Optimizing and assigning price levels for air traffic management

Pricing policies could encourage airline companies to modify departure times and routes of their flights in order to reach a route-slot allocation target that minimizes the en-route congestion. The problem of restricting the number of price levels and the assignment of one price level to each sector at each time period is studied using a simulation based on a Logit discrete choice model. In this model, an option is defined as the combination of a departure time and a route. For each flight, a utility is associated to each option and takes into account the flying cost, the cost of ground delay and the prices of crossed sectors. The optimization of the pricing policy considers average flows and minimizes the quadratic difference between desired and expected flows on each option. A heuristic algorithm that involves successive iterations of simulated annealing and gradient methods performs this optimization. The simulated annealing assigns a price level to each sector at each time period, and the gradient algorithm computes new values of price levels. The test of the method on constructed examples indicates that the use of only four price levels does not significantly deteriorate the performance of the system with respect to the use of independent prices.     

Joint RP–SP data in a mixed logit analysis of trip timing decisions

In the current paper, a departure time choice model including travel time variability is estimated, combining stated preference and revealed preference data. We account for response scale differences between RP and SP data and, applying the mixed logit model, test for correlation of scheduling sensitivity across RP and SP choices within individuals. The analysis implies systematic differences in the RP and SP data. With support of the evaluation from the Stockholm trial, this indicates that SP is less trustworthy for trip timing analysis and forecasting, presumably because there are temporal differences in RP and SP choice situations.     

Impacts of vehicle restrictions on urban transport flows: The case of Santiago,Chile

Regression models are employed to quantify the effects of vehicle restrictions on private and public transport passenger flows in Santiago, Chile using trip flow data for cars, buses and the city's Metro rail system. Estimates are derived for the effects of two restrictions: a permanent measure applied from April through August 2008 to vehicles without catalytic converters and additional measures that banned the use of vehicles with catalytic converters between 7:30 am and 9 pm on days declared as environmental “pre-emergencies” due to high air pollution levels. The estimates show that the permanent restriction had no impact on the use of private cars while the additional restriction curtailed their use by 5.5%. Also, on pre-emergency days the flow of passengers to the Metro increased by about 3% while the bus network showed no statistically significant increase. The pre-emergency restrictions thus had an effect on the ridership of the Metro but not on the bus network as alternatives to the use of private cars.     

一体化发展策略下的轨道交通枢纽建设

轨道交通的枢纽建设在实现城市交通发展一体化中起着至关重要的作用。在总结上海轨道交通多个枢纽建设和运营经验的基础上，提出在一体化发展策略下建设轨道交通枢纽的创新理念，包括“由多线集中换乘到分散换乘”的网络规划理念、由“零距离换乘”到“以时间换空间”的运营安全理念、注重客流组织“单循环”流线的换乘效率理念、实现立体化交通组织的“人车分流”理念、注重细节的“以人为本”理念以及车站周边空间的一体化开发理念，最后从规划、设计、运营、服务、开发五个层面提出轨道交通枢纽建设的具体要求，为未来轨道交通枢纽的规划和建设提供参考。     

The sensitivity of the valuation of travel time savings to the specification of unobserved effects

The behavioural value of travel time savings (VTTS) remains a controversial data item in the evaluation of transport projects. Over the last 40 years we have seen numerous empirical studies on the amount that an individual or firm is willing to pay to save a unit of travel time (or more precisely transfer a fixed amount of time from one activity to another). With a few exceptions, the majority of empirical studies have used essentially the same data metric and model estimation procedure. The most popular approaches have used either revealed preference (RP) or stated preference (SP) data and a multinomial logit (MNL) choice model to identify marginal rates of substitution between travel time and price of a trip. A few more advanced studies have integrated RP and SP data and have explored the gains in behavioural power of models that relax, to varying degrees, the underlying assumptions that produce the MNL model. This paper highlights the potential gains from a more carefully considered structure of the unobserved effects which condition the form of a discrete choice model, and hence the possible misinference from the simpler MNL specification. We demonstrate the implication for VTTS estimates of serial correlation between the SP treatments, covariance amongst alternatives, presence of individual specific (random) effects or heterogeneity, and differential variance of the unobserved effects.     

