Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

TRANSIT TRAVEL TIME FORECASTS FOR LOCATION-BASED QUERIES: IMPLEMENTATION AND EVALUATION

Shin Hyoung PARK Yeon J. JEONG Ph.D Candidate Ph.D Course Civil and Urban Engineering Civil and Urban Engineering Seoul National University Seoul National University #135-402, San 56-1, Shillim9-Dong, #135-402, San 56-1, Shillim9-Dong, Gwanak-Gu, Seoul, Korea, 151-744 Gwanak-Gu, Seoul, Korea, 151-744 Fax: +82-2-885-3801 Fax: +82-2-885-3801 E-mail: [email protected] E-mail: [email protected]

Tschangho John KIM Endowed Professor Urban and Regional Systems University of Illinois at Urbana-Champaign 111 Temple Buell Hall, 611 Lorado Taft Dr. Champaign, IL, USA 61820 Fax: +1- 217-244-1717 E-mail: [email protected]

Abstract: Recently, the market for location-based services (LBS) has been rapidly expanding. As an LBS value-added service, a concierge service provides users with a minimum total cost route using multiple modes. One of the core features in a concierge service is a travel time forecast which undoubtedly affects decisions by users as to the selection of modes, routes and time of travel. Multimodal travel time forecasts thus become an essential part of providing LBS. Many researchers have endeavored to develop reliable travel time forecasting models. Most of these studies, however, have shown general forecasting performances without carefully evaluating performances of models with actual time data. The purpose of this paper is to implement a transit travel time forecasting model and evaluate the performance. The nonparametric regression model, developed in an earlier study has been implemented and evaluated using real-time transit data.

Key Words: LBS, concierge service, travel time forecast, nonparametric regression

1. INTRODUCTION

Recently, the market for location-based services (LBS) has been rapidly expanding and some initial services have been made available in some markets. As an LBS value-added services, a new service such as concierge service provides users with a minimum total cost route for both shopping and travel using automobiles as well as transit systems. One of the core features in a concierge service is a travel time forecast, which undoubtedly affects the decisions by the users as to the selection of modes, routes and time of travel.

Travel time forecasting models have been developed and studied intensively because they have critical impacts on various Intelligent Transportation Systems (ITS) applications such as Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS) (Smith and Demetsky, 1997; Ben-Akiva et al., 1995; Dougherty et al., 1993). Transit travel time forecasts now become an essential part of providing LBS as seen in Kim (2004), Kang and Kim (2005) and Kang et al. (2006). Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

The purpose of this study is to forecast transit travel time using real-time traffic data coming from both buses and subway systems, and evaluate the performance of the model developed by You and Kim (1999a), (1999b), (2000) with actual travel time data collected in real time. This is a significant contribution since provision of real-time transit information and easy access to it would most likely boost the use of mass transit systems and provide more accurate responses to the users of LBS.

Many researchers have endeavored to develop reliable travel time forecasting models using various methods including historical profile approaches, time series models, neural networks, nonparametric regression models, traffic simulation models, and dynamic traffic assignment (DTA) models (Ben-Akiva et al., 1994; Ben-Akiva et al., 1995; Davis et al., 1990; Gilmore and Abe, 1995; Mahmassani et al., 1991; Peeta and Mahmassani, 1995; Sen et al., 1997; Uchida and Yamasita, 1997; You and Kim, 1999a, 1999b, 2000). This previous research has adopted various types of travel time forecasting mechanisms and shows relatively acceptable forecasting error margins. Most of these studies, however, have shown general forecasting performances without carefully evaluating performances of models with real time data.

