Transit Travel Time Forecasts for Location-Based Queries: Implementation and Evaluation

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: Predict : X( t + 15) Given : X(t) X(t−15 ) where, X(t) : Current Travel Time Condition X(t−15 ) : Condition at 15 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.

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