The value and effects of introducing high standard train and bus concepts in Blekinge, Sweden

A set of improvements were introduced on a railway line in the south of Sweden in January 1992, including a new train concept, improved timetable and lower fares. The new train replaced a combination of old rail cars and standard coaches (bus). Furthermore, 18 months later, a new high comfort coach (bus) service was introduced as a complement to the train timetable to increase the number of departures. Forecasts from a national four-step model system, based on Revealed Preference data, which did not cover explanatory factors like mode concept, comfort, mode image, was made and compared with a forecast based on stated preference data choice model. Both type of forecasts were compared to travel statistics. Stated Preference data have been collected from car drivers, coach and train passengers. The set of improvements have been included in the SP alternatives presented to the subjects. A structured logit model, a regression model and a linear programming model are compared. Unstructured and structured logit models are compared. Logit models have been estimated and used to form a mode choice model which predicts the changes in market shares between the train and car. The choice model forecast coincide, accidentally, with actual demand 8–9 months after the introduction of the set of improvements (a 40% increase in journeys). The set of improvements, including the new train service, has proven to be a success with more than 100% increase in travel after only two years of operation. Differences in estimated logit models for 1991 and 1992 are explained as partly due to asymmetry effects and partly to shortcomings in the SP data collection technique. Logit model estimates for train and coach are compared. The value of high standard coach service, running in parallel to the train, is estimated. The new coach gained few new passengers and also a much lower monetary value than the train by both train and bus passengers.     

北京地铁换乘站客流预测模型研究

根据北京地铁全网一票换乘和一卡通无障碍换乘机制,以及早晚高峰出行等特点,对各种地铁客流预测模型进行分析,研究北京地铁换乘站客流预测模型的应用。依据换乘站不同类型的客流,以北京地铁历史客流和实时客流数据为基础,探讨采用历史平均预测法、基于最小二乘支持向量机时间序列预测法、分峰段混合预测法、基于概率树全路网预测方法等对进站客流、出站客流、换乘客流和站内客流进行预测。     

Performance assessment of UK airports: Evidence from a Bayesian dynamic frontier model

This paper analyzes the cost efficiency of UK airports over the period 1998-2008, using a Bayesian dynamic frontier model. This model provides a more structural explanation for the variation in airports inefficiency than has been presented by previous models, and also allows for cost inefficiency effects. On average, the dynamic frontier results, estimated via the Markov Chain Monte-Carlo simulation, indicate that UK airports improved their efficiency over time. Factors found to be important determinants of cost efficiency include airport size, price regulation, price cap variations and airport competition. Policy implications of the results are derived.     

Urban freight demand forecasting: A mixed quantity/delivery/vehicle-based model

The research germinates from the statement that the cities have to solve the impacts due to freight transport in order to improve their sustainability implementing sets of city logistics measures. But city logistics measures involve several actors and choice dimensions. It is therefore important to have methods and models able to assess the effectiveness of the measures to be implemented. The current models were mainly developed to simulate some aspects of urban freight transport, and are not able to forecast many impacts of implementing traffic and transportation measures at an urban scale.This paper presents a modelling approach that tries to point out the relations existing among city logistics measures, actors and choice dimensions. It comprises three model sub-systems to estimate the quantity O–D matrices by transport service type (e.g. retailer on own account or wholesaler on own account or by carrier), the delivery O–D matrices by delivery time period, and the vehicle O–D matrices according to delivery tour departure time and vehicle type.This modelling system is a multi-stage model and considers a discrete choice approach for each decisional level. It was first tested using some data collected in the inner area of Rome, including traffic counts and interviews with retailers and truck-drivers. The model estimations were also compared with the experimental ones, and quite satisfactory results were obtained.     

