Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015 Application of Traffic State Prediction Methods to Urban Expressway Network in the City of Seoul Youngho Kim a, Woojin Kang b, Minju Park c a,b,c The Korea Transport Institute, 370 Sicheong-daero, Sejong-si, 339-007, Korea a E-mail: [email protected] b E-mail: [email protected] c Corresponding author: E-mail: [email protected] Abstract: This paper proposes a traffic state prediction method based on two perspectives; short-term and long-term prediction. Modified KNN method is used for short-term prediction from recent 2 years of historical data set. Pattern of the day of the week is used to predict long-term. To overcome the gap between the result of short-term and long-term prediction, the weighted average for two predicted results is considered as the final predicted result. The proposed method is tested in the real urban expressway network and the performance of the proposed method is evaluated in this paper. Keywords: K-nearest neighbor, K-NN, short-term prediction, long-term prediction 1. INTRODUCTION Drivers usually precede a trip by checking out the traffic conditions for their route using computers, smart phones, or navigation systems. Even after they determine their travel route based on the information prior to the departure, they constantly look for the optimal travel route through navigation systems while driving. This is why the predicted traffic information is getting more important the advanced route planning. The predicted traffic information is produced in two steps, traffic state estimation and traffic state prediction. The traffic state estimation is to determine the value of traffic variables of the target node-link system based on the traffic data observed from the real road. The traffic prediction is to project the estimated traffic states onto the unknown future traffic condition. The estimated traffic information is currently provided by most traffic management centers, based on the traffic data collected from various traffic observation systems. Even though various traffic prediction algorithms have been developed theoretically, they have not been implemented practically. Predicted data will be evaluated by future real traffic data. Figure 1 shows these steps well. This paper proposed a traffic state prediction method for the congestion caused by fluctuations in traffic demand. Only speed data of each link is used to predict future traffic state. The proposed method was tested in the real urban expressway network and the performance of the proposed method was evaluated. This paper is composed of 5 parts. 1885 Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015 Current traffic condition at time t Future traffic condition at time RSE RSE Mobile Mobile Navigation Navigation VDS VDS Current Current Traffic Traffic Condition Condition Traffic State Estimation Traffic State Estimation Traffic Traffic Estimation Estimation Link i-2 Link i-1 Link i Link i+1 Link i+2 Link i-2 Link i-1 Link i Link i+1 Link i+2 (Speed, (Speed, Flow Flow in link) in link) Projection Evaluation Historical Databases Predicted data at : : : : (Speed, travel time in each link) Speed Speed Speed Speed Prediction Predicted information Flow Flow Flow Flow Link i-2 Link i-1 Link i Link i+1 Link i+2 (Speed, Flow : : : : in link) Figure 1. Scope of the research 2. LITERATURE REVIEW The traffic prediction is classified into short-term and long-term prediction in terms of prediction horizon. The longer the prediction horizon, the predicted traffic conditions rely more on the statistical assumptions based on historical traffic data, behavioral assumptions, and economic, social or demographic considerations like Figure 2 (Van Lint, 2004). If the prediction horizon is short, the predicted traffic conditions are determined mainly by the spatio-temporal evolution of the current and the near-past traffic conditions. For the long-term prediction statistical approaches like ARIMA or regression model can be proposed however, the long-term prediction method has not been intensively investigated yet. Chrobok et al. (2000) analyzed historical traffic data and identified that traffic behavior is different depending on daily characteristics, seasonal differences, and special events. He suggested that the historical data should be used for the traffic forecast but did not propose any specific methods for the application. On the contrary, a lot of former research deals with the short-term prediction method. For the short-term prediction, model-driven approach and data-driven approach have been proposed so far. Each approach has its own advantages and disadvantages. 1886 Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015 Figure 2. Comparison between long-term and short-term prediction <Van Lint, 2004> The model-based approach has a very solid theoretical background and provides full insight into the traffic phenomena based on the theory. The model-based approach is classified into microscopic, macroscopic, and mesoscopic traffic prediction models. Microscopic traffic prediction model describes individual vehicles' trajectories (Behrisch et al., 2011). Macroscopic traffic prediction model describes aggregate properties of traffic like traffic volume, density and average flow based on the analogy of fluid mechanics (see for example Kotsialos, 2002). Mesoscopic traffic prediction model combines microscopic and macroscopic models (see Ben-Akiva, 1998; Mahmassani, 1995). The model-based approach shows competence in describing the traffic behavior for the changes in the supply side like traffic control, construction, or weather. However, the model-based approach needs very accurate and reliable input data such as traffic volume, O-D flow pattern, road capacity which accurate values are not easy to achieve in the practice. The performance of the model is largely influenced by the accuracy of the data. The data-driven approach does not require extensive expertise on traffic flow modeling. Many ready-to-use softwares are available to implement in the practice and shows promising results as the traffic prediction tools. Data-driven approach includes ARIMA model (Williams 2001), support vector regression models (Chun-Hsin et al., 2003), Bayesian network (A. Pascale, M. Nicoli, 2011), multivariate spatial-temporal autoregressive model (Min, Wynter, 2011). The data-driven approach requires significant efforts for the selection of input and output model and enough data set for various traffic conditions. The usage of data-driven approach has been limited so far due to the shortage of database of traffic data. However, ITS implementation makes it possible to get large amount of reliable traffic data and the data-driven approach has become increasingly important for the traffic prediction. This paper deals with the congestion caused by changes in the traffic demand. The congestion caused by changes in the supply side due to external conditions like weather, construction, incidents is not investigated. As a Consequence, a traffic prediction algorithm based on the data-driven approach is proposed in this paper. 1887 Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015 3. METHODOLOGY In this paper, the traffic prediction algorithm is proposed for the practical application to the traffic information provision systems in the traffic management center. The traffic prediction algorithms developed so far have limitation of transferability and are very site sensitive. The prediction algorithm proposed in this paper has minimum location specific features and can be applied widely, which is critical for the real application. In Figure 3, the traffic prediction algorithms are classified into long-term prediction and short-term prediction depending on whether the current traffic condition influences on the traffic condition at the prediction time. In the long-term prediction, the predicted traffic condition (i.e. after 1 hour from the current time) is assumed not to be influenced by the current traffic condition. In the short-term prediction the predicted traffic condition (i.e. within 1 hour from the current time) is assumed to be influenced by the current traffic condition. While there is no significant change in the traffic information predicted by the long-term prediction algorithm the traffic information predicted by the short-term prediction algorithm should be updated if more reliable and recent data becomes available. Figure 3. Schematic representation of traffic prediction The values from long-term prediction and the short-term prediction usually show different values in the boundary of the two predictions. In order to provide smoothly varying predicted values without severe changes, two predicted values should be averaged with suitable weight. Usually in a short period from the current time, short-term prediction has more weight than long-term prediction. In a long period from the current time, long-term prediction has more weight than long-term prediction. 3.1 Long-term prediction As the long-term prediction is not influenced by the current traffic condition, the long-term prediction does not use the current traffic data but the historical traffic data. The long-term prediction is mainly based on the statistical approach considering correlation between traffic data and non-traffic data which describes the external conditions. 1888 Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015 The assumption