Heterogeneous urban traffic data and their integration through kernel-based interpolation

Purpose This paper aims to present collection and analysis of heterogeneous urban traffic data, and integration of them through a kernel-based approach for assessing performance of urban transport network facilities. The recent development in sensing and information technology opens up opportunities for researching the use of this vast amount of new urban traffic data. This paper contributes to analysis and management of urban transport facilities. Design/methodology/approach In this paper, the data fusion algorithm are developed by using a kernel-based interpolation approach. Our objective is to reconstruct the underlying urban traffic pattern with fine spatial and temporal granularity through processing and integrating data from different sources. The fusion algorithm can work with data collected in different space-time resolution, with different level of accuracy and from different kinds of sensors. The properties and performance of the fusion algorithm is evaluated by using a virtual test bed produced by VISSIM microscopic simulation. The methodology is demonstrated through a real-world application in Central London. Findings The results show that the proposed algorithm is able to reconstruct accurately the underlying traffic flow pattern on transport network facilities with ordinary data sources on both virtual and real-world test beds. The data sources considered herein include loop detectors, cameras and GPS devices. The proposed data fusion algorithm does not require assumption and calibration of any underlying model. It is easy to implement and compute through advanced technique such as parallel computing. Originality/value The presented study is among the first utilizing and integrating heterogeneous urban traffic data from a major city like London. Unlike many other existing studies, the proposed method is data driven and does not require any assumption of underlying model. The formulation of the data fusion algorithm also allows it to be parallelized for large-scale applications. The study contributes to the application of Big Data analytics to infrastructure management.

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An anti-interference detection algorithm for parking monitoring systems

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-04-2019-0034 ◽

2019 ◽

Vol 12 (4) ◽

pp. 481-496

Author(s):

Ani Dong ◽

Zusheng Zhang ◽

Jiaming Chen

Keyword(s):

Data Fusion ◽

Local Level ◽

Detection Algorithm ◽

Magnetic Sensors ◽

Sensor Nodes ◽

Detection Accuracy ◽

Fusion Algorithm ◽

Content Type ◽

Occupancy Detection ◽

Local Decision

Purpose Magnetic sensors have recently been proposed for parking occupancy detection. However, there has adjacent interference problem, i.e. the magnetic signal is easy to be interfered by the vehicles which are parking on adjacent spaces. The purpose of this paper is to propose a sensing algorithm to eliminate the adjacent interference. Design/methodology/approach The magnetic signals are converted to the pattern representation sequences, and the similarity is calculated using the pattern distance. The detection algorithm includes two levels: local decision and data fusion. In the local decision level, the sampled signals can be divided into three classes: vacant, occupied and uncertain. Then a collaborative decision is used to fusion the signals which belong to the uncertain class for the second level. Findings An experiment system included 60 sensor nodes that were deployed on bay parking spaces. Experiment results show that the proposed algorithm has better detection accuracy than existing algorithms. Originality/value This paper proposes a data fusion algorithm to eliminate adjacent interference. To balance the energy consumption and detection accuracy, the algorithm includes two levels: local decision and data fusion. In most of cases, the local decision can obtain the accurate detection result. Only the signals that cannot be correctly detected at the local level need data fusion operation.

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Modeling and Data Fusion of Dynamic Highway Traffic

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2644-11 ◽

2017 ◽

Vol 2644 (1) ◽

pp. 92-99 ◽

Cited By ~ 4

Author(s):

Flavia Ottaviano ◽

Fabing Cui ◽

Andy H. F. Chow

Keyword(s):

Data Fusion ◽

San Francisco ◽

Traffic Engineering ◽

Big Data Analytics ◽

Data Sources ◽

Transmission Model ◽

Cell Transmission Model ◽

Highway Traffic ◽

Short Term ◽

Fusion Algorithm

This paper presents a data fusion framework for processing and integrating data collected from heterogeneous sources on motorways to generate short-term predictions. Considering the heterogeneity in spatiotemporal granularity in data from different sources, an adaptive kernel-based smoothing method was first used to project all data onto a common space–time grid. The data were then integrated through a Kalman filter framework build based on the cell transmission model for generating short-term traffic state prediction. The algorithms were applied and tested with real traffic data collected from the California I-880 corridor in the San Francisco Bay Area from the Mobile Century experiment. Results revealed that the proposed fusion algorithm can work with data sources that are different in their spatiotemporal granularity and improve the accuracy of state estimation through incorporating multiple data sources. The present work contributed to the field of traffic engineering and management with the application of big data analytics.

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An intelligent urban traffic data fusion analysis method based on improved artificial neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-179273 ◽

2019 ◽

Vol 37 (4) ◽

pp. 4413-4423 ◽

Cited By ~ 1

Author(s):

Xiaodan Liu ◽

Chunliang Li

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Data Fusion ◽

Urban Traffic ◽

Traffic Data ◽

Analysis Method ◽

Artificial Neural ◽

Fusion Analysis

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Design of a new nonlinear model predictive fault tolerant control system using multi-sensor data fusion technique based on UKF algorithm

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-10-2014-0274 ◽

2015 ◽

Vol 34 (4) ◽

pp. 1286-1301

Author(s):

Mohammad Ghesmat ◽

Akbar Khalkhali

Keyword(s):

