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

Youngho Kim a, Woojin Kang b, Minju Park c a,b,c The 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.

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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.

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Figure 2. Comparison between long-term and short-term prediction

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.

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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.

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The assumption of the long-term prediction is that if the condition is the same then the same traffic situations repeat. Therefore, for the long-term prediction, the classification of the traffic demand pattern is important (Chung, 2003). Traffic demand is very fluctuating depending on the day of week, holidays, and vacation and special days (christmas eve and new year's eve). There are 9 public holidays and 2 national holidays (the New Year and Korean Thanksgiving) in Korea. Normal public holidays are one-day long and two national holidays are usually 3 days long, but it can be longer if preceded or followed by a weekend. During two national holidays every year more than half the population travels for family gatherings, which may be a unique situation to (Kim and Kang, 2011). That is why the traffic pattern of the national holidays should be differently classified from public holidays. The summer vacation usually lasts from the last week July to the first week of August in korea. The traffic pattern of these two weeks is different from the normal week. However, the winter vacation does not show a specific traffic pattern, except two special days like Christmas Eve and New year's Eve.

Table 1. Traffic pattern classification Type Traffic pattern classification 7 pattern: Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Day of the week Saturday

New year's day (Jan. 1), Independence Movement day (Mar. 1), Children's Day (May 5), Buddha's Birthday (Apr. 8*), Memorial day (Jun. 6), National Liberation Day (Aug. 15), National Foundation Day (Oct. 3), Holidays Proclamation Day (Oct. 9), Christmas (Dec. 25)

New year's Day according to Lunar Calendar(Jan. 1*) Full moon Festival (Aug. 15*)

Special day Christmas Eve (Dec. 24), New year's Eve (Dec. 31) *: Lunar calendar

The predicted traffic data are determined as the median of the recent 10 values in the same traffic pattern rather than mean value because the median value is more robust than the mean values for the outlier data. The recent 10 values are selected in order to consider the seasonal variations.

3.2 Short-term prediction The short-term prediction model considers evolution of spatial traffic phenomena over time based on the current as well as the near-past traffic data. Most data-driven approaches of the short-term prediction model have limitation that they follow the current traffic situation with a certain time lag. Significant improvement has not been shown regarding the problem even though many researchers tried to overcome the problem. The proposed short-term prediction algorithm minimizes the time lag of the current short-term traffic prediction algorithm and increases the reliability of the prediction by considering the spatial evolution of traffic phenomena. The short-term prediction model is based on K-nearest neighbor (K-NN) algorithm. The K-NN algorithm is one of the non-parametric regression learning algorithms and widely used

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for prediction problems due to the simplicity of the learning process and the high prediction accuracy (Tam and Lam, 2009; Robinson and Polak; 2005; Kasai and Warita, 2015). In the short-term prediction based on the K-NN algorithm, the time series of speed for a given interval (N) is analyzed to search for the closest pattern in the historical data set. The k time series of speed in the historical data set which are closest to the time series of the current speed are selected. The speed values are predicted by a weighted average of future time series of the selected k time-series. The weighting of the k speeds is set to be the reciprocal of the Euclidean norm, which is calculated based on speed. Bajwa et al (2005) applied the K-NN method to travel time prediction. They define the traffic pattern as a matrix on spatial as well as temporal scale. For the search of the similar traffic pattern in the historical dataset the whole section of the road with sufficient length of time to be predicted is included. Their method shows promising results for the recurrent congestion but could not be applied to the non-recurrent congestion because the non-availability of a similar pattern for the whole section during the enough time period from the historical database. For the expansion their method should be improved.

