Passenger Delay Research Articles

Collaborative train timetabling and passenger flow control in oversaturated metro lines considering state-dependence

Congestion downgrades the moving speed of passengers in trains and platforms. This in turn affects congestion. That is, the moving speed of passengers interacts with congestion. It is state-dependent and obeys the fundamental diagram. This interaction makes the system unstable and can result in substantial passenger delays and heightened security risks in oversaturated metro lines. This study aims to rectify this by developing a more realistic collaborative optimization approach. Firstly, we formulate a deterministic fluid queuing network model to accurately capture the dynamic propagation of congestion in oversaturated metro lines. This model incorporates the fundamental diagram, thereby effectively considering the interaction between passenger movement speeds and congestion. A recursive algorithm is devised to solve the fluid queuing network model. The complexity of the algorithm is independent of the capacities of the stations and trains. This provides the possibility of analyzing large-scale metro networks. Based on the queuing network model, a mixed-integer nonlinear programming model aimed at minimizing the total passenger waiting time is built. To solve the model effectively, we employ an alternating direction method of multipliers framework to decompose the model into nonlinear programming and integer nonlinear programming problems, which are respectively solved by improved simultaneous perturbation stochastic approximation and adaptive large neighborhood search algorithms. Finally, a small-scale case and a real-world case with data from Chengdu metro lines are implemented to demonstrate the performance and effectiveness of the proposed approach. The experimental results provide some interesting insights. (1) Without consideration of state-dependence, the queuing model underestimates the passenger waiting time and overestimates the service levels. (2) As demand increases, excluding extreme oversaturation scenarios, the optimized results of the state-independent model progressively deteriorate compared to our state-dependent model, with the largest percentage decline being 50.23%. This is especially noticeable when the train capacity is limited. (3) The contribution ratio of passenger flow control in mitigating congestion gradually surpasses that of train timetabling with increasing demand. In the small-scale case, the former contribution ratio to optimization rises from negligible to 98.19%.

Alternative service network design for bus systems responding to time-varying road disruptions

In practice, road disruptions occur frequently, interrupting multiple bus routes at the same time and causing widespread passenger delays. Typically, these disrupted roads are repaired sequentially and then gradually put into service. In response to such time-varying road disruptions, this paper aims to assist bus operators in developing effective alternative service networks for passengers. The proposed approach involves the joint optimization of service-based route adjustments, bus timetables, and passenger assignment to minimize the total passenger cost and weighted bus operation time. Specifically, a novel service-based adjustment strategy is introduced to flexibly adapt each bus service to time-varying road disruptions. An integer programming model is built for the studied problem based on the set of passengers’ time-space itineraries. To efficiently generate these time-space itineraries and solve models for large-scale problems, this paper develops a hierarchical solution framework. The framework consists of three key parts: (1) a column generation procedure to iteratively explore passengers’ spatial paths; (2) a customized extension algorithm to extend these spatial paths to time-space itineraries; and (3) a tailored adaptive large neighbourhood search heuristic to solve the final itinerary-based model. After that, the overall methodology is tested with both an illustrative example and a real-world example in Beijing. Experimental results show that our methodology produces a high-performance solution with only 7.3% of unserved passengers. Besides, compared to the two benchmark adjustment strategies, our service-based adjustment strategy reduces the average itinerary cost for all passengers by 27.0% and 43.3%, respectively.

