DETERMINANTS OF BUS DWELL TIME

Bus dwell time data collection typically involves labor-intensive ride checks. This paper reports an analysis of bus dwell times that use archived automatic vehicle location (AVL)/automatic passenger counter (APC) data reported at the level of individual bus stops. The archived data provide a large number of observations that serve to better understand the determinants of dwells, including analysis of rare events, such as lift operations. The analysis of bus dwell times at bus stops is applicable to TriMet, the transit provider for the Portland metropolitan area, and transit agencies in general. The determinants of dwell time include passenger activity, lift operations, and other effects, such as low floor bus, time of day, and route type. Introduction Bus dwell time data collection typically involves labor-intensive ride checks. This paper reports an analysis of bus dwell times that use archived automatic vehicle location (AVL)/automatic passenger counter (APC) data reported at the level of individual bus stops. The archived AVL/APC data provides a rich set of dwell time observations to better understand the determinants of dwells. In addition, the large quantity of data allows analysis of rare events, such as lift operations. The analysis of bus dwell times at bus stops was originally used to estimate delay associated with bus lift use operations for passengers with disabilities in the Tri-County Metropolitan Transportation District of Oregon (TriMet), the transit provider for Journal of Public Transportation, Vol. 7, No. 1, 2004 2 2 the Portland metropolitan area (Dueker, et al. 2001). In addition, the analysis yielded useful information about dwell times that has applicability to transit agencies in general. The estimated models provide a system-wide baseline. Stop-level, route-level, operator-specific, and passenger boarding-level analyses can follow. This paper includes examples of applying the model results to simulate dwell times for different times of day, route types, and various levels of passenger boardings and alightings. The effects of fare payment method and bicycle rack usage on dwell times was unable to be incorporated, but suggest how future research could extend the model. Prior Work Literature on bus dwell times is sparse, due to the cost and time required for manual data collection. Consequently, most prior analyses tend to be route-specific, focus on analyzing various issues causing bus delay, and are based on small samples. Previous studies on dwell time have used ordinary least squares (OLS) regression to relate dwell time to boardings and alightings, with separate equations estimated for different operating characteristics likely to affect dwell time. Kraft and Bergen (1974) found that passenger service time requirements for AM and PM peaks are similar, midday requirements are greater than those in peak periods, boarding times exceed alighting times, and rear door and front door alighting times are the same. They also found that dwell time is equal to 2 seconds plus 4.5 seconds per boarding passenger for cash and change fare structures, and 1.5 seconds plus 1.9 seconds for exact fare. Levinson’s (1983) landmark study of transit travel time performance reported that dwell time is equal to 5 seconds plus 2.75 seconds per boarding or alighting passenger. Guenthner and Sinha (1983) found a 10-20 second penalty for each stop plus a 3-5 second penalty for each passenger boarding or alighting. However, dwell time models based on small samples have low explanatory power, even when controlling for factors such as lift activity, fare structure, and number of doors. Guenthner and Hamet (1988) looked at the relationship between dwell time and fare structure, controlling for the amount of passenger activity. Lin and Wilson (1992) reviewed prior work and formulated a model of dwells as a function of boardings, alightings, and interference with standees, which was then applied to light rail transit dwells. Bertini and El-Geneidy (2004) modeled dwell time for a single inbound radial route in the morning peak period in their analysis of trip Determinants of Bus Dwell Time 2 3 level running time. They incorporated the results of the dwell time analysis directly into the trip time model by estimating parameters for number of dwells and number of boarding and alighting passengers. Data Issues Dwell time is defined as “the time in seconds that a transit vehicle is stopped for the purpose of serving passengers. It includes the total passenger service time plus the time needed to open and close doors” (HCM 1985). In the past, dwell time data collection consisted of placing observers at highly utilized bus stops to measure passenger service times, and by ride checks or onboard observers for dwells at bus stops along routes. The ride check procedure as prescribed in the Transit Capacity and Quality of Service Manual consists of the following steps to collect field data for estimating passenger service times: 1. From a position on the transit vehicle, record the stop number or name at each stop. 2. Record the time that the vehicle comes to a complete stop. 3. Record the time that the doors have fully opened. 