Actual Arrival Time Research Articles

Bus arrival and departure time updates in the Greater Sydney Area

Public Transport Authorities generate large quantities of data as part of their daily operations, including vehicle positions, arrival times, and dynamic routing options. This information is essential feedback into the system for planning routes and timetables, modelling passenger demand growth and evaluating operational performance. The General Transit Feed Specification (GTFS) is a data format that allows public transport data to be consumed by a wide variety of software applications. There are barriers to widespread consumption of GTFS data related to location-specific data extensions, non-human-readable formats and error cleaning. This paper describes a flexible dataset of actual bus arrivals and departure times created with a pipeline for GTFS realtime feeds designed to address these challenges. The paper describes the pipeline, verifies the quality of the data and presents an output of 25 months of actual bus arrival and departure times for Sydney, Australia. We conclude by discussing relevance to researchers and practitioners of the pipeline outputs in general and the sample data specifically.

Stability of a Queue Fed by Scheduled Traffic at Critical Loading

Performance of a queueing system with scheduled arrivals A scheduled arrival sequence is one in which customers are scheduled to arrive at constant interarrival times, but each customer’s actual arrival time is perturbed from her scheduled arrival time by a random perturbation. In “Stability of a Queue Fed by Scheduled Traffic at Critical Loading”, V.F. Araman and P.W. Glynn consider a single server queue with deterministic service times in which customers arrive following a scheduled arrival process. Unlike a queue fed by renewal traffic, this queue is shown to be stable even when the utilization is equal to one. It is also shown that for finite mean perturbations, a necessary and sufficient condition for stability is when the positive part of the perturbation has bounded support, with no requirement on the negative part of the perturbation. Perhaps surprisingly, this criterion is not reversible, in the sense that such a queue can be stable for a scheduled traffic process in forward time, but unstable for the time-reversal of the same traffic process.

Efficient demand management for the on-time arrival problem: A convexified multi-objective approach assuming macroscopic traffic dynamics

This work proposes a novel solution approach to address the On-Time Arrival (OTA) problem, considering macroscopic traffic dynamics. In this context, all drivers who intend to use the road infrastructure are required to communicate their origin–destination pair and desired arrival time to a central scheduler prior to starting their trip. In response, the scheduler assigns a departure time and a multi-regional route to each driver to make possible their on-time arrival at the destination. The OTA problem is formulated as a nonconvex, nonlinear, multi-objective optimization problem considering two objective criteria. The first criterion aims at minimizing the travel time of all drivers in the network to prevent congestion, while the second criterion seeks to minimize the discrepancy between the desired and actual arrival time. The proposed formulation is solved efficiently through an approximated convex solution that leverages the Normal Boundary Intersection (NBI) method to efficiently generate a representative sample of the Pareto Front. Additionally, two solution methodologies based on “knee” solution and the Nash Bargaining Game is proposed which can be used to select a unique solution across all the Pareto points, that offers a reasonable trade-off between the two objective criteria. Finally, simulation results demonstrate that the proposed solution can significantly reduce congestion while ensuring that most drivers will arrive at their destination on their desired time.

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.

Accuracy of Prehospital Services' Estimated Time to Arrival for Ground Transport to the Emergency Department

BackgroundEmergency medical services (EMS) transporting patients to the emergency department (ED) typically call ahead to provide an estimated time to arrival (ETA). Accurate ETA facilitates ED preparation and resource allotment in anticipation of patient arrival. ObjectiveThe study purposed to determine the accuracy of ETA provided by EMS ground units. MethodsWe performed a single-center, prospective, observational study of ED patients arriving via EMS ground transport. The primary outcome was the time difference between EMS-reported ETA and actual time of arrival (ATA). The difference between ATA and ETA was compared using the two-sided Wilcoxon Signed-Rank Test. Subgroup analysis was performed to evaluate ETA accuracy for specific types of transports and assess variability by month and time of day. ResultsWe included 1176 patient transports in the final analysis. The overall median difference ATA-ETA was 3 min (interquartile range 1–5 min) with a range of −26–48 minutes (Z = −25.139, p < 0.001). EMS underestimated ETA in 961 cases (81.7%), and 94 ETAs (8.0%) were accurate to within 1 min. The largest difference between ATA and ETA occurred between 07:00–07:59 and 16:00–16:59 (5 min, interquartile range 2–7). ConclusionOur data demonstrate that prehospital providers underestimate time to ED arrival in most ground transports; however, the median difference between estimated and actual time to arrival is small.

