Traffic big data prediction and visualization using Fast Incremental Model Trees-Drift Detection (FIMT-DD)

Wibisono, Ari; Jatmiko, Wisnu; Wisesa, Hanief Arief; Hardjono, Benny; Mursanto, Petrus

doi:10.1016/j.knosys.2015.10.028

Cited by 67 publications

(36 citation statements)

References 18 publications

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“…This data series provides traffic flow information for 15-min periods since 2009 on most of road links in UK. The data set obtained from the loop sensor id AL2989A (TMU Site 30012533) containing traffic flow between 2009 and 2013 was used in [51] for the validation of traffic forecasting approach.…”

Section: Data Setsmentioning

confidence: 99%

Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

2017

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Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for shortterm traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to nonstationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.

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Section: Data Setsmentioning

confidence: 99%

Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

2017

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“…Guo et al, [19] developed visualizations for spatial, temporal, and multi-dimensional views of traffic data at intersections. Wibisono et al, [20] developed roadway performance maps and charts for several highways in the United Kingdom. Finally, several studies by led by G. Andrienko and N. Andrienko [21,22] developed novel visualizations for identifying congested roadway segments using spatial and temporal abstraction techniques.…”

Section: Visualization Of Bottlenecks and Congestionmentioning

confidence: 99%

Visualization Tools for Traffic Bottleneck Analysis

Franz¹,

Pack²,

Lund³

et al. 2017

IJT

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“…Visualization of big traffic data can facilitate observers to have an intuitive understanding of big data. Wibisono A and others used a fast incremental model to analyze and predict very large traffic data sets, and visually predict traffic flow in sensor points generated in actual map simulation [16]. Zhang J et al used a spatial interpolation method to discretize bus passenger flow into continuous regional distribution on the basis of big data of Beijing bus IC card and analyzed the spatial and temporal variation trend of all-day passenger flow in Beijing [17].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro

2019

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The rapid development of cities has brought new challenges and opportunities to traditional traffic management. The usage of smart cards promotes the upgrading of intelligent transportation systems, and also produces considerable big data. As an important part of the urban comprehensive transportation system, Nanjing metro has more than 1 million inbound and outbound records of traffic smart cards used by residents every day. How to process these traffic data and present them visually is an urgent problem in modern traffic management. In this study, five working days with normal weather conditions in Nanjing were selected, and the swiping records of the smart cards were extracted, and the space–time characteristics were analyzed. In terms of time analysis, this research analyzed the 24-h fluctuation of daily average passenger flow, peak hour coefficient of passenger flow, 24-h fluctuation of passenger flow on different metro lines, passenger flow intensity on different metro lines and passenger flow comparison at different stations. In spatial analysis, this study uses thermodynamic charts to represent the inflow and outflow of passengers at different stations during early and evening peak periods. The analysis results and visualized images directly reflect the area where Nanjing metro congestion is located, and also shows the commuting characteristics of residents. It can solve the problem of urban congestion, carry out the rational layout of urban functional areas, and promote the sustainable development of people and cities.

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Traffic big data prediction and visualization using Fast Incremental Model Trees-Drift Detection (FIMT-DD)

Cited by 67 publications

References 18 publications

Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

Visualization Tools for Traffic Bottleneck Analysis

Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro

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