Analisis Pencapaian Ground Time Pesawat Airbus A350-900 Maskapai Singapore Airlines Terhadap Kinerja Petugas Ramp Handling di Bandar Udara Internasional Juanda Surabaya

Ground time is one of the important things in handling aircraft while in the apron area and Ramp handling officers are responsible for block on to block off activities and play an important role in improving service functions in the apron area. Ground time that does not comply with company standards can affect flight schedules and operational costs for an airline. This research aims to analyze the ground time achievements of the Airbus A350-900 aircraft on the performance of ramp handling officers. The method used in this research is a quantitative approach with data collection techniques, namely observation and interviews. The data analysis technique uses SPSS 25 which includes descriptive analysis by processing primary data in the form of ground time records for the Airbus A350-900 aircraft for two months which has been adjusted to secondary data, namely company documents to determine the average (mean) ground time each month.Based on the results of descriptive analysis to find out the average ground time, the results obtained in August were 55.53 minutes and in September it was 55.93 minutes, meaning that in general the ground time was still achieved and the performance of the ramp handling officers was very good because they reached the ground time on time. . If ground time is looked at based on the daily schedule, there are still several delays caused by several factors, namely delays in the arrival of planes from the origin airport to the destination airport, weather factors and delays for passengers with baggage.

Unraveling Extreme Weather Impacts on Air Transportation and Passenger Delays Using Location-Based Data

Unraveling Extreme Weather Impacts on Air Transportation and Passenger Delays Using Location-Based Data

Joint optimization of metro travel reservation and compartment capacity allocation for an overcrowded metro line

ABSTRACT In some mega cities, metro lines often experience severe congestion during peak hours, posing serious operational risks. To alleviate passenger delay and address overcrowded conditions, this study proposes a novel model for metro travel reservation and carriage reserved capacity. The model comprises three components: off-station queuing restrictions, dynamic loading constraints, and carriage reserved capacity constraints. A particle swarm optimization algorithm is applied to allocate reservation quota by considering time-variant and location-dependent passenger demand and train supply. The paper applies the model to Nanjing Metro Line 3, using historical data to estimate passenger demand and compartment capacity, and evaluates its performance in four scenarios. The results demonstrate proposed approach efficiently obtains high-quality joint reservation plans, resulting in a notable reduction of approximately 20% in platform delay time and 50% in off-platform queuing time in suitable scenarios. The paper also discusses the implementation challenges and limitations, and provides recommendations for effective application.

Flexible optimal bus‐schedule bridging for metro operation‐interruption

AbstractUnder the sudden interruption of the metro, it is significant to dispatch emergency bridging buses to evacuate stranded passengers to improve linkage management and service reliability. Aiming at the problem of emergency bridging bus scheduling, considering the remaining capacity of conventional buses, passenger tolerance, and site convenience, a flexible combined emergency bridging bus scheduling model is constructed based on the constraints of vehicle capacity, dispatching capacity, and maximum evacuation times of emergency bridging bus, aiming at minimizing the maximum evacuation time and passenger delay. The improved Harris hawk algorithm is used to solve the model, and the evacuation plan of the demand‐responsive and station‐station bridging lines is obtained. The maximum evacuation time is 41 min, and the average delay of stranded passengers is 19 min. The results show that the maximum evacuation time is 10% and 27% less than the fixed combined and single scheduling. The average delay is 16% and 42% less than the fixed combination and traditional single scheduling. The sensitivity analysis of the influencing factors of emergency bridging bus scheduling is conducted. The results show that the flexible combined emergency bridging bus scheduling model constructed in this paper can improve evacuation efficiency and reduce passenger travel delays.

Passenger‐based adaptive transit signal priority for BRT systems with multiple loading areas

AbstractAn adaptive transit signal priority strategy is presented in this paper with the objective of passenger delay minimization at isolated intersections serving conflicting bus rapid transit (BRT) routes. The proposed passenger‐based adaptive signal priority for BRT systems (PASPB) is designed to optimize both green times and phase sequences at the start of each cycle and for a prespecified decision horizon. Since a public transportation (PT) vehicle travel time model capable of estimating the dwell time at stops with multiple loading areas has not yet been developed, PT vehicle dwell time is modeled in this study by analyzing the cases of passenger service by single or double PT vehicles. The problem is formulated as a mixed‐integer nonlinear program (MINLP) and at each execution, the optimization is conducted by genetic algorithm. The model is deployed to a real‐field intersection with conflicting BRT routes under the SUMO microsimulation environment. The results show that PASPB outperforms the SYNCHRO optimal solution and phase insertion strategy regarding PT passenger delay. Besides, the sensitivity analysis proves that at high demand levels of the PT system or general traffic, PASPB presents the best performance in terms of general traffic, PT, and total passenger delay compared to other models.

