Layover Time Research Articles

Optimal Allocation of Layover Time in a Smart DC Railway Metro Traction System

Optimal Allocation of Layover Time in a Smart DC Railway Metro Traction System

0177 Quantifying the duration and timing of pilot sleep during layovers

Abstract Introduction Pilots flying long range (8-16 h) and ultra long range (16+ h) flights crossing several time zones must decide when and how much to sleep during layover between the outbound and return flight. Some pilots choose to sleep on home base time (HBT), while others attempt to sleep on local layover time. We sought broad patterns in the timing and amount of pilot sleep during layovers. Methods Using actigraphy, verified by self-report diaries, we collected sleep/wake data on 229 pilots (flying 460 total trips) from nine routes ranging from 10 to 17 hours in scheduled duration. All trips originated in the United States and seven were westbound routes, and two were eastbound routes. Scheduled layover durations ranged from 24 to 56 hours. We collected continuous sleep data three days before the trip, during both flights, during layover, and at least three days after the trip. Results Across all routes, pilots slept an average of 7 hours 58 minutes per 24 hours of layover (± 1 hour 53 minutes SD). Neither the amount nor timing of layover sleep depended on any of the following variables: 1) time (relative to home base) that the layover began; 2) direction of travel (westbound vs eastbound); 3) crew role (flying vs relief); and 4) total amount of sleep during the outbound flight. On layovers lasting 48 hours or longer, the second 24 hours showed more pilots sleeping during local night compared to the first 24 hours. Interindividual differences in the timing and amount of layover sleep were very large (larger than group differences between routes, between crews, and between flight directions.) On average, on layover days pilots obtained 17 minutes less sleep compared to pre-trip days and 32 minutes less sleep compared to post-trip days. Conclusion There are many complex factors that contribute to the timing and amount of layover sleep. We found that interindividual differences are larger than the variables that are constrained by the operational environment, which suggests that the timing and duration of layover sleep may be impacted more by individual traits rather than environmental or operational factors. Support (if any) United Airlines

Analisis Kebutuhan Frekuensi Kapal Ro-Ro Dalam Melayani Penyeberangan

Air Putih - Sei Selari crossing of Bengkalis regency has a distance of 5 miles. The total number of Ro-Ro vessels operating at the parallel crossing of Air Putih – Sei Selari is 6 vessels with a rolling system where 4 vessels operate and 2 vessels are off / standby. The number of trips served per day is an average of 20 trips. However, the overall number of ships operating on a fairly close trajectory is not balanced with optimal ship operating time service. As a result there is a queue of ships to dock. In this study used several methods of analysis to solve the problems that occur, including load factor analysis, frequency analysis required and analysis of the needs of the number of ships. From the results of the analysis it was found that the ideal number of ship frequencies under normal and congested conditions were 21 trips/day and 24 trips/day respectively. Scheduling is based on ship service time during normal and busy times, both of that condition have Sailing Time 35 minutes, headways 90 minutes at normal condition and 103 minutes at congested, Layover time 20 minutes at normal times and 25 minutes at congested, so as to minimize the length of waiting time for ships to dock on the wharf. With a total of 6 ships, the ship operating schedule per day uses a ship rolling system with the division of 3 ships in operation and 3 ships off / standby.

