Scheduled Departure Times Research Articles

Identifying alternative stops for first and last-mile urban travel planning

Urban travelers today are seeking increasingly more information to plan their optimal trip, based on additional factors other than scheduled departure times. Still, some route planning applications provide a simple approach with a few parameter settings (e.g. to minimize travel time between two specific places at a certain time) and without any multimodal solutions. Our approach provides travelers with a set of non-dominated nearby stops that presents a number of traveler preferences in an easily comprehensible and quickly calculable manner. We display first and last-mile stops that fall on a Pareto front based on multiple criteria such as travel time, number of transfers, and frequency of service. Our algorithm combines stop and route-based information to quickly present the traveler with numerous nearby quality options for their itinerary decision making. We expand this algorithm to include multimodal itineraries with the incorporation of free-floating scooters to investigate the change in stop and itinerary characteristics. We then analyze the results on the star-shaped public transportation network of Göttingen, Germany, to show what advantages stops on the Pareto front have as well as demonstrate the increased effect on frequency and service lines when incorporating a broadened multimodal approach.

A Statistical Review of European Carriers’ Flight Delays and the Assessment of Delay Factors

Disruption is an inherent risk that might arise because of the complexity and dynamic nature of air transport operations. In the aviation business, disruption can happen for a variety of reasons, including poor weather, strikes, and political factors. This study aims to analyse flight delays that European airlines encounter and assess the dependencies between different operation parameters through correspondence analysis in contingency tables that are visually represented using correspondence maps. This study examined data from selected European airlines between 2018 and 2022, which contained information on the length of delay, the reasons associated with it, and the specific characteristics of each flight, such as the type of flight, the type of aircraft, and the scheduled departure time. The result showed that even though there was a large decrease in the overall number of flights operated during the pandemic period in 2020 and 2021, the percentage of delayed flights still varies above 55%. Long-haul flights and larger aircraft tend to have longer delays. Except for 2020 and 2021, the percentage of delayed flights for different lengths of delays and scheduled departure times did not significantly change across the review years. The evaluation reason for delay appeared to increase with time due to airport or authority restrictions, while there was a tendency for the number of delayed flights related to technical aircraft, equipment, and ground operations to decrease. By developing an analysis of the root causes of flight delays using case studies that have already been published by different researchers and determining the degree to which an operating parameter contributes, we may provide guidelines for future studies that will uncover ways to minimize flight delays.

How Complex Systems Sometimes Follow Murphy’s Law: Train Delay Prediction at a Station Using Delays at Previous Stops as the Features

Prediction or estimation of the delay of a train is essential to analyze customer behavior. In case of longer delays, there is a high probability that the customer will abort their travel or change the mode/time of travel. The delay propagation from previous stations to the next station creates a chain of these types of customer reactions. Thus, using delays at the previous station to predict the next station’s delay is a good approach to align with customer behavior. The present research attempts to predict the delays at a station using the delays at previous stations. The previous delays at stations are generated by creating lags in the original delays and creating them as one of the prediction features. The present research uses the delay data acquired from Bane NOR from 1 January 2021 to 28 February 2023. This data contains the scheduled and actual departure and arrival times of different trains between Oslo and Trondheim (up and down the line) in the specified period. The machine learning models based on neural networks were used on the data in the present research. Different prediction algorithms, i.e., recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM), were used. The prediction results are compared to look at the insights of the train delays in the given period. In conclusion, this study highlights how extreme feature engineering can negatively affect the output of a model.

Methodology for calculating the intensity of passenger demands for transport services by passenger taxi cars

The article provides formulas that make it possible to calculate the intensity of the incoming and outgoing flow of passenger demands for transport services by passenger taxi cars in such large transport hubs of the Moscow agglomeration as the airports of the Moscow Aviation Hub, which are typical examples of a queuing system. The main differences between the incoming and outgoing flow are noted. As a result of the fact that the incoming flow is subject to elements of randomness, the article discusses a method for determining the distribution law of a random variable, which is the probability of passengers arriving pi(t) at the airport at time t before the scheduled departure time of the aircraft. Using the standard Pearson’s criterion χ2, the hypothesis about the expected distribution law is tested. The intensities of incoming and outgoing flow calculated according to the above formulas can be used as input data for simulation modeling of the queuing system for passengers at transport hubs by various modes of transport in order to optimize the transportation process and improve the quality of passenger service. Keywords transport hub, urban agglomeration, taxi transportation, queuing system, incoming flow, random variable distribution law, Pearson’s criterion χ2

