You (expect to) get what you pay for: A system approach to delay, fare, and complaints

In this paper, an analytical framework integrating delay, fare, and complaints with passenger air travel has been laid out. Examining aggregate monthly data for US domestic air travel, we have identified causal relationships among fare, complaints, and levels of delay. An analytical framework is proposed that formalizes these relationships in an integrated manner. This integrated framework is then estimated in a set of simultaneous equations by using 118 months of data from January 1997 to October 2006. Results show that complaints are influenced by levels of delays. However, complaints are positively influenced by average yield. These findings lead us to support the central hypothesis that complaints are responsive to levels of delays, but they tend to vary according to fare. That is, air travelers are less likely to complain in return for lower fares, even when faced with the same or even higher levels of delays. These findings have important policy implications, including the passengers’ bill of rights and regulator’s choice between market and operational performances.     

Airport demand management: The operations research and economics perspectives and potential synergies

Airport demand management aims to mitigate air traffic congestion by limiting the imbalances between demand and capacity at busy airports through administrative measures (e.g., slot controls) or economic incentives (e.g., congestion pricing, slot auctions). This paper provides an integrated synthesis of the contributions of the fields of operations research/management science (OR/MS) and economics on the subject matter. From an operating standpoint, assessing the benefits of demand management requires estimates of airport capacity and models of airport on-time performance. From a managerial standpoint, the design of demand management mechanisms can be supported by decision-making models of flight scheduling. From an economic standpoint, the welfare impact of congestion pricing, slot controls and slot auctions depends on the market structure at the airport. This paper proposes an integrated framework that underscores the interdependencies between these operating, managerial and economic aspects to foster cross-disciplinary approaches toward more effective demand management policies at busy airports worldwide.     

Airline delays,congestion internalization and non-price spillover effects of low cost carrier entry

This paper develops an econometric model of flight delays to investigate the influence of competition and dominance on the incentives of carriers to maintain on-time performance. We consider both the route and the airport levels to inspect the local and global effects of competition, with a unifying framework to test the hypotheses of 1. airport congestion internalization and 2. the market competition-quality relationship in a single econometric model. In particular, we examine the impacts of the entry of low cost carriers (LCC) on the flight delays of incumbent full service carriers in the Brazilian airline industry. The main results indicate a highly significant effect of airport congestion self-internalization in parallel with route-level quality competition. Additionally, the potential competition caused by LCC presence provokes a global effect that suggests the existence of non-price spillovers of the LCC entry to non-entered routes.     

The effect of slow zones on ridership: An analysis of the Chicago Transit Authority “El” Blue Line

Transit agencies frequently upgrade rail tracks to bring the system to a state of good repair (SGR) and to improve the speed and reliability of urban rail transit service. For safety during construction, agencies establish slow zones in which trains must reduce speed. Slow zones create delays and schedule disruptions that result in customer dissatisfaction and discontinued use of transit, either temporarily or permanently. While transit agencies are understandably concerned about the possible negative effects of slow zones, empirical research has not specifically examined the relationship between slow zones and ridership. This paper partially fills that gap. Using data collected from the Chicago Transit Authority (CTA) Customer Experience Survey, CTA Slow Zone Maps, and, the Automatic Fare Collection System (AFC), it examines whether recurring service delays due to slow zones affect transit rider behavior and if the transit loyalty programs, such as smart card systems, increase or decrease rider defections. Findings suggest that slow zones increase headway deviation which reduces ridership. Smart card customers are more sensitive to slow zones as they are more likely to stop using transit as a result of delay. The findings of this paper have two major policy implications for transit agencies: (1) loyalty card users, often the most reliable source of revenue, are most at risk for defection during construction and (2) it is critical to minimize construction disruptions and delays in the long run by maintaining state of good repair. The results of this paper can likely be used as the basis for supporting immediate funding requests to bring the system to an acceptable state of good repair as well as stimulating ideas about funding reform for transit.     

Individual freight effects,capacity utilization,and Amtrak service quality

This paper offers a new perspective on the post-deregulation rail industry. We hypothesize that a link exists between individual freight effects and Amtrak service quality. Specifically, we investigate the relationship between freight control of the infrastructure on which Amtrak trains operate and Amtrak train delays. Our sample consists of 1117 directional station-pairs for fiscal years 2002 through 2007 on 28 Amtrak non-Northeast Corridor passenger routes. We found that freight effects have a significant impact on Amtrak train delays after controlling for other important delay determinants such as the capacity utilization rate. The impact is higher on long-distance routes. We also observed significant differences between the effects of different freight railroads. For example, Amtrak operations on infrastructure controlled by several Class I railroads experienced significantly larger delays than baseline operations, while Amtrak train delays on Burlington Northern and Santa Fe’s tracks actually fell below baseline levels.