Step 1. Pattern Extraction and Take related links from Current speed

. Links considered according to speed in link i : Traffic Flow • Speeds in selected links for Pattern L

Time Speed Speed Speed Link i- Link i-1 Link i Link Link step (Link i) (Link i+1) (Link i-1) 2 i+1 i+2 t-5 83 86 88 t-4 85 82 85 t-3 82 85 83 t-2 72 70 82 t-1 51 55 70 t 45 45 55

Step 2. Calculate Euclidean distance and Take 3 patterns Step 3. Take the average speed of one hour (k=3) with the lowest Euclidean distance ahead speeds from selected k-Nearest Neighbors Real data Historical data k=1 k=2 k=3 Predicted Speed 83 86 88 82 82 78 78 88 82 t-5 82.5 81.7 83 t+1 35.3 85 82 85 85 82 85 85 82 85 t-4 82.2 85.5 84 t+2 32.2 82 85 83 82 7785 8383 89 86 8585 8583 83 t-3 85 85 85 t+3 20.0 72 70 82 72 8570 8282 85 55 8370 8882 89 t-2 72 72.6 71 t+4 33.3 51 55 70 51 8255 8570 83 43 7855 8570 87 t-1 55 58 52 45 45 55 45 7245 7055 82 42 7745 8855 86 t 44 42 44 t+9 60.2 51 55 70 86 72 55 t+1 36 32 36 t+10 72 45 45 55 Average 53 85 55 speed t+11 75 t+12 80 73 78 t+12 77 Figure 4. Steps of short-term prediction

The K-NN algorithm proposed in this paper considers traffic states in the upstream as well as the downstream traffic condition in limited spatial scale, by which the method can alleviate the shortcomings of time-lag problems in the literature review. To overcome another time-lag, this paper is limiting the time to the same time in same date from the historical data. This assumption is based on the fact that similar pattern of congestion repeats in similar time zone. In step 1, as mentioned above, neighboring links of link i are determined according to the speed of link i in current time and create matrix for 3 links during 30 min. Step 2 calculates the Euclidean distance between matrix in step 2 and historical speed data. In this step, depending on the size of the impact that influences the traffic speed, the weight of the each

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link is reflected. Then, take 3 neighbors (k=3) with the lowest Euclidean distance. Finally, in step 3, take each speed of 12 steps (one hour prediction horizon) from each of 3 selected patterns and calculate average speed of each step for 3 selected neighbors. The calculated average speed is the short-term prediction speed for the following one hour.

4. CASE STUDY

4.1 Site description

Seoul proper comprises 605.25 with radius of approximately 15km and has 10.5 million residents. Seoul Capital Area including the surrounding of metropolis and forms the world's largest with 25.6 million people. Therefore, most roads including the expressway in Seoul are congested especially in the peak hours. In Figure 5, Seoul urban expressway network consists in 7 expressways, i.e. Naebu Expressway (43.6km), Gangbyeon Expressway(80.9km), Olympic Highway(82.7km), Gyeongbu Expresswway (downtown section: 56.7km), Dongbu Expressway(55.7km), Bukbu Expressway(16.7km), Seobu Expressway(18.4km).

Bukbu Expressway Dongbu Expressway

Naebu Expressway

Gangbyeon Expressway

Olympic Expressway Seobu Expressway

Figure 5. Schematic representation of traffic prediction

The expressway is equipped with traffic observation system using not only fixed-location detectors such as video detectors, dedicated short range communications (DSRC) but also vehicle-based system applying brand call-taxi. The traffic data are collected from both types of traffic observation system. The expressway traffic management center produces speed and traffic volume data for its own node-link system. For the traffic prediction traffic volume and speed can be used. But the traffic volume collected from the fixed-location system does not show reliable value. Therefore, the future traffic state is predicted based on the speed data.

4.2 Performance evaluation

This chapter explains and evaluates the results of long-term and short-term prediction. To analysis long-term prediction, 10 days of history which reflect the day of a week were chosen and to analysis short-term prediction, recent 2 years of speed data were used as history data. For the performance evaluation, the prediction was carried out on a week without any special events (2013/10/27-2013/11/01) on Seoul city express way. The prediction was

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proceeded on both short-term and long-term on each link of the Seoul city’s node-link system. To overcome the gap between the result of short-term and long-term prediction, the weighted average for two predicted results were selected as the final predicted result. In this paper, the weight of short and long-term prediction changes in proportion to the time as indicated in Figure 6.

