Analysis of Airline Passenger Trip Delays

The purpose of the Air Transportation System (ATS) is to provide safe and efficient transportation of passengers and cargo. The on-time performance of the ATS is measured by flight-based metrics such as flight delays and flight cancellations. Researchers have shown that flight-based metrics do not accurately reflect the passenger trip experience, and especially underestimate the impacts of cancelled flights and missed connections on passenger trip time. This paper describes a segment-based trend analysis in passenger trip time for the years 2004 and 2005. This paper uses the “estimated passenger trip delay (EPTD)” caused by delayed and cancelled single-segment flights to measure the passenger on-time performance. The EPTD passenger-based metric captures the passenger delays (40M hours) caused by small amount of cancelled flights (<2%). The trend analysis indicates that a small increase in operations (+0.5%) in conjunction with an increase in cancellations (+1.5%) and higher load factors (+5.4%), resulted in 17.4% increase in Estimated Total Passenger Trip Delays in 2005 over 2004. This result reveals the complex nonlinear relationship between the number of operations, the number of cancellations, load factor and passenger trip delays. INTRODUCTION On-Time Performance of passenger trips is a critical measurement of the quality of service provided by the Air Transportation System (ATS). It is a significant factor in the service-profit chain that drives airline profitability, productivity and customer loyalty and satisfaction [1]. For a given flight, passenger trip time is determined by flight times, as well as the time accrued by passengers following missed connections and cancellations. The behavior of the ATS as a transportation system can be modeled by a two tiered flow model: the vehicle tier and the passenger tier, as shown in Figure 1. The flight schedule is the input of the vehicle tier. Affected by a combination of different factors such as traffic volume and weather condition, disruptions (flight delays and flight cancellations) occur, and impose time penalties on passenger trip time. Researchers have shown that flight-based performance measurements do not accurately reflect the passenger trip experience [3]. Flight-based performance measurements use “flight” as the measuring unit instead of “passenger”. A delayed flight with 30 passengers and a delayed flight with 200 passengers are both one count of “delayed flight” in flight-based performance measurements Flight-based performance measurements do not consider passenger factors such as load factor, aircraft size, and number of passenger loaded. Cancelling a flight with high load factor and large size has much stronger impact on passenger trip time than cancelling a flight with low load factor and smaller size. But these two flight cancellations have no difference in flight-based performance measurements. TRB 2007 Annual Meeting CD-ROM Paper revised from original submittal. Danyi Wang and Dr. Lance Sherry 2 The difference between the flight-based and passenger-based approach is shown by the following example. Day 1 and day 2 both have 200 scheduled flights and 10 of them are cancelled. The on-time performance of these two days will have no difference when flight-based metrics are used (both 5% cancellation rate). However, the passenger trip performance of these two days could have a big difference: if the ten flights in day 1 are operated by a single airline and cancelled late in the afternoon, the responsible airline won’t be able to re-booking all the disrupted passengers in the same day due to shortage of empty seats, and available flights. Parts of the passengers have to wait until the next day. These ten cancelled flights in day 1 will generate significant amount of passenger trip delays. If the ten cancelled flights in day 2 are evenly distributed throughout the day and are from different airlines with lower load factors, the responsible airlines will have enough time and resources to re-booked passengers. The passenger delays generated in day 2 is much less than those generated in Day 1. Flight cancellation has more complicated effect on passenger on-time performance, since it many factors, such as flight frequency, flight delay, cancelled time, load factor and aircraft size, are involved in the re-booking process [2]. This paper uses “Estimate Passenger Trip Delay (EPTD)” as a metric to evaluate the NAS on-time performance from passenger’s perspective. The new metric calculates passenger trip delays caused by delayed fights and cancelled flights. Compared with the flight-based metrics, EPTD captures the impacts of flight cancellations on passenger trip delay, and more accurately reflect the travel experience of passengers. The passenger-based metric and measurement could be useful for: • Traffic flow management • Airline operation centers • FAA strategy planning • Flight reservations and ticket purchases by customers This paper describes an analysis of the trends in passenger-based metrics between the years 2004 and 2005. The results indicate that a small increase in operations (+0.5%) in conjunction with an increase in cancellations (+1.5%) and higher load factors (+5.4%), resulted in 17.4% increase in total Estimated Passenger Trip Delays (EPTD). This illustrates the non-linear