4. Count and record the number of passengers alighting and the number of passengers boarding. (The data collection form calls for front and rear door specific counts). 5. Record the time that the major passenger flows end. 6. When passenger flows stop, count the number of passengers remaining on board. (Note: If the seating capacity of the transit vehicle is known, the number of passengers on board may be estimated by counting the number of vacant seats or the number of standees). 7. Record time when doors have fully closed. 8. Record time when vehicle starts to move. (Note: Waits at timepoints or at signalized intersections where dwell is extended for cycle should be noted but not included in the dwell time. Delays at bus stops when a driver is responding to a passenger information request are everyday events and should be included in the calculation of dwell time. Time lost dealing with fare disputes, lost property or other events should not be included.) 9. Note any special circumstances. In particular, any wheelchair movement times should be noted. Whether this is included in the mean dwell time Journal of Public Transportation, Vol. 7, No. 1, 2004 2 4 depends on the system. Dwell times due to infrequent wheelchair movements are often not built into the schedule but rely on the recovery time allowance at the end of each run. The observer must use judgment in certain cases. At nearside stops before signalized intersections the driver may wait with doors open as a courtesy to any late-arriving passengers. The doors will be closed prior to a green light. This additional waiting time should not be counted as dwell time but as intersection delay time. (TCRP 1999) Automating the collection of dwell time data through the employment of AVL and APC technologies compromises the procedures outlined above. The dwell time is measured as specified, but the time the bus stops and starts is not recorded, nor is the starting and stopping of passenger flows. Our analysis deleted dwells of over 180 seconds (3 minutes). This censoring was done to purge the analysis of dwells that are abnormal. Also, TriMet’s Automated Passenger Counters (APC) record total boardings and alightings rather than door-specific counts. Finally, there is no guarantee that operators will behave similarly in closing the doors while awaiting for traffic to clear or traffic signals to change. These compromises to the conventional measurement of dwell time are offset by the ability to collect data on large numbers of dwells, with any “special circumstances” included in the error term of OLS regression models. Automating Collection of Dwell Time Data Uses of Archived AVL/APC Data to Improve Transit Performance and Management (Furth, et al. forthcoming), identifies the bus stop as the appropriate spatial unit for data aggregation and integration. This integration of scheduled and actual arrival time at the level of the individual stop is crucial for research on bus operations and control strategies. Integrating data at the bus stop level supports real time applications, such as automated stop annunciation and next-stop arrival time information. Importantly, if bus stop data are archived, operations performance and monitoring analysis can also be supported (Furth, et al. forthcoming). TriMet has automated the collection and recording of bus dwell time and passenger activity at the bus stop level, and archives the data consistent with the TCRP recommendations. TriMet operates 97 bus routes, 38 miles of light rail transit, and 5 miles of streetcar service within the tri-county Portland metropolitan region. TriMet’s bus lines carry approximately 200,000 trips per day, serving a total popuDeterminants of Bus Dwell Time 2 5 lation of 1.3 million persons within an area of 1,530 square kilometers (590 square miles). TriMet implemented an automated Bus Dispatch System (BDS) in 1997 as a part of an overall operation and monitoring control system upgrade. The main components of the BDS include: 1. AVL based upon differential global positioning system (GPS) technology, supplemented by dead reckoning sensors 2. Voice and data communication system using radio and cellular digital packet data (CDPD) networks 3. On-board computer and control head displaying schedule adherence information to operators and showing dispatchers detection and reporting of schedule and route adherence 4. APCs on front and rear doors of 70% of vehicles in the bus fleet 5. Computer-aided dispatch (CAD) center The BDS reports detail operating information in real time by polling bus location every 90 seconds, which facilitates a variety of control actions by dispatchers and field supervisors. In addition, the BDS collects detailed stop-level data that are downloaded from the bus at the end of e