Hydropower station scheduling with ship arrival prediction and energy storage

Effectiveness improvement in power generation and navigation for grid-connected hydropower stations have emerged as a significant concern due to the challenges such as discrepancies between declared and actual ship arrival times, as well as unstable power generation. To address these issues, this paper proposes a multi-objective real-time scheduling model. The proposed model incorporates energy storage and ship arrival prediction. An energy storage mechanism is introduced to stabilize power generation by charging the power storage equipment during surplus generation and discharging it during periods of insufficient generation at the hydropower stations. To facilitate the scheduling with the eneragy storage mechanism, the arrival time of ships to the stations are predicted. We use the maximization of generation minus grid load demand and the maximization of navigability assurance rate as two objective functions in the scheduling process. The model uses the Non-Dominated Sorting Beluga Whale Optimization (NSBWO) algorithm to optimize and solve the real-time discharge flow scheduling of the hydropower stations in different time periods. The NSBWO algorithm combines the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Beluga Whale Optimization (BWO). The experimental results show that the proposed method has advantages in predicting the expected arrival time of ships and scheduling the discharge flow. The prediction using XGBoost model reaches accuracy with more than 0.9, and the discharged flow obtained from scheduling meets the demand of hydropower stations grid load while also improves the navigation benefits. This study provides theoretical analysis with its practical applications in a real hyropower station as a case study for solving hydropower scheduling problems.

Online learning for route planning with on-time arrival reliability

Consider a network where travel times on edges are i.i.d. over T rounds with unknown distributions. One wishes to choose departure times and routes sequentially between a given origin-destination pair across T rounds to minimize the expectations of: 1) number of rounds when the travel time exceeds an upper bound, and 2) summation over all rounds of the square of the difference between the given target and actual arrival times. We provide an efficient online learning algorithm for this problem.

Evaluation and prediction of punctuality of vessel arrival at port: a case study of Hong Kong

ABSTRACT The punctuality of vessel arrival at port is a crucial issue in contemporary port operations. Uncertainties in vessel arrival can lead to port handling inefficiency and result in economic losses. Although vessels typically report their estimated time of arrival (ETA) en-route to the destination port, their actual time of arrival (ATA) often differs from the reported ETA due to various factors. To address this issue and enhance terminal operational efficiency, we first quantitatively evaluate vessel arrival uncertainty in different time slides prior to arrival at the port using 2021 vessel arrival data for Hong Kong port (HKP). Our results confirm that the overall vessel arrival uncertainty decreases as vessels approach the HKP. Then, we implement a random forest (RF) approach to predict vessel arrival time. Our model reduces the error in ship ATA data prediction by approximately 40% (from 25.5 h to 15.5 h) using the root mean squared error metric and 20% (from 13.8 h to 11.0 h) using the mean absolute error metric compared with the reported ETA data. The proposed vessel arrival time evaluation and prediction models are applicable to port management and operation, laying the foundation for future research on port daily operations.

A bi-layer model for berth allocation problem based on proactive-reactive strategy

To reduce the impact of equipment failure, vessel arrival time delay and other unexpected factors, container terminal managers need to develop a more robust berth scheduling plan while considering the recovery costs. To deal with the berth allocation problem (BAP) with uncertain arrival time, a proactive-reactive hybrid strategy to deal with the impact of uncertainty is proposed. According to the characteristics of BAP and proactive-reactive strategy, a bi-layer programming model is constructed. A robust baseline plan with buffer time is generated in the first layer, and a recovered scheduling plan is generated in the second layer according to the actual arrival time. The decisions between the two layers of models affect each other. In addition, an Adaptive cross-entropy algorithm (ACEA) is designed to solve the first layer model. Different from the traditional cross-entropy algorithm (CEA), an adaptive search strategy is added to further search and optimize the neighborhood of the elite solutions. Furthermore, heuristic adjustment strategies (HAS) are designed to deal with the second layer. The computational results demonstrate that the bi-layer model proposed in this paper can better balance the baseline schedule cost and the recovered schedule cost. Numerical experiments are carried out to assess the effectiveness of the proposed model and the efficiency of the proposed algorithms.