Joint design of electric bus transit service and wireless charging facilities

Cities worldwide are deploying electric buses rapidly to enhance eco-friendly public transportation. Most existing electric transit systems rely solely on stationary charging at terminals and have to equip buses with large-sized batteries to maintain operation in networks. Consequently, the stationary-charging electric bus (SE-Bus) systems are subject to high battery costs, limited range per charge, long charging time (layover time), etc. These limitations may be addressed by integrating the new wireless power transfer technology, which provides contactless charging to the operating buses without extra delays. To guide wireless-charging electric bus (WE-Bus) designs in corridors, this paper proposes an optimization model to jointly optimize (i) the electric supply system and (ii) the transit service that are interdependent.Specifically, we construct parsimonious models for system metrics using five design variables/functions, i.e., battery size, the number of wireless power transfer (WPT) pads, stationary charging rate, service headways, and stop densities. Accordingly, we formulate the optimal design problem to minimize the generalized system cost as the sum of the transit agency’s and patrons’ costs. The problem is attacked by first decomposing into two sub-problems regarding (i) electric supply system design and (ii) transit service design and then solving them iteratively. The (i) sub-problem is converted to a linear programming problem by linearization, and the (ii) sub-problem is analyzed with optimal conditions. Numerical tests show the proposed solution approach produces accurate results.Extensive numerical experiments unveil several findings. First, the WE-Bus systems outperform the SE-Bus in various scenarios except when wireless charging facilities are too costly. Second, the optimized WE-Bus designs can reduce costs not only to the agency (with smaller-sized bus battery and reduced fleet cost) but also to patrons (who enjoy improved services with shorter headways). Additional parameter analyses also investigate the impacts of demand levels, wireless charging rates, depths of discharge, etc. Lastly, a case study in Chengdu (China) illustrates the application of the proposed model.

Biosecurity and Lairage Time versus Pork Meat Quality Traits in a Farm–Abattoir Continuum

The modern pig production chain is increasingly focused on biosecurity, quality, and safety of meat and is associated with many challenges impacting world meat markets, such as animal disease outbreaks and sanitary restrictions, trade regulations and quality requirements. To overcome such challenges and assure more consistent pork meat quality (and safety), there is a need to develop an effective and reliable monitoring system in a farm-abattoir continuum that can be based on selected biomarkers. This study assessed interrelations of selected stress and inflammation biomarkers (acute phase proteins (APP)) between farm biosecurity score versus pork meat quality traits after two different lairage periods. Briefly, the maximum recorded levels of stress hormones (436.2 and 241.2 ng/mL, for cortisol and Chromogranin A (CgA), respectively) and APP (389.4 and 400.9 μg/mL, Pig Major Acute Proteins (MAP) and Haptoglobin (Hp), respectively) at four commercial farms were within the recommended threshold values. Cortisol and APP were negatively correlated to the internal and total biosecurity scores of farms. The increase of level of both sets of biomarkers was found at bleeding (after transportation and lairage period), but with lower values after long (18-20 h) versus short (1-3 h) lairage lay-over time. In general, negative correlation was confirmed between stress and inflammation biomarkers and carcass/meat quality traits. The farm total biosecurity level significantly affected chilling yield, meat temperature, and a* value. Pig-MAP emerged as a good biomarker with a promising potential for assessment and anticipation of broad aspects in the pork meat chain. It can be used for detection of failures in the pig production system and might be incorporated in certification programs for the pork meat industry.

Flight reliability during periods of high uncertainty

In times of great uncertainty for the airline industry, travelers are in search of reliable itineraries now more than ever. With condensed airline schedules and less options, air travelers must rely on making flight connections and manage layover times to arrive at their final destination on time. In an era with readily available information, passengers expect accurate and transparent reliability information to help improve decision making for multi-leg itineraries. However, often for reliability in air travel, this information is incomplete or not useful. In this paper we utilize historical probability distributions of flight arrival and departure times using publicly available data to give an intuitive and predictive flight itinerary reliability metric. The COVID-19 pandemic significantly affected air-travel in the US and this uncertainty is still being felt with cancellations and delays due to staff shortages and reduced demand. Therefore, we extend the stochastic network model from our previous research to air travel during COVID-19 to see the effects on flight reliability. Using this model, we conduct computational experiments to evaluate air travel through multiple reliability metrics. We show that during periods of high uncertainty, predictive historical distributions of flight data considering recency and seasonal effects are less accurate given many cancellations and a reduced flight schedule.