The problem of homogeneity of rail passenger delay compensation scheme rules in Great Britain: impacts on passenger engagement and operator revenues

BackgroundA rail passenger delay compensation scheme aiming at improving attractiveness of rail services and providing minimum customer service standards for delayed passengers operates in the European Union and Great Britain. British rail passengers are eligible to claim 50% of fare for delays of more than 30 min and 100% for delays of over 1 h. The scheme rules were chosen arbitrarily and are homogeneous across all ticket types and journey lengths. As longer journeys are usually more expensive and subjected to longer delays, long distance operators are likely to see more passengers being eligible to claim compensation. This, combined with higher engagement rates due to differences in sensitivity to lateness or opportunity cost of not claiming compensation is likely to have an impact on the differing revenue burden for operating companies.ObjectiveAgainst this background, this study aims to quantify the revenue impact of homogeneity of scheme rules for different types of train operators to advance understanding of the scheme’s costs and motivate further research into the economic rationale behind the scheme’s provision and design.MethodologyAn econometric model was constructed to empirically test the impact of performance levels and train operator characteristics on the compensation payments made to passengers through the ’Delay Repay’ scheme in Great Britain.ResultsThe combined differences in the nature of operation and engagement levels mean that with delay levels and engagement increasing with journey length and fare, short, medium and long distance train operating companies (TOCs) repay on average respectively 0.3%, 0.8% and 1.8% of their ticket revenues, increasing the scheme’s proportionate burden on the revenues of long distance operators. Further research is needed to either explain the economic or regulatory reasons behind the differing revenue impact of the scheme on different types of TOCs or suggest how the scheme can be redesigned to take these differences into account.

Improving the scheduling of railway maintenance projects by minimizing passenger delays subject to event requests of railway operators

In the Netherlands, it is expected that passenger activities on railway networks will double by 2050. To manage the passenger demand, railway capacity planning needs to be adapted. One fundamental aspect of the railway capacity planning is the scheduling of large maintenance projects. These maintenance requests should not be scheduled during major events to avoid the disruption of service. To do so, passenger operators can submit event request, i.e., a time period and location in which no maintenance project should be scheduled. Currently, these event requests are considered as hard constraints and the flexibility in maintenance scheduling decreases resulting in conflicts. In this study, the focus is on scheduling maintenance projects to minimize passenger delays while relaxing the hard constraints of event requests. This problem is addressed by introducing a Mixed Integer Linear Program (MILP) that minimizes passenger delays while scheduling maintenance projects, which includes capacity constraints for alternative services in event request areas during maintenance projects. The computational complexity of the proposed model is reduced by adding valid inequalities from the Single Machine Scheduling problem and using a simulated annealing meta-heuristic to find a favorable initial solution guess. Then, the MILP is solved to global optimality with Branch-and-Bound. A case study on the Dutch railway network shows improvements when event requests are not considered as hard constraints and an increase in the flexibility to schedule maintenance projects. This allows decision makers to choose from a set of optimal maintenance schedules with different characteristics.

Impact of Ambient Temperature, Passenger Load, and Delay on the Consumption of Battery Electric Buses

The operation of state-of-the-art battery electric buses (BEBs) has started recently, and their consumption model is unknown. Therefore, the service providers are lenient regarding the energy used. Our aim was to reveal the effects of ambient temperature and bus service characteristics, such as passenger load and delay, on the energy consumption. Real-time consumption data of BEBs were analyzed in Nyíregyháza, Hungary, and the functional relationships among the aforementioned variables were revealed. We found that while ambient temperature is negatively correlated with consumption in the cold winter periods, the consumption surplus is positively correlated with passenger load and generally positively correlated with the delay on the trip because of road traffic. In the case of delay, the consumption increase is caused by more frequent acceleration and the greater consumption of auxiliary systems due to longer running times. Accordingly, the energy consumption can be estimated if the characteristics data are available. The results can be used by bus service operators to plan the daily schedule and predict the energy consumption of a BEB.