Extensive Model and Matheuristic Algorithm for the Train Platforming Problem with Two-Train-Capacity Tracks: A Case Study of Prague Central Station

This paper provides a deeper insight into the train platforming problem (TPP). Many studies have focused on different versions of train scheduling and routing problems, and most of them assume that the platform track’s capacity is one train. However, especially in busy and complex railway stations, most platform tracks are divided into two parts, allowing two trains to simultaneously share the same platform track for passenger boarding/alighting. This results in more efficient train assignment to the platform tracks. In addition, consideration of the track capacity makes the problem more difficult because directions of trains are problematic. Motivated by this challenge, we consider the TPP with two-train-capacity tracks. We first describe the problem in detail and then propose a mixed-integer programming model. The objective of the considered problem is to minimize the total weighted train delays, which are defined as the difference between the departure times calculated by the mathematical model (M1) and the scheduled departure times of the trains in the timetable. Because of the NP-hard nature of the problem, the proposed M1 may not find feasible solutions for large-size problems. Thus, a matheuristic algorithm (MA) is developed to solve large-size problems. We used randomly generated test problems to demonstrate the performance of the proposed M1 and MA. Experimental results showed that MA outperforms M1 in both solution quality and solution time. Additionally, a case study was conducted at the central station of Prague, Czechia.

Optimising Airport Ground Resource Allocation for Multiple Aircraft Using Machine Learning-Based Arrival Time Prediction

Managing aircraft turnaround is a complex process due to various factors, including passenger handling. Airport ground handling, resource planning, optimal manpower, and equipment utilisation are some cost-cutting strategies, particularly for airlines and ground handling service teams. Scheduled aircraft arrival and departure times are critical aspects of the entire ground management and passenger handling process. This research aimed to optimise airport ground resource allocation for multiple aircraft using machine learning-based prediction methodologies to enhance the prediction of aircraft arrival time, an uncontrollable variable. Our proposed models include a multiple linear regression (MLR) model and a multilayer perceptron (MLP)-based model, both of which are used for predicting round-trip arrival times. Additionally, we developed a MLP-based model for multiclass classification of arrival delays based on departure time and delay from the same airport. Under normal weather conditions and operational scenarios, the models were able to predict round-trip arrival times with a root mean squared error of 8 min for each origin–destination pair and classify arrival delays with an average accuracy of 93.5%. Our findings suggest that machine learning-based approaches can be used to predict round-trip arrival times based on the departure time from the same airport, and thereby accurately estimate the number of actual flight movements per hour well in advance. This predictability enables optimised ground resource planning for multiple aircraft based on constrained airport resource deployment and utilisation.

Departure Time Choice in Schedule-Based Transit Assignment

This paper investigates existing departure time models for a schedule-based transit assignment and their parametrization. It analyzes the impact of the temporal resolution of travel demand and suggests functions for evaluating the adaptation time as part of the utility of a path. The adaptation time quantifies the time between the preferred and the scheduled departure times. The findings of the analysis suggested that travel demand should be discretized into intervals of 1 min, with interval borders right between the full minute, that is, ±0.5 min. It was shown that longer time intervals led to arbitrary run volumes, even for origin–destination pairs with just one transit line and a fixed headway. Although a linear relationship between adaptation time and adaptation disutility is a common assumption in several publications, it cannot represent certain types of passenger behavior. For some trip purposes, passengers may be insensitive to small adaptation times, but highly sensitive to large adaptations. This requires a nonlinear evaluation function.

Minimum costs paths in intermodal transportation networks with stochastic travel times and overbookings

In intermodal transportation, it is essential to balance the trade-off between the cost and duration of a route. The duration of a path is inherently stochastic because of delays and the possibility of overbooking. We study a problem faced by a company that supports shippers with advice for the route selection. The challenge is to find Pareto-optimal solutions regarding the route’s costs and the probability of arriving before a specific deadline. We show how this probability can be calculated in a network with scheduled departure times and the possibility of overbookings. To solve this problem, we give an optimal algorithm, but as its running time becomes too long for larger networks, we also develop a heuristic. The idea of this heuristic is to replace the stochastic variables by deterministic risk measures and solve the resulting deterministic optimization problem. The heuristic produces, in a fraction of the optimal algorithm’s running time, solutions of which the costs are only a few percent higher than the optimal costs.