Effects of number of electric vehicles charging/discharging on total electricity costs in commercial buildings with time-of-use energy and demand charges

Electric vehicle (EV) penetration has been increasing in the modern electricity grid and has been complemented by the growth of EV charging infrastructure. This paper addresses the gap in the literature on the EV effects of total electricity costs on commercial buildings by incorporating V0G, V1G, and V2B charging. The electricity costs are minimized in 14 commercial buildings with real load profiles, demand, and energy charges. The scientific contributions of this study are the incorporation of demand charges, quantification of EV, and smart charging electricity costs and benefits using several representative long-term datasets, and the derivation of approximate equations that simplify the estimation of EV economic impacts. Our analysis is primarily based on an idealized uniform EV commuter fleet case study. The V1G and V2B charging electricity costs as a function of the number of EVs initially diverge with increasing charging demand and then become parallel to one another with the V2B electricity costs being lower than V1G costs. A longer EV layover time leads to higher numbers of V2B charging stations that can be installed such that original (pre-EV) electricity costs are not exceeded as compared to a shorter layover time. Sensitivity analyses based on changing the final state of charge (SOC) of EVs between 90% and 80% and initial SOC between 50% and 40% (thereby keeping charging energy demand constant) show that the total electricity costs are the same for V0G and V1G charging, while for V2B charging, the total electricity costs decrease as final SOC decreases.

Evaluation of the Needs of Ferry Boats on the Tanjung Kalian – Tanjung Api-Api Bangka Belitung Province

Tanjung Kalian Ferry Port is a ferry port that connects Sumatra Island and Bangka Island which has a distance of 30 miles. The Tanjung Kalian – Tanjung Api-Api route has 13 (thirteen) ships that operate for a travel time of 240 minutes or 4 hours. High productivity of passengers and vehicles on the Tanjung Kalian – Tanjung Api-Api route which causes the ship's carrying capacity to exceed the provided capacity, resulting in load accumulation. The method of analysis of the carrying capacity on the ship uses the analysis of the daily average load factor calculation. The primary data used is a survey of productivity data for departures and arrivals for 15 days from April 21 – May 5, 2021, survey data for ship sailing times, and survey data for ship layover time for the Tanjung Kalian – Tanjung Api-Api route. By looking at the increasing productivity of passengers and vehicles at the Tanjung Kalian Ferry Port, there is an increase in the needs of service users that must be met, including the frequency of ships and the number of ships operating. In accordance with the results of the analysis, Tanjung Kalian Ferry Port must increase the frequency of ships to 19 trips/day. This is used to overcome the number of service users who are not transported.

Customer Choice Modelling: A Multi-Level Consensus Clustering Approach

Customer Choice Modeling aims to model the decision-making process of customers, or segments of customers, through their choices and preferences identified by the analysis of their behaviors in one or more specific contexts. Clustering techniques are used in this context to identify patterns in their choices and preferences, to define segments of customers with similar behaviors, and to model how customers of different segments respond to competing products and offers. However, data clustering is an unsupervised learning task by nature, that is the grouping of customers with similar behaviors in clusters must be performed without prior knowledge about the nature and the number of intrinsic groups of data instances, i.e., customers, in the data space. Thus, the choice of both the clustering algorithm used and its parameterization, and of the evaluation method used to assess the relevance of the resulting clusters are central issues. Consensus clustering, or ensemble clustering, aims to solve these issues by combining the results of different clustering algorithms and parameterizations to generate a more robust and relevant final clustering result. We present a Multi-level Consensus Clustering approach combining the results of several clustering algorithmic configurations to generate a hierarchy of consensus clusters in which each cluster represents an agreement between different clustering results. A closed sets based approach is used to identified relevant agreements, and a graphical hierarchical representation of the consensus cluster construction process and their inclusion relationships is provided to the end-user. This approach was developed and experimented in travel industry context with Amadeus SAS. Experiments show how it can provide a better segmentation, and refine the customer segments by identifying relevant sub-segments represented as sub-clusters in the hierarchical representation, for Customer Choice Modeling. The clustering of travelers was able to distinguish relevant segments of customers with similar needs and desires (i.e., customers purchasing tickets according to different criteria, like price, duration of flight, lay-over time, etc.) and at different levels of precision, which is a major issue for improving the personalization of recommendations in flight search queries.

Min-Time TS: task resource mapping algorithm in cloud computing

Internet-established cloud environment provides enormous amount of ubiquitous, distributed resources such as processors, memory, and software. In recent years, task scheduling and resource allocation has become a significant issue in cloud computing. Task scheduling plays a crucial role in cloud system performance. For achieving elegant performance, arises a need of efficient scheduling technique. Numerous algorithms developed for scheduling. In this paper, we have proposed an efficient approach which schedules the tasks to the available cloud resources by forming a task pair. Min-Time TS algorithm forms a finite number of task sets; each set consists a pair of tasks one from Group G1 and another from Group G2 and are scheduled with minimum layover time.