90 Actual speed Short-term speed Long-term speed 85 Combined speed

80

75 Speed(km/hr)

rate Weighted average 1 Long-term 70 Short-term 0 Current time After 1hour

65 500 600 700 800 900 1000 1100 1200 1300 1400 1500 Time(min)

Figure 6. Combination of short-term and long-term prediction

The mean absolute percentage error (MAPE) is used for evaluating the prediction result. MAPE is a measure of accuracy of a method for predicted speed values. Figure 7 and Figure 8 show the result of short-term prediction and long-term prediction for two links in Olympic highway (Length 26.2km, 15 Links). In Figure 7, if current pattern matches up with the pattern from historical data set at the same time of historical days, the prediction show similar result with real pattern without time lag. However, it may be difficult to predict huge fluctuation in a moment among tiny fluctuation because this pattern cannot be found from historical data set. This mismatch causes bigger MAPE in congestion state. We also compared the result of predicted speed after 15min, 30min, 45min and 60min with real speed for a day. The result shows that as the prediction term goes longer the MAPE goes bigger.

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Short-term predicted speed Short-term predicted speed (→Dongho Bridge) (Yangwha Bridge→Seongsan Bridge) 100 110 Actual Actual 90 15min 100 15min 30min 30min 80 45min 45min 90 60min 60min 70 80 60 70 current pattern matches

50 up with the pattern from Speed(km/h) Speed(km/h) 60 historical data set 40 MAPE : 10.45 15min 50 MAPE 15min: 2.85 30 MAPE 30min: 14.17 MAPE 30min: 3.72 MAPE : 16.98 20 45min 40 MAPE 45min: 4.10

MAPE 60min: 18.19 MAPE 60min: 4.18 10 30 0 500 1000 1500 0 500 1000 1500 Time(min) Time(min) Figure 7. Short-term predicted speed result for a day (2013/10/30)

Long-term predicted speed Long-term predicted speed (Seongsu Bridge→Dongho Bridge) (Yangwha Bridge→Seongsan Bridge) 100 110 Actual speed Actual speed 90 Predicted speed 100 Predicted speed

80 90

80 )

) 70

hr hr 60 70

50 60

Speed(km/ Speed(km/ 40 50

30 40

20 30 MAPE predicted: 15.60 MAPE predicted: 4.19 10 20 0 500 1000 1500 0 500 1000 1500 Time(min) Time(min)

Figure 8. Long-term predicted speed result for a day (2013/10/30)

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Table2 shows the MAPE for each link of Olympic-highway for a day. Results on the left side show the MAPE of short-term prediction and right side results show the MAPE of combined speed of short-term and long-term prediction. The links with heavy speed fluctuation and congestion shows bigger MAPE. The MAPE results of some links were improved after the combination process of short-term and long-term.

Table 2. MAPE for each link of Olympic-highway (2013/10/30) Short-term MAPE Combined Speed MAPE Link Name 15min 30min 45min 60min 15min 30min 45min 60min

------Cheonho Bridge ↓ 1.52 1.75 1.97 2.02 1.47 1.59 1.59 1.56 ↓ 2.94 3.39 3.27 3.73 2.87 2.99 2.72 2.65 Jamsil Railroad Bridge 3.31 3.63 3.66 4.02 3.19 3.35 3.34 3.59 ↓ 11.88 18.19 18.99 20.35 12.02 17.65 18.08 17.37 ↓ Cheongdam Bridge ↓ 11.16 18.33 23.39 25.43 11.06 16.51 18.18 13.69 Youngdong Bridge

↓ 10.45 14.17 16.98 18.19 10.63 13.71 15.15 15.16 Seongsu Bridge

↓ 8.91 11.84 14.58 17.08 9.17 11.98 13.74 13.70 Dongho Bridge

↓ 7.54 9.89 11.53 13.92 7.63 10.02 11.14 12.25

↓ 12.92 18.70 21.90 20.44 12.63 17.43 19.43 17.50

↓ 5.19 6.61 7.13 6.98 5.16 6.42 6.96 7.98 Dongjak Bridge ↓ 19.07 26.31 30.86 33.68 19.59 27.22 31.75 38.53 ↓ 5.92 6.40 6.79 7.13 5.98 6.68 7.17 8.85 Yeouido Upper ↓ 4.04 5.17 5.52 5.23 4.06 4.99 5.21 6.14 Yeouido Lower ↓ 3.02 4.10 4.27 4.54 3.15 4.13 4.43 5.24 Yangwha Bridge ↓ 2.85 3.72 4.10 4.18 2.86 3.56 3.73 3.85 Seongsan Bridge ------Average 7.38 10.15 11.66 12.46 7.43 9.88 10.84 11.20