Agent-based modelling of high-speed railway interdependent critical infrastructures facing physical and cyber threats

Globally, high-speed rail systems serve nearly 2 billion passenger-km daily. By virtue, they are a critical infrastructure like telecommunication and power networks. Accordingly, they become a catalyst for societal and economic growth stemming from the mobility business. The highspeed rail operations are very complex and interdependent, owing to the escalated demands for long-distance interconnected transportation. In recent years, there have been unreasonable delays for passengers as a new norm due to unfortunate train cancellations and relaxation of mobility performance requirements. Therefore, accurate measurements, monitoring and prediction of disruptive impacts and service performance metrices are indispensable. Within the scope of high-speed rail services, this paper examines how agent-based and multi-agent-based models are utilized to address such the challenges. Our findings reveal that the current use of agents or multi-agent models has some limitations for practical applications. Previous studies showed that mathematical methods to assess the resilience of critical infrastructures, railway scheduling, and vehicle dispatching can yield more satisfactory outcomes, although the approaches can be relatively time-consuming. In contrast, agent-based and multi-agent-based models can shorten processing time and uncover disruptive events more promptly. The paper thus showcases several emerging concepts, including i) the utilization of big data for crisis management, ii) interconnectivity analysis of high-speed rail infrastructures, and iii) enhancement of transport resilience. In addition, our findings identify the most influential agents and their possible applications to enhance systems resilience of highspeed rail networks when dealing with unforeseen physical and cyber threats.

Handling uncertainty in train timetable rescheduling: A review of the literature and future research directions

External and internal factors can cause disturbances or disruptions in daily train operations, leading to deviations from official timetables and passenger delays. As a result, efficient train timetable rescheduling (TTR) methods are necessary to restore disrupted train services. Although TTR has been a popular research topic in recent years, the uncertain characteristics of railways have not been sufficiently addressed. This review first identifies the primary uncertainties of TTR and examines their impacts on both TTR and passenger routing during disturbances or disruptions. It finds that only a few uncertainties have been investigated, and the existing solution methods do not adequately meet practical requirements, such as considering the dynamic nature of disturbances or disruptions, which is crucial for real-world applications. Therefore, the review highlights problems associated with TTR uncertainties that need urgent attention and suggests promising methodologies that could effectively address these issues as future research directions. This review aims to help practitioners develop improved automatic train-dispatching systems with better train-rescheduling performance under disturbances or disruptions compared to current systems.

Integrated Demand Responsive transport in Low-Demand Areas: A case study of Canberra, Australia

This paper evaluates Integrated-Demand Responsive Transport (I-DRT) as a solution to the challenges faced by traditional public transport (PT) systems in low-demand urban areas. The study investigates the implications of replacing local PT with I-DRT in low-demand urban areas. A multi-objective model, incorporating operational cost, environmental impact, passengers' travel time, and inequity is used to simulate the I-DRT performance. The analysis compares the performance of I-DRT and existing local bus lines in Belconnen, Canberra, Australia, based on number of utilised vehicles, operational cost, fuel consumption, average travel time, individual passenger travel time, delay, and inequity in delay distribution.

Integrated Reinforcement Learning and Optimization for Railway Timetable Rescheduling

The railway timetable rescheduling problem is regarded as an efficient way to handle disturbances. Typically, it is tackled using a mixed integer linear programming (MILP) formulation. In this paper, an algorithm that combines both reinforcement learning and optimization is proposed to solve the railway timetable rescheduling problem. Specifically, a value-based reinforcement learning algorithm is implemented to determine the independent integer variables of the MILP problem. Then, the values of all the integer variables can be derived from these independent integer variables. With the solution for the integer variables, the MILP problem can be transformed into a linear programming problem, which can be solved efficiently. The simulation results show that the proposed method can reduce passenger delays compared with the baseline, while also reducing the solution time.