Hierarchical optimal control framework to automatic train regulation combined with energy-efficient speed trajectory calculation in metro lines

In high-density metro lines, frequent disturbances could lead to a domino effect of train delays if no adjustment strategy is imposed timely. In this paper, to enhance train timetable adherence and reduce energy consumption, we provide a hierarchical optimal control framework for the automatic train regulation problem with energy-efficient speed trajectory based on the model predictive control method. Specifically, by considering the dynamic passenger flows, the non-linear train regulation model is proposed at the higher level to enhance timetable adherence, while at the lower level the energy-efficient speed trajectories of the trains are generated on-line in a distributed manner. The interaction of real-time information exists between the higher level and the lower level, i.e., the upper model provides the train running time and the numbers of onboard passengers to the lower model, while the lower model feedbacks the updated train information (e.g., the actual arrival time) to the upper model, which provides the potential for the proposed framework to respond to disturbances at the operational stage. Moreover, a customized model predictive control method combined with Radau pseudospectral method (RPM) is designed to generate the energy-efficient train speed trajectory at the lower level in response to uncertain operational conditions. Numerical cases based on the Beijing Metro Yizhuang line demonstrate the effectiveness and robustness of our proposed hierarchical optimal control framework to deal with uncertain disturbances.

Calculation of Theoretical Travel Time and Automatic Picking of Actual Travel Time in Seismic Data

We used the ray tracing technique based on the IASP91 Earth model to calculate the travel times in order to identify the phases. This technique can calculate the travel times for the seismic phases in the conventional travel time tables. The waveform data received from the stations in the Guangxi area are selected for analysis and discussion. The outcomes of the numerical modeling and its use demonstrate that there is good agreement in terms of the absolute differences between the calculated and theoretical travel times from the ISAP91 tables. The relative residuals are determined directly from the actual arrival times picking during the correlation analysis, and the validity of the travel time method for picking seismic phases by correlation analysis can be demonstrated.

Deep learning for hetero–homo conversion in channel-domain for phase aberration correction in ultrasound imaging

Echo imaging in ultrasound computed tomography (USCT) using the synthetic aperture technique is performed with the assumption that the speed of sound is constant in the system. However, tissue heterogeneity causes a mismatch between the predicted arrival time and the actual arrival time of the echo signal, which will result in phase aberration, leading to the quality degradation of the reconstructed B-mode image. The conventional correction methods that use the correlation of each different channel require the presence of strong point scatterers and involve the problem of local solutions due to excessive correction. In this study, we propose a novel approach to correcting the signal distortion due to sound speed heterogeneity using a deep neural network (DNN). The DNN was trained to convert the distorted radio frequency (RF) inputs for the heterogeneous medium to the distortion-free RF outputs for the homogeneous medium. The network with U-net architecture using ResNet-34 as a backbone was trained using the hetero–homo corresponding channel-domain RF data generated via numerical simulations. The trained network performed phase aberration correction in the channel-domain RF, with the B-mode images reconstructed with the corrected RF demonstrating a higher contrast and an improved resolution compared with uncorrected cases. It was also demonstrated that the DNN model is robust to both varied reflection intensities and varied sound speed heterogeneities. The successful results demonstrated that the proposed DNN-based method is effective for phase aberration correction in US imaging.

Evaluation of timeliness and models of transporting critically ill children for intensive care: the DEPICT mixed-methods study

