STATISTICAL METHOD FOR PRE-TRIP TRAFFIC CONDITIONS PREDICTION

Hiroshi WARITA Metropolitan Expressway Public Corporation, 43-5, -Hakozakicho, Chuo-ku, 103-0015, TEL: +81-3-5640-4857, Fax: +81-3-5640-4881, E-mail: [email protected] Hirohisa MORITA Professor, Research Institute of Science & Technology, Nihon University 7-24-1, Narashinodai, Funabashi, Chiba 274-0063, Japan TEL: 81-47-469-5504, Fax: +81-47-469-2581, E-mail: [email protected] Edward CHUNG Visiting Professor, Center for Collaborative Research, University of Tokyo 4-6-1 Komaba, -ku, Tokyo 153-8904, JAPAN TEL: +81-3-5452-6529, FAX: +81-3-5452-6420, E-mail: edward @iis.u-tokyo.ac.jp Masao KUWAHARA Professor, Center for Collaborative Research, University of Tokyo 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, JAPAN TEL: +81-3-5452-6418, FAX: +81-3-5452-6420, E-mail: [email protected] Atsushi TANAKA Tokyo Headquarters, Oriental Consultants Co.,Ltd. 3-5-7 Hisamoto, Takatsu-ku, Kawasaki City, Kanagawa, 213-0011, Japan TEL: +81-44 812 8813 FAX: +81-44 812 8823 E-mail: [email protected]

ABSTRACT Traffic information on the Metropolitan Expressway network is supplied via various media. Current available real time information is associated with present traffic condition. However, current real time information is not suitable for pre-trip decisions as the information does not factor in the time interval to joining the expressway network and this is subject to considerable prediction errors. This research involved the proposal of a method for matching historic traffic data with real-time traffic data as a means of predicting the travel times and bottleneck status immediately prior to departure. The method was tested on major routes and producing satisfactory accuracy, and therefore demonstrating that the method has attained a deployable level of accuracy. We further propose an effective method of supplying this data to facilitate informed departure time decisions.

INTRODUCTION Delays caused by chronic traffic congestion on the Metropolitan Expressway are a major problem. For this reason, it is important to take measures to reduce traffic congestion and develop transport networks. At the same time, it is important to provide road traffic information so road users can choose the optimal routes and time periods, thus dispersing transport demand. Various kinds of information on the Metropolitan Expressway, such as information on traffic congestion and travel time, are provided via diverse media, including information boards, Internet and mobile terminals, so that drivers can use the information when making travel plans, just before they depart or when driving on the Expressway. Pre-trip information on traffic

1 congestion and travel time is provided via the Internet, mobile terminals and other media, but the information reflects conditions at the time of accessing the information. For this reason, the conditions of traffic congestion are likely to change by the time the driver actually reaches the Expressway. Therefore, it is necessary to provide pre-trip information that can anticipate possible changes in traffic volumes. The scope of road traffic information is being expanded, and the types of road traffic information providers and tools are diversifying, but the content remains unchanged. In order to keep up with the changing demand, the content of the information provided needs to be refined. This paper proposes and examines the applicability of a method of matching accumulated traffic data with data for the day of prediction to predict pre-trip road traffic conditions. It also proposes a method of providing information that may be effective in enabling drivers to decide departure time and make other choices.

OUTLINE OF THE PREDICTION METHOD

CONCEPT Figure 1 illustrates the concept of this method which has to account for the fluctuations in the travel times. As shown, we focused on the fact that the fluctuations in travel conditions immediately prior to departure also affect traffic conditions expected in the near future and, by matching historic traffic data with real-time traffic data, a method of predicting traffic conditions after the present time was developed. The prediction method was developed for Route No. 3 line in the inbound direction, and was verified on 9 other routes.

WHEN PREDICTIVE VALUES ARE GIVEN Recent survey of Metropolitan Expressway users (see Reference (6)) showed that nearly half need pre-trip information one hour before they use the Metropolitan Expressway. The results of surveys conducted at the entry and exit points of the Metropolitan Expressway indicate that the average distance covered when the driver approaches the Expressway (distance between an on-ramp and the center of its destination zone) is 21 km. If half of the distance represents the distance to the entrance and the vehicle runs on surface streets at an average speed of 20 km/h, it takes about 30 minutes to get from the starting point to the Expressway. From these results, it is assumed that drivers can easily reach the entrance of the Metropolitan Expressway if they have one hour. The authors decided to add an additional hour, to account for a possible change in departure time, and provide pre-trip information up to two hours ahead.

