scholarly journals Prediction of Road Network Traffic State Using the NARX Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ziwen Song ◽  
Feng Sun ◽  
Rongji Zhang ◽  
Yingcui Du ◽  
Chenchen Li
Keyword(s):  
Neural Network ◽  
Road Network ◽  
Flow Speed ◽  
Prediction Performance ◽  
Traffic Information ◽  
Index Formula ◽  
Fundamental Diagram ◽  
The Road ◽  
Traffic State

To provide reliable traffic information and more convenient visual feedback to traffic managers and travelers, we proposed a prediction model that combines a neural network and a Macroscopic Fundamental Diagram (MFD) for predicting the traffic state of regional road networks over long periods. The method is broadly divided into the following steps. To obtain the current traffic state of the road network, the traffic state efficiency index formula proposed in this paper is used to derive it, and the MFD of the current state is drawn by using the classification of the design speed and free flow speed of the classified road. Then, based on the collected data from the monitoring stations and the weighting formula of the grade roads, the problem of insufficient measured data is solved. Meanwhile, the prediction performance of NARX, LSTM, and GRU is experimentally compared with traffic prediction, and it is found that NARX NN can predict long-term flow and the prediction performance is slightly better than both LSTM and GRU models. Afterward, the predicted data from the four stations were integrated based on the classified road weighting formula. Finally, according to the traffic state classification interval, the traffic state of the road network for the next day is obtained from the current MFD, the predicted traffic flow, and the corresponding speed. The results indicate that the combination of the NARX NN with the MFD is an effective attempt to predict and describe the long-term traffic state at the macroscopic level.

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

scholarly journals Macroscopic Fundamental Diagram based Road Network Characteristics Identification and Simulation

2020 ◽  
Vol 165 ◽  
pp. 04051
Author(s):  
Yi Yu ◽  
Liang Wang ◽  
Xianglun Mo ◽  
Yao Yu ◽  
Mei liu
Keyword(s):  
Road Network ◽  
Key Factors ◽  
Link Length ◽  
Fundamental Diagram ◽  
Road Density ◽  
Network Characteristics ◽  
The Road ◽  
Macroscopic Fundamental Diagram ◽  
Flow Equilibrium ◽  
The Relationship

As an inherent property of the road network, macroscopic fundamental diagram (MFD) method can effectively describe the traffic status of the urban roads and identify the relationship among key factors, such as traffic flow and occupancy. Currently, using MFD is easily affected by various network inner factors including topology and road density, so in this paper we propose a method to identify inner characteristic of road network and do a series of comparisons under different scenarios with fixed traffic input circumstance. The differential impact of data collector setting locations are discussed with a aim to reveal the respective location setting suitable for various networks conditions in initial; then road topology and density are designed in road network and simulated MFD performances with flow equilibrium affections. It is shown as the dispersion decreasing of link length or road density of network, the network exhibits better operation efficiency so as to increase the output of link flow and the dissipative ability of the road network. Meanwhile, the equivalent of entrances and exits is proved as another important factor has same impact on MFD.

scholarly journals How Does Travel Demand Follow the Change in Infrastructure? Multiple-Year Eigenvector Centrality Analysis

2021 ◽  
Vol 13 (23) ◽  
pp. 13366
Author(s):  
Hiroe Ando ◽  
Fumitaka Kurauchi
Keyword(s):  
Road Network ◽  
Travel Demand ◽  
Time Lag ◽  
Physical Structure ◽  
Eigenvector Centrality ◽  
Demand And Supply ◽  
The Road ◽  
Centrality Analysis ◽  
The Impact

The road network is one of the most permanent elements of the physical structure of cities, and the long-term impacts should be considered for effective and efficient road network improvement. It is therefore important to catch up on how the road will be used after construction. However, we do not have much knowledge on the pattern and time lag in the change process of travel demand and supply in the real situation. To explore such changes, this study proposes to evaluate a network with eigenvector centrality (EC) measures that can evaluate the importance of nodes in a network. We believe the analysis based on topological properties by the graph theory is suitable to verify the evolution of road networks. This study analysed long-term changes over 20 years in an actual city to understand the impact of road network improvements. The EC analysis with the weights of traffic indices obtained from survey data evaluates the connectivity of road services on the supply side, and traffic concentration on the demand side.