Background Centralisation of paediatric intensive care has increased the need for specialist critical care transport teams to transfer sick children from general hospitals to tertiary centres. National audit data show variation in how quickly transport teams reach the patient’s bedside and in the models of care provided during transport; however, the impact of this variation on clinical outcomes and the experience of patients, families and clinicians is unknown. Objectives We aimed to understand if and how clinical outcomes and experience of children transported for intensive care are affected by timeliness of access to a transport team and different models of transport care. Methods We used a mixed-methods approach with a convergent triangulation study design. There were four study workstreams: a retrospective analysis of linked national clinical audit data (2014–16) (workstream A), a prospective questionnaire study to collect experience data from parents of transported children and qualitative analysis of interviews with patients, families and clinicians (workstream B), health economic evaluation of paediatric transport services (workstream C) and mathematical modelling evaluating the potential impact of alternative service configurations (workstream D). Results Transport data from over 9000 children were analysed in workstream A. Transport teams reached the patient bedside within 3 hours of accepting the referral in &gt; 85% of transports, and there was no apparent association between time to bedside and 30-day mortality. Similarly, the grade of the transport team leader or stabilisation approach did not appear to affect mortality. Patient-related critical incidents were associated with higher mortality (adjusted odds ratio 3.07, 95% confidence interval 1.48 to 6.35). In workstream B, 2133 parents completed experience questionnaires pertaining to 2084 unique transports of 1998 children. Interviews were conducted with 30 parents and 48 staff. Regardless of the actual time to bedside, parent satisfaction was higher when parents were kept informed about the team’s arrival time and when their expectation matched the actual arrival time. Satisfaction was lower when parents were unsure who the team leader was or when they were not told who the team leader was. Staff confidence, rather than seniority, and the choice for parents to travel with their child in the ambulance were identified as key factors associated with a positive experience. The health economic evaluation found that team composition was variable between transport teams, but not significantly associated with cost and outcome measures. Modelling showed marginal benefit in changing current transport team locations, some benefit in reallocating existing teams and suggested where additional transport teams could be allocated in winter to cope with the expected surge in demand. Limitations Our analysis plans were limited by the impact of the pandemic. Unmeasured confounding may have affected workstream A findings. Conclusions There is no evidence that reducing the current 3-hour time-to-bedside target for transport teams will improve patient outcomes, although timeliness is an important consideration for parents and staff. Improving communication during transport and providing parents the choice to travel in the ambulance with their child are two key service changes to enhance patient/family experience. Future work More research is needed to develop suitable risk-adjustment tools for paediatric transport and to validate the short patient-related experience measure developed in this study. Trial registration This trial is registered as ClinicalTrials.gov NCT03520192. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 34. See the NIHR Journals Library website for further project information.

Two-stage optimization of airport ferry service delay considering flight uncertainty

As an important part of the ground service for flights parked at remote stands, the scheduling optimization of ferry vehicles is the key factor to improve the apron support capacity. Considering the uncertainty of actual arrival time and departure time of flights, a two-stage optimization model for flight on-time service rate and service delay time is proposed in this paper. At the first stage, a capacity network with four types of nodes and five types of arcs is constructed. By setting appropriate arc capacity and cost parameters, a programming model based on the minimum cost flow is constructed to optimize the number of flights that can be served on time by a limited number of ferry vehicles. At the second stage, a new capacity network and a time-space network combined with a heuristic algorithm are constructed, and integer linear programming models are proposed to optimize the delay time of unserved flights in the first stage. The efficiency of the proposed method is verified by flight data from Beijing Capital International Airport and further analysis is carried out. This study can provide a decision-making reference for the scheduling optimization of airport ferry vehicles.

Robust berth scheduling using machine learning for vessel arrival time prediction

In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach.

BEST-Blockchain-Enabled Secure and Trusted Public Emergency Services for Smart Cities Environment.

In the last few years, the Internet of things (IoT) has recently gained attention in developing various smart city applications such as smart healthcare, smart supply chain, smart home, smart grid, etc. The existing literature focuses on the smart healthcare system as a public emergency service (PES) to provide timely treatment to the patient. However, little attention is given to a distributed smart fire brigade system as a PES to protect human life and properties from severe fire damage. The traditional PES are developed on a centralised system, which requires high computation and does not ensure timely service fulfilment. Furthermore, these traditional PESs suffer from a lack of trust, transparency, data integrity, and a single point of failure issue. In this context, this paper proposes a Blockchain-Enabled Secure and Trusted (BEST) framework for PES in the smart city environment. The BEST framework focuses on providing a fire brigade service as a PES to the smart home based on IoT device information to protect it from serious fire damage. Further, we used two edge computing servers, an IoT controller and a service controller. The IoT and service controller are used for local storage and to enhance the data processing speed of PES requests and PES fulfilments, respectively. The IoT controller manages an access control list to keep track of registered IoT gateways and their IoT devices, avoiding misguiding the PES department. The service controller utilised the queue model to handle the PES requests based on the minimum service queue length. Further, various smart contracts are designed on the Hyperledger Fabric platform to automatically call a PES either in the presence or absence of the smart-home owner under uncertain environmental conditions. The performance evaluation of the proposed BEST framework indicates the benefits of utilising the distributed environment and the smart contract logic. The various simulation results are evaluated in terms of service queue length, utilisation, actual arrival time, expected arrival time, number of PES departments, number of PES providers, and end-to-end delay. These simulation results show the effectiveness and feasibility of the BEST framework.

