Introduction to Real World Traffic

The case study on a real-world traffic network comprised of 0.

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.

The model was built by adding complexity and the simulated results are analysed and compared for real-world traffic performance.

Our simulation results based on a real-world traffic data set demonstrate the advantages of the proposed approaches.

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.

To validate the proposed system, experiments are performed on the real-world traffic data provided by the Aliyun Tianchi platform.

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.

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.

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.

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.

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.

Imitation learning on real-world data has the potential to improve the simulation of real-world traffic.

The optimal speed control strategy is evaluated in both a simulated traffic scenario and a real-world traffic scenario.

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.

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.

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.

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.

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.

We demonstrate this claim by applying our methodology to real-world traffic from DNS servers that use partial prefix-preserving anonymization.

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.

We evaluate the framework on the real-world traffic dataset and obtain a consistent improvement of 6.

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.

In a real-world traffic scene, objects can appear in different sizes and pose different details.

The proposed prediction method is carefully evaluated with real-world traffic data collected on Hwy 55 in Minnesota.

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.

In this paper, we analyze how FL impacts the performance of object detection in a real-world traffic environment.

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.

We evaluated both models on a real-world traffic dataset captured by surround-view fisheye cameras mounted on top of a vehicle.

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

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.

We evaluate our approach on a real-world traffic dataset from a major ISP and Mobile Network Operator.

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.

We use real-world traffic data from two locations (i.

We evaluate our model on multiple real-world traffic datasets and superior performance is achieved over state-of-the-art baselines.

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.

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.

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

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.

We successfully show the overall evaluation of these proving grounds in terms of their capability to accommodate real-world traffic scenarios.

Experiments on three real-world traffic datasets have verified the superiority of the proposed model.

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.

Additionally, we collect a real-world traffic flow dataset to evaluate the effectiveness of the approach.

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.

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.

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.

Extensive evaluations on real-world traffic flow data demonstrate the superiority of the proposed method.

We collect two real-world traffic datasets and construct closed- and open-world evaluations to verify the effectiveness of FineWP.

Extensive experiments on two real-world traffic datasets demonstrate the superiority of our proposed approach.

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.

We collect real-world traffic datasets from 1,300 DApps with more than 169,000 flows.

We demonstrate how our classification scheme and all its configurations perform well on real-world traffic from a high performance computing network environment.

We develop a microscopic simulation model of evacuation traffic in SUMO and calibrate it using real-world traffic data collected during the evacuation period of hurricane Irma for a segment in the Interstate highway in Florida.

Experimental results show the effectiveness of the proposed framework compared to state-of-the-art methods, and indicate the potential to be deployed into real-world traffic prediction systems.

We demonstrate the capability and effectiveness of VisCas with two case studies conducted on real-world traffic congestion and air pollution datasets with domain experts.

The uneven distribution hidden in the real-world traffic will cause performance degradation of the existing schemes.

Experiments on real-world traffic datasets demonstrate that the proposed model outperforms other baselines and has interpretability in real scenarios.

We evaluate the effectiveness of the model on a real-world traffic dataset that we collected by using a tcpdump utility of Linux.

Large scale evaluation experiments were conducted using two real-world traffic datasets, and the experimental results indicate that the proposed model can achieve better balance between forecasting accuracy and computational efficiency compared to benchmark models.

A catalog with scenarios recorded in real-world traffic for resimulation must be assembled, defining a set of requirements the driving function must pass before release.

For this purpose, vessel traffic data obtained in the western Baltic Sea based on the automatic identification system (AIS) is analysed to extract all close encounters between ships considered as real-world traffic situations with a potential risk of collision.

8% more accurate than the DHSTNet technique, for two different real-world traffic datasets.

The proposed stochastic link-based FD was calibrated and validated using real-world traffic data obtained from two selected road segments in Hong Kong.

By experiments with a real-world traffic accident dataset, we demonstrated the effectiveness and applicability of the framework.

Extensive experiments on real-world traffic flows demonstrate that DeFlow-Net outperforms existing solutions using standard convolutions, and spatial partition by pre-conceived regions further enhances the performance.

ΙΜΤ links a real-world traffic model, a dynamic emissions model and a Lagrangian dispersion model coupled with a boundary layer flow module.

4% faster than the state-of-the-art model and while being able to generalize to different real-world traffic scenes.

