A learning-based comprehensive evaluation model for traffic data quality in intelligent transportation systems

2015 ◽  
Vol 75 (19) ◽  
pp. 11683-11698 ◽  
Author(s):  
Yidong Li ◽  
Dewang Chen
Keyword(s):  
Data Quality ◽  
Intelligent Transportation Systems ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Data ◽  
Comprehensive Evaluation Model

scholarly journals A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 815 ◽  
Author(s):  
Minghui Ma ◽  
Shidong Liang ◽  
Yifei Qin
Keyword(s):  
Data Quality ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Nearest Neighbor ◽  
Transportation Systems ◽  
Traffic Data ◽  
K Nearest Neighbor ◽  
Time Dimension ◽  
Nearest Neighbor Searching ◽  
Searching Strategy

Traffic data are the basis of traffic control, planning, management, and other implementations. Incomplete traffic data that are not conducive to all aspects of transport research and related activities can have adverse effects such as traffic status identification error and poor control performance. For intelligent transportation systems, the data recovery strategy has become increasingly important since the application of the traffic system relies on the traffic data quality. In this study, a bidirectional k-nearest neighbor searching strategy was constructed for effectively detecting and recovering abnormal data considering the symmetric time network and the correlation of the traffic data in time dimension. Moreover, the state vector of the proposed bidirectional searching strategy was designed based the bidirectional retrieval for enhancing the accuracy. In addition, the proposed bidirectional searching strategy shows significantly more accuracy compared to those of the previous methods.

scholarly journals An Improved Stacked Denoise Autoencoder with Elu Activation Function for Traffic Data Imputation

2019 ◽  
Vol 8 (11) ◽  
pp. 3951-3954
Keyword(s):  
Deep Learning ◽  
Missing Data ◽  
Intelligent Transportation Systems ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
Activation Function ◽  
Transportation Systems ◽  
Data Imputation ◽  
Traffic Data ◽  
Spatio Temporal

Traffic data plays a major role in transport related applications. The problem of missing data has greatly impact the performance of Intelligent transportation systems(ITS). In this work impute the missing traffic data with spatio-temporal exploitation for high precision result under various missing rates. Deep learning based stacked denoise autoencoder is proposed with efficient Elu activation function to remove noise and impute the missing value.This imputed value will be used in analyses and prediction of vehicle traffic. Results are discussed that the proposed method outperforms well in state of the art approaches.

Costs and Benefits of Using Intelligent Transportation Systems as an Alternative Data Source: Case Study

2002 ◽  
Vol 1804 (1) ◽  
pp. 85-90
Author(s):  
Pat S. Hu ◽  
Richard T. Goeltz ◽  
Richard L. Schmoyer
Keyword(s):  
Intelligent Transportation Systems ◽  
Value Added ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Data ◽  
Costs And Benefits ◽  
Data Resource ◽  
Address Data ◽  
Data Source

Intelligent transportation systems (ITSs) are an alternative data source that could lead to win–win situations: this source will not only benefit the transportation operations and planning communities by allowing them to access more and better data, but it will also enhance the appeal of ITS deployment by significantly broadening its originally intended benefits. Use of ITS-generated data as an alternative data resource is reflected in the archived data user services (ADUS) in the national ITS architecture. Usually, an agency will evaluate the costs and the benefits of ADUS before it decides whether to deploy ADUS. The costs and benefits of ADUS are examined on the basis of results from a case study in which ITS-generated traffic data were analyzed to determine whether they can help meet such traffic data needs as estimating the total travel volume and the total vehicle miles traveled. The cost is measured in terms of the effort needed to archive and reformat the data, revamp the software, and address data quality and data integration issues. The benefits are measured in terms of the value added by the ITS-generated data. Although the costs are high to use ITS-generated data for purposes other than the originally intended use, the research has proved that ITS-generated data can improve transportation decisions by, in this case, improving traffic estimates.

A Novel Traffic Flow Forecasting Method Based on the Artificial Neural Networks and Intelligent Transportation Systems Data Mining

2013 ◽  
Vol 842 ◽  
pp. 708-711 ◽  
Author(s):  
Wei He ◽  
Tao Lu ◽  
Cheng Qiang Yu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Intelligent Transportation Systems ◽  
Rbf Neural Network ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Information ◽  
Traffic Data ◽  
Ann Model ◽  
Artificial Neural

Useful information often hides in traffic management system. To mine useful data, prior knowledge has been used to train the artificial neural network (ANN) to identify the traffic conditions in the traffic information forecasting. Subjective information has hence been introduced into the ANN model. To solve this problem, a new ANN model is proposed based on the data mining technology in this work. The Self-Organized Feature Map (SOFM) is firstly employed to cluster the traffic data through an unsupervised learning and provide the labels for these data. Then labeled data were used to train the GA-Chaos optimized RBF neural network. Herein, the GA-Chaos algorithm is used to train the RBF parameters. Experimental tests use practical data sets from the Intelligent Transportation Systems (ITS) to validate the performance of the proposed ANN model. The results show that the proposed method can extract the potential patterns hidden in the traffic data and can accurately predict the future traffic state. The prediction accuracy is beyond 95%.

Empirical Approaches to Outlier Detection in Intelligent Transportation Systems Data

2003 ◽  
Vol 1840 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Eun Sug Park ◽  
Shawn Turner ◽  
Clifford H. Spiegelman
Keyword(s):  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
San Antonio ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Screening Methods ◽  
Traffic Data ◽  
New Methods ◽  
System Data ◽  
Empirical Approaches

Novel methods for implementation of detector-level multivariate screening methods are presented. The methods use present data and classify data as outliers on the basis of comparisons with empirical cutoff points derived from extensive archived data rather than from standard statistical tables. In addition, while many of the ideas of the classical Hotelling’s T2-statistic are used, modern statistical trend removal and blocking are incorporated. The methods are applied to intelligent transportation system data from San Antonio and Austin, Texas. These examples show how the suggested new methods perform with high-quality traffic data and apparently lower-quality traffic data. All algorithms were implemented by using the SAS programming language.

Intelligent Lighting Control System in Large-Scale Sports Competition Venues

2018 ◽  
pp. 172-182 ◽  
Author(s):  
Shengmin CAO
Keyword(s):  
Control System ◽  
Large Scale ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Practical Significance ◽  
Lighting Control ◽  
Sports Events ◽  
Planning And Design ◽  
Comprehensive Evaluation Model

This paper mainly studies the application of intelligent lighting control system in different sports events in large sports competition venues. We take the Xiantao Stadium, a large­scale sports competition venue in Zaozhuang City, Shandong Province as an example, to study its intelligent lighting control system. In this paper, the PID (proportion – integral – derivative) incremental control model and the Karatsuba multiplication model are used, and the intelligent lighting control system is designed and implemented by multi­level fuzzy comprehensive evaluation model. Finally, the paper evaluates the actual effect of the intelligent lighting control system. The research shows that the intelligent lighting control system designed in this paper can accurately control the lighting of different sports in large stadiums. The research in this paper has important practical significance for the planning and design of large­scale sports competition venues.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

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