Enhancing urban metro system resilience under disruptive events through multi-agent reinforcement learning

With urban rail transit (URT) systems playing an increasingly crucial role in megacity mobility, the rising frequency of service disruptions may pose substantial passenger delays and safety concerns. This emphasizes the need to prioritize resilience enhancements for the URT system. Bus bridging, an effective resilience-enhancing measure, faces challenges that effectively capture the unique features of bridging buses and stranded passengers, and rely on predetermined routes and fixed frequencies, diminishing service flexibility and efficiency in dynamic bridging environments. To overcome these challenges, we propose a novel approach using a multi-agent reinforcement learning model. This model treats bus bridging as a collaborative multi-agent task with the competition. Specifically, it enables bridging buses to adaptively select the next visiting station based on environmental characteristics. Furthermore, a competitive reward function is introduced to facilitate effective collaboration among all agents while preventing malicious competition. The case study results demonstrate that the proposed enhancing resilience method outperforms traditional reinforcement learning method and classic bus bridging strategy and can reduce the average waiting time and average travel time, especially in resource-limited and complex scenarios.

Bus routing fine-tuning for integrated network-based demand and bus bridging for a disrupted railway system

Unanticipated commuter railway disruptions inevitably tend to result in passenger bunching and frustration with considerable delays. Bus bridging strategy is a timely response approach to operationally handle and ease these scheduling disturbance issues. Conventional strategies, in this case, are apt to adding new/temporary bus routes, or, alternatively, sharply amending existing routes. Nonetheless, previous approaches have mainly been dedicated to reducing the delay of stranded passengers. This was done without integrating and considering the ordinary demand with the stranded demand through optimally handling time-dependent total passenger demand based on available service capacity. Consequently, this study creates a method on how to fine-tune bus-routing changes to handle these railway disruptions. This method allows for adjusting partial bus routes to optimally solve the combined problem of the railway passengers with bus bridging and to service all the network-based bus stops. We propose a mixed integer nonlinear programming model with multitype vehicles using a rolling horizon method to minimize total passenger delay from integrated bus bridging and existing bus services. Considering the extensive problem of the bus network with variable headways, we use a customized branch-and-bound algorithm incorporating an elitist genetic algorithm and the Monte Carlo simulation method to solve the model. A case study of the Shanghai Jinshan Railway shows that the result of total passenger delay of the proposed bus bridging modeling with rolling horizon is less by 37.23% than without rolling horizon for representing the advance of knowledge, and less by 60.7% than the do-nothing situation for representing the advance over practice.

Vertidrome Airside Level of Service: Performance-Based Evaluation of Vertiport Airside Operations

This paper presents the Vertidrome Airside Level of Service (VALoS) framework, a novel performance metric designed to evaluate airside traffic flow operations at vertidromes in the context of Urban Air Mobility (UAM). As the UAM industry rapidly evolves, the need for a comprehensive evaluation framework becomes increasingly important. The VALoS framework provides a performance-based approach to evaluating vertidrome traffic flow performance, considering metrics like average passenger delay, air taxi in-flight delay, and vertidrome punctuality. Unlike existing Level of Service approaches, the VALoS framework unifies the requirements of various stakeholders, the passenger, the air taxi operator, and the vertidrome operator each with their own performance metric and target. It provides a multi-faceted approach covering airside air and ground traffic flows, arrivals and departures, and performance changes during strategic planning and tactical execution phases. The VALoS is evaluated at 15-min intervals while considering changing stakeholder performance targets and operational uncertainties. For the reference use case, the study demonstrates the significant impact of short-term disruptions, while stochastic deviations can be neglected. Higher traffic volumes due to changing demand/capacity ratios result in higher VALoS variability. The VALoS framework, together with a fast-time simulation, provides a versatile method for exploring future vertidrome traffic flows and supporting strategic vertidrome airside planning and integration. This integrated approach is essential for the evolving UAM vertidrome industry; aligning the interests of different stakeholders and promoting sustainable and efficient vertidrome planning and operation.