Strategic Evacuation for Hurricanes and Regional Events with and without Autonomous Vehicles

A scheduling algorithm is developed for optimal planning of large-scale, complex evacuations to minimize total delay plus travel time across residents. The algorithm is applied to the eight-county Houston-Galveston region and land use setting under the 2017 Hurricane Harvey scenario with multiple destinations. Autonomous vehicle (AV) use under central guidance is also tested, to demonstrate the evacuation time benefits of AVs. Higher share of AVs delivers more efficient evacuation performance, thanks to greater reliability on evacuation order compliance, lower headways, and higher road capacity. Furthermore, 100% AV use delivers lower overall evacuation costs and network clearance times and less uncertainty in travel times (via lower standard deviation in). Based on evaluations of different evacuation schedules, a 50% compressed evacuation time span resulted in longer travel times and network congestion. A 50% longer evacuation time span reduced residents' total travel time and network congestion, but increased the evacuation cost. As expected, evacuation efficiency falls when evacuees do not comply with evaucation schedules. Large shares of AVs will not be possible in the near future, so methods to enhance evacuees' compliance behavior (e.g., enforced and prioritized evacuation orders) should be considered until a meaningful level of AV technical maturity and penetration rate is available. This paper demonstrates the benefits of scheduled departure times, AV use, and evacuation order compliance, which help balance conflicting objectives during emergencies.

Predictability improvement of Scheduled Flights Departure Time Variation using Supervised Machine Learning

The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estimated Departure Time(ETD) for each individual flight is currently derived from Air Traffic Flow Management System (ATFMS), which are solely determined based on individual flight plan Estimated Off Block Time(EOBT) or subsequent delays updated by Airline. Even if normal weather conditions prevail, aircraft departure times will differ from ETOTs determined by the ATFMS due to a number of factors such as congestion, early/delayed inbound flight (linked flights), reactionary delays and air traffic flow management slot changes. This paper presents a model that predicts departure time variance based on the previous leg departure time using a combination of exponential moving average and machine learning methods. The model correctly classifies the departure time (Early, On Time, Delay) based on the previous leg departure state, allowing the ATFM system to measure the arrival time of a capacity constrained airport with greater accuracy and better assess demand requirements. The results show that the proposed model with M5P Regression tree provides the best results, with Mean Absolute Error and Root Mean Square Error (RMSE) of 3.43 and 4.83, respectively, indicating a 50% improvement over previous research findings. Whereas, with logistic regression, the classification of departure time (Early, On Time, Delay) is achieved a better accuracy of 91 %, which is higher than previous works.

Scheduling strategy for transit routes with modular autonomous vehicles

The Modular Autonomous Vehicle (MAV) systems allow a vehicle module to join onto and detach from other modules to dynamically adjust vehicle capacity. It potentially renders transit agencies more flexibility to deal with the temporal fluctuations of passenger demand. In this work, we propose a strategy for flexible MAV scheduling on transit routes to meet the time-varying passenger demand. The proposed strategy is formulated as a bi-objective optimization model considering both the utilization of vehicles and service quality. The model determines the scheduled departure times from the terminals, the length of MAV for each scheduled trip, and the assignment of modules to all scheduled trips, simultaneously. The ε-constraint method is adopted to solve the developed model and the fuzzy satisfying approach is employed to select the best possible solution. We implement the proposed strategy in a real-world case study in comparison with a traditional strategy to demonstrate the effectiveness of the proposed strategy. The results show that the proposed strategy can remarkably improve the utilization of vehicles and also make passengers more convenient. Specifically, it leads to an 84.9% reduction in the total empty seat, as well as a 12.62% reduction in the total passenger waiting time.

Strategic Flight Cancellation Under Ground Delay Program Uncertainty

Under certain capacity constraints, flight operators will strategically cancel flights hours to days in advance of their scheduled departure times to improve their overall operating schedule. Although there are consequences for unnecessarily canceling flights (i.e., displaced passengers, lost revenue, etc.), any benefits associated with such cancellations are best realized if made early, often before any traffic flow rate limitation is imposed. This paper proposes a framework for optimizing an adaptive decision strategy based on the evolution of the forecast uncertainty. Using an ensemble forecast, a scenario tree is generated to highlight both key planning scenarios and the likelihood of these scenarios developing over the forecast horizon. By aligning decision points at the initial and intermediary nodes in the tree, the recommended flight cancellations and cancellation timing are optimized for minimum expected cost with respect to the forecast uncertainty. The proposed approach is applied to a case day and analyzes the recommended cancellations across the forecast horizon against different predicted scenarios. The results demonstrate that by directly planning for future strategy adaptation lower costs are incurred earlier, when a higher degree of uncertainty exists, as compared with decisions based on a single decision point.