Recovery of flights disrupted by snowstorm

PurposeTo reduce the time of flight rescheduling, reduce the total delay cost of all flights to a minimum and put forward more references for passengers to take flights, this paper aims to mainly study the recovery of flights affected by snow disaster within the minimum delay time.Design/methodology/approachThe temporal and spatial network flight recovery model is used to optimize all flights of various types of aircraft, and the adjusted flight schedule based on minute delay time is obtained. In addition, for passenger travel flights, the impact of passenger delay cost on the total delay time is minimized as an objective function to calculate the passenger delay cost.FindingsIn this paper, the actual departure time of aircraft is sorted in ascending order. Up to five planes can take off from the runway every 5 min, and the 10-min decision interval is successively delayed. The actual arrival time is sorted by the same method and the sequential delay is calculated to obtain the adjusted flight schedule. As a result, it takes less time to reschedule flights.Originality/valueIn this paper, heuristic algorithm is used to adjust the schedule of delayed flights flexibly, which is convenient for manual modification. This decision method has good robustness and can partially adjust the interrupted flights without affecting other scheduled flights while maintaining the stable operation of the whole plan, greatly improving the efficiency of civil aviation operations and reducing the impact of flight delays.

Cooperative scheduling optimization for ground-handling vehicles by considering flights’ uncertainty

The ground-handling vehicle services of airports are facing significant challenges due to the uncertainty of actual flight arrival time, leading to low resource utilization efficiency and unreasonable service sequence. Moreover, there are different properties (service objects and service orders) of ground-handling vehicles that are not only challenging for modeling schedules of various vehicles but also increase the difficulty when dealing with coordination optimization among all different kinds of ground-handling vehicles. In this paper, the refuellers and shuttle buses would be integrated to provide a more realistic and coordinated optimization schedule for ground-handling vehicles’ dispatching. Specifically, multi-objective mixed integer programming is developed to optimize the priority of services and corresponding service time window by minimizing service costs and time losses. Then, the uncertainty of the flights’ actual arrival time is described by the method of chance constraints. Subsequently, the proposed model is utilized at the Beijing Capital Airport to indicate the effectiveness of the proposed modeling framework. Results demonstrate that significant service performance gains (reduced by 66% in time exceeding the planned departure time, 45% in extra waiting time of vehicles, 40% in flights not departing on schedule, and increased by 31% in the absorption of delay time, etc.), which demonstrates its application in solving schedule problems among other airports.

Improving Efficiency in Congested Traffic Networks: Pareto-Improving Reservations through Agent-Based Timetabling

In an urban transportation network, congestion occurs in the form of a queue behind a bottleneck. Many studies have considered a reservation-based optimization approach for queuing systems. To control the traffic density behind a bottleneck so that it does not exceed the link capacity, and to reduce the emissions and improve the sustainability of cities, we propose a new mobility service system to offer a Pareto-improving schedule for both the portion of agents making reservations and others with fixed departure time schedules. This reservation system takes the agents’ (i.e., users or vehicles here) actual arrival and departure times from a conventional system without reservations as the preferred time windows at both the origins and destinations. Such a centralized mobility service system could maintain or improve the end-to-end traveling performance for all users. The proposed reservation and end-to-end timetabling problem is formulated as a multicommodity flow optimization problem in a discretized space–time network. We use a modified dynamic programming method for the reservation strategy on the space–time network and further adopt the alternative direction method of multiplier (ADMM) based on prime and dual theory to solve the large-scale instances. A comprehensive discussion is also provided regarding the technical challenges and potential solutions when operating such a system in a real-world setting.

A stochastic approach for planning airport ground support resources

AbstractAirport ground operations are one of the main causes of late departures. Hence, reliable work plans for ground support resources can help mitigate these delays while limiting the under‐/over‐utilization of equipment. In this paper, we propose a stochastic approach for modeling the routing problems encountered during scheduling of each activity. We embed Monte Carlo simulation within an optimization approach to assess solutions during the search and guide the algorithm toward more robust schedules that account for resource cost. Actual flight arrival data, random service, and travel times are used in the simulation process. Results prove that our method clearly increases the robustness of the obtained schedules and the number of aircraft being handled within the planned time. To further identify potential causes of delay, we propose a reliability analysis based on the study of survival functions for each turnaround activity, combined with the dispersion of the resources' arrival to the parking stand.