Airlines’ competition in aircraft size and service frequency in duopoly markets

We are interested in how airlines make decisions on aircraft size and service frequency in a competitive environment. We apply three game-theoretic models to analyze airlines’ choices in duopoly markets: one short-haul market and one long-haul market. We study how airlines’ choices in a competitive environment may vary with flight distance, and also do sensitivity analysis to explore how the equilibrium results may change when air travel demand is higher, as it may happen in the future.Our research considers the competition factor in airlines’ decisions on both aircraft size and service frequency, and the impact of these decisions on both the cost and demand sides of airlines’ business. Different from previous studies, our research is based on cost, market share and demand models derived from empirical studies.     

The built environment and vehicle ownership modeling: Evidence from 32 diverse regions in the U.S.

Vehicle ownership is an important determinant of the travel demand forecasting process. Vehicle ownership models are used by policy makers to identify factors that affect vehicle miles traveled, and therefore address problems related to energy consumption, air pollution, and traffic congestion. For the conventional travel demand forecasting, it logically follows land use forecasting, before trip generation, which is commonly treated as step one. The most critical limitation of the vehicle ownership models, especially in the conventional process, is that they are often related mainly to sociodemographic variables, not so much to built environmental variables. In this study, by pooling regional household travel survey data from 32 diverse regions (almost 92,000 households) of the U.S., and by controlling for socio-demographic and the built environmental variables, we estimated a vehicle ownership model that contributes to the understanding of vehicle ownership and improves the accuracy of travel demand forecasts. Two main findings of this research are: 1) The number of vehicles owned by a household increases with socio-demographic variables and decreases with almost all of the built environmental variables. For the urban planning and design practices, this finding suggests that car shedding occurs as built environments become more dense, mixed, connected, and transit-served. 2) We used both count regression and discrete choice models, and the results suggest that count regression models have better predictive accuracy. The model developed in this study can be directly used for travel demand modeling and forecasting by metropolitan planning organizations.     

Congestion pricing,transit subsidies and dedicated bus lanes: Efficient and practical solutions to congestion

We analyze urban congestion management policies through numerical analysis of a simple model that: allows users to choose between car, bus or an outside option (biking); consider congestion interactions between cars and buses; and allow for optimization of frequency, vehicle size, spacing between stops and percentage of capacity to be dedicated to bus lanes. We compare resulting service levels, social welfare and consumer surplus for a number of different policies and find that: (i) dedicated bus lanes is a better stand-alone policy than transit subsidization or congestion pricing. The latter is marginally better than subsidization but has a negative impact in consumer surplus. (ii) Efficient transit subsidies are quite large since in many cases first-best transit price is negative; establishing dedicated bus lanes or implementing congestion pricing render subsidies unnecessary for high demand levels. (iii) Both subsidization and dedicated bus lanes would count with public support while congestion pricing would probably encounter opposition. (iv) Transit subsidies and/or congestion pricing do not induce large changes on optimal bus size, frequency, circulation speeds and spacing between stops in mixed-traffic conditions: dedicated bus lanes do. (v) In all cases analyzed, revenues from congestion pricing are enough to cover transit subsidies; the optimal percentage of capacity that should be devoted for bus traffic is around one third.     

A hierarchical clustering and routing procedure for large scale disaster relief logistics planning

We describe a hierarchical cluster and route procedure (HOGCR) for coordinating vehicle routing in large-scale post-disaster distribution and evacuation activities. The HOGCR is a multi-level clustering algorithm that groups demand nodes into smaller clusters at each planning level, enabling the optimal solution of cluster routing problems. The routing problems are represented as capacitated network flow models that are solved optimally and independently by CPLEX on a parallel computing platform. The HOGCR preserves the consistency among parent and child cluster solutions obtained at consecutive levels. We assess the performance of the algorithm by using large scale scenarios and find satisfactory results.     