LOCATIONS RESEARCHED AND DATA USED The prediction method was studied for the Yoga-Tanimachi section of Route No. 3 (see Figure 2). The following data were used. - Period: One years and two months from June 1, 2002 to May 31, 2003 - Type: Five-minute data for each section, including travel time and number of vehicles running in the section (traffic density × length of section) - Date of prediction: June 26 (Thursday) to June 29 (Sunday), 2003, August 28 (Thursday) to August 31 (Sunday), 2003

2 Days with similar Emerging conditions also pre-trip trends similar Miyakezaka Route No. 3 (Shibuya) Inbound junction Clock wise Ttravel time on days Yoga with similar trends Sangenjaya Ikejiri Shibuya Takagicho Inner Tanimachi circular Distance: Approximately 12 km route

Travel time time Travel junction Traffic volume: Some 40,000 vehicles a day

Travel time today Ichinohashi Counter clock junction wise

Current time time Figure 1 Method Concept Figure 2 Route Covered in the Study

PREDICTION METHOD Past accumulated data Data for the day of the trip Figure 3 gives an overview of prediction Matching procedures. Pre-trip g 1 2 3 ࡮࡮࡮࡮࡮࡮࡮ two-hou Number of vehicles r period ඙㑆ᚲⷐᤨ㑆 running in the ඙㑆ᚲⷐᤨ㑆 section (ST) PAST DATA 12 3඙㑆ᚲⷐᤨ㑆඙㑆ᚲⷐᤨ㑆࡮࡮࡮࡮࡮࡮࡮ Current ඙㑆ᚲⷐᤨ㑆඙㑆ᚲⷐᤨ㑆 time for matchin for Number඙㑆ᚲⷐᤨ㑆 of vehicles

The authors classify accumulated data into period Two-hour running in the section Squared error for the same (SP) time and the same time zone three categories: weekdays, Saturdays, and Ǜ(SPij-STij)2 1’ 2’ 3’ ࡮࡮࡮࡮࡮࡮࡮ Sundays and use data accumulated over one Days are sorted in the order of smallest errors first year. Ranking Date 㧝 **/**

Two-hour period Two-hour 㧞 **/**

used for prediction prediction for used 㧟 **/** DATA USED FOR MATCHING To match historic data with real-time data, Calculation of predictive values vehicle speed data for each section, travel Extracts the section travel times 1’ 2’ 3’ ࡮࡮࡮࡮࡮࡮࡮ for the top 10 days with similar Section඙㑆ᚲⷐᤨ㑆඙㑆ᚲⷐᤨ㑆 travel time time, number of vehicles running and other tendencies (top 10 days) factors were compared. The median for the section travel 1’ 2’ 3’ ࡮࡮࡮࡮࡮࡮࡮ For data matching, the authors use data on times for the top 10 days is Predicted section calculated travel time the number of vehicles running in the (median) section (traffic density × length of section), The time slice value for the Predicted predicted section travel time is travel time which involves the factors of distance and calculated. traffic volume in addition to vehicle speed The predicted section vehicle speed is 1’ 2’ 3’ ࡮࡮࡮࡮࡮࡮࡮ calculated by dividing the length of Predicted section and makes it possible to pinpoint the the section by the predicted section vehicle speed travel time. (Bottleneck status) condition of upstream vehicles. Figure 3 Prediction Procedures

MATCHING For data matching, the accumulated daily squared error for the number of vehicles running in each section at the same time was calculated using historic data and real-time data, and days with small errors were extracted as showing similar tendencies.

SCOPE OF MATCHING Since Route No. 3 inbound is affected by upstream traffic congestion on the , this was also taken into consideration when deciding the spatial scope of matching. As a result, for the scope of matching, the adjoining section of the Inner Circular Route was added to the Yoga-Tanimachi section (17 sections, 12.0km). Specifically, the section leading up to the Ichinohashi junction (2 sections, 1.3km) was added for the inner route of the Loop and the

3 section leading up to the Miyakezaka junction (2 sections, 1.4km) was added for the outer route (see Figure 4). In order to take into account the volume of traffic flowing from the upstream of the section subject to prediction, a method covering Route No. 3 (the subject route) and its upstream (Tomei Expressway, 12 sections, 18.5km) was studied. In the section “Verification of the Prediction Method” below, however, a method of including the upstream section only for the

