Short-term traffic flow forecasting of urban network based on dynamic STARIMA model

Min, Xinyu; Hu, Jianming; Chen, Qi; Zhang, Tongshuai; Zhang, Yi

doi:10.1109/itsc.2009.5309741

Cited by 48 publications

(28 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently studies had shown that traffic forecasting models are effective within cities (Min et al, 2009;Cortez et al, 2012;Vlahogianni et al, 2014). These models allow one to obtain predicted traffic-based on-road emissions.…”

Section: Discussionmentioning

confidence: 99%

Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

Chang

Wang

et al. 2020

Geosci. Model Dev.

View full text Add to dashboard Cite

Abstract. Rapid urbanization in China has led to heavy traffic flows in street networks within cities, especially in eastern China, the economically developed region. This has increased the risk of exposure to vehicle-related pollutants. To evaluate the impact of vehicle emissions and provide an on-road emission inventory with higher spatiotemporal resolution for street-network air quality models, in this study, we developed the Real-time On-road Emission (ROE v1.0) model to calculate street-scale on-road hot emissions by using real-time big data for traffic provided by the Gaode Map navigation application. This Python-based model obtains street-scale traffic data from the map application programming interface (API), which are open-access and updated every minute for each road segment. The results of application of the model to Guangzhou, one of the three major cities in China, showed on-road vehicle emissions of carbon monoxide (CO), nitrogen oxide (NOx), hydrocarbons (HCs), PM2.5, and PM10 to be 35.22×104, 12.05×104, 4.10×104, 0.49×104, and 0.55×104 Mg yr−1, respectively. The spatial distribution reveals that the emission hotspots are located in some highway-intensive areas and suburban town centers. Emission contribution shows that the dominant contributors are light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) in urban areas and LDVs and heavy-duty trucks (HDTs) in suburban areas, indicating that the traffic control policies regarding trucks in urban areas are effective. In this study, the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) was applied to investigate the impact of traffic volume change on street-scale photochemistry in the urban areas by using the on-road emission results from the ROE model. The modeling results indicate that the daytime NOx concentrations on national holidays are 26.5 % and 9.1 % lower than those on normal weekdays and normal weekends, respectively. Conversely, the national holiday O3 concentrations exceed normal weekday and normal weekend amounts by 13.9 % and 10.6 %, respectively, owing to changes in the ratio of emission of volatile organic compounds (VOCs) and NOx. Thus, not only the on-road emissions but also other emissions should be controlled in order to improve the air quality in Guangzhou. More significantly, the newly developed ROE model may provide promising and effective methodologies for analyzing real-time street-level traffic emissions and high-resolution air quality assessment for more typical cities or urban districts.

show abstract

Section: Discussionmentioning

confidence: 99%

Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

Chang

Wang

et al. 2020

Geosci. Model Dev.

View full text Add to dashboard Cite

show abstract

“…If R(A) = R A , there is no solution for Equation (12). If Rank(A) = Rank A < n, there are infinite solutions for Φ i .…”

Section: S(mentioning

confidence: 99%

Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks

Chen

Zhang

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

Abstract: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.

show abstract

“…In the former case, the data processing part is not an issue. Recently, more and more research efforts have been devoted toward developing systems for region-wide traffic flow forecasting [18], [22]. When it comes to a whole region, the data issue can be very pronounced.…”

Section: Architecture Based On Mapreduce For Distributed Modelingmentioning

confidence: 99%

Distributed Modeling in a MapReduce Framework for Data-Driven Traffic Flow Forecasting

Chen¹,

Zhong

Lin

et al. 2013

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

With the availability of increasingly more new data sources collected for transportation in recent years, the computational effort for traffic flow forecasting in standalone modes has become increasingly demanding for large-scale networks. Distributed modeling strategies can be utilized to reduce the computational effort. In this paper, we present a MapReduce-based approach to processing distributed data to design a MapReduce framework of a traffic forecasting system, including its system architecture and data-processing algorithms. The work presented here can be applied to many traffic forecasting systems with models requiring a learning process (e.g., the neural network approach). We show that the learning process of the forecasting model under our framework can be accelerated from a computational perspective. Meanwhile, model fusion, which is the key problem of distributed modeling, is explicitly treated in this paper to enhance the capability of the forecasting system in data processing and storage.

show abstract

Short-term traffic flow forecasting of urban network based on dynamic STARIMA model

Cited by 48 publications

References 10 publications

Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks

Distributed Modeling in a MapReduce Framework for Data-Driven Traffic Flow Forecasting

Contact Info

Product

Resources

About