scholarly journals An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 144
Author(s):  
Alexander Genser ◽  
Noel Hautle ◽  
Michail Makridis ◽  
Anastasios Kouvelas
Keyword(s):  
Traffic Management ◽  
Multiple Linear Regression Model ◽  
Ground Truth ◽  
Distance Matrix ◽  
Heterogeneous Data ◽  
License Plate ◽  
Multiple Sensors ◽  
Traffic State ◽  
Comparison Results ◽  
Additional Processing

A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology.

scholarly journals Macroscopic Traffic State Estimation: Understanding Traffic Sensing Data-Based Estimation Errors

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Paul B. C. van Erp ◽  
Victor L. Knoop ◽  
Serge P. Hoogendoorn
Keyword(s):  
State Estimation ◽  
Traffic Management ◽  
Estimation Error ◽  
Ground Truth ◽  
Traffic Information ◽  
Estimation Errors ◽  
Sensing Data ◽  
Traffic Conditions ◽  
Traffic State ◽  
Traffic State Estimation

Traffic state estimation is a crucial element in traffic management systems and in providing traffic information to road users. In this article, we evaluate traffic sensing data-based estimation error characteristics in macroscopic traffic state estimation. We consider two types of sensing data, that is, loop-detector data and probe speed data. These data are used to estimate the mean speed in a discrete space-time mesh. We assume that there are no errors in the sensing data. This allows us to study the errors resulting from the differences in characteristics between the sensing data and desired estimate together with the incomplete description of the relation between the two. The aim of the study is to evaluate the dependency of this estimation error on the traffic conditions and sensing data characteristics. For this purpose, we use microscopic traffic simulation, where we compare the estimates with the ground truth using Edie’s definitions. The study exposes a relation between the error distribution characteristics and traffic conditions. Furthermore, we find that it is important to account for the correlation between individual probe data-based estimation errors. Knowledge related to these estimation errors contributes to making better use of the available sensing data in traffic state estimation.

scholarly journals Spatio-Temporal Visual Analysis for Urban Traffic Characters Based on Video Surveillance Camera Data

2021 ◽  
Vol 10 (3) ◽  
pp. 177
Author(s):  
Haochen Zou ◽  
Keyan Cao ◽  
Chong Jiang
Keyword(s):  
Video Surveillance ◽  
Traffic Management ◽  
Visual Analysis ◽  
Urban Traffic ◽  
License Plate ◽  
Road Vehicle ◽  
Urban Road ◽  
Surveillance Camera ◽  
Vehicle Density ◽  
Spatio Temporal

Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation.

Traffic State Estimation in Heterogeneous Networks with Stochastic Demand and Supply: Mixed Lagrangian–Eulerian Approach

2019 ◽  
Vol 2673 (11) ◽  
pp. 114-126 ◽  
Author(s):  
Mohammadreza Kavianipour ◽  
Ramin Saedi ◽  
Ali Zockaie ◽  
Meead Saberi
Keyword(s):  
State Estimation ◽  
Network Traffic ◽  
Ground Truth ◽  
Solution Algorithm ◽  
Estimation Problem ◽  
Demand And Supply ◽  
Traffic State ◽  
Probe Vehicles ◽  
Loop Detectors ◽  
Traffic State Estimation

A network fundamental diagram (NFD) represents the relationship between network-wide average flow and average density. Network traffic state estimation to observe NFD when congestion is heterogeneously distributed, as a result of a time-varying and asymmetric demand matrix, is a challenging problem. Recent studies have formulated the NFD estimation problem using both fixed measurements and probe trajectories. They are often based on a given ground-truth NFD for a single day demand. Stochastic variations in network demand and supply may significantly affect the approximation of an NFD. This study proposes a modified framework to estimate network traffic states to observe NFD while capturing the stochasticity in transportation networks. A mixed integer problem with non-linear constraints is formulated to address stochasticity in the NFD estimation problem. To solve this Nondeterministic Polynomial-hard (NP-hard) problem, a solution algorithm based on the simulated annealing method is applied. The problem is formulated and the solution algorithm is implemented to find an optimal configuration of loop detectors and probe vehicles to estimate the NFD of the Chicago downtown network and capture its day-to-day variations, considering a given available budget. Ground-truth NFDs and estimated NFDs based on a subset of loop detectors and probe vehicles are calculated using a simulation-based dynamic traffic assignment model, which is the best surrogate available to replicate real-world conditions. The main contribution of this study is to capture stochasticity in the demand and supply sides to find a more robust subset of links and trajectories to be acquired for the NFD estimation.

Research of License Plate Recognition System Based on Labview

2015 ◽  
Vol 734 ◽  
pp. 646-649
Author(s):  
Zhong Hua Hu ◽  
Chen Tang
Keyword(s):  
Virtual Instrument ◽  
Character Recognition ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
License Plate ◽  
Morphological Operations ◽  
License Plate Recognition ◽  
Plate Location ◽  
Traffic Management System

The vehicle license plate recognition system is the intelligent traffic management system based on the image and the character recognition technology, which is an important part of the intelligent transportation system. This paper introduces a method of vehicle license plate location based on edge detection and morphological operations, virtual instrument is combined with machine vision of the license plate recognition method [1]. Finally the license plate number of the vehicle is get. Experiment results show that such method can simplify the algorithm and has some correct location rate.

scholarly journals A Dynamic Day-To-Day Departure Time and Route Choice Model for Bounded-Rational Individuals

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lingjuan Chen ◽  
Yu Wang ◽  
Dongfang Ma
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Choice Model ◽  
Route Choice ◽  
User Equilibrium ◽  
Navigation Systems ◽  
Dynamic Learning ◽  
Traffic State ◽  
Departure Time ◽  
Navigation Information