SGDAN-A Spatio-Temporal Graph Dual-Attention Neural Network for Quantified Flight Delay Prediction.

There has been a lot of research on flight delays. But it is more useful and difficult to estimate the departure delay time especially three hours before the scheduled time of departure, from which passengers can reasonably plan their travel time and the airline and airport staff can schedule flights more reasonably. In this paper, we develop a Spatio-temporal Graph Dual-Attention Neural Network (SGDAN) to learn the departure delay time for each flight with real-time conditions at three hours before the scheduled time of departure. Specifically, it first models the air traffic network as graph sequences, what is, using a heterogeneous graph to model a flight and its adjacent flights with the same departure or arrival airport in a special time interval, and using a sequence to model the flight and its previous flights that share the same aircraft. The main contributions of this paper are using heterogeneous graph-level attention to learn the influence between the flight and its adjacent flight together with sequence-level attention to learn the influence between the flight and its previous flight in the flight sequence. With aggregating features from the learned influence from both graph-level and sequence-level attention, SGDAN can generate node embedding to estimate the departure delay time. Experiments on a real-world large-scale data set show that SGDAN produces better results than state-of-the-art models in the accurate flight delay time estimation task.

A Review of Commercial Electric Vehicle Charging Methods

Electric Vehicles (EVs) are rapidly becoming the forerunners of vehicle technology. First electric vehicles were overlooked because of not having adequate battery capacity and because of low efficiency of their electric motors. Developing semiconductor and battery technologies increased the interest in the EVs. Nevertheless, current batteries still have insufficient capacity. As a result of this, vehicles must be recharged at short distances (approximately 150 km). Due to scheduled departure and arrival times EVs appear to be more suitable for city buses rather than regular automobiles. Thanks to correct charging technology and the availability of renewable energy for electric buses, the cities have less noise and CO2 emissions. The energy consumption of internal combustion engines is higher than of the electric motors. In this paper, studies on the commercial electric vehicle charging methods will be reviewed and the plug-in charging processes will be described in detail. This study strives to answer the questions of how plug-in charging process communication has performed between the EV and Electric Vehicle Supply Equipment (EVSE).

Scheduled block time setting and on-time performance of U.S. and Chinese airlines—A comparative analysis

Scheduled block time (SBT) is the duration between scheduled departure time and scheduled arrival time of a given flight. SBTs have a significant impact on airline operations and performance, and airlines consider various factors in setting them. Recent studies have proposed several models for understanding U.S. airlines’ SBT-setting behavior and have examined the contribution of SBT-setting to airline on-time performance (OTP). How airlines in different countries and regions set their SBTs and to what degree differences in SBT-setting behavior explain observed differences in OTP is still unknown. Here we develop econometric models of SBTs based on historical distributions of actual block time to reveal the differences in SBT-setting behavior between major airlines in China and the U.S. We find that Chinese airlines focus on the left tail of the distributions while the U.S. airlines consider the left tail, middle, and inner right tail. They also have different ways of accounting for taxi-out time in setting SBTs, with the U.S. focusing on historical time distributions while in China taxi-out time is based on airport category. We then perform counterfactual analysis by using one nation’s model to predict the other nation’s SBTs and the resulting difference in OTP. Results indicate that the OTP of Chinese airlines would match those of the U.S. if they followed U.S. practices in setting SBTs, while U.S. airlines would have slightly worse OTP than Chinese carriers if they set SBTs according to Chinese methods. Our findings highlight the difference in SBT-setting behavior of major airlines in China and U.S. and its contribution to OTP. The model and methods presented here may also be applied to study and compare SBT setting and its impact on OTP in other parts of the world.

Heuristic approaches for flight retiming in an integrated airline scheduling problem of a regional carrier

Flight retiming in airline scheduling consists in slightly modifying the scheduled departure time of some flights with the goal of providing a better service with a cheaper cost. In this research, the departure times must be selected from a small discrete set of options. The whole problem embeds flight retiming, fleet assignment, aircraft routing and crew pairing. Thus, the aim is to determine the departure times of the flights, the fleet assignment and the minimum cost aircraft and crew routes. The objective function takes into account a large cost associated with each crew member, a penalization for short or long connection times, a cost for crew members changing aircraft along their routes, and a minor penalty associated with the use of each aircraft. The constraints enforce aircraft maintenance and crew working rules. In this setting, flight retiming is allowed to potentially reduce the total costs and increase the robustness of the solution against delays by decreasing the number of aircraft changes.We propose and compare four heuristic algorithms based on a Mixed Integer Linear Programming model for the whole problem. The model contains path variables representing the crew pairings, and arc variables representing the aircraft routes. In the heuristic algorithms, column generation is applied on the path variables, and different flight retiming options are considered. The algorithms are tested on real-world instances of a regional carrier flying in the Canary Islands to evaluate their advantages and drawbacks. In particular, one of the algorithms, that uses the solution of the Linear Programming relaxation of the model to select promising options for the departure of the flights, turns out to be the most effective one. The obtained results show that costs can be significantly reduced through flight retiming while still keeping the computing times reasonably short. In addition, we perform a sensitivity analysis by including more retiming options and by using different aircraft and crew costs. Finally, we report the results on larger size instances obtained by combining real-world ones.

