Modeling Transit Trip Time Using Archived Bus Dispatch System Data

Transit travel time and operating speed influence service attractiveness, operating cost, and system efficiency. The objective of this paper is to estimate the values of parameters that affect the total travel time for a particular bus route in Portland, Oregon. The Metropolitan Transportation District of Oregon ~TriMet! provides transit service in the three-county Portland metropolitan area. TriMe has implemented a Bus Dispatch System ~BDS! as a part of its overall service control and management system. This BDS provides a rich array of archived data that were used in this study to develop the trip time model for the route under study. This trip time model provides heightened understanding of the factors that affect the trip time on the route. The value of the model was revealed when sensitivity analyses were performed using data from the studied route. This analysis concluded that improvements can be readily achieved after understanding the factors that affect total trip time. Finally, some conclusions and recommendations are presented and suggestions for additional research are provided. DOI: 10.1061/~ASCE!0733-947X~2004!130:1~56! CE Database subject headings: Public transportation; Buses; Travel time; Transportation models. ~every 90 s! that enables the implementation of a variety of conIntroduction Transit travel time and operating speeds influence service attractiveness, operating cost, and system efficiency. If these data are available, they can also provide important descriptions of system performance for use in day-to-day operations management, route planning and scheduling, the transportation planning process ~Levinson 1983!, and continuing performance measurement and evaluation ~NCHRP 1999!. The objective of this paper is to estimate the values of parameters that affect the total travel time for a particular bus route in Portland, Oregon. This study will use very detailed stop-level data that are automatically collected and archived for each bus, route, and stop every day. The Tri-County Metropolitan Transportation District of Oregon ~TriMet! operates 97 bus routes and a 38-mile light rail line within the tricounty Portland metropolitan region. TriMet’s bus lines carry approximately 200,000 trips per day, serving a total population of 1.3 million persons within an area of 1,530 sq kilometers ~590 sq miles!. The route chosen for this study was a 12.7-km ~7.9-mile! segment of TriMet’s Route 14 inbound ~westbound!, shown in Fig. 1. This is a heavily used inbound route that runs through southeast Portland toward downtown during the morning commute period. TriMet provides 105 scheduled trips per weekday on Route 14 with 64 stops. Scheduled trip times Assistant Professor, Dept. of Civil and Environmental Engineering, Portland State Univ., P.O. Box 751, Portland, OR 97207-0751. E-mail: [email protected] PhD Student, School of Urban Studies and Planning, Portland State Univ., P.O. Box 751, Portland, OR 97207-0751. E-mail: [email protected] Note. Discussion open until June 1, 2004. Separate discussions must be submitted for individual papers. To extend the closing date by one month, a written request must be filed with the ASCE Managing Editor. The manuscript for this paper was submitted for review and possible publication on May 16, 2002; approved on February 6, 2003. This paper is part of the Journal of Transportation Engineering, Vol. 130, No. 1, January 1, 2004. ©ASCE, ISSN 0733-947X/2004/1-56–67/$18.00. 56 / JOURNAL OF TRANSPORTATION ENGINEERING © ASCE / JANUAR range between 40 and 45 min and scheduled headways range between 3 and 55 min ~the mean headway is 11.4 min!. The analysis here concentrates on morning peak inbound service between SE 94th/Foster Ave. ~location ID 1831! and the North Terminal, a layover and bus staging area near NW 4th/Hoyt St. ~location ID 9573!, near downtown Portland. In order to demonstrate the feasibility of this modeling approach, 14 inbound trips between 6:00 a.m. and 10:00 a.m. on Tuesday, February 1, 2001 were analyzed. This paper describes how a trip time model was applied to the sample studied. It is shown that a trip time model can provide heightened understanding of the factors that affect transit trip time and lead to better operational decision-making and performance assessment. The value of the model will be revealed when sensitivity analyses are performed using data from the route studied.