scholarly journals PENGEMBANGAN JARINGAN JALAN NASIONAL DI SULAWESI UTARA DAN GORONTALO

2020 ◽  
Vol 20 (1) ◽  
pp. 67-76
Author(s):  
Triono Junoasmono ◽  
Hansen Samuel Arberto Gultom ◽  
Brian Sixon Christian Umboh ◽  
Anastasia Caroline Sutandi
Keyword(s):  
Road Network ◽  
Road Traffic ◽  
Secondary Data ◽  
Traffic Modeling ◽  
The Road ◽  
North Sulawesi ◽  
Road Development ◽  
Traffic Volumes ◽  
National Road

Abstract The development of the road network is needed to determine the extent of the road network of a city or region that requires handling and development, both in the long term, medium term and short term. The purpose of this study is to obtain a master plan for the development of the national road network in North Sulawesi and Gorontalo Provinces, as a basis for planning the development of the road network for the next 5 years. The data used are primary and secondary data. Based on the results of traffic modeling, the majority of national roads in North Sulawesi Province and in Gorontalo Province have relatively small traffic volumes. The projection results, from 2020 to 2025, show that there are 7 roads that require handling and capacity improvement. Keywords: road network, national road, traffic modeling, road capacity, road development  Abstrak Pengembangan jaringan jalan diperlukan untuk mengetahui sejauh mana jaringan jalan suatu kota atau wilayah memerlukan penanganan maupun pengembangan, baik untuk jangka panjang, jangka menengah, maupun jangka pendek. Tujuan penelitian ini adalah untuk mendapatkan suatu rencana induk pengembangan jaringan jalan nasional di Provinsi Sulawesi Utara dan di Provinsi Gorontalo, sebagai basis perencanaan pengembangan jaringan jalan hingga 5 tahun yang akan datang. Data yang digunakan adalah data primer dan data sekunder. Berdasarkan hasil pemodelan lalu lintas, mayoritas jalan nasional di Provinsi Sulawesi Utara dan di Provinsi Gorontalo memiliki volume lalu lintas yang relatif kecil. Hasil proyeksi dari tahun 2020 sampai dengan tahun 2025, menunjukkan bahwa terdapat 7 ruas jalan yang memerlukan penanganan dan peningkatan kapasitas. Kata-kata kunci: jaringan jalan, jalan nasional, pemodelan lalu lintas, kapasitas jalan, pengembangan jalan

scholarly journals Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

2018 ◽  
Vol 10 (9) ◽  
pp. 1461 ◽  
Author(s):  
Yongyang Xu ◽  
Zhong Xie ◽  
Yaxing Feng ◽  
Zhanlong Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Road Network ◽  
Deep Convolutional Neural Network ◽  
Road Extraction ◽  
Remote Sensing Imagery ◽  
The Road

The road network plays an important role in the modern traffic system; as development occurs, the road structure changes frequently. Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution remote sensing imagery is becoming increasingly popular, and has become a new tool to update the geospatial database. Considering that the training dataset of the deep convolutional neural network will be clipped to a fixed size, which lead to the roads run through each sample, and that different kinds of road types have different widths, this work provides a segmentation model that was designed based on densely connected convolutional networks (DenseNet) and introduces the local and global attention units. The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information. A dataset from Google Earth was used to validate the method, and experiments showed that the proposed deep convolutional neural network can extract the road network accurately and effectively. This method also achieves a harmonic mean of precision and recall higher than other machine learning and deep learning methods.

scholarly journals Dynamic Evaluation on the Traffic State of an Urban Gated Community by Opening the Micro-Inter-Road Network

Technologies ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 71 ◽  
Author(s):  
Lin Dong ◽  
Akira Rinoshika ◽  
Zhixian Tang
Keyword(s):  
Road Network ◽  
Entropy Method ◽  
Urban Traffic ◽  
Traffic Information ◽  
Gated Community ◽  
Entropy Model ◽  
Evaluation Standard ◽  
Traffic System ◽  
Traffic State ◽  
Selection Framework