2. THE MODEL

In this paper, a nonparametric regression model, developed in an earlier study (You and Kim, 1999a, 1999b, 2000), has been implemented and evaluated using real-time transit data. In essence, when predictions occur every 15 minutes, a nonparametric regression can be stated as follows:

)15( :Predict tX + )15( :Given X(t) 15 −15 )X(t

where, X(t) : Current Travel Time Condition − )X(t :15 Condition 15at minutes before

Suppose there is a part of time series data representing a nonlinear system. By segmenting the original time series into a number of finite levels, it is possible to obtain local linear subsystems. A group of similar past cases to the present condition is identified (parameter K in required input data), and each slope of the past cases is compared with the slope of the present condition (parameter k in required input data) (see Figure 1). Thus this concept attempts to identify similar values as well as similar slopes. By having k sets of identified similar cases, the future conditions are finally estimated with the forward values of the past cases. Further detailed explanation of the model and solution algorithms can be found in You and Kim (1999a), (1999b), (2000). Figure 1 illustrates a simplified concept of nonparametric regression, adopting the k-Nearest Neighbor (k-NN) smoothing method.

Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

Original Time Series

Projected Time Series Forward

Backward

Present Past

FIGURE 1 A conceptual diagram of nonparametric regression in forecasting

3. MODEL IMPLEMENTATION AND EVALUATION

3.1 Data The Seoul Metropolitan area in Korea has been chosen for implementation and evaluation of the model. The current bus system for the Seoul Metropolitan area was implemented in July 2004. A detailed description of the bus system can be found in the map provided by the Seoul Industry Promotion Foundation. The system includes 657 bus lines and 10 subway lines serving 408 stops. Including all data covering buses and subways available in the study area would produce a set too large to use in evaluating the performance of the model. As a result, for this analysis we have chosen a part of the Seoul Metropolitan area denoted as zone 4 in Figure 2.

Figure 2 Zonal numbering systems for bus routes Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

z Zone 0: Jongno-gu, Jung-gu, Yongsan-gu z Zone 1: Dobong-gu, Gangbuk-gu, Seongbuk-gu, Nowon-gu z Zone 2: Dongdaemun-gu, Jungnang-gu, Seongdong-gu, Gwangjin-gu z Zone 3: Gangdong-gu, Songpa-gu z Zone 4: Seocho-gu, Gangnam-gu z Zone 5: Dongjak-gu, Gwanak-gu, Geumcheon-gu z Zone 6: Gangseo-gu, Yangcheon-gu, Guro-gu, Yeongdeungpo-gu z Zone 7: Eunpyeong-gu, Mapo-gu, Seodaemun-gu

We obtained detailed line data for 10 bus and subway lines as shown in Table 1. There are 445 bus stops and 42 subway stations in the data. Data on bus stops, bus network and actual bus travel time were provided by the Samsung SDS and Korea geo-Spatial Information and Communication (KSIC). All subway related data was obtained from the Seoul Metropolitan Subway Cooperation. Data were rearranged according to schemas for bus line table and bus schedule table as shown in Tables 2 and 3. These tables are examples of table design of database used for development of customized program. They define not only name, type and length of field in the table, but also whether it is primary key or not, and whether it is foreign key or not.

We obtained actual operation time data logs between 7:00 AM – 9:00 AM for each of the 10 lines for one month. Forecasts utilized the nonparametric regression model developed by You and Kim (1999a), (1999b), (2000).

TABLE 1 Transport modes and lines used for the analysis

Mode Line Number Origin Destination Line 2 Gyodae Jamsil Line 3 Apgoojeong Sooseo Subway Line 7 Exp. Bus Terminal Cheongdam Line 8 Jamsil Bockjeong Bundang Seonreung Seohyun 401 Munjeong Dong Exp. Bus Terminal 402 Suseo Sinsa Station Main Line Bus 461 Munjeong Dong Nambu Terminal 471 Sinsa Station Segok Dong 3413 Jamsil Station Suseo Police Station 3422 Garak Market Sinsa Middle School Branch Line Bus 4312 Munjeong Dong Gangnam Station 4420 Bockjeong Station Gangnam Station 9403 Gumi Dong Jamsil Station Wide Area Bus 9406 Ori Station Nonhyun Station

Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

TABLE 2 A schema for bus line Information table in bus database Table ID T_Bus_Line_Info Table Name BusLineInfo Field Name Type/Length PK/NN Remark 1 LineID NUMBER (7) PK, FK Line Identification 2 LineNum NUMBER (4) FK Line Number Blue: Main Line; Green: Branch Line 3 Type CHAR (10) Red: Wide Area; Yellow: Circular Line Wide Area: 1,400 Won 4 Fare NUMBER (4) Others: 800 Won

TABLE 3 A schema for bus schedule information table by line Table ID P_Line_Number Table Name LineNumber Field Name Type/Length PK/NN Remark 1 DATE DATE PK Day of month 2 DAY VARCHAR (10) PK Day of week Identification number for the fi 3 Station_1 ID NUMBER (6) PK rst station of the bus line Identification number for the se 4 Station_2 ID NUMBER (6) PK cond station of the bus line Identification number for the th 5 Station_3 ID NUMBER (6) PK ird station of the bus line ......

3.2 Implementation For implementation of the forecasting model, a Windows XP computer with an Intel Pentium- IV-3GHz processor and 512 MB RAM was utilized. To develop customized computer programs, we used Visual Basic 6.0 with MS Access 2002 for DBMS.

The core concept of the model implementation is shown in Figure 3. In this research, the nearest neighbor formulation of nonparametric regression is implemented with a two-step traffic pattern search algorithm With a historical database, the first search process attempts to locate the most similar K sets of data values to the current traffic data using travel time data. If other traffic data sets such as traffic volume and occupancy rate data are available, these data sets are also considered in this process. A predefined search range can be given for the first search process. For instance, in order to predict "15-minute ahead" traffic condition at 7:00 AM, the first search process locates similar conditions that have occurred previously between 6:30 and 7:30 AM from a historical database when the search range is set to ±0.5 hour. The second search process applies the standard deviation of the differences to refine the intermediate search results, which are K sets of traffic data obtained from the first search process. By analyzing the standard deviations of the differences, the most similar k sets are selected among K sets of traffic data. Finally, the k-NN smoother is calculated using the next 15-minute traffic data (i.e., forward values in Figure 1) of the selected k data sets.

Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

FIGURE 3 The conceptual process for implementing transit travel forecasting model

FIGURE 4 A schematic flow chart for model implementation

Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

A schematic flowchart of the implementation process is shown in Figure 4. Detailed explanation of each step has been omitted here since the implementation process was exactly the same as was reported in You and Kim (1999a), (1999b), (2000).

3.3 Evaluation The average search time for the study results is shown in Table 4. Search time for any mode without transfers seems to be acceptable, but the search time for the case with transfers should be improved. Considering that the study area is only a part of the entire Metropolitan area, the search time would likely be considerably lengthy once the entire Seoul Metropolitan region was included as part of the subject area.

Forecast accuracy was evaluated by comparing the study results with the actual time data gathered for each line. We used past data to forecast the current travel time for which actual data are available, as shown in Figure 5. In addition, we also compare the study results with two commercial operators currently providing services as shown in Tables 5 and 6. The services provided by company A can be found at http://www.algoga.go.kr/ and by company B at http://bus.seoul.go.kr/.

Travel Time

Forecasted

Simulated Calendar Time t t(+1) Travel Time

Observed

Calendar Time t(-1) t

FIGURE 5 Comparisons between forecasted and observed travel time using the same data set

Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

TABLE 4 Average search time Mode and Transfer Search Time (second) Bus without transfer 4 Bus with transfer 11 Subway without transfer 1 Bus-Subway with Transfer 12

TABLE 5 Search time comparisons for Nonhyun to Dogok Dong without transfer Mode Actual This Study Company A* Company B# Bus (Tuesday) 19 min 52 sec 19 min 19 sec 23 min 26 min Bus (Sunday) 16 min 13 sec 13 min 50 sec 23 min 26 min * For more information on company A, see http://www.algoga.go.kr/ # For more information on company B, see http://bus.seoul.go.kr/.

