Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks
With the rapid increase of private vehicles, traffic congestion has become a worldwide problem. Various models have been proposed to undertake traffic prediction. Among them, autoregressive integrated moving average (ARIMA) models are quite popular for their good performance (simple, low complexity,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-02-01
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| Series: | Applied Sciences |
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| Online Access: | http://www.mdpi.com/2076-3417/8/2/277 |
| _version_ | 1828432574809112576 |
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| author | Jianbin Chen Demin Li Guanglin Zhang Xiaolu Zhang |
| author_facet | Jianbin Chen Demin Li Guanglin Zhang Xiaolu Zhang |
| author_sort | Jianbin Chen |
| collection | DOAJ |
| description | With the rapid increase of private vehicles, traffic congestion has become a worldwide problem. Various models have been proposed to undertake traffic prediction. Among them, autoregressive integrated moving average (ARIMA) models are quite popular for their good performance (simple, low complexity, etc.) in traffic prediction. Localized Space-Time ARIMA (LSTARIMA) improves ARIMA’s prediction accuracy by extending the widely used STARIMA with a dynamic weight matrix. In this paper, a localized space-time autoregressive (LSTAR) model was proposed and a new parameters estimation method was formulated based on the LSTARIMA model to reduce computational complexity for real-time prediction purposes. Moreover, two theorems are given and verified for parameter estimation of our proposed LSTAR model. The simulation results showed that LSTAR provided better prediction accuracy when compared to other time series models such as Shift, autoregressive (AR), seasonal moving average (Seasonal MA), and Space-Time AR (STAR). We found that the prediction accuracy of LSTAR was a bit lower than the LSTARIMA model in the simulation results. However, the computational complexity of the LSTAR model was also lower than the LSTARIMA model. Therefore, there exists a tradeoff between the prediction accuracy and the computational complexity for the two models. |
| first_indexed | 2024-12-10T18:22:16Z |
| format | Article |
| id | doaj.art-45f7bd0a1db24d08acca32fc9df8e822 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-10T18:22:16Z |
| publishDate | 2018-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-45f7bd0a1db24d08acca32fc9df8e8222022-12-22T01:38:10ZengMDPI AGApplied Sciences2076-34172018-02-018227710.3390/app8020277app8020277Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road NetworksJianbin Chen0Demin Li1Guanglin Zhang2Xiaolu Zhang3College of Information Science & Technology, Donghua University, Shanghai 201620, ChinaCollege of Information Science & Technology, Donghua University, Shanghai 201620, ChinaCollege of Information Science & Technology, Donghua University, Shanghai 201620, ChinaCollege of Information Science & Technology, Donghua University, Shanghai 201620, ChinaWith the rapid increase of private vehicles, traffic congestion has become a worldwide problem. Various models have been proposed to undertake traffic prediction. Among them, autoregressive integrated moving average (ARIMA) models are quite popular for their good performance (simple, low complexity, etc.) in traffic prediction. Localized Space-Time ARIMA (LSTARIMA) improves ARIMA’s prediction accuracy by extending the widely used STARIMA with a dynamic weight matrix. In this paper, a localized space-time autoregressive (LSTAR) model was proposed and a new parameters estimation method was formulated based on the LSTARIMA model to reduce computational complexity for real-time prediction purposes. Moreover, two theorems are given and verified for parameter estimation of our proposed LSTAR model. The simulation results showed that LSTAR provided better prediction accuracy when compared to other time series models such as Shift, autoregressive (AR), seasonal moving average (Seasonal MA), and Space-Time AR (STAR). We found that the prediction accuracy of LSTAR was a bit lower than the LSTARIMA model in the simulation results. However, the computational complexity of the LSTAR model was also lower than the LSTARIMA model. Therefore, there exists a tradeoff between the prediction accuracy and the computational complexity for the two models.http://www.mdpi.com/2076-3417/8/2/277LSTARSTARIMAparameters estimationtraffic flow predictionurban road network |
| spellingShingle | Jianbin Chen Demin Li Guanglin Zhang Xiaolu Zhang Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks Applied Sciences LSTAR STARIMA parameters estimation traffic flow prediction urban road network |
| title | Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks |
| title_full | Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks |
| title_fullStr | Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks |
| title_full_unstemmed | Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks |
| title_short | Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks |
| title_sort | localized space time autoregressive parameters estimation for traffic flow prediction in urban road networks |
| topic | LSTAR STARIMA parameters estimation traffic flow prediction urban road network |
| url | http://www.mdpi.com/2076-3417/8/2/277 |
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