An Automatic Train Operation Based Real-Time Rescheduling Model for High-Speed Railway

With the continuous development of the Automatic Train Operation (ATO) system in high-speed railways, automatic driving is progressively supplanting manual operations, ushering in a new era of predictability and reliability for high-speed railway transport. Concurrently, the advent of the ATO system provides a notable impact on real-time rescheduling during disruptions, as it equips dispatchers with precise insights into train operation statuses. This paper is dedicated to a thorough analysis of how the transition to automatic driving in train operations influences the real-time rescheduling model. Based on the distinctive impact of the ATO system on real-time rescheduling, we have proposed a mixed-integer linear programming model that combines train re-timing, reordering, and the minimization of passenger delays. To validate the effectiveness of our model, we present several experiments conducted using data from the Beijing–Shanghai high-speed railway line. The results unequivocally demonstrate that our ATO-based model significantly mitigates train delay time, demonstrating its practical value in optimizing high-speed railway operations.

Towards a resilience assessment framework for the airport passenger terminal operations

Airport terminals can generate and intensify the undesired phenomena that take place on transportation networks in the wake of disruptive events. The more resilient a terminal is, the more easily it will bounce back to the desired state, causing less nuisance to the users. In this study, we expand the state-of-the-art airport terminal resilience assessment through fast-time simulation and tailored resilience metrics. From the point of view of simulation, we establish delay management strategies based on flight schedule alterations as a response to a disruptive event. In the baseline scenario, no disruption is set, whereas passenger arrival to the airport is blocked for 1 or 2 h in the disrupted scenarios. A previously verified and validated simulation model of a Brazilian domestic airport was used to represent the passenger departing flow, encompassing check-in and security screening. From the resilience perspective, a systematic assessment procedure for new resilience metrics is unveiled, providing means for exploring the metrics’ formulations and their relationship to the operational strategies. Following the simulation runs, resilience metrics for individual passenger and aircraft delays were investigated, something yet scarcely explored in the literature. The results show that the proposed metrics deliver significantly different outcomes from those associated with the current literature. Consequently, we can conclude that a resilience assessment concerning the airport terminal that disregards individual delays may be missing important information. The systematic approach we propose is a contribution in that sense, even though this topic requires more research, considering the vast nature of the possible disruptive events and their subsequent operational impacts.

Multi-route coordination for bus systems in response to road disruptions

Road disruptions frequently occur in practice, resulting in a productivity loss of bus services and widespread passenger delays. These negative impacts are significant, especially when multiple routes are influenced at the same time. In order to mitigate these impacts, this paper proposes a multi-route coordination approach that collaboratively adjusts multiple bus routes and optimizes bus timetables to provide effective alternative bus services for passengers. Three adjusting strategies are adopted for bus routes with varying passenger demand: detouring, short-running, and cancellation. To address the multi-route coordination problem, a column-generation-based two-stage framework is developed. Concretely, a column generation technique is utilized in the first stage to iteratively generate candidate adaptive bus routes and passenger paths. Following that, an integrated integer programming model is built in the second stage to simultaneously determine bus timetables and the combinations of those adaptive bus routes. After realizing the low computational efficiency for large-scale problems, this paper designs a customized decomposition algorithm based on set partitioning to solve the presented model and obtain near-optimal solutions efficiently. Finally, the proposed methodology is applied to an illustrative Sioux Falls network and a real-world bus network in Beijing to verify its validity and effectiveness. Three comparative analyses are conducted to discuss the advantages of the three adjusting strategies, to investigate the benefits of coordinating bus timetables, and to explore the applicability of different adjusting strategies, respectively.