Multidisciplinary mission and system design tool for a reusable electric propulsion space tug

According to the Global Exploration Roadmap, a Getaway in the lunar vicinity will enable the human exploration of the Moon, paving the way toward Mars and further in the deep space. A reusable transportation system, the Lunar Space Tug (LST), can be a key support transportation system to achieve a continuous and sustainable connection between the Earth and the orbital lunar outpost. Exploiting an electrical propulsion technology consisting in Hall Thrusters (HTs), this reusable space cargo system can maximize the delivered payload, with a lower propellant consumption with respect to a chemical-based platform. This significant improvement comes with two main throwbacks: a long transfer time between Earth and Moon and a complex multidisciplinary design of the overall LST. The first issue can be overcome with good mission planning in order to reduce the layover times and maximize the use of the LST. However, the peculiarities of the adopted propulsion technology shape the overall mass and power distribution of the space tug, as well as its transfer trajectory. The design complexity introduced by this subsystem can be effectively investigated if the mission analysis, trajectory generation and subsystem sizing are merged together in one software. In fact, the low-thrust transfer trajectories are highly affected by orbital perturbation and eclipse periods. At the same time, the high demand in power of the propulsion system puts constraints on the mass breakdown and power allocations of the LST, therefore on the reachable thrust level. The trajectory and subsystems sizing limitations call for a particular accurate mission analysis in order to succeed. Only combining all those elements, it is possible to define feasible design boundaries without performing countless simulations trying to optimize each element as a stand-alone part.Politecnico di Torino, in collaboration with the European Space Research and Technology Centre (ESTEC), developed a MatLab-based preliminary design tool for electric propulsion space tug missions, called MultidisciplinAry desiGN Electric Tug tOol (MAGNETO). Starting from the mission analysis of the lunar space tug, the tool building blocks and capabilities are presented. Moreover, the build-in trajectory module will be analysed in-depth. The potentiality of the trajectory generation tool in MAGNETO enables a refined design of the initial design envelope of the LST. For this reason, the improved results will be confronted with the previous LST design tool developed by Politecnico di Torino.

Market structure and quality determination for complementary products: Alliances and service quality in the airline industry

This paper explores the effect of market structure on quality determination for complementary products. The focus is on the airline industry and the effect of airline alliances on flight frequency, an important element of service quality. With zero layover cost, the choice of flight frequencies has the same double-marginalization structure as in the usual alliance model, leading to a higher frequency in the alliance case as double marginalization is eliminated, along with a lower full trip price and higher traffic. The surprising result of the paper emerges with high-cost layover time, where double marginalization in frequencies is absent and where an alliance reduces service quality via a lower frequency, with the full price potentially rising (in which case traffic falls).

Are passengers compensated for incurring an airport layover? Estimating the value of layover time in the U.S. airline industry

American, Delta, and United have organized their operations into extensive hub-and-spoke networks that typically require passengers originating and concluding travel in non-hub cities to board a connecting flight at a hub en route to the final destination. From the passenger perspective, layovers are detrimental since the addition to total travel time relative to a nonstop itinerary is a cost incurred by the passenger. An airline is able to reduce a passenger's layover time by narrowing the gap between flights at the connecting airport. However, narrowing this flight gap has the adverse effect of increasing airport congestion. Taking these perspectives into account, it is clear that layover time influences a prospective passenger's purchasing decision and an airline's flight scheduling decision. Using published fare and itinerary data from Google Flights, this paper provides insight into both decisions by providing empirical estimates on the value of layover time in the U.S. airline industry. This paper finds that passengers are compensated with a fare that is $42.74-$47.60 cheaper per hour of layover time. Of the three dominant legacy carriers, United passengers are found to be compensated at an even higher rate of $61.89 per hour.