Design of limited-stop bus service with capacity constraint and stochastic travel time

This paper develops a mathematical model for the optimal stopping design of limited-stop bus service, which allows each bus vehicle to skip some stops. To better reflect the reality, this paper considers the vehicle capacity and stochastic travel time. Also, vehicles are all allowed to skip stops whereas any stop is not allowed to be skipped by two consecutive vehicles. A hybrid artificial bee colony (ABC) and Monte Carlo method is developed to solve the optimal stopping strategy. Finally, the model and solution method are validated by a numerical example, and a sensitivity analysis is performed on the passenger demand.     

Comparing the p-median and flow-refueling models for locating alternative-fuel stations

The p-median and flow-refueling models are two of the more popular models for optimal location of alternative-fuel stations. The p-median model, one of the most widely used location models of any kind, locates p facilities and allocates demand nodes to them to minimize total weighted distance traveled. In comparison, the flow-refueling location model (FRLM) is a path-based demand model that locates p stations to maximize the number of trips on their shortest paths that can be refueled. For a path to be considered refuelable, one or more stations must be located on the path in a way that allows the round trip to be completed without running out of fuel, given the vehicle driving range. In this paper, we analyze how well the facilities located by each model perform on the other’s objective function on road networks in Florida. While each objective function degrades somewhat when facilities are located by the other model, the stations located by the flow-refueling model generally do better on the p-median objective than the stations located by the p-median model do on the flow-refueling objective. This difference between the two models is even more pronounced at the state scale than at the metropolitan scale. In addition, the optimal locations for the FRLM tend to be more much more stable as p increases than those located by the p-median model.     

Estimating local car ownership models

Many studies in the transport demand literature have shown that income is an important factor in determining how many cars a household owns. When the models used to measure the strength of this relationship are estimated on cross-sectional data, they typically yield one overall value as the estimate. Local circumstances will, however, vary. This paper illustrates the use of the Geographically Weighted Regression technique to estimate the individual strength of this relationship for each of the United Kingdom electoral wards. Use of this type of model enables a wards’ income elasticity to be based on both the local estimate of the strength of this relationship and the current local level of car ownership. How the use of this local elasticity changes future forecasts of the size of the vehicle fleet is illustrated.     

Driving demand: What can gasoline refueling patterns tell us about planning an alternative fuel network?

Knowing which variables predict gasoline demand can help inform which are useful in determining future demand at an alternative fuel station such as those for bio-fuels, natural gas, hydrogen, or fast-charge electricity. This study explores the spatial distribution of demand by comparing two main classes of variables: those without a displacement component such as population in a census block group, and those that imply a vector or directionality such as vehicle kilometers traveled. The spatial distribution of these variables is compared to the spatial distribution of demand for gasoline using regression. Many models examining the transition from gasoline to an alternative fuel assume a demand pattern for fuel a priori in order to estimate potential demand at a future alternative fuel station. This paper studies not the models themselves but the variables used to predict demand. The results indicate that vehicle kilometers traveled (VKT) is the best variable to pinpoint where demand for fuel will occur. However, travel to the central business district of the metropolitan area does not appear to translate into demand for fuel in proportion to the VKT. While gasoline demand does appear to vary with population as well, the location of demand is much less specific than that predicted by VKT. The results also suggest that the route between home and the nearest freeway entrance may help predict a large portion of refueling and merits further investigation. This possible tendency can be used to create a new variable called “population-traffic” which appears to describe the spatial distribution of demand well. The good performance of this independent variable in regressions suggests that stations sited along the freeway may serve customers needs and provide the necessary concentration of demand for initial alternative fuel stations. A practical application of this work would be to help define refueling demand patterns in a rollout of alternative fueled vehicles in a neighborhood or town.     

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement.Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.     

