Passenger Itinerary Research Articles

From passenger itineraries to climate impact: Analyzing the implications of a new mid-range aircraft on the global air transportation system

Aircraft design usually focuses on utilizing new technologies to improve mission performance of individual aircraft and, thus, reduce cost, emissions and climate impact of the global air transportation system (ATS). However, efficiency gains in the ATS can also be achieved by improving the alignment of payload–range capabilities and aircraft–route assignment of the overall fleet. Therefore, this article analyzes the implications—from passenger itineraries to climate impact—of a new mid-range aircraft (NMRA), which addresses the current lack of modern, designated mid-range aircraft in commercial production. The analysis is based on simulating and comparing two future scenarios with similar technology levels: one with and one without a NMRA. The core of the applied novel simulation framework is highly integrated models for passenger itinerary preferences, fleet development and aircraft–route assignment. Models for passenger demand as well as aircraft trajectories, emissions and climate impact complement the simulation framework. The results show that introducing a NMRA to the ATS improves the quality of travel for passengers and the alignment of payload–range capabilities and aircraft–route assignment for the overall aircraft fleet. As a consequence, the NMRA reduces the fuel consumption, emissions and climate impact of the ATS in our future scenarios by up to 1.4% (pessimistic) and 2.1% (optimistic). We conclude that a higher fleet diversification and a better alignment of payload–range capabilities and aircraft–route assignment through introducing a NMRA can marginally improve the fuel efficiency and climate impact of the ATS.

A Schedule-Based Model for Passenger-Oriented Train Planning With Operating Cost and Capacity Constraints

In the planning stage, train operators design timetables to serve passenger trips and a train circulation plan to support these timetables. These designs consider not only operating costs but also passenger convenience. In this study, we developed an optimization model for a new problem that focuses on timetabling and train-unit scheduling while also considering passenger itinerary choices in a schedule-based train system. This optimization model minimizes passenger travel costs within the constraints of a limited budget available for operating costs. The model is solved by an iterative heuristic that simulates the interaction between train operations and passenger itinerary choices. The heuristic solves the timetabling and train-unit scheduling problem using a decomposition approach to increase computational efficiency, while passenger loading is solved by a user-equilibrium passenger assignment model. An example based on the high-speed railway network in southern China was used to demonstrate the effectiveness of the proposed model and method.

Assessing spread risk of COVID-19 in early 2020

A novel coronavirus emerged in late 2019, named as the coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO). This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread at the early stage of the transmission. A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data. We found that the cordon sanitaire of the primary city was likely to have occurred during the latter stages of peak population numbers leaving the city, with travellers departing into neighbouring cities and other megacities in China. We estimated that there were 59,912 international air passengers, of which 834 (95% uncertainty interval: 478–1,349) had COVID-19 infection, with a strong correlation seen between the predicted risks of importation and the number of imported cases found. Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks, our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.

Integrated Aircraft and Passenger Recovery With Enhancements in Modeling, Solution Algorithm, and Intermodalism

During schedule disruption of airline operations, aircraft assigned to some flights become unavailable (are held on the ground) for a certain time period. An effective recovery plan becomes essential for airlines to reduce potential losses and prevent the propagation of disruption and delay. In this study, we addressed integrated aircraft and passenger recovery for such schedule disruption. In addition to modeling and reducing the size of model for solving the problem efficiently with modified time-band network and candidate passenger itinerary generation, we also introduce an intermodal concept to expand airline aviation networks by including ground transportation modes. With this intermodal network, airlines would have more flexibility for performing aircraft recovery based on business interest priorities and would protect passengers from suffering extensive delays. We applied the modeling to a pure aviation network and the intermodal network and compared both outcomes with those from the existing literature. The comparison shows that the algorithms generating the modified time-band network can reduce redundant arcs in the network and, thus, the feasible solution space of mix integer linear programming (MILP) without compromising optimality. Considering a real-time intermodal network in disruption management helps to reduce the number of stranded passengers and the total disruption cost.

Passenger itinerary inference model for congested urban rail networks

The paper develops a data-driven approach for the inference of passenger itineraries in urban heavy rail systems, the Passenger Itinerary Inference Model (PIIM). In light of increasing demand and crowding, the model can be used to assess the impact of near capacity operations on customers, evaluate system performance, and understand passenger behavior when choosing alternative routes. PIIM uses data from Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) systems and is applicable to general urban rail networks with entry and exit fare transactions. PIIM consists of three main modules: the left behind model, the route fractions inference model, and the inference model. The left behind model estimates the probability of any passenger being left behind by station and time interval. The route fractions inference model estimates the route choice fractions given the left behind probabilities. The inference model maps each pair of fare transaction records (entry and exit) to a set of feasible itineraries and infers the train(s) boarded by passengers based on the left behind probabilities and the route choice fractions. Synthetic data using AFC and AVL data from a major, congested urban rail system were generated to validate the model. The results show that the model accurately predicts the itineraries used by passengers, the left behind probabilities, route choice fractions, train loads, and other performance metrics of interest. PIIM is also used with actual data to illustrate its applicability.

