

^{[14]}The solution approach is then tested on medium and large realistic instances generated from real-world traffic on Paris-Orly airport to show the benefit of our approach.

^{[15]}We evaluate the performance of our method with synthetically generated but realistic traffic as well as on real-world traffic from a Tor exit node on the Internet.

^{[16]}In this study, the emissions in real-world traffic from Euro VI-compliant HDTs were compared to those from older classes, represented by Euro V, using high-resolution time-of-flight chemical ionization mass spectrometry.

^{[17]}Extensive experiments on real-world traffic surveillance benchmarks demonstrate the real-time performance of the proposed model while keeping comparable accuracy with state-of-the-art.

^{[18]}We demonstrate this claim by applying our methodology to real-world traffic from DNS servers that use partial prefix-preserving anonymization.

^{[19]}We compared the performance of the proposed load balancer with iptables DNAT and loopback based on the RFC2544 performance standard, and also performed tests simulating real-world traffic patterns by using IMIX traffic streams.

^{[20]}We evaluate the framework on the real-world traffic dataset and obtain a consistent improvement of 6.

^{[21]}Our testbed evaluations confirm that, by using the live video streams derived from real-world traffic cameras, Soudain ensures the real-time requirement and achieves up to 25% improvement on the detection accuracy, compared with multiple state-of-the-art alternatives.

^{[22]}In a real-world traffic scene, objects can appear in different sizes and pose different details.

^{[23]}The proposed prediction method is carefully evaluated with real-world traffic data collected on Hwy 55 in Minnesota.

^{[24]}This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data.

^{[25]}In this paper, we analyze how FL impacts the performance of object detection in a real-world traffic environment.

^{[26]}We then apply our model to a real-world traffic sensor dataset to study traffic patterns during different configurations of the traffic lights at an intersection.

^{[27]}We evaluated both models on a real-world traffic dataset captured by surround-view fisheye cameras mounted on top of a vehicle.

^{[28]}Traditional iteration based approach as well as estimators adopted from control theory are discussed, benchmarked, and validated on real-world traffic data.

^{[29]}It is a challenging task for self-driving vehicles in Real-World traffic scenarios to find a trade-off between the real-time performance and the high accuracy of the detection, recognition, and tracking in videos.

^{[30]}We evaluate our approach on a real-world traffic dataset from a major ISP and Mobile Network Operator.

^{[31]}We present a machine learning approach using Long-Short Term Memory Neural Networks (LSTM-NN), trained over real-world traffic data during hurricane evacuation (hurricanes Irma and Matthew) using different combinations of input features and forecast horizons.

^{[32]}We use real-world traffic data from two locations (i.

^{[33]}We evaluate our model on multiple real-world traffic datasets and superior performance is achieved over state-of-the-art baselines.

^{[34]}Trace-driven experiments with real-world traffic data show that the proposed approach derives accurate traffic conditions with the average accuracy as 80%, based on only 50 probe vehicles’ intervention.

^{[35]}Traditional iteration based approach as well as estimators adopted from control theory were benchmarked and validated on real-world traffic data as well as via microscopic traffic simulation.

^{[36]}We conduct extensive numerical experiments on several real-world traffic data sets, and our results demonstrate the effectiveness of LATC in diverse missing scenarios.

^{[37]}We generate a large dataset of 30 million cycles by approximately replicating real-world traffic arrival patterns from archived loop detector data in a microscopic traffic simulator.

^{[38]}We successfully show the overall evaluation of these proving grounds in terms of their capability to accommodate real-world traffic scenarios.

^{[39]}Experiments on three real-world traffic datasets have verified the superiority of the proposed model.

^{[40]}A simple LSTM model, LSTM encoder-decoder model, CNN-LSTM model and a Conv-LSTM model were designed and evaluated using a real-world traffic volume dataset for multiple prediction horizons.

^{[41]}Additionally, we collect a real-world traffic flow dataset to evaluate the effectiveness of the approach.

^{[42]}We carry out experiments on synthetic traffic grid and real-world traffic network of Monaco city to compare with the existing A2C and Q-learning algorithms.

^{[43]}The experiment results suggest the proposed model can achieve 94% for F1 measure in the macro average of five labels on real-world traffic data.

^{[44]}Numerous continuum models have been proposed for freeway traffic, but their performance for real-world traffic flows has not been rigorously evaluated and compared in the literature.

^{[45]}Extensive evaluations on real-world traffic flow data demonstrate the superiority of the proposed method.

^{[46]}We collect two real-world traffic datasets and construct closed- and open-world evaluations to verify the effectiveness of FineWP.

^{[47]}Extensive experiments on two real-world traffic datasets demonstrate the superiority of our proposed approach.

^{[48]}We use a real-world traffic dataset from the United States Department of Transportation Federal Highway Administration to evaluate optimal control decision determination performance of ReDS in comparison with the state-of-the-art methods.

^{[49]}We collect real-world traffic datasets from 1,300 DApps with more than 169,000 flows.

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## real world traffic datum

Collecting real world traffic data in driving studies is very time consuming and expensive.^{[1]}Simulations performed on the real world traffic data illustrates promising imputation as well as temporal prediction performance even in an online setup.

^{[2]}In particular, the design of advanced traffic forecasting algorithms in large scale urban and inter-urban road networks are described along with their implementation and utilization on large amounts of real world traffic data.

^{[3]}We validated our congestion forecasting framework on the real world traffic data of Nashville, USA and identified the onset of congestion in each of the neighboring segments of any congestion source with an average precision of 0.

^{[4]}To evaluate our mechanism, we use real world traffic data collected from Shanghai taxis and compare it with existing work.

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