The opening of a gated community to expand the micro-road network in an urban traffic system is an importance research topic related to urban congestion. To satisfy the demands of opening an early choosing case, this paper proposes a comprehensive selection framework on qualified communities and their appropriate opening times by describing the traffic state at the boundary road network accurately. The traffic entropy model and fuzzy c-means (FCM) method are used in this paper. In the framework, a new opening evaluation entropy model is built using basic theory of the thermodynamic traffic entropy method. The traffic state entropy values of the boundary road network and entropy production are calculated to determinate the opening time. In addition, a specific fuzzy range evaluation standard at a preset gated community is drawn with an FCM algorithm to verify the opening determination. A case study based on the traffic information in a simulated gated community in Shanghai is evaluated and proves that the findings of opening evaluation are in accordance with the actual situation. It is found that the micro-inter-road network of a gated community should be opened as the entropy value reaches 2.5. As the travel time is less than 20 s, the correlation between the opening entropy value and the journey delay time exhibits a good linear correlation, which indicates smooth traffic flow.

scholarly journals The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fan Hou ◽  
Yue Zhang ◽  
Xinli Fu ◽  
Lele Jiao ◽  
Wen Zheng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Road Network ◽  
Short Term Memory ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Point Of Interest ◽  
Flow Prediction ◽  
Long Short Term Memory ◽  
On The Road

Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). The model can capture the complex spatial dependence of road nodes on the road network and use LSGC (local spectrogram convolution) to capture spatial correlation features from the K-order local neighbors of the road segment nodes in the road network. It is more accurate to extract the information of neighbor nodes by replacing the single-hop neighborhood matrix with K-order local neighborhoods to expand the receptive field of graph convolution. The high-order neighborhood of road nodes is also fully considered instead of only extracting features from first-order neighbor nodes. In addition, an external attribute enhancement unit is designed to extract external factors (weather, point of interest, time, etc.) that affect traffic flow in order to improve the accuracy of the model’s traffic flow prediction. The experimental results show that when considering the static, dynamic, and static and dynamic combination, the model has excellent performance: RMSE (4.0406, 4.0362, 4.0234), MAE (2.7184, 2.7044, 2.7030), accuracy (0.7132, 0.7190, 0.7223).

scholarly journals A Novel Multisensor Traffic State Assessment System Based on Incomplete Data

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Yiliang Zeng ◽  
Jinhui Lan ◽  
Bin Ran ◽  
Yaoliang Jiang
Keyword(s):  
Traffic Flow ◽  
Incomplete Data ◽  
Road Traffic ◽  
Flow Speed ◽  
State Assessment ◽  
Assessment System ◽  
Flow Data ◽  
The Road ◽  
Traffic State ◽  
Probe Vehicle

A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system.

scholarly journals Proactive Traffic Information Control in Emergency Evacuation Network

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Zhengfeng Huang
Keyword(s):  
Real Time ◽  
Road Network ◽  
Emergency Evacuation ◽  
Traffic Information ◽  
Information Control ◽  
Traffic Condition ◽  
The Road ◽  
Real Time Information ◽  
Near Future ◽  
Time Information

Traffic demand in emergency evacuation is usually too large to be effectively managed with reactive traffic information control methods. These methods adapt to the road traffic passively by publishing real-time information without consideration of the routing behavior feedback produced by evacuees. Other remedy measures have to be prepared in case of nonrecurring congestion under these methods. To use the network capacity fully to mitigate near-future evacuation traffic congestion, we propose proactive traffic information control (PTIC) model. Based on the mechanism between information and routing behavior feedback, this model can change the route choice of evacuees in advance by dissipating strategic traffic information. Generally, the near-future traffic condition is difficult to accurately predict because it is uncertain in evacuation. Assume that the value of traffic information obeys certain distribution within a range, and then real-time traffic information may reflect the most-likely near-future traffic condition. Unlike the real-time information, the proactive traffic information is a selection within the range to achieve a desired level of the road network performance index (total system travel time). In the aspect of the solution algorithm, differential equilibrium decomposed optimization (D-EDO) is proposed to compare with other heuristic methods. A field study on a road network around a large stadium is used to validate the PTIC.

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