Aircraft and passenger recovery during an aircraft’s unexpected unavailability

Airlines design their initial schedules under the assumption that all resources will be available on time and flights will operate as planned. However, some disruptions occur due to mechanical failures and unexpected delays of maintenance, making the aircraft unavailable for a certain period of time. These deviations from the initial plan result in high operational costs in addition to the serious inconveniences experienced by passengers. In order to handle aircraft and passenger recovery problems simultaneously, we work on integrated networks at which aircraft routings and passenger itineraries are superimposed. Consequently, we could calculate the actual profit and cancellation cost by evaluating each passenger itinerary while considering the seat capacity limitations. In our computational results, we use a daily schedule of a major U.S. airline and clearly demonstrate that there is an optimal trade-off between operating and passenger-related costs.

Komponen Sebuah Perjalanan Wisata (Tour)

<p><em>A Tour consists of six important elements. Those are (1) transportation which can be land, sea, or air transportation. (2) Acommodation that can be an expensive resort or a small and simple motor inn. (3) Dining can be a qick-service food, buffet dinner, or table service dining. (4) Sightseeing; which can be a city tour, rural or village tour to historical sites, traditional architecture, or (5) Attraction is actually the most important pulling factor motivation of the tourists’ visit to a destination which can be a performance, an adventure activities, etc. (6) Shopping which can be a big super-market, traditional art market, or galleries. A tour plan is made in the form of 3 different itineraries. Those are (1) summary itinerary, (2) Detail Passenger Itinerary or DPI, and (3) Escort itinerary which is also welknown as Manual. A tour itinerary must consider all six elements in accordance with the clients’ interests, budget, number, and time alocation for a tour.</em></p>

A math-heuristic algorithm for the integrated air service recovery

A sophisticated flight schedule might be easily disrupted due to adverse weather, aircraft mechanical failures, crew absences, etc. Airlines incur huge costs stemming from such flight schedule disruptions in addition to the serious inconveniences experienced by passengers. Therefore, an efficient recovery solution that simultaneously decreases an airline's recovery cost while simultaneously mitigating passenger dissatisfaction is of great importance to the airline industry. In this paper, we study the integrated airline service recovery problem in which the aircraft and passenger schedule recovery problems are simultaneously addressed, with the objective of minimizing aircraft recovery and operating costs, passenger itinerary delay cost, and passenger itinerary cancellation cost.Recognizing the inherent difficulty in modeling the integrated airline service recovery problem within a single formulation (due to its huge solution space and quick response requirement), we propose a three-stage sequential math-heuristic framework to efficiently solve this problem, wherein the flight schedules and aircraft rotations are recovered in the first stage, Then, a flight rescheduling problem and passenger schedule recovery problems are iteratively solved in the next two stages. Time-space network flow representations, along with mixed-integer programming formulations, and algorithms that take advantages of the underlying problem structures, are proposed for each of three stages. This algorithm was tested on realistic data provided by the ROADEF 2009 challenge and the computational results reveal that our algorithm generated the best solution in nearly 72% of the test instances, and a near-optimal solution was achieved in the remaining instances within an acceptable timeframe. Furthermore, we also ran additional computational runs to explore the underlying characteristics of the proposed algorithm, and the recorded insights can serve as a useful guide during practical implementations of this algorithm.

An Axiomatic Design Approach to Passenger Itinerary Enumeration in Reconfigurable Transportation Systems

Transportation systems represent a critical infrastructure upon which nations' economies and national security depend. As infrastructure systems, they must be planned and operated to accommodate the uncertain and continually evolving needs of their passengers and freight. New roads are planned or existing roads are closed for maintenance or due to operational breakdowns. Reconfigurable transportation systems are those which adapt to these changes quickly and efficiently. They are not overdesigned with capabilities that may be left unused; instead, capabilities are added only when needed, thus supporting the need for resilient infrastructure. An axiomatic-design-for-large-flexible-systems approach is chosen as a methodology for its deep roots in engineering design. It addresses systems where the functionality not only evolves over time, but also can be fulfilled by one or more system resources, and is used here to enumerate passenger itineraries. This paper builds upon a recent work in which axiomatic design was used to develop a theory of degrees of freedom in transportation systems for their reconfigurable design and operation. The methodological developments are then demonstrated on a small subsection of the Mexico City transportation system to demonstrate its wide-ranging utility in reconfigurability decision-making at the planning and operation timescales. In addition, further comparisons of axiomatic design to traditional graph theory are made, indicating the mathematical basis of the former in the latter.