TABLE 6 Search time comparisons for Gangnam to Garak Market with transfer Mode Actual This Study Company A* Company B# 46 min 54 se Bus (1/18/05:Tuesday) 43 min 17 sec 54 min 58 min c 36 min 39 se Bus (1/23/05: Sunday) 36 min 19 sec 54 min 58 min c * For more information on company A, see http://www.algoga.go.kr/ # For more information on company B, see http://bus.seoul.go.kr/.

The results are quite encouraging. When compared to company A and company B, the study results are quite close to actual travel times as shown in Tables 5 and 6. It is interesting to note that results by both companies shown Tables 5 and 6 are the same regardless of which day of the week travel occurs on, while this study’s results differ depending on the day of the week. Presumably this is because this study uses historical data base for 1 month and both companies use fixed data or distance related data. For further illustration of the study results, forecasted travel times for various times of day for different lines are shown in Tables 7, 8 and 9.

TABLE 7 Travel Time Forecast for Line Number 402 (Daechi station to Gangnam station without transfer on 1/19: Wednesday) Time of Day(AM) Travel Time Forecast 7:00 12 min 29 sec 7:15 15 min 36 sec 7:30 13 min 09 sec 7:45 14 min 07 sec 8:00 13 min 48 sec 8:15 12 min 41 sec 8:30 16 min 07 sec Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

TABLE 8 Travel time forecast for line number 402 and 471 (Dogok Dong to Segok Dong transferring bus line number 402 to bus line number 471 at Yangje Station on 1/19: Wednesday) Time of Day(AM) Travel Time Forecast 7:00 24 min 00 sec 7:15 26 min 47 sec 7:30 25 min 06 sec 7:45 26 min 04 sec 8:00 28 min 21 sec 8:15 26 min 47 sec 8:30 28 min 43 sec

TABLE 9 Travel time forecast for line number 4420 (Segok Dong to Dogok station transferring bus line number 4420 to subway at Suseo station on 1/20: Thursday)

Time of Day(AM) Travel Time Forecast 7:00 18 min 42 sec 7:15 19 min 19 sec 7:30 19 min 19 sec 7:45 19 min 29 sec 8:00 19 min 46 sec 8:15 19 min 50 sec 8:30 18 min 53 sec

4. CONCLUSION

The nonparametric regression model developed in an earlier study by You and Kim (1999a), (1999b), (2000) has been implemented and evaluated using actual and real-time transit data.

The results appear very reasonable and quite close to actual transit times. The results are also compared to the commercially available forecasts provided by two companies. It is interesting to note that results by both companies shown in Tables 5 and 6 are the same regardless of day of week traveled, while this study’s results differ in travel time depending on the day of week. Presumably, the two companies may have been using either fixed link travel time data or distance related data. The forecasting results for various times of day show different travel times for the distance between the same origin and destination, indicating that using a historical data base for the past month for future forecasts using a nonparametric regression model would provide more accurate transit time forecasts for location-based transit travel time queries.

The search time for the study results are mixed: time for any mode without transfers seems to be acceptable, but the search time for the case with transfers needs to be improved. Considering particularly that the study area is only a part of the entire Metropolitan area, the search time could become considerably long once the entire Seoul Metropolitan region Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007 becomes the subject area.

As noted earlier, the market for location-based services (LBS) has been rapidly expanding, with some initial services have becoming available in limited markets. As an LBS value- added service in metropolitan areas, accurate transit time forecasts would undoubtedly affect decisions by users as to the selection of modes, routes and time of travel. Multimodal travel time forecasts thus become an essential part of providing successful LBS and this study paved a ground for developing an operational multimodal travel services for LBS markets.

ACKNOWLEDGEMENT

The study is a part of the projects titled, "Development of Route Guidance Systems based on Mobile Tracking Technology" funded by the Ministry of Construction and Transportation, Korea. This research is also funded by Engineering Research Institute and BK21 Safe and Sustainable Infrastructure Research Group in Seoul National University. The generous funding by the agencies is gratefully acknowledged. The opinions expressed in the paper, however, are solely of the authors and do not necessarily reflect those by the funding agencies.