relationship between number of operations, number of cancellations, load factors and passenger trip delays. This paper is organized as follows. The next section describes previous research on passenger metrics. The third section describes the algorithms used to estimate passenger trip delay. The fourth section describes the results of the analysis of annual trends in passenger metrics. The last section provides conclusions and future work. PREVIOUS WORK Bratu and Barnhart’s research [3] on flight schedule reliability analyzed the impacts of disrupted activities on passenger trip time used proprietary airline data to investigate the impact of delayed flights, cancelled flights and missed connections, on passenger trip time. They estimated passengers scheduled on the cancelled flights, or missed their connections have experienced an average delay of 303 minutes in August 2000. Their investigation validated and measured the discrepancy between passenger delays and flight delays with real data. They identified that flightbased metrics alone are a poor proxy for passenger delays for hub-and-spoke airlines. The limitation of Bratu and Barnhart’s research is that all the results are constrained by one-month (August 2000) passenger booking data provided by a single legacy airline. So their research emphasized more on “analysis of a single airline performance” instead of “predictability of the system performance”, and it can not be expended to a system-level analysis. Ball, Lovell, Mukherjee and Subramanian [6] developed a passenger delay analytical model, which works as part of the NAS Strategy Simulator. The analytical model is based on a decision tree which determines the probability of delayed, missed connection, cancelled and on-time flight legs. The advantage of Ball’s research is that they are trying to use it in a predictable system-level model. But Ball’s research heavily depends on Bratu and Barnhart’s results. They assume a fixed passenger delay (7 hours) for all the passengers encountering missed connection or flight cancellations. This assumption ignores the specific properties of different airport and routes, and assumes a homogenous transportation network. TRB 2007 Annual Meeting CD-ROM Paper revised from original submittal. Danyi Wang and Dr. Lance Sherry 3 Table 1 lists major difference between Bratu and Barnhart (2005), Ball, Lovell, Mukherjee and Subramanian (2006), and this paper in estimating passenger trip delay. TABLE 1 Difference in Estimating Passenger Trip Delay Aspects Bratu & Barnhart (2005) Ball (2006) Wang (2006) Source of Data One-month passenger booking data from an single legacy airline, combined with ASPM flight operation data ASPM and Bratu & Barnhart’s Statistics Publicly accessible databases from BTS (Bureau of Transportation Statistics): AOTP*, T-100*. Goals Analyze airline flight schedule reliability Estimate passenger delays in the ATS Measure the ATS on-time performance from a passenger’s perspective Perspective Airline’s perspective Passenger’s perspective Passenger’s perspective Level of Detail Detailed analysis on performance of a single airline in one month Very high level analysis on system-level performance Detailed analysis on systemlevel performance METHODOLOGY Passenger trip data is proprietary airline data that can only be provided by airlines. Special algorithms are developed [3] to estimate passenger trip delays using publicly accessible single-segment flight data. Passenger trip delay is caused by two activities: delayed flight and cancelled flight: 1. The “Passenger Trip Delay due to delayed flights” is computed by processing the data for each flight in the AOTP database for a given route and specified period (e.g. 365 days) to compute the delay time for the flight. This time is then multiplied to the average number of passengers for this flight (from the T-100 data-base) to derive the passenger delay time for the flight. The equation for this process is listed below: (1) where i = flight with arrival delay > 15 minutes Pax(i) = number of passengers on delayed flight i ActArrTime(i) = actual arrival time for flight i SchArrTime(i) = scheduled arrival time for flight i 2. The “Passenger Trip Delay due to cancelled flights” is computed as the time difference between the scheduled arrive time of the cancelled flight, and the actual arrive time of the re-booked flight. Passengers on cancelled flight are assumed to be re-booked to the nearest available flight which flies the same origin-destination pair and operated by the same carrier. The passenger will experience a trip time that now includes both the flight delay of the re-booked flight plus the additional time the passengers must wait for the re-booked flight. In general, passengers from a cancelled flight will be relocated to 2 or 3 different flights due to the large number of passengers need to be re-booked on the same route, and the limited empty seats on each available flight on the route. A 15 hours upper-bound is derived from (Bratu and Barnhart 2005) and reflects an estimate of the upper-bound of passenger trip delays due to cancelled flights. We assume the disrupted passengers with an unacceptably long delay exceeding 15 hours will be re-routed or re-accommodated on another airline, and their passenger trip delays are set to be 15 hours. The equation for this process is listed below: