DEVELOPMENT OF A KEY PERFORMANCE INDICATOR TO COMPARE REGULARITY OF SERVICE BETWEEN URBAN BUS OPERATORS Paper submitted for the Transportation Research Board 90th Annual Meeting (2011) and for consideration for the Transportation Research Record Date submitted: 30th July 2010 Date revised: 11th November 2010
Corresponding author: Mark Trompet Railway and Transport Strategy Centre, Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London Project Manager of the International Bus Benchmarking Group Skempton Building, SW7 2AZ London; Tel +(20)75941519; Fax +(20)75946107 E-mail:
[email protected] Xiang Liu Railway and Transport Strategy Centre, Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London Skempton Building, SW7 2AZ London; Tel +(20)75946092; Fax +(20)75946107 E-mail:
[email protected] Daniel J. Graham Railway and Transport Strategy Centre, Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London Skempton Building, SW7 2AZ London; Tel +(20)75946088; Fax +(20)75946107 E-mail:
[email protected]
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ABSTRACT This paper evaluates options for a key performance indicator that comparably illustrates differences in performance with regard to maintaining service regularity on high frequency routes between urban bus operators. The data used for this study was collected by the International Bus Benchmarking Group, facilitated by Imperial College London, and relates to twelve medium to large sized urban bus operators from different countries. Through two annual rounds of data collection, lessons were learned on feasible data characteristics, required sample size and data cleaning processes. The following four key performance indicator alternatives were tested and their strengths and weaknesses described: ‘Excess Wait Time’, ‘Standard deviation of the difference between the scheduled and the actual headway’ and % of service within a fixed and relative number of minutes from the scheduled headway, also referred to as respectively ‘Wait assessment’ and ‘Service regularity’. The results suggest that while all four methodologies illustrate a different, interesting view on service regularity performance, the Excess Wait Time methodology is the best option when the key performance indicator should reflect the customer experience of the regularity of service. KEYWORDS: Regularity, Headway adherence, Bus operations, Key performance indicator, Benchmarking, Excess Wait Time, Wait assessment
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1. INTRODUCTION A regular bus service has a direct positive impact on how customers perceive availability and timeliness of service. In contrary, research showed that bus headway irregularity discourages commuters’ use of public transit (Polus (1); Fu and Yang (2)). Out of the eight service areas defined by the European quality norm EN13816 (3), both availability of service and journey time are considered as most important by passengers in different countries. The members of the International Bus Benchmarking Group (IBBG) have therefore expressed the wish to compare their regularity performance and, if feasible, to understand how other operators perform better. The International Bus Benchmarking Group (IBBG), which is facilitated by the Railway and Transport Strategy Centre at Imperial College London, is now in its seventh year. Its current members are TMB Barcelona, STIB Brussels, Dublin Bus, Los Angeles County Metropolitan Transportation Authority, Carris Lisbon, London Buses, Milan ATM, STM Montreal, NYCT New York, RATP Paris, STA Sydney Buses, Singapore SMRT and CMBC Vancouver. All members provide normal passenger public bus service operations in large urban areas. The research questions put forward in the service regularity benchmarking pilot were: Which indicators could be used in comparing regularity of service between operators in different cities? What and which quantity of data is necessary to calculate the indicator? How do anomalies in the dataset need to be addressed before analysis? What are the strengths and weaknesses of different regularity key performance indicators when used in benchmarking? This paper describes the lessons learned by the IBBG in the past two years while developing a key performance indicator which comparably illustrates differences in performance with regard to maintaining service regularity on high frequency routes between urban bus operators. To describe the iterative steps taken in the development process, the remainder of this paper is structured as follows: section 2 provides a definition for regularity and lists relevant regularity indicators, both discussed in literature and in use by IBBG members. Section 3 describes the dataset which is used for this study and lists the lessons learned about anomalies in the received data and respective efforts made to ‘clean’ and to structure the data in a way it could be used for the analysis. Sections 4 to 6 describe the methods chosen to reflect the regularity performance of the IBBG organisations and present the results using the ‘cleaned’ dataset. The following four key performance indicator alternatives were tested and their strengths and weaknesses described: ‘Excess Wait Time’, ‘Standard deviation of the differences between the scheduled and the actual headway’ and two methods showing percentages of service which are considered regular; either within a fixed number of minutes from the scheduled headway (Wait assessment) or within a time threshold that is relative to the scheduled headway (Service regularity). Overall conclusions are drawn and together with recommendations for future development discussed in Section 7.