Route No. 3 is used for the reason of data Miyakezaka JCT collection. Route No.3 Judging from the variation in travel times for the Tomei Expressway Shibuya Line extracted days due to the time of matching and Tanimachi Yokohama- Yoga other factors, the authors also decided that the machida JCT Ichinohashi JCT temporal scope of matching should be two hours. hid Figure 4 Spatial Scope of Matching

METHOD OF CALCULATING PREDICTIVE VALUES From the results of matching, the top 10 days that showed similar tendencies were extracted. For these top 10 days, the median of travel times for five-minute periods for a given section was calculated as the predicted travel time for the section. These medians were added up by time slices method (tracing the trajectory with the passage of time) to obtain the predicted travel time for the Yoga-Tanimachi section. The reason the medians for the top 10 days were used instead of those for the days that show the most similar tendencies is that there is variation in travel time even among the top 10 days, indicating that specific data may still remain for the 10 days. The predicted travel time for a given section and the length of the section were used to calculate the vehicle speed for the section, which was used as a predictive value for traffic congestion.

VERIFICATION OF THE PREDICTION METHOD

EVALUATION METHOD In order to evaluate the applicability of the prediction method, prediction accuracy levels were analyzed for nine routes of the Metropolitan Expressway (see Figure 5). Prediction accuracy levels were analyzed for 10 days, and predictive values for six two-hour periods starting between 6:00 a.m. and 8:00 p.m. were covered in the analyses (see Table 1 and Figure 6). A focus group survey conducted by the Metropolitan Expressway Public Corporation (see Reference (6)) show that the permissible prediction error for pre-trip information is ±10 minutes, and therefore, this was used to evaluate the predictions. This method’s predicted values for the inbound line of the Expressway No. 3 (Shibuya Line) were compared with real-time information provided by the Metropolitan Expressway telephone service (hereinafter referred to as instantaneous values) and statistical data on average travel time provided via the Metropolitan Expressway Public Corporation’s Web site (see Figure 6). In this context, an instantaneous value is the sum of the travel times for subsections at the time of access. Statistical data is the median travel times calculated taking into account the characteristics of travel times for particular days of the week and seasons observed over the past several years.

4 RESULTS OF PREDICTION Of the cases verified, Figure 7 compares the predictions for travel times and the condition of traffic congestion with actual measured values for Route No. 3 (inbound) on Thursday, June 26. The figure suggests that the predictive values are almost consistent with the actual ones in terms of the tendency of travel time to increase or decrease and the length of congestion. Figure 8 shows the results of prediction by route, time zone and day of the week. According to this figure, even if accidents and construction work are taken into consideration, an overall average accuracy level of 80% or higher is attained. By route, an accuracy level of 80-90% is almost achieved, but the accuracy and the Horikiri-Yoga section of Route No. 6 (9) is somewhat low, at around 60%. This route was affected by the new route that came into service (Central Circular Oji Route), and only five months of accumulated data was available for matching. This lowers the accuracy level due to the lack of similar days ie. smaller database. By time, the accuracy level for the early morning periods starting at 6:00 and 8:00 a.m. is high, and by contrast, that for the evening is somewhat low. By the day of the week, the accuracy level for Sundays, Mondays and Thursdays exceeds 80%, but that for Fridays and Saturdays is somewhat low. The reason for low accuracy levels for the evening periods, as well as Fridays and Saturdays, is that the variation in travel time is greater than for other time periods and other days of the week. Bijogi Number Route Length (1) Route No. 3 (Yoga -Tanimachi) 12km Kohoku (2) Route No. 3 (Tanimachi - Yoga) 12km (3)21km (3) Route No. 5 (Bijogi - Takebashi) 21km 㧔BijogiЈ Horikiri (6)(7)18km (4) Bay Shore Route Takehashi㧕 㧔KasaiЊKohoku㧕 (Showajima - Kasai) 16km (5) Bay Shore Route Takehashi (Chidori-cho – ) 25km (4)16km (6) Tani machi 㧔Showajima 18km Chidori-cho (Kasai -Kohoku) (1)(2)12km ЈKasai㧕 㧔YogaЊTani machi㧕 (7) Central Circular Route (Kohoku - Kasai) 18km Yoga Kasai (8)(9)28km (5)25km (8) Route No. 3 (Yoga) - Route No. 6 (Horikiri) 28km 㧔YogaЊHorikiri㧕 㧔Chidori-choЈHaneda Airport㧕 (9) Route No. 6 (Horikiri) 28km Showajima - Route No. 3 (Yoga) Haneda Airport Figure 5 Routes Analyzed