A pair-based task scheduling algorithm for cloud computing environment

In the cloud computing environment, scheduling algorithms show the vital role of finding a possible schedule of the tasks. Extant literatures have shown that the task scheduling problem is NP-Complete as the objective is to obtain the minimum overall execution time. In this paper, we address the problem of scheduling a set of l tasks with a set of |G| groups to a set of m clouds, such that the overall layover time is minimized. Note that overall layover time is the sum of the timing gaps between paired tasks. Here, we present a pair-based task scheduling algorithm for cloud computing environment, which is based on the well-known optimization algorithm, called Hungarian algorithm. The proposed algorithm considers an unequal number of tasks and clouds, and pairs the tasks to make the scheduling decision. We simulate the proposed algorithm and compare it with three existing algorithms, first-come-first-served, Hungarian algorithm with lease time and Hungarian algorithm with converse lease time in twenty-two different datasets. The performance evaluation shows that the proposed algorithm produces better layover time in comparison to existing algorithms. The proposed algorithm is analyzed theoretically and shown to require O (kpl2) time for k iterations, p repetitions and l tasks.

Improving Bus Operations through Integrated Dynamic Holding Control and Schedule Optimization

Bus bunching can lead to unreliable bus services if not controlled properly. Passengers will suffer from the uncertainty of travel time and the excessive waiting time. Existing dynamic holding strategies to address bus bunching have two major limitations. First, existing models often rely on large slack time to ensure the validity of the underlying model. Such large slack time can significantly reduce the bus operation efficiency by increasing the overall route travel times. Second, the existing holding strategies rarely consider the impact on the schedule planning. Undesirable results such as bus overloading issues arise when the bus fleet size is limited. This paper explores analytically the relationship between the slack time and the effect of holding control. The optimal slack time determined based on the derived relationship is found to be ten times smaller than in previous models based on numerical simulation results. An optimization model is developed with passenger-orient objective function in terms of travel cost and constraints such as fleet size limit, layover time at terminals, and other schedule planning factors. The optimal choice of control stops, control parameters, and slack time can be achieved by solving the optimization. The proposed model is validated with a case study established based on field data collected from Chengdu, China. The numerical simulation uses the field passenger demand, bus average travel time, travel time variance of road segments, and signal timings. Results show that the proposed model significantly reduce passengers average travel time compared with existing methods.

Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks

An increasing number of cities are transitioning from fossil fuel-powered buses for public transport to battery electric buses. Evaluating the energy demand of buses has become an important prerequisite for the planning and deployment of large electric bus fleets and the required charging infrastructure. A number of state-of-the-art approaches to determining the energy requirements of electric buses use individual specific energy demand values or rely on standard driving cycles, though these do not consider local bus route characteristics. Others require high-resolution measurements of the vehicles’ driving profiles, which is impractical for large bus fleets. This paper presents a longitudinal dynamics model to calculate the energy demand for electric buses. The model is designed to be easily applied to large bus networks using real data sources that are commonly available to bus transit operators. This data can be derived from low-resolution data collected from day-to-day operations, where only the arrival and departure time of the buses at each bus stop are available. This approach offers a practical alternative to state-of-the-art methods and requires no high-resolution velocity profiles, which are difficult to obtain, while still taking into account the details of the operational characteristics of the transportation network under consideration. The application of the model is demonstrated in a case study to electrify the complete public bus network in Singapore. The results showed that the heterogeneity of driving conditions observed in a large network leads to a high variance in energy requirements between different bus lines and at different times of day. This confirms the need to take the characteristics of each individual bus route into account. In the case-study, a fully electric public bus fleet would require about 1.4 GWh per day for revenue service, which is about one per cent of Singapore’s daily electricity demand. Another finding is that 50% of the bus lines require less than 40 kWh per terminus-to-terminus journey, which indicates a good potential for fast opportunity charging during layover time. The results of the model should serve as the basis for further studies into battery sizes, charging strategies and charging infrastructure requirements.