Integrated aircraft and passenger recovery with cruise time controllability

Disruptions in airline operations can result in infeasibilities in aircraft and passenger schedules. Airlines typically recover aircraft schedules and disruptions in passenger itineraries sequentially. However, passengers are severely affected by disruptions and recovery decisions. In this paper, we present a mathematical formulation for the integrated aircraft and passenger recovery problem that considers aircraft and passenger related costs simultaneously. Using the superimposition of aircraft and passenger itinerary networks, passengers are explicitly modeled in order to use realistic passenger related costs. In addition to the common routing recovery actions, we integrate several passenger recovery actions and cruise speed control in our solution approach. Cruise speed control is a very beneficial action for mitigating delays. On the other hand, it adds complexity to the problem due to the nonlinearity in fuel cost function. The problem is formulated as a mixed integer nonlinear programming (MINLP) model. We show that the problem can be reformulated as conic quadratic mixed integer programming (CQMIP) problem which can be solved with commercial optimization software such as IBM ILOG CPLEX. Our computational experiments have shown that we could handle several simultaneous disruptions optimally on a four-hub network of a major U.S. airline within less than a minute on the average. We conclude that proposed approach is able to find optimal tradeoff between operating and passenger-related costs in real time.

Declarative approach to cyclic steady state space refinement: periodic process scheduling

Problems of cyclic scheduling are usually observed in flexible manufacturing systems which produce multitype parts where the automated guided vehicle system plays the role of a material handling system, as well as in various other multimodal transportation systems where goods and/or passenger itinerary planning plays a pivotal role. The schedulability analysis of the processes executed in the so-called systems of concurrent cyclic processes (SCCPs) can be executed within a declarative modeling framework. Consequently, the considered SCCP scheduling problem can be seen as a constraint satisfaction problem. Such a representation provides a unified way for evaluating the performance of local cyclic processes as well as of multimodal processes supported by them. Here, the crucial issue is that of a control procedure (e.g., a set of dispatching rules), which would guarantee the cyclic behavior of the SCCP. In this context, we discuss the sufficient conditions guaranteeing the schedulability of both local and multimodal cyclic processes, and we propose a recursive approach in designing them.

Risk Aversion to Short Connections in Airline Itinerary Choice

Network airlines traditionally attempt to minimize passenger connecting times at hub airports, assuming that passengers prefer minimum scheduled elapsed times for their trips. However, minimizing connecting times creates schedule peaks at hub airports. These peaks are extremely cost-intensive in terms of additional personnel, resources, runway capacity, and schedule recovery. Consequently, passenger connecting times should be minimized only if the anticipated revenue gain of minimizing passenger connecting times is larger than the increase in operating cost (i.e., if this policy increases overall operating profit). The extent to which a change in elapsed time affects passenger itinerary choice—and thus an airline's market share—is analyzed. An existing airline itinerary choice survey is extended to test the assumption that passenger demand is affected by the length of connecting times. Previous studies have not explicitly focused on the connecting time at hubs in their models. The hypothesis is that passe...

Risk Aversion to Short Connections in Airline Itinerary Choice

Network airlines traditionally attempt to minimize passenger connecting times at hub airports, assuming that passengers prefer minimum scheduled elapsed times for their trips. However, minimizing connecting times creates schedule peaks at hub airports. These peaks are extremely cost-intensive in terms of additional personnel, resources, runway capacity, and schedule recovery. Consequently, passenger connecting times should be minimized only if the anticipated revenue gain of minimizing passenger connecting times is larger than the increase in operating cost (i.e., if this policy increases overall operating profit). The extent to which a change in elapsed time affects passenger itinerary choice–-and thus an airline's market share–-is analyzed. An existing airline itinerary choice survey is extended to test the assumption that passenger demand is affected by the length of connecting times. Previous studies have not explicitly focused on the connecting time at hubs in their models. The hypothesis is that passengers might incur lower utilities from shorter connecting time versus longer connecting times based on the potential discomfort of a hasty connection and the misconnection risk associated with short connecting times. A stated preference experiment is performed, and socioeconomic data are collected to estimate a choice model. Multinomial logit model results support this hypothesis and refute the traditional network airline assumption.

The impact of infrastructure-related taxes and fees on domestic airline fares in the US

This paper estimates the impact of infrastructure-related add-on taxes and fees on the direct cost of domestic air travel in the US. We work with the large DB1A database of the Bureau of Transportation Statistics of the US Department of Transportation to conclude that the effective tax rate ( ETR) on the average base fare ( BF) was 15.5% in the Second Quarter of 2002. Depending on the filtering rules for extremely low and high fares in the database, the ETR ranges from 15.4% to 16.3%. The incidence is much heavier on the least expensive tickets, because three out of the four add-on taxes and fees are based on the passenger's itinerary and are independent of the BF. Partly as a result, the ETR on tickets issued by low-cost carriers is somewhat greater than on those issued by other airlines. A comparative analysis of 1993 data indicates that the ETR was 10.9% at the time. A large portion of the increase between 1993 and 2002 is due to a striking 25% decline in the yields achieved by the airlines during that period.