Shared autonomous vehicles and their potential impacts on household vehicle ownership: An exploratory empirical assessment

Emerging transportation technologies have the potential to significantly reshape the transportation systems and household vehicle ownership. Key among these transportation technologies are the autonomous vehicles, particularly when introduced in shared vehicle fleets. In this paper, we focus on the potential impact that fleets of shared autonomous vehicles might have on household vehicle ownership. To obtain initial insights into this issue, we asked a sample of university personnel and members of the American Automobile Association as to how likely they would consider relinquishing one of their household's personal vehicles if shared autonomous vehicles were available (thus reducing their household vehicle ownership level by one). For single-vehicle households, this would be relinquishing their only vehicle, and for multivehicle households (households owning two or more vehicles) this would be relinquishing just one of their vehicles. Possible responses to the question about relinquishing a household vehicle if shared autonomous vehicles are present are: extremely unlikely, unlikely, unsure, likely, and extremely likely. To determine the factors that influence this response, random parameters ordered probit models are estimated to account for the likelihood that considerable unobserved heterogeneity is likely to be present in the data. The findings show that a wide range of socioeconomic factors affects people's likelihood of vehicle relinquishment in the presence of shared autonomous vehicles. Key among these are gender effects, generational elements, commuting patterns, and respondents' vehicle crash history and experiences. While people's opinions of shared autonomous vehicles are evolving with the continual introduction of new autonomous vehicle technologies and shifting travel behavior, the results of this study provide important initial insights into the likely effects of shared autonomous vehicles on household vehicle ownership.     

Long-term route traffic forecasts and flight schedule pattern for a medium-sized European airport

This paper develops a methodology for assessing the future route network and flight schedule at a medium-sized European airport. The starting point is the existing origin and destination demand from the base airport across the world. This is expanded using growth rates by country or region for the period up to year 2015. The future origin and destination demand is then converted into route traffic, subject to a threshold for direct service. Where demand falls below this level, traffic is reallocated via various appropriate hubs. A model of frequency and aircraft size is applied to estimate the future air service on each route and a schedule created, allocating these flights to specific times of day. The scenario where the base airport operates as a hub is then investigated. This involves identifying a suitable hub model, considering geographical and competitive issues. The origin and destination demand is increased, resulting in an enlarged network of routes. Flights are then scheduled within the constraints of an optimal wave pattern. The outputs are of considerable interest in long-term airport planning and also offer an insight into future airline network strategies and opportunities.     

Inter-regional and inter-temporal analysis of travel behaviour for Asian metropolitan cities: Case studies of Bangkok, Kuala Lumpur, Manila, and Nagoya

The unprecedented demand for travel experienced in Asia, in conjunction with the economic development of the1980s, has resulted in a number of detrimental effects on urban systems. Economic development has certainly intensified per capita income enhancing personal mobility. In Asia, private vehicle ownership and usage have continued to be recognised as an obligatory element of travel for many. Undoubtedly there is a direct relationship between vehicle ownership and public transport usage. Inter-regional and inter-temporal investigations of travel behaviour in Asian cities are therefore necessary to develop an understanding of the future transportation system including suitability and the role of public transport. Since travel data are scarce in Asian countries, inter-regional or inter-temporal travel behaviour investigations do not exist to date. Several travel demand models are developed using discrete choice modelling techniques and Bangkok, Kuala Lumpur, Manila, and Nagoya as case studies. Estimation results of the mode choice models are successfully incorporated to compare travel behaviour trends in selected cities in Asia. The developed models are tested for spatial and temporal transferability.     

Determining flight frequencies on an airline network with demand–supply interactions

This paper determines flight frequencies on an airline network with demand–supply interactions between passenger demand and flight frequencies. The model consists of two submodels, a passenger airline flight choice model and an airline flight frequency programming model. The demand–supply interactions relevant to determining flight frequency on an airline’s network are analyzed by integrating these two submodels. The necessary condition for the convergence of the demand–supply interaction is discussed. An example demonstrates the feasibility of applying the proposed models. The results are more accurate than those obtained without considering demand–supply interactions, and the models provide ways to consider demand–supply interactions well in advance to determine flight frequencies on an airline network.     

