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Development of Traffic Forecast Systems

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Development of Traffic Forecast Systems Development of traffic forecast systems Youngho Kim • The Korea Transport Institute • Research fellow • [email protected] Contents 1. Research Background 2. Methodology • Long-term prediction • Short-term prediction • Combined prediction 3. Prediction Result 4. Case Study (Seoul city) 5. Conclusion 1 Motivation of the research Reliability & Predictability 2 Traffic Prediction New road constructionMain and titleextension to reduce the congestion are almost impossible due to lack of budget and difficulty of securing the lands for the roads Back grounds ⇒ To make the best use of the existing roads, the proactive traffic control strategy and traffic demand management necessary based on the traffic prediction information 3 Predicted traffic information (주요 교통관련 분야) Real time Information Optimal travel route during the trip (주요 교통관련 분야) Predicted Optimal travel route and departure Information time before the trip Optimal departure time choice + Optimal travel route choice Optimal travel route choice 4 State of the art (Data based approach) Germany- Bayerninfo England- Highway Agency France - Autoroutes USA- INRIX 5 State of the art (Model based approach) PTV - OPTIMA TSS - AIMSUN 6 State of the art (Model based approach) Autobahn.NRW 14:15 14:30 14:45 15:00 7 State of the art (Data based approach) Google map Traffic information 6:00AM Oct. 10. 2015 8 System development transferability Road type Available data set Model driven approach Data driven approach (Causality) (Correlation) Long-term Short-term (cf. Climate forecast) (cf. Weather forecast) Recurrent congestion – non recurrent congestion Output Ground truth value(?) Accuracy evaluation Analysis of prediction effects 9 System development 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) 10 Vision of traffic forecast system 11 TRAFFIC PREDICTION ALGORITHM Input data Data processing Output data Traffic data based on node-link (speed, flow, occupancy) Long-term prediction algorithm Combination of two prediction (Long-term prediction + Short-term Non traffic data Short-term prediction algorithm prediction) (Weather, Construction, Incident) Prediction horizon Short-term prediction 1hour Long-term prediction Current time 24 hours Long-term Algorithm Short-term Algorithm Input data 5-min speed and flow data (historical data) 5-min speed and flow data (real-time and historical data) Output data 5-min predicted speed data 5-min predicted speed data Categories: day of week, special days(Christmas Eve and New Year’s Eve..) and holidays Modified k-Nearest Neighbor Methodology Long-term prediction: (historical data : 3years) the median of the most recent 10 data in the same categories Prediction Prediction range: at anytime from current time Prediction range: within 1 hour from current time Horizon 12 LONG-TERM PREDICTION Assumption of long-term prediction: the evolution of traffic is reproducible across days with similar attributes The median of the most recent 10 data in the same categories The median shows more robust predictions against outliers than the mean Type Traffic pattern classification Day of the week Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday New Year's Day (Jan. 1), Independence Movement day (Mar. 1), Children's Day (May 5), Buddha's Short holidays Birthday (Apr. 8*), Memorial day (Jun. 6), National Liberation Day (Aug. 15), National Foundation Day (Oct. 3), Hangul Proclamation Day (Oct. 9), Christmas (Dec. 25) Long holidays New Year's Day according to Lunar Calendar (Jan. 1*), Chuseok (Aug. 15*) Special days Christmas Eve (Dec. 24), New Year's Eve (Dec. 31) *: Holidays in lunar calendar 13 SHORT-TERM PREDICTION Pattern matching algorithm: looking for the best matching historical traffic data set with the real-time data 100 Actual 90 15min 30min 80 45min 60min - use traffic data collected not only 70 from current links but also from 60 50 neighboring links Speed(km/h) 40 - the training