Data Fusion ◽

Nonlinear Model ◽

Unscented Kalman Filter ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

Sensor Data ◽

Fusion Algorithm ◽

Content Type ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion

Purpose – There are high expectations for reliability, safety and fault tolerance are high in chemical plants. Control systems are capable of potential faults in the plant processing systems. This paper proposes is a new Fault Tolerant Control (FTC) system to identify the probable fault occurrences in the plant. Design/methodology/approach – A Fault Diagnosis and Isolation (FDI) module has been devised based on the estimated state of system. An Unscented Kalman Filter (UKF) is the main innovation of the FDI module to identify the faults. A Multi-Sensor Data Fusion algorithm is utilized to integrate the UKF output data to enhance fault identification. The UKF employs an augmented state vector to estimate system states and faults simultaneously. A control mechanism is designed to compensate for the undesirable effects of the detected faults. Findings – The performance of the Nonlinear Model Predictive Controller (NMPC) without any fault compensation is compared with the proposed FTC scheme under different fault scenarios. Analysis of the simulation results indicates that the FDI method is able to identify the faults accurately. The proposed FTC approach facilitates recovery of the closed loop performance after the faults have been isolated. Originality/value – A significant contribution of the paper is the design of an FTC system by using UKF to estimate faults and enhance the accuracy of data. This is done by applying a data fusion algorithm and controlling the system by the NMPC after eliminating the effects of faults.

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A new fusion structure model for real-time urban traffic state estimation by multisource traffic data fusion

2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) ◽

10.1109/apnoms.2016.7737227 ◽

2016 ◽

Author(s):

Pan Zhang ◽

Lanlan Rui ◽

Xuesong Qiu ◽

Ruichang Shi

Keyword(s):

Data Fusion ◽

State Estimation ◽

Real Time ◽

Urban Traffic ◽

Structure Model ◽

Traffic Data ◽

Traffic State ◽

Traffic State Estimation

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A Data Fusion Approach of Multiple Maintenance Data Sources for Real-World Reliability Modelling

Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015) - Lecture Notes in Mechanical Engineering ◽

10.1007/978-3-319-27064-7_7 ◽

2016 ◽

pp. 69-77 ◽

Cited By ~ 1

Author(s):

Kazi Arif-Uz-Zaman ◽

Michael E. Cholette ◽

Fengfeng Li ◽

Lin Ma ◽

Azharul Karim

Keyword(s):

Data Fusion ◽

Real World ◽

Data Sources ◽

Reliability Modelling ◽

Fusion Approach

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Optimization of the transport network by the case of Cherkasy city

Journal of Mechanical Engineering and Transport ◽

10.31649/2413-4503-2021-13-1-13-22 ◽

2021 ◽

Vol 13 (1) ◽

pp. 13-22

Author(s):

Viktor Bilichenko ◽

◽

Liudmyla Tarandushka ◽

Nataliia Kostian ◽

Oleksandr Pylypenko ◽

...

Keyword(s):

Public Transport ◽

Urban Traffic ◽

Urban Transport ◽

Transport Network ◽

Transport Infrastructure ◽

Model Parameters ◽

Passenger Transport ◽

The Public ◽

Route Network ◽

The City

The article explores the possibility of optimization of the public transport network by reducing the number of duplicate routes. In the course of the research the existing network of urban passenger transport of Cherkasy and the structure of the transport fleet of motor transport enterprises providing relevant services are analyzed. The length of the different routes of the network and the intensity of their movement are determined. It has been found that the density of the public passenger transport route network (8.1 km/km2) is much higher than the normative value. The indices of duplication of each bus and trolleybus route of the network with other routes are calculated. In order to study the demand for urban passenger transportation, a population survey was conducted. A mathematical model for optimizing the movement of trolleybuses and buses on duplicate routes is constructed. The model takes into account the degree of duplication of one route by another, the percentage distribution of passengers by type of transport and the limitation of vehicles by passenger capacity. The values of the model parameters, which determine the damage to the urban environment by one run, are calculated at the tariff rates for damage to the vehicle 1 km of the city road and the emission into the atmosphere of the exhaust gas (for buses). Optimization of the Cherkasy public transport network on routes with complete duplication (coincidence of route routes of two modes of transport is not less than 75%). According to the optimization results, a new itinerary network is proposed, which provides minimal duplication of routes, which in turn will lead to reduction of the accident rate, reduction of environmental pollution and increase of the efficiency of operation of the entire transport infrastructure of the city. The implementation of the results of this study will reduce the overall economic and environmental losses of passengers and transport, which will lead to a more efficient functioning of urban transport. The constructed model can be used to plan urban traffic on new routes, as well as to construct a dual task of calculating the cost of passenger hours, provided that passenger traffic is moving with optimum intensity.

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Study on Traffic Multi-Source Data Fusion

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2019040105 ◽

2019 ◽

Vol 13 (2) ◽

pp. 63-75

Author(s):

Suping Liu ◽

Dongbo Zhang ◽

Jialin Li

Keyword(s):

Data Fusion ◽

Traffic Flow ◽

Traffic Congestion ◽

Traffic Management ◽

Analysis Data ◽

Urban Traffic ◽

Data Sources ◽

Flow Parameters ◽

Fusion Methods ◽

Single Detector

In order to alleviate urban traffic congestion, it is necessary to obtain roadway network traffic flow parameters to estimate the traffic conditions. Single-detector data may not be sufficient to obtain a comprehensive, effective, accurate and high-quality traffic flow data. Neural networks and regression analysis data fusion methods are employed to expand data sources as well as for improving data quality. The multi-source detector data can provide fundamental support for traffic management. An empirical analysis was conducted using acquisition technology employed by the Beijing urban expressway to estimate traffic flow parameters. The results show that the proposed data fusion method is feasible and provides reliable data sources.

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