Airline strategies for aircraft size and airline frequency with changing demand and competition: A simultaneous-equations approach for traffic on the north Atlantic

Airport planners need to know the forecast demand on the facilities provided airside at airports. For this they need to know how airlines will deal with traffic in terms of the size of aircraft and frequency of service. In response to increasing demand, airlines may increase capacity by increasing the frequency of flights or they may choose to increase aircraft size. This may yield operating cost economies. If the airports they operate from are capacity constrained they will be limited in the extent that they can change frequency that will limit their ability to compete with the number of frequencies offered. Consequently, these airports are excluded as are major hubs as frequencies will be influenced by connecting passengers. Routes are identified on the north Atlantic that can be analysed and conclusions are suggested on the basis of three stage least-squares estimates for pooled time series-cross section data. An increase in passengers on the whole will result in a larger increase in frequency than in aircraft size but the impact of competition does not yield significant results due to the strategy of excluding certain categories of airport.     

Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data

As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the “China Optics Valley” in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields ‘better’ configuration in meeting real-world bike riding needs.     

The split delivery vehicle routing problem with minimum delivery amounts

In the vehicle routing problem, a fleet of vehicles must service the demands of customers in a least-cost way. By allowing multiple vehicles to service the same customer (i.e., splitting deliveries), substantial savings in travel costs are possible. However, split deliveries are often an inconvenience to the customer who would prefer to have demand serviced in a single visit. We consider the vehicle routing problem in which split deliveries are allowed only if a minimum fraction of a customer’s demand is serviced by a vehicle. We develop a heuristic method for solving this problem and report computational results on a wide range of problem sets.     

Throughput time distribution analysis for a one-block warehouse

We develop discrete time models for the throughput time distribution of orders arriving to a one-block warehouse. The models accommodate single- or multi-line orders, and we show how to use them to determine the optimal batch size, given a desired probability of on-time order fulfillment. Experiments suggest that the optimal batch size is slightly higher than one would choose if minimizing average throughput time.     

基于公交信号优先的算法研究

公交优先是利用先进的检测系统,为公交车辆提供优先信号。针对公交信号优先的实施对其他社会车辆的影响,以为公交车辆提供优先信号的同时,降低交叉口车辆平均延误为目标,建立基于发出频率的公交信号优先算法和基于需求强度的公交信号优先算法。     

The two-stage batch ordering strategy of logistics service capacity with demand update

This paper discusses a logistics service integrator (LSI) orders logistics service capacity from a functional logistics service provider (FLSP) before the selling season with two opportunities. The optimal two-stage batch ordering strategy of LSI with demand update is studied. Standard batch size is introduced into LSI’s two-stage capacity ordering strategy model, which is built upon the decision-making sequence and actually observed demand signals between the two ordering instants. The model solution method is designed based on scenario analysis and enumerative algorithm. Sensitivity analysis of the optimal adjustment ordering time point is then carried out and a numerical analysis is presented.     

Analysis of the potential demand for battery electric vehicle sharing: Mode share and spatiotemporal distribution

Carsharing is considered one of the solutions to urban transport problems. As a new mode in the urban transport system in China, there are still initial questions of how carsharing will perform and what the impacts will be. Accordingly, this study considers battery electric vehicle sharing and investigates its potential demand, with Beijing as the case study. A nested logit model is established and calibrated to analyze mode choice behavior. Further, real trip data is used to estimate the potential demand for battery electric vehicle sharing. In addition, the temporal and spatial distribution of potential demand, the impact of battery electric vehicle sharing on the mode split, and the impact of pricing strategies are analyzed. The results show that an optimistic mode split of battery electric vehicle sharing is 4.23% when the average distance between travelers and stations is 0.5 km. The main source of potential demand is public transport. However, the substitution effect of battery electric vehicle sharing for private vehicles is weak. The potential trips are concentrated in the morning peak period, mainly starting in residential or integrative areas, and ending in commercial areas or green spaces. Commuting and long-distance trips are more sensitive to decreases in price, such that they are more likely to be completed as battery electric vehicle sharing trips. This price decrease could also increase the potential trip ratio during the evening peak period. These findings are useful to governments and operators for implementing policies such as station planning, relocation, and pricing strategies.