The sky is the limit: Assessing aircraft market diffusion with agent-based modeling

This paper presents an agent-based model for the diffusion of new aircraft models. Expanding on the classical economic decision framework, where investment decision-making is entirely based on profitability, our more holistic modeling approach takes into account profitability, flexibility, as well as the environmental impact of new aircraft models in the adoption decision process. Technical parameters, such as the range and passenger number per aircraft model considered, various types of pollutant emissions of the aircraft engine, as well as daily operational data, are used as covariates in the model. In validation for the most common mid-range passenger aircraft models of Airbus and Boeing, our agent-based model produces results that are comparable to observed real-world data on the market diffusion of existing mainstream aircraft models. This result shows the applicability and usefulness of our model, which can subsequently also be applied for studying the diffusion of aircraft models embodying new generations of components. Our simulation shows that a price reduction or a decrease in pollutant emissions of new aircraft models can be expected to lead to more adoption and faster diffusion. Furthermore, our modeling approach demonstrates that a holistic and systematic framework that includes not only profitability (in terms of payback time) but also flexibility (in terms of optimal range and amount of passengers) and environmental impact (in terms of deviation from regulatory standards) can be helpful for modeling the investment decision-making process of airlines.     

Estimating Tourist Demand through Cointegration Analysis: Swedish Data

The purpose of this paper is to use co-integration analysis to estimate the long-run relationship between monthly tourist flows to Sweden from American, European and Scandinavian countries. Also, the factors that influence arrivals, such as income, price, exchange rate, the Chernobyl nuclear accident and the 1991 Gulf War are estimated. An econometric model approach of cointegration analysis is adopted to estimate the effect of the above factors on the number of visitors to Sweden and the area No.6 in Sweden (SW:6). Monthly time-series data for the period 1980-1998 were used. The estimated model does not indicate any statistically significant effect of the Chernobyl nuclear accident, or the 1991 Gulf War on international tourism demand. On the other hand, the estimated model does indicate statistically significant effect of income, exchange rate and the consumer price index (CPI) on international tourism demand. The estimation and diagnostic testing strategy supports the specification of the model.     

A study on travelers' transport mode choice behavior using the mixed logit model: A case study of the Seoul-Jeju route

This study is to analyze passengers' choice of the mode of transportation when air transportation is in competition with high speed rail (HSR). The Seoul-Jeju route analyzed as an empirical case study, in which the construction of an undersea tunnel to connect Seoul and Jeju city by HSR has been considered. The study also included two new variables, ‘safety of transportation’ and ‘availability of duty free shopping’ in addition to traditional transport choice variables such as travel time, travel costs and frequency of service to reflect special characteristics of the market. As data gathering tools, SP techniques and mixed logit model, for analytical methodology, were utilized. The authors found that the goodness of fit of the models was improved with new variables. The models also showed that the characteristics of business passengers and leisure passengers in choosing the mode of transportation were different. Business passengers were apt to choose a safety secured mode of transportation regardless of fare while leisure passengers preferred to use duty free shops more than business passengers.     

Identifying differences in willingness to pay due to dimensionality in stated choice experiments: a cross country analysis

This paper explores the influence of both cultural and socio-economic characteristics on the perception of complexity and cognitive load associated with stated choice (SC) experiments. Complexity is analysed in terms of five design dimensions which were systematically varied according to a macro experimental design. To study the influence of cross country differences on willingness to pay estimates, we combined datasets collected in Sydney, Santiago de Chile and Taichung city in Taiwan, all of them related to an equivalent route choice experiment. Several mixed logit models were specified and estimated; our results show that design dimensions do have an impact on the behavioural outputs of discrete choice models estimated on SC data. However, these influences seem to be data-specific, suggesting that the impact of design dimensions upon SC outcomes may be local and not necessarily transferable across different countries and cultures.     