We implement a highly efficient prototype and conduct extensive experiments on two large-scale real-world traffic data sets collected by a modern ride-hailing platform.

Experiments are carried out on two real-world traffic networks, including traffic speed and flow datasets.

The traditional fixed-profile test methods only indicate the effect of powertrain efficiency on fuel economy and cannot reflect the influences of control algorithms or driving behaviors on fuel consumption under real-world traffic conditions.

The proposed methodology is demonstrated using a real-world traffic network in Southern California under earthquake considering deterministic and stochastic repair durations.

Thus, it does not provide sufficient expressiveness for users to define custom metamorphic relations nor means to automatically generate meaningful inputs based on such expressive metamorphic relations that reflect real-world traffic behaviors.

The proposed method is qualitatively and quantitatively evaluated in real-world traffic scenarios with various baselines.

The proposed solution surpasses state-of-art baselines based on real-world traffic datasets, which treat various real-time delays differently.

We test ZTT in a diversity of real-world traffic scenarios and show that it is able to obtain a significant performance boost over static configurations suggested by experts.

Extensive experiments conducted on two real-world traffic datasets demonstrate that the proposed VGRAN model outperforms state-of-the-art approaches while capturing innate ambiguity of the predicted results.

The proposal is validated through a simulation study based on real-world traffic data collected in Santiago, Chile.

By conducting simulations on real-world traffic datasets, we demonstrate that our proposed weak signal detection scheme can achieve throughput gain of up to 3.

Experimental results based on real-world traffic sensor data indicate that the structures of traffic causality graphs are time-varying; the traffic cause-effect interaction among different road segments during the peak time is more significant than that during the nonpeak time; and the critical road segments can be identified, which are mainly located at the intersections of arterial roads, undertaking the convergence and dispersion of large traffic flows.

Experiments on five real-world traffic flow datasets demonstrate that ASTGNN outperforms the state-of-the-art baselines.

Experimental results on real-world traffic data show that the proposed method outperforms coupled Bayesian robust principal component analysis (coupled BRPCA), the rank sparsity tensor decomposition (RSTD), and standard normal deviates (SND) in detecting NRTC.

This approach is implemented on real-world traffic flow data and demonstrates the prominent superiority.

An experimental evaluation of queries that reflect the requirements has been conducted using the real-world traffic simulation tool PTV Vissim.

Experimental results on the public traffic network datasets and real-world traffic network datasets generated by ourselves demonstrate our proposed model achieves better performance than the state-of-the-art ones.

While the use of real-world data for model parameter identification is a well-known method, NDS are commonly used to explore the behavior of drivers or to analyze real-world traffic situations.

Experimental results based on real-world traffic data demonstrate that our proposed method outperforms other methods from various aspects.

Real-world traffic sign recognition is an important step toward building autonomous vehicles, most of which highly dependent on deep neural networks (DNNs).

It is necessary to validate their functional performance in the installed state thoroughly, reliably, and efficiently under realistic conditions before deploying them in real-world traffic.

Then the SGDE-S2S model and some other baseline models are tested on real-world traffic speed data of Chengdu, China.

We validate it against two real-world traffic datasets and provide discussion why the entropy of codes is a reliable performance indicator.

The utility and efficiency of the recommendation framework are demonstrated using real-world traffic data.

To evaluate the proposed model, four months of real-world traffic data are collected in Xiaoshan District, Hangzhou, China.

We validate the accuracy of the analytical model by comparing it with simulations using real-world traffic data and vehicle distributions.

The key problem in Fog-RAN optimization is how to cluster complementary RRHs into communities and allocate adequate numbers of BBUs for the fog servers, since real-world traffic and mobility patterns are highly dynamic to model, and it is not trivial to find an optimal RRH clustering and BBU allocation scheme from potentially enormous numbers of candidates.

With extensive experimental analysis, we have shown that our model has significantly outperformed the state-of-the-art models on three real-world traffic datasets from the Performance Measurement System (PeMS).

The original method of the input data collection, from the real world traffic, has been also presented.

Collecting real world traffic data in driving studies is very time consuming and expensive.

At last, we evaluate DA&FD under different topologies, real world traffic and load scenarios, both by simulation and in real testbed.

The model is trained and tested using a large dataset of 1311 cyclist trajectories, recorded at an urban intersection in real world traffic.