A heterogeneous fleet liner ship scheduling problem with port time uncertainty

We deal with a schedule design problem for a heterogeneous fleet liner shipping service under uncertain waiting and handling times at ports. In a liner shipping service, longer than expected waiting and handling times at a port may cause a delay from scheduled departure time. We consider the problem to find the departure times at ports and sailing times of ships between ports so that the total fuel burn is minimized while targeted overall service level (a performance measure based on on-time departure probabilities) is achieved. We consider two new aspects of the problem. The first one is the heterogeneous fleet where each ship type may have different fuel efficiency, i.e. a different fuel burn function. The second one is considering critical ports on the route, i.e. considering the fact that on-time performance at some critical ports might be more important for the shipping company. We propose a model which finds different service levels for different ship type-port pairs by considering importance of ports and fuel efficiencies of ships. We also give a new overall service level measure for the entire route by combining service levels for different ship type-ports pairs. We propose a chance constrained nonlinear mixed integer programming formulation for the problem. Finally, we give computational results that show the effects of several experimental factors on fuel consumption, speed and service level.

Congestion pricing and environmental cost at Guangzhou Baiyun International Airport

To solve the problems related to resource constraints during peak periods—especially flight delays—congestion pricing, which is an important demand management tool, is proposed to ease airport congestion. A steady-state congestion model that includes the costs of airlines, passengers and the environment is established to calculate congestion fees for different times of day and different queue lengths. For the atomistic airline market, airline congestion fees are approximately 5000RMB to 10,000RMB during off-peak time. However, during peak time, congestion fees exceed 10,000RMB and can even reach 35,000RMB. These results show that airport congestion pricing can motivate airlines to move certain flights from the peak period and can slightly alter the scheduled departure times of other flights to avoid the rush of departures that occur on the hour at busy airports, thereby reducing the cost of delays caused by congestion. Moreover, congestion pricing that includes environmental cost has been shown to contribute to energy conservation and emissions reduction.

Implications of Service Failure on Customer Perception and Patronage of Shuttle Services in South East Nigeria

This study examined customers’ perception and patronage level of ABC Transport Shuttle Services in relation to the service failure(s) recorded in the recent past. A survey research was conducted using 386 respondents drawn from the past, present and potential customers of the company in Owerri, Uyo, Calabar, Onitsha, Warri, Port Harcourt, Enugu, Aba and Umuahia. Structured questionnaire were administered on these passengers while an oral interview was conducted with the ABC General Shuttle Manager. The study revealed that major areas of service failure in ABC Shuttle services include poor adherence to scheduled departure time(s), unfriendly system resulting from glaring service failures, delay in service delivery, aging buses and poor service failure management efforts. The study recommended among others, total overhauling of the shuttle system, strict adherence to scheduled departure(s) as well as periodic training of staff on effective customer relationship management and timely, efficient/effective service delivery.

Reliability evaluation of a multi-state air transportation network meeting multiple travel demands

In last decades, air transportation plays an important role in global economy. Several scholars have studied optimizing air transportation system or proposed reliability evaluation algorithms from airline management viewpoints. This work evaluates the reliability of an air transportation system from the perspective of travel agency instead. An air transportation system can be modeled as a multi-state air transportation network (MATN) wherein each node represents an airport and each arc denotes a flight carrying passengers between a pair of airports from scheduled departure time to scheduled arrival time. Significantly, this study focuses on investigating the reliability of multiple travel demands. Therefore, the reliability of an MATN is defined as the probability that a set of demands can be carried successfully under constraints of time and number of stopovers. This study employs the concept of minimal paths in reliability evaluation. Subsequently, a searching procedure is added to the proposed algorithm. In addition, an illustrative example and a case study are utilized to demonstrate the proposed algorithm and discuss the implications of reliability evaluation for the management of travel agency.