set for the K-NN 30 algorithm is bounded to the same 20 10 time of day to search for similar 0 500 1000 1500 Time(min) traffic data. 14 SHORT-TERM PREDICTION Q (veh/h) 80km/h Free flow synchronized 40km/h congested K (veh/km) 15 SHORT-TERM PREDICTION 16 Combination of long- and short-term prediction ()=()·() + (1 - ())·() (): Predicted Travel Speed at Time, t (): Predicted Short-term Travel Speed at Time, t (): Predicted Long-term Travel Speed at Time, t (): Weight Function which returns weight value as a function of prediction horizon 90 1 Actual speed Short-term speed 0.9 Long-term speed 85 Combined speed 0.8 0.7 80 termprediction - 0.6 0.5 75 Speed(km/hr) 0.4 rate Weighted average 0.3 1 Long-term 70 Short-term Weight for Short Weight 0 0.2 Current time After 1hour 0.1 65 5 10 15 20 25 30 35 40 45 50 55 60 500 600 700 800 900 1000 1100 1200 1300 1400 1500 Prediction horizon(min) Time(min) 17 Results 18 Test site Seoul urban expressway (6 expressways) - Total length: 295.2 km 19 Performance evaluation for a day 20 Performance evaluation for a week Short-term Long-term Combined MAPE (%) MAPE (%) MAPE (%) Olympic expressway Link section 15 30 45 60 15 30 45 60 - min min min min Min Min min min - Total length: 41.2 km - - - - - - - - - Cheonho Bridge - Test period: 1 week ↓ 3.3 3.8 4.1 4.4 5.4 3.4 3.9 4.2 5.04 Olympic Bridge ↓ 6.1 8.5 10.3 11.6 18.9 7.1 10.5 12.8 16.6 2013/10/25~11/01 Jamsil Railroad Bridge ↓ 4.6 5.6 6.7 7.7 16.0 5.4 7.6 9.6 14.1 Jamsil Bridge ↓ 7.1 10.5 12.7 14.6 21.6 8.0 12.3 15.1 19.3 Cheongdam Bridge ↓ 9.6 14.5 19.1 22.2 25.5 10.4 16.1 20.2 23.3 Youngdong Bridge ↓ 9.2 13.6 16.9 19.1 19.6 9.4 13.9 16.5 18.9 Seongsu Bridge ↓ 11.8 15.7 18.1 20.7 21.1 12.2 16.0 18.0 19.6 Dongho Bridge ↓ 8.7 11.3 13.9 16.2 15.1 8.9 11.4 13.2 14.0 Hannam Bridge ↓ 8.6 13.1 16.8 18.8 17.0 8.6 12.7 15.1 15.0 Banpo Bridge ↓ 7.1 8.63 9.6 10.3 10.7 7.0 8.2 8.8 9.2 Dongjak Bridge ↓ 12.1 17.8 22.1 25.0 28.3 12.8 19.1 23.2 27.2 Hangang Bridge ↓ 6.4 7.6 8.3 8.8 9.6 6.4 7.6 8.2 9.5 Yeouido Upper ↓ 4.9 6.1 6.7 7.1 7.8 5.0 6.1 6.6 7.9 Yeouido Lower ↓ 6.2 7.5 8.5 9.3 12.3 6.6 8.4 9.6 11.8 Yangwha Bridge ↓ 7.4 9.2 10.5 11.1 14.4 7.9 10.2 11.8 14.1 Seongsan Bridge - - - - - - - - - Average 7.5 10.2 12.3 13.8 16.2 7.9 10.9 12.8 15.0 1 21 Performance evaluation for a week for whole network Seoul urban expressway Short-term Long-term Combined Expressway MAPE (%) MAPE (%) MAPE (%) (6 expressways) name 15 30 45 60 15 30 45 60 - min Min min Min Min Min Min min - Total length: 295.2 km Olympic 8.17 10.53 12.12 13.24 18.59 8.73 11.78 13.90 17.81 - Test period: 1 week Gangbyeon 8.86 11.06 12.43 13.36 19.62 9.28 12.18 14.18 18.48 2013/10/25~11/01 Gyeongbu 6.45 8.33 9.64 10.60 11.62 6.55 8.43 9.56 10.91 Naebu 6.15 7.94 9.25 10.41 14.31 6.43 8.67 10.30 13.33 Dongbu 9.10 10.94 12.01 12.70 14.09 9.24 11.13 12.11 13.40 Bukbu 9.11 11.22 12.50 13.35 17.91 9.45 12.10 13.79 16.87 Average 7.97 10.00 11.33 12.27 16.02 8.28 10.71 12.31 15.13 1 22 Traffic forecast system (main page) Real-time and predicted speed Date selection for in Seoul the Long-term expressway prediction Congestion and event report Time selection http://topis.seoul.go.kr/predict/ 23 Traffic forecast system (accuracy evaluation page) Modified KNN parameter MAPE for all expressways (15min, 30min, 45min, 60min, Long-term) Contour map for selected expressway during recent 24 hours (15min, 30min, 45min, 60min, Long-term) 24 Conclusions New traffic prediction method is proposed to provide travel information prior to departure as well as during trips The proposed traffic prediction algorithm combines short- term (i.e. real-time data based) prediction and long-term (i.e. recurrent historical data based) prediction - short-term: modified k nearest neighbors - long-term: median speed for 10 days with similar patterns Traffic forecasting service using the proposed algorithm is about to open in Seoul Future works : Prediction considering the effects in the supply side such as changes resulting from precipitation, construction, or incidents; Urban street network 25.
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