Days June 26 (Thursday) to June 30 (Monday), August 25 (Monday) and August 28 (Thursday) to August 31 (Sunday), 2003 Time zones 6:00-8:00, 8:00-10:00, 10:00-12:00, 12:00-14:00, 16:00-18:00 and 18:00-20:00 (including accidents and construction work) Prediction period Two-hour period from each hour (every five minutes; 24 samples per case) Table 1 Periods and Time Zones for Which Accuracy Levels Were Analyzed Actual measured value Error Statistical data

Value predicted using this method Instantaneous travel time The access-time value is constant because it is for the time when pre-trip information is provided. Travel time Travel

8:00 10:00 Prediction period (Every five minutes) Time when information is provided (users look at the information) Figure 6 Conceptual Diagram of Evaluation of Predictive Values

5 Bottleneck status Travel time Measured Predicted value

JCT JCT value JCT JCT Yoga Yoga

Yoga Yoga Travel time (minutes) Tanimachi Tanimachi Tanimachi Tanimachi 12km 12km 0 10 20 30 40 50 60 8:00 Predicted value 10:00 12:00 Measured value

14:00 16:00

18:00 : Congestion (Traveling speed 20km/h or les) : Slight congestion (Traveling speed above 20km/h below 40km/h) Figure 7 Comparisons of Traffic Congestion and Travel Time By route

Frequency of prediction errors coming within the range of ± By time zone Frequency of prediction errors coming 10 minutes within ±10 minutes 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%

(1) Route No. 3 6:00 91% 87% (Yoga 㸢Tanimachi) 8:00 86% (2) Route No. 3 99% 10:00 78% (Tanimachi 㸢 Yoga) (3) Route No. 5 12:00 85% 68% (Bijogi 㸢 Takebashi) 16:00 77% (4) Bay Shore Route 94% 18:00 76% (Showajima 㸢 Kasai) (5) Bay Shore Route 81% By the day of Frequency of prediction errors coming (Chidoricho 㸢 Airport Central) the week within ±10 minutes (6) Central Circular Route 79% (Kasai 㸢Kohoku) 0% 20% 40% 60% 80% 100% (7) Central Circular Route 93% (Kohoku 㸢Kasai) Sunday 92% (8) Yoga on Route No. 3 75% Monday 84% 㸢Horikiri on Route No. 6 (9) Horikiri on Route No. 6 Tursday 82% 64% 㸢Yoga on Route No. 3 Friday 78% Average 82% Saturday 75% Figure 8 Results of Prediction

COMPARISON WITH CURRENTLY PROVIDED DATA Figure 9 and Table 2 compare the value predicted using this method with statistical data and instantaneous travel time currently provided by the Metropolitan Expressway Public Corporation for Route No. 3 (inbound) on two specific days. According to these figure and table, the predictive values obtained using this method accurately reflect the fluctuations in travel time during the two-hour period and attain the highest accuracy level. The statistical data have a high accuracy level in the morning, but the accuracy level for the afternoon, which sees greater fluctuations in travel time, declines. The instantaneous travel time have a high accuracy level

6 when they are provided, but if fluctuations in travel time become large, the accuracy level falls with the passage of time. A look at the accuracy level at the time of prediction indicates that some of the predictive values obtained using this method attain an accuracy level comparable to or higher than that for instantaneous travel time, suggesting that they can be utilized as information for vehicle drivers running on the Expressway, including information boards that display travel time. From the results of the above prediction, it can be said that data obtained using this method compare favorably not only with statistical data but also with real-time data, making it possible to provide information that shows future tendencies.