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Table3 shows the average MAPE for each link of Olympic-highway between 2013/10/27 and 2013/11/01. The combination showed improved results as table 2 shows. Like this, the combined speed of short-term and long-term prediction and predicted travel time could be offered to the drivers through various method such as smart phones, web pages, navigations and etc. Figure 8 shows an example of information offered on webpage

Table 3. MAPE for each link of Olympic-highway for a week (2013/10/27-2013/11/01) Short-term MAPE Combined Speed MAPE Link Name 15min 30min 45min 60min 15min 30min 45min 60min

------Cheonho Bridge ↓ 1.84 2.11 2.32 2.44 1.78 1.97 2.05 2.31 Olympic Bridge ↓ 4.61 6.25 7.37 7.89 4.65 6.01 6.59 6.31 Jamsil Railroad Bridge 2.89 3.52 4.06 4.69 2.82 3.25 3.44 3.28 ↓ Jamsil Bridge 6.15 9.12 10.68 12.19 6.68 10.19 12.14 15.07 ↓ Cheongdam Bridge ↓ 9.17 13.91 18.30 21.30 9.85 15.05 18.54 20.44 Youngdong Bridge

↓ 8.94 13.55 16.62 18.79 9.18 13.76 16.06 18.33 Seongsu Bridge

↓ 11.19 14.83 17.18 19.94 11.58 15.24 17.22 19.20 Dongho Bridge

↓ 8.75 11.23 13.50 15.59 8.91 11.46 13.16 14.41 Hannam Bridge

↓ 8.77 13.52 17.29 19.27 8.78 13.11 15.61 15.67 Banpo Bridge

↓ 6.64 8.22 9.18 9.75 6.51 7.80 8.38 8.86 Dongjak Bridge ↓ 11.38 16.68 20.64 23.30 11.99 17.76 21.61 25.64 Hangang Bridge ↓ 6.27 7.58 8.26 8.88 6.31 7.62 8.30 9.62 Yeouido Upper ↓ 4.78 5.88 6.31 6.64 4.77 5.74 6.13 7.17 Yeouido Lower ↓ 5.45 6.93 7.80 8.44 5.81 7.58 8.58 10.21 Yangwha Bridge ↓ 5.61 6.95 8.00 8.57 5.76 7.30 8.38 9.71 Seongsan Bridge ------Average 6.83 9.35 11.17 12.51 7.03 9.59 11.08 12.42

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The weighted average of the short-term and the long-term prediction has been offered to the drivers through web pages during the new year’s day, 2015 according to the lunar calendar. Figure 9 shows an example of information offered on webpage.

Figure 9. Example of service for predicted result

5. CONCLUSION

Many drivers want to plan their trip efficiently in advance by scheduling the departure time and travel route so that they can reach their planned destination without much traffic delay. Such demands from the drivers increased the importance of the traffic prediction. Intelligent transportation systems (ITS) provides advanced prediction information from huge data, however inappropriate information may cause human drivers’ distress and confusion and which can finally lead to serious congestion. This may be due to lack of reliable data. Although the government and research centers have huge data, the data practically used for the traffic prediction is very limited. For the traffic prediction, traffic volume and speed can be used, but the traffic volume collected from the fixed-location system does not show reliable value. Therefore, this paper only used the speed data to predict the future traffic. In this paper the traffic prediction algorithms were 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. The prediction was proceeded on both short-term and long-term on each link of the Seoul city’s node-link system. To overcome the gap between the result of short-term and long-term prediction, the weighted average for two predicted results were selected as the final predicted result (combined speed). To evaluate the performance of short-term, long-term and combined prediction, the prediction was carried out on a week without any special events (2013/10/27-2013/11/01) on Olympic express way. The prediction shows similar result to the actual speed pattern.

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If we could apply more conditions like weather, specific events (accidents, constructions and etc.) and holidays to the historical data, the prediction will show more precise result. This may be done on the future work. The predicted information may be useful to the commuting drivers, a forwarding agents, taxi drivers, local governments’ event preparation and even also to the road managements.

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