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Methods for analysis of passenger tr...

Wang, Danyi.

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Methods for analysis of passenger trip performance in a complex networked transportation system.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Methods for analysis of passenger trip performance in a complex networked transportation system./
Author:	Wang, Danyi.
Description:	276 p.
Notes:	Adviser: Lance Sherry.
Contained By:	Dissertation Abstracts International68-07B.
Subject:	Engineering, Aerospace. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3269824
ISBN:	9780549099130

Methods for analysis of passenger trip performance in a complex networked transportation system.
Wang, Danyi.

Methods for analysis of passenger trip performance in a complex networked transportation system. - 276 p.

Adviser: Lance Sherry.

Thesis (Ph.D.)--George Mason University, 2007.

5. Congestion Flight Delay, Load Factor, Flight Cancellation Time, and Airline Cooperation Policy are the most significant factors affecting total passenger trip delay in the system.

ISBN: 9780549099130Subjects--Topical Terms:

1018395
Engineering, Aerospace.

Methods for analysis of passenger trip performance in a complex networked transportation system.
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020 $a 9780549099130
035 $a (UMI)AAI3269824
035 $a AAI3269824
040 $a UMI $c UMI
100 1 $a Wang, Danyi. $3 1272047
245 1 0 $a Methods for analysis of passenger trip performance in a complex networked transportation system.
300 $a 276 p.
500 $a Adviser: Lance Sherry.
500 $a Source: Dissertation Abstracts International, Volume: 68-07, Section: B, page: 4626.
502 $a Thesis (Ph.D.)--George Mason University, 2007.
520 $a 5. Congestion Flight Delay, Load Factor, Flight Cancellation Time, and Airline Cooperation Policy are the most significant factors affecting total passenger trip delay in the system.
520 $a The estimated passenger trip delay was for 1,030 routes between the 35 busiest airports in the United States in 2006. The major findings of this research are listed as follows:
520 $a The purpose of the Air Transportation System (ATS) is to provide safe and efficient transportation service of passengers and cargo. The on-time performance of a passenger's trip is a critical performance measurement of the Quality of Service (QOS) provided by any Air Transportation System. QOS has been correlated with airline profitability, productivity, customer loyalty and customer satisfaction (Heskett et al. 1994).
520 $a Btatu and Barnhart have shown that official government and airline on-time performance metrics (i.e. flight-centric measures of air transportation) fail to accurately reflect the passenger experience (Btatu and Barnhart, 2005). Flight-based metrics do not include the trip delays accrued by passengers who were re-booked due to cancelled flights or missed connections. Also, flight-based metrics do not quantify the magnitude of the delay (only the likelihood) and thus fails to provide the consumer with a useful assessment of the impact of a delay. Passenger-centric metrics have not been developed because of the unavailability of airline proprietary data, which is also protected by anti-trust collusion concerns and civil liberty privacy restrictions. Moveover, the growth of the ATS is trending out of the historical range.
520 $a The objectives of this research were to (1) estimate ATS-wide passenger trip delay using publicly accessible flight data, and (2) investigate passenger trip dynamics out of the range of historical data by building a passenger flow simulation model to predict impact on passenger trip time given anticipated changes in the future. The first objective enables researchers to conduct historical analysis on passenger on-time performance without proprietary itinerary data, and the second objective enables researchers to conduct experiments outside the range of historic data.
520 $a 1. High passenger trip delays are disproportionately generated by cancelled flights and missed connections. Passengers scheduled on cancelled flights or missed connections represent 3 percent of total enplanements, but generated 45 percent of total passenger trip delay. On average, passengers scheduled on cancelled flights experienced 607 minutes delay, and passengers who missed the connections experienced 341 minutes delay in 2006. The heavily skewed distribution of passenger trip delay reveals the fact that a small proportion of passengers experience heavy delays, which can not be reflected by flight-based performance metrics.
520 $a 2. Trend analysis for passenger trip delays from 2000 to 2006 shows the increase in flight operations slowed down and leveled off in 2006, while enplanements kept increasing. This is due to the continuous increase in load factor. Load factor has increased from 69% in 2003 to 80% in 2006. Passenger performance is very sensitive to changes in flight operations: annual total passenger trip delay was increased by 17% and 7% from 2004 to 2005, and from 2005 to 2006, while flight operations barely increased (0.5% from 2004 to 2005, and no increase from 2005 to 2006) during the same time period.
520 $a 3. Passenger trip delay is shown to have an asymmetric performance of passenger trip delay in terms of routes. Seventeen percent of the 1030 routes generated 50 percent of total passenger trip delays. An interesting observation is that routes between the New York metropolitan area and the Washington D.C. metropolitan area have the highest average passenger trip delays in the system.
520 $a 4. In terms of airports, there is also an asymmetric performance of passenger trip delay. Nine of the 35 busiest airports generated 50 percent of total passenger trip delays. Some airports, especially major hubs, impact the passenger trip delays significantly more than others. Recognition of this asymmetric performance can help reduce the total passenger trip delay propagation in the air transportation network by making changes primarily in major airports, such as Atlanta, GA (ATL), Chicago O'Hare (ORD) and Newark (EWR) airports.
590 $a School code: 0883.
650 4 $a Engineering, Aerospace. $3 1018395
650 4 $a Engineering, System Science. $3 1018128
690 $a 0538
690 $a 0790
710 2 $a George Mason University. $3 1019450
773 0 $t Dissertation Abstracts International $g 68-07B.
790 $a 0883
790 1 0 $a Sherry, Lance, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3269824