Thursday, June 26 Actuallyታ᷹୯ measured value Friday, June 27 70 Value predicted using this 70 Actually measured value ᧄᚻᴺ䈮䉋䉎 ታ᷹୯ method Value predicted using this 60 60 ᧄᚻᴺ䈮䉋䉎 Statistical⛔⸘୯ data method ⛔⸘୯ 50 instantaneous⍍ᤨ୯ travel data 50 Statistical data Instantaneous⍍ᤨ୯ travel data 40 40 30 30 20 20 10 10 Travel time (minutes) Travel time (minutes) 0 0 㪏㪑㪇㪇 㪏㪑㪊㪇 㪐㪑㪇㪇 㪐㪑㪊㪇 㪏㪑㪇㪇 㪏㪑㪊㪇 㪐㪑㪇㪇 㪐㪑㪊㪇 㪈㪏㪑㪇㪇 㪈㪏㪑㪊㪇 㪈㪐㪑㪇㪇 㪈㪐㪑㪊㪇 㪉㪇㪑㪇㪇 㪈㪉㪑㪇㪇 㪈㪉㪑㪊㪇 㪈㪊㪑㪇㪇 㪈㪊㪑㪊㪇 㪈㪋㪑㪇㪇 㪈㪇㪑㪇㪇 㪈㪍㪑㪇㪇 㪈㪍㪑㪊㪇 㪈㪎㪑㪇㪇 㪈㪎㪑㪊㪇 㪈㪏㪑㪇㪇 㪈㪇㪑㪇㪇 㪈㪉㪑㪇㪇 㪈㪉㪑㪊㪇 㪈㪊㪑㪇㪇 㪈㪊㪑㪊㪇 㪈㪋㪑㪇㪇 㪈㪍㪑㪇㪇 㪈㪍㪑㪊㪇 㪈㪎㪑㪇㪇 㪈㪎㪑㪊㪇 㪈㪏㪑㪇㪇 㪈㪏㪑㪇㪇 㪈㪏㪑㪊㪇 㪈㪐㪑㪇㪇 㪈㪐㪑㪊㪇 㪉㪇㪑㪇㪇 Figure 9 Comparisons with Currently Provided Data (Route No. 3)

Item Predictive Statistical Instantaneo value data us travel time Overall percentage of prediction errors coming within ±10 95% 75% 78% minutes Percentage of prediction errors at the time of prediction coming within ±10 minutes 100% 67% 100% Percentage of prediction errors half an hour after the time of prediction coming within ±10 minutes 100% 83% 100% Percentage of prediction errors one hour after the time of prediction coming within ±10 minutes 100% 75% 67% Percentage of prediction errors one and a half hours after the time of prediction within±10 minutes 75% 67% 75% Percentage of prediction errors two hours after the time of prediction within ±10 minutes 100% 83% 50% Table 2 Accuracy Level Comparison Table

EFFECTIVE DATA PROVISION METHODS FOR DEPARTURE TIME DECISIONS

The aforementioned prediction results were used to test effective methods of providing the data to facilitate informed departure time decisions. Figure 10 shows travel time and traffic congestion data provided on the Internet, etc. As illustrated, measured values up to the time of issue and predicted values are shown in a continuous time series. This means that even infrequent road users can ascertain clear times and select their departure time accordingly. Providing average travel times obtained from historic statistical data (40th -60th percentile value) simultaneously is a useful service for frequent road users allowing them to ascertain whether travel times are likely to be longer on the day in question.

7 Bottleneck status Route No. 3 Shibuya Line YogaSangenjaya Takagicho Tanimachi Current and Predicted Travel Time for Yoga-Tanimachi Ikejiri Shibuya Sector of the Route No. 3 Shibuya Line Yoga toll gate 40 6:00 34 74.7666667 79 83Legend 79.23333333 81.9333333 75.76666667Vehicle 76 75.4333333 71.96666667 speed: 73.9 0-20 72.7 74.833333 km/h75.83333 66.36666667 ) 35 18.9 75.2 80.1333 84.0333333 79.86666667 82.9 76.56666667Vehicle 77 73.2333333 speed:70.4 69.36666667 20-40 71.4 72.7km/h75.6 66.16666667 Average range of Vehicle speed: 40 km/h or 30 Predicted travel time 18.7 75.5666667 81.1667 84.8 80.3 83.9 75.03333333 75 75.6666667 72.86666667 71.16666667 71.1333333 71.966667 75.73333 61.13333333 travel times 12.7 75.3333333 82.5667 86.2 82.76666667 85.1 76.26666667 76 76.9666667 74.26666667 72.2 72.4 73.066667 77.2 65.63333333 25 7:00 Travel time for the period 13.3 76.4666667 80.2333 85.6 80.03333333 83.1 74.63333333 75 71.8666667 68.6 68.93333333 71.5666667 71.8 74.53333 53.63333333

20 up to the present present the to 12.433 75.8666667 78.4 81.6 77.56666667 82.8 73.83333333 74 68.3666667 62.43333333 65.36666667 68.8 69.433333 71.7 61.1 15 11.633 76.7666667 81.2667 86.2 80.53333333 84.8 76.96666667 77 69 65.2 63.8 69.1666667 66.333333 60.83333 51.06666667 Travel time (minutes

Congestion for the period up period the for Congestion 12.167 74.8666667 78.4 83.2 76.73333333 81.1333333 73.13333333 73 69.9 66.36666667 66.8 69.6333333 50.9 43.76667 52.46666667 10 8:00 11.4 76.4666667 78.9167 83.2833333 78.5 80.2 73.88333333 74 64.1333333 56.78333333 57.43333333 64.4333333 56.166667 42.75 47.21666667 5 11.033 76.8333333 79.6833 84.0833333 78.88333333 79.25 72.16666667 72 55.0166667 50.58333333 45.98333333 50.1833333 43.15 35.06667 45.58333333

0 11.55 75.7166667 78.1833 83.2166667 77.31666667 80 71.36666667 71 55.0166667 38.3 38.08333333 35.7166667 34.416667 34.96667 39.51666667

13.65 73.0166667 75.0833 80.2 73.25 69.75 45.28333333 46 27.9833333 30.53333333 33.93333333 35.1666667 33.9 36.8 41.5

5:00 5:30 6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 9:00 14.917 70.8833333 67.7833 64.5666667 53.95 31.65 25.3 25 25.9333333 30.95 35.6 34.4666667 29.966667 34.51667 40.06666667

15.733 52.6166667 52.75 49.45 30.93333333 24.6833333 22.18333333 22 23.0166667 28.66666667 33.48333333 30.4333333 28.3 33.93333 39.2 Current time 15.717 31.0166667 25.1667 28.8166667 25.23333333 23.4 21.66666667 22 21.4166667 27.63333333 30.88333333 28.15 28.666667 33.51667 39.85 Predicted congestion Predicted

16.183 26.5 28.2 22.9166667 24.45 23.3333333 23.8 24 26.4666667 30.88333333 36.11666667 30.3333333 30.233333 34.51667 39.53333333 Figure 10 Image of Data Provided

CONCLUSION This paper has devised a prediction method that matches historic road traffic data with real-time road traffic data in order to provide drivers with travel time and traffic congestion information before they depart. Verification of the method for major routes of the Metropolitan Expressway indicates that a high accuracy level was attained for all the routes. Furthermore, values obtained using the method are more accurate than the statistical and instantaneous travel time currently being provided as pre-trip information. We would like to propose this method as it is highly efficient as well. Issues to be addressed in the future include expanding matching criteria and time zones in an effort to further raise the accuracy level of prediction. To put the method to practical use, it is necessary to provide information at the time of an accident and develop a specific system for providing information in such cases as when trip sections are selected at random.

ACKNOWLEDGEMENTS In this research, discussions of the study group for travel time predictions organized by the Kuwahara Laboratory of the University of Tokyo’s Institute of Industrial Science, particularly those with graduate student Shamas ul Islam Bajwa, were very instructive. The Japan Highway Public Corporation and Mr. Yoriyuki Sunaga of the Metropolitan Expressway Public Corporation provided valuable data for analysis. The authors would like to express their gratitude for their contributions to this research.

REFERENCES (1) Hiroshi Warita㧘Tomoaki Okada, Atsushi Tanaka “EVALUATION OF OPERATION FOR TRAVEL TIME INFORMATION ON THE METROPOLITAN EXPRESSWAY”, Proceedings of 8th World Congress on Intelligent Transport Systems㧘Sydney㧘2001 (2) H. , Y. Ohba, M. Kuwahara, “The Comparison of Two Type Travel Time Prediction Methods Using Toll Collection System Data”, First ITS Symposium 2002 Proceedings, pp. 515-520, 2002 (3) Shamas ul Islam Bajwa, Edward Chung, Masao Kuwahara, ”Travel Time Prediction on expressways using traffic detectors”㧘Japan Society of Civil Engineers, Proceedings, Vol.26 CD-ROM㧘2002 (4) H. Warita, T. Okada, A. Tanaka, ”A Study of Raising the Accuracy Level for Information on Travel Time”, Proceedings of the 21st Meeting for Presentation of Studies in Traffic Engineering, pp. 301-3-4, 2001 (5) Shamas ul Islam Bajwa, Edward Chung, Masao Kuwahara, ”SENSITIVITY ANALYSIS OF SHORT-TERM TRAVEL TIME PREDICTION MODEL’S PARAMETERS”, Proceedings of 10th World Congress on Intelligent Transport Systems,Madrid,2003 (6) H. Warita, S. Tazawa, S. Takehira, “A User Evaluation Survey concerning Provision of Information on Travel Time”, the 25th Japan Road Conference, 2003 (7) Metropolitan Expressway Public Corporation, “A Report on the 24th Metropolitan Expressway Origin Destination Survey”, 1998

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