Airline Alliances and Service Quality

Convenient scheduling, characterized by adequate flight frequency, is the main quality attribute for airline services. However, the effect of airline alliances on this important dimension of service quality has received almost no attention in the literature. This paper fills this gap by providing such an analysis in a model where flight frequency affects schedule delay and connecting layover time. While an alliance raises service quality when layover time has zero cost, the reverse occurs when layover time is costly. The source of this surprising result is that costly layovers eliminate the additive structure of the full trip price, which consists of the sum of the subfares plus the weighted sum of the reciprocal flight frequencies when layover cost is zero. The paper also shows that nonaligned carriers adjust frequencies to suit passenger preferences in business and leisure markets, while an alliance is less responsive to such preference differences. With hub-airport congestion, greater internalization by allied carriers tends to reduce frequency, but this force is not enough to overturn the positive alliance effect in the low-cost layover case.

Defining Reserve Times for Metro Systems: An Analytical Approach

The aim of this paper is to provide an analytical approach for determining operational parameters for metro systems so as to support the planning and implementation of energy-saving strategies. Indeed, one of the main targets of train operating companies is to identify and implement suitable strategies for reducing energy consumption. For this purpose, researchers and practitioners have developed energy-efficient driving profiles with the aim of optimising train motion. However, as such profiles generally entail an increase in travel times, the operating parameters in the planned timetable need to be appropriately recalibrated. Against this background, this paper develops a suitable methodology for estimating reserve times which represent the main rate of extra time needed to put ecodriving strategies in place. Our proposal is to exploit layover times (i.e., times spent by a train at the terminus waiting for the next trip) for energy-saving purposes, keeping buffer times intact in order to preserve the flexibility and robustness of the timetable in case of delays. In order to show its feasibility, the approach was applied in the case of a real metro context, whose service frequency was duly taken into account. In particular, after stochastic analysis of the parameters involved for calibrating suitable buffer times, different operating schemes were simulated by analysing the relationship between layover times, number of convoys, and feasible headway values. Finally, some operation configurations are analysed in order to quantify the amount of energy that can be saved.

Динамическая система показателей, определяющая техническую оснащенность инфраструктуры железнодорожных станций

Objective: To obtain technical equipment of infrastructure and terminal capacity relations in order to determine the necessary gridiron of tracks, as well as to assess efficiency and optimization of existing stations’ functioning. Methods: Based on synthesis and analysis of stations’ operation, as well as transport systems’ simulation modeling theory. Results: Parameters, influencing the present and designed terminal capacity, were identified. Cause-and-effect relations of events, resulting in railway machinery being brought in non-serviceable condition, are established. Dynamic system of indices was suggested, allowing for a more precise solution of the following tasks: to identify the necessary and sufficient gridiron of tracks for passing the specified amount of traffic, to assess the most probable amount of traffic at the station and its loading. Criteria of input and output flows of traffic at the station, as well as lay-over and trains' delays were stated, the former make it possible to analyze the suggested technical equipment and station functioning. Practical importance: On the basis of obtained relations it is possible to specify optimum relationship of trains’ lay-over time at station yards, the reserve of gridiron of tracks’ elements time in use and the number of routes being implemented at the station, as well as the necessary gridiron of tracks within the given аmount of train traffic. The results of the research may be applied in designing new railway stations, as well as optimization of station functioning and reconstruction of the existing stations.

Are Passengers Compensated for Incurring an Airport Layover? Estimating the Value of Layover Time in the U.S. Airline Industry

American, Delta, and United have organized their operations into extensive hub-and-spoke networks that typically require passengers originating and concluding travel in non-hub cities to board a connecting flight at a hub en route to the final destination. From the passenger perspective, layovers are detrimental since the addition to total travel time relative to a nonstop itinerary is a cost incurred by the passenger. An airline is able to reduce a passenger's layover time by narrowing the gap between flights at the connecting airport. However, narrowing this flight gap has the adverse effect of increasing airport congestion. Taking these perspectives into account, it is clear that layover time influences a prospective passenger's purchasing decision and an airline's flight scheduling decision. Using published fare and itinerary data from Google Flights, this paper provides insight into both decisions by providing empirical estimates on the value of layover time in the U.S. airline industry. This paper finds that passengers are compensated with a fare that is $42.74-$47.60 cheaper per hour of layover time. Of the three dominant legacy carriers, United passengers are found to be compensated at an even higher rate of $61.89 per hour.