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2. REGULARITY AND RELATED LITERATURE 2.1 Service Regularity Time based service quality indicators are usually split in two groups: punctuality and regularity indicators. ‘Adherence to timetable’ is generally measured for ‘low frequency’ routes through a punctuality indicator. Certifications based on EN 13816 (1), such as AFNOR 286, define a punctual service as one where 80% of buses arrive between the scheduled time and 3 minutes late from the published timetable or 90% of buses arrive between the scheduled time and 5 minutes late from the published timetable. As customers would take specific notice of the service timetable for low frequency routes to ‘plan’ their arrival at the bus stop, it is more important for low frequency buses to be ‘on-time’. For high frequency routes, passengers are more likely to arrive randomly (Furth and Muller (4); Csikos and Currie (5)). Buses are expected to arrive in frequent regular intervals and from a customer point of view there is less need to ‘plan’ the arrival time at the bus stop. For these high frequency routes bus operators often do not publish timetables, but post the headway in minutes at different times of the day. Therefore, ‘adherence to headway’ is key, which is measured through a regularity indicator. One universal definition for the headway length to mark the difference between high and low frequency routes could not be found in literature, however a 10 minutes headway seems a boundary used most frequently. For example, the Transit Capacity and Quality of Service Manual (6) suggests that for headways < 10 minutes passengers do not need schedules, however for, although still frequent, headways of 10-14 minutes passengers may consult schedules. Furth and Muller (4) suggest that the headway threshold between short and long waiting behaviour is 10 to 12 minutes. Amongst the IBBG members, New York City Transit bus service performance indicator system also draws the line between high and low frequency buses at 10 minutes headways (Nakanishi (7)). Other operators like London Buses define a high frequency bus service as “5 or more buses an hour, e.g. headways of up to 12 minutes” and low frequency service as 4 or less buses an hour (8). 2.2 Literature Review of Regularity Indicators The authors could not find evidence of published research on regularity indicators specifically used in benchmarking to reflect service regularity performance between different urban bus operators. This paper aims to add knowledge to this area. However, much research has been published with regards to regularity measures in general. The TCRP ‘Guidebook for Developing a Transit Performance Measurement System’ (9) presents four possible regularity indicators: • Headway adherence, which is the preferred measure within the TCQSM (6) and is in the 2nd edition defined as the coefficient of variation of headway deviations divided by the average scheduled headway. • Service regularity: the percentage of headways that deviate no more than a specific amount from the scheduled interval. The specific amount can be a percentage of the headway, or an absolute number of minutes. The latter is sometimes referred to as ‘wait assessment’. This ‘service regularity’ is also used by European quality certification institutions such as CEN, AFNOR and AENOR.
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Headway ratio: the observed headway divided by the scheduled headway, multiplied by 100. The guidebook mentions that headway ratio assumes that scheduled headways are constant over the measurement period. In reality, as also the IBBG pilot has shown, this is very uncommon, which makes this indicator less applicable for benchmarking purposes. • Headway regularity index using Gini’s ratio (Henderson, Kwong et al (10)). The guidebook states that this indicator performs well in identifying vehicle bunching, however that the indicator is difficult to visualize and or explain. As Ruan and Lin (11) outline in their overview of regularity indicators, the most common used metric is the average passenger waiting time (A.W.T) proposed by Osuna and Newell (12). Assuming uniform passenger arrival, the A.W.T is derived as the sum of one half of the average headway and the ratio of headway variance to twice the average headway, i.e. E(A.W.T)=0.5E(headway)+V(Headway)/2E(Headway). Golshani (13) also described AWT as an efficiency indicator for service regularity, however his definition is: the global value of passengers’ waiting times dividing by the number of passengers. Golshani further proposes the standard deviation of headway, as well as the irregularity index, which gives an indication of long gaps between vehicles and is defined as the ratio of the mean square headway to the mean headway squared. 2.3 Regularity Indicators in use by International Bus Benchmarking Group Members Four organisations: Dublin Bus, LACMTA Los Angeles, STM Montreal and Sydney Buses, do not (officially and continuously) measure regularity of service for high frequency bus routes. New York City Transit (NYCT) regularly review their performance indicator for headway regularity. From 1994 to 2000 NYCT used the percentage of headways between actual trips that are within ±50% of the scheduled headway (for headways