Accurate prediction of travellers’ day-to-day departure time and route choice is critical in advanced traffic management systems. There have been several related works about route choice with the assumption that the departure time for individual travellers is known beforehand. With real-time traffic state information provided by navigation systems and previous historical experience, travellers will dynamically update their departure time, which is neglected in existing works. In this study, we aim to describe travellers’ spatial-temporary choice behaviour taking navigation information into account and propose a bounded-rational day-to-day dynamic learning and adjustment model. The new model contains three steps. First, the real-time navigation guidance on each discrete day is obtained, and the self-learned experience of travellers’ choices with navigation information is presented; then, the day-to-day revision process of the choices is derived to maximize departure and route choice prospect; next, by aggregating each individual’s behaviour and calculating route choice probability, a bounded-rational continuous day-to-day dynamic model is provided. Numerical experiments suggest that the proposed model converges to a spatial-temporal oscillating equilibrium not a fixed-point stable status, and the final equilibrium trend is different from classical user equilibrium. The findings of the study are helpful to improve the prediction accuracy of traffic state in urban street networks.

scholarly journals Genereting Transition States of Isomerization Reactions with Deep Learning

2020 ◽  
Author(s):  
Lagnajit Pattanaik ◽  
John Ingraham ◽  
Colin Grambow ◽  
William H. Green
Keyword(s):  
Transition State ◽  
Transition States ◽  
Three Dimensional ◽  
Ground Truth ◽  
Distance Matrix ◽  
Quality Data ◽  
Quantum Chemistry Calculations ◽  
Quantitative Understanding ◽  
Speed Accuracy ◽  
Transition State Structures

Lack of quality data and difficulty generating these data hinder quantitative understanding of reaction kinetics. Specifically, conventional methods to generate transition state structures are deficient in speed, accuracy, or scope. We describe a novel method to generate three-dimensional transition state structures for isomerization reactions using reactant and product geometries. Our approach relies on a graph neural network to predict the transition state distance matrix and a least squares optimization to reconstruct the coordinates based on which entries of the distance matrix the model perceives to be important. We feed the structures generated by our algorithm through a rigorous quantum mechanics workflow to ensure the predicted transition state corresponds to the ground truth reactant and product. In both generating viable geometries and predicting accurate transition states, our method achieves excellent results. We envision workflows like this, which combine neural networks and quantum chemistry calculations, will become the preferred methods for computing chemical reactions.

scholarly journals Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

2021 ◽  
Vol 11 (23) ◽  
pp. 11530
Author(s):  
Pangwei Wang ◽  
Xiao Liu ◽  
Yunfeng Wang ◽  
Tianren Wang ◽  
Juan Zhang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Short Term ◽  
Spatiotemporal Features ◽  
Traffic State ◽  
State Prediction ◽  
Comparison Results ◽  
Perception System

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 minutes respectively.

scholarly journals Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm

2020 ◽  
Author(s):  
Ahmed Hamed ◽  
Ahmed Sobhy ◽  
Hamed Nassar
Keyword(s):  
Heterogeneous Data ◽  
Italian Society ◽  
Test Results ◽  
Recall Accuracy ◽  
Classification Techniques ◽  
Comparison Results ◽  
The World

Abstract The coronavirus 2019 disease (COVID-19) is wreaking havoc around the world, and great efforts are underway to control it. Millions of people are now being tested and their data keeps accumulating in large volumes. This data can be used to classify newly tested persons as whether they have the disease or not. However, normal classification techniques are hampered by the fact that the data is typically both incomplete and heterogeneous. To address this two-fold obstacle, we propose a KNN variant (KNNV) algorithm which accurately and efficiently classifies COVID-19. The main two ideas behind the proposed algorithm are that for each instance to be classified it chooses the parameter K adaptively and calculates the distances to other instances in a novel way. The KNNV was implemented and tested on a COVID-19 dataset from the Italian society of medical and intervention radiology society. It was also compared to three algorithms of its category. The test results show that the KNNV can efficiently and accurately classify COVID-19 patients. The comparison results show that the algorithm greatly outperforms all its competitors in terms of four metrics: precision, recall, accuracy, and F-Score.

scholarly journals Research and Implementation of Fast-LPRNet Algorithm for License Plate Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhichao Wang ◽  
Yu Jiang ◽  
Jiaxin Liu ◽  
Siyu Gong ◽  
Jian Yao ◽  
...  
Keyword(s):  
Traffic Management ◽  
Recognition Rate ◽  
Recognition System ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Generalization Ability ◽  
Traffic Management System ◽  
High Recognition Accuracy ◽  
Fully Connected

The license plate recognition is an important part of the intelligent traffic management system, and the application of deep learning to the license plate recognition system can effectively improve the speed and accuracy of recognition. Aiming at the problems of traditional license plate recognition algorithms such as the low accuracy, slow speed, and the recognition rate being easily affected by the environment, a Convolutional Neural Network- (CNN-) based license plate recognition algorithm-Fast-LPRNet is proposed. This algorithm uses the nonsegment recognition method, removes the fully connected layer, and reduces the number of parameters. The algorithm—which has strong generalization ability, scalability, and robustness—performs license plate recognition on the FPGA hardware. Increaseing the depth of network on the basis of the Fast-LPRNet structure, the dataset of Chinese City Parking Dataset (CCPD) can be recognized with an accuracy beyond 90%. The experimental results show that the license plate recognition algorithm has high recognition accuracy, strong generalization ability, and good robustness.

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