Modeling the mixed storage strategy for quay crane double cycling in container terminals

A mixed storage strategy was proposed to improve the efficiency of yard operations and horizontal transportation to corporate with quay crane double cycling. The effects of the mixed storage strategy on terminal operations, including truck travel distance, yard crane operations and the number of required trucks, were analyzed. An approach based on cycle-time models, the queuing theory was proposed to evaluate the performances from long-term run. Results show using the mixed storage strategy, the truck travel distance can be decreased and the number of required trucks and yard crane’s operation time can be reduced by 16% and 26% respectively.     

CNG2020 strategy and airline efficiency: A Network Epsilon-Based Measure with managerial disposability

In this paper, we analyze the impacts of CNG2020 strategy on the efficiency of 29 international airlines during 2021–2023. Predictions are made through neural networks based on the empirical data from 2008 to 2015. Following the principle of CNG2020 strategy, we calculate the emission limit for each airline. Then we propose a new model, Network Epsilon-Based Measure with managerial disposability, to discuss the difference in efficiency of the airlines when the CNG2020 strategy is considered. The main findings are: (1) For most of these 29 airlines, the CNG2020 strategy has little influence on their efficiency. (2) The efficiency has little variation under the following conditions: when the carbon price is 8 $/ton CO₂-eq, when the price is 14 $/ton CO₂-eq, and when the price is 20 $/ton CO₂-eq. (3) Scandinavian is identified as the benchmarking airline.     

Accounting for differences in modelled estimates of RP,SP and RP/SP direct petrol price elasticities for car mode choice: A warning

Using a sample of 245 direct petrol price elasticities of car travel collected from 52 published mode choice studies, a random coefficient regression model is estimated to account for heterogeneity in the influence of the type of data used in the various studies (RP, SP and a combination of RP/SP). The focus on the type of data is designed to highlight a concern that has emerged in the way that an increasing number of researchers and consultants derive elasticities from stand-alone stated preference studies, and apply them. It is well known that this is not valid without model calibration (usually via the mode-specific constants), since the elasticity formula uses the probability of an alternative being chosen. To understand the extent of possible behavioural response bias when calibration is ignored is important, signalling a possible adjustment process to correct for the absence of calibration relative to calibrated RP and or RP/SP derived elasticities.     

Efficient aircraft spare parts inventory management under demand uncertainty

In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MTTF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed.     

Investigating household vehicle ownership,mode choice and trip sharing decisions using a combined revealed preference/stated preference Nested Logit model: case study in Bangkok Metropolitan Region

This study proposes a Nested Logit model to investigate household travel behaviour in respect to vehicle ownership, mode choice and trip sharing decisions. The model is analysed using revealed preference (RP) and stated preference (SP) data since a combined estimation of RP/SP data is an effective method of expressing complex travel behaviour and forecasting travel demand for new transport services. In the proposed model, the nesting structure has two levels. The upper level shows car ownership, motorcycle ownership, and no vehicle-ownership choices, and the lower level shows the mode choice combinations for two-traveller households. Trip sharing is considered as one of the mode-choice options in the model. The proposed model is analysed using data from the Bangkok Metropolitan Region. The analysis conducted informs that Central Business District (CBD) travel, long distance travel, household income, job status, age of travellers and presence of school children in households are key aspects in household travel decisions. Based on these aspects, households make important decisions on vehicle ownership, mode choice and trip sharing. In addition, this study reveals commuters’ hidden preferences for modes that are not in existence, in particular the Mass Rapid Transit System in the Bangkok Metropolitan Region due to be fully implemented in 2010.     

Varying Elasticities and Forecasting Performance

This study assumes that tourists' demand reactions to income and price changes are asymmetric at different phases of the business cycle. In order to test this hypothesis, we analyzed the demand for international tourism in five source markets using a modified growth rate (MGR) model. The empirical evidence demonstrates that income elasticity is indeed asymmetric across the business cycle in four source markets. In addition, asymmetric price effects were found for one source market. To compare forecasting performance, we also estimated a time‐varying parameter (TVP) model. The results show that the MGR model generally outperforms the TVP model. Copyright © 2013 John Wiley & Sons, Ltd.