REFERENCES

Ben-Akiva, M., Koutsopoulos, H. N. and Mukundan, A. (1994) A Dynamic Traffic Model System for ATMS/ATIS Operations. IVHS Journal, vol. 2(1), 1-19. Ben-Akiva, M., Cascetta, E. and Gunn, H. (1995) An On-Line Dynamic Traffic Prediction Model for an Inter-Urban Motorway Network. Urban Traffic Networks: Dynamic Flow Modeling and Control, Gartner, N. H. and Improta, G. (eds.), New York: Springer- Verlag, 83-122. Davis, G. A., Nihan, N. L., Hamed, M. M. and Jacobson, L. N. (1990) Adaptive Forecasting of Traffic Congestion. Transportation Research Record, no. 1287, 29-33. Dougherty, M. S., Kirby, H. R. and Boyle, R. D. (1993) The Use of Neural Networks to Recognise and Predict Traffic Congestion. Traffic Engineering and Control, vol. 34(6), 311-314. Gilmore, J. F. and Abe, N. (1995) Neural Network Models for Traffic Control and Congestion Prediction. IVHS Journal, vol. 2(3), 231-252. Kang, S. and T. J. Kim. Solving a Location-Based Concierge Service Problem: A Heuristic Approach using Genetic Algorithm. Presented at Research Symposium on Societies and Cities in the Age of Instant Access, Salt Lake City, UT, November 2005 Kang, S., S. Oh and T. J. Kim. (2006) A Heuristic Algorithm for Solving a Multimodal Location-Based Concierge Service Problem, In Transportation Research Record: Journal of Transportation Research Board (Accepted, but unpublished). Kim, T. J. (2004) Multi-modal routing and navigation cost functions for location-based services (LBS). In D.H. Lee ed., Urban and Regional Transportation Modeling: Essays in Honor of David Boyce, Edward Elgar, Northampton, MA. Mahmassani, H. S., Peeta, S., Chang, G-L. and Junchaya, T. (1991) A Review of Dynamic Assignment and Traffic Simulation Models for ADIS/ATMS Applications. In Technical Report DTFH61-90-R-00074, Center for Transportation Research, The University of Proceedings of the Eastern Asia Society for Transportation Studies, Vol.6, 2007

Texas at Austin. Peeta, S. and Mahmassani, H. S. (1995) Multiple User Classes Real-Time Traffic Assignment for on-Line ATIS/ATMS Operations: A Rolling Horizon Solution Framework. Preprint at the Transportation Research Board 74th Annual meeting, Washington, D.C. Sen, A., Sööt, S., Ligas, J. and Tian, X. (1997) Arterial Link Travel Time Estimation: Probes, Detectors and Assignment-type Models. Preprint at the Transportation Research Board 76th Annual meeting, Washington, D.C. Seoul Industry Promotion Foundation, http://english.seoul.go.kr/images/SeoulBusMap. pdf Smith, B. L. and Demetsky, M. J. (1997) Traffic Flow Forecasting: Comparison of Modeling Approaches. Journal of Transportation Engineering, vol. 123, no. 4, 261-266 Uchida, T. and Yamasita, S. (1997) Vector ARMA Time Series Models for Short-Term Prediction of Traffic Demand. Journal of the Eastern Asia Society for Transportation Studies, vol. 2, no. 4, 1247-1261. You, J. and Kim, T. J. (1999a) An Integrated Urban Systems Model with GIS. Journal of Geographical Systems, vol. 1(4), 305-321. You, J. and Kim, T. J. (1999b) Implementation of a Hybrid Travel Time Forecasting Model with GIS-T. In Proceedings of the 3rd Bi-Annual Conference of the Eastern Asia Society for Transportation Studies, Taipei, Taiwan, September 1999. You, J. and T. J. Kim. (2000) Development and Evaluation of a Hybrid Travel Time Forecasting Model, Transportation Research Part C, Volume 8, Issues 1-6, 231-256.