Passenger oriented railway disruption management by adapting timetables and rolling stock schedules

In passenger railway operations, unforeseen events require railway operators to adjust their timetable and their resource schedules. The passengers will also adapt their routes to their destinations. When determining the new timetable and rolling stock schedule, the railway operator has to take passenger behavior into account. The operator should increase the capacity of trains for which the operator expects more demand than on a regular day. Furthermore, the operator could increase the frequency of the trains that serve stations with an additional demand.This paper describes a real-time disruption management approach which integrates the rescheduling of the rolling stock and the timetable by taking the changed passenger demand into account. The timetable decisions are limited to additional stops of trains at stations at which they normally would not call. Several variants of the approach are suggested, with the difference in how to determine which additional stops should be executed.Real-time rescheduling requires fast solutions. Therefore a heuristic approach is used. We demonstrate the performance of the several variants of our algorithm on realistic instances of Netherlands Railways, the major railway operator in the Netherlands.     

Insights on disruptions as opportunities for transport policy change

Policy change is characterised as being slow and incremental over long time periods. In discussing a radical shift to a low carbon economy, many researchers identify a need for a more significant and rapid change to transport policy and travel patterns. However, it is not clear what is meant by rapid policy change and what conditions might be needed to support its delivery.Our contention in this paper is that notions of habit and stability dominate thinking about transport trends and the policy responses to them. We limit variability in our data collection and seek to design policies and transport systems that broadly support the continuation of existing practices. This framing of the policy context limits the scale of change deemed plausible and the scope of activities and actions that could be used to effect it.This paper identifies evidence from two sources to support the contention that more radical policy change is possible. First, there is a substantial and on-going churn in household travel behaviour which, harnessed properly over the medium term, could provide the raw material for steering behaviour change. Secondly, there is a growing evidence base analysing significant events at local, regional and national level which highlight how travellers can adapt to major change to network conditions, service availability and social norms. Taken together, we contend that the population is far more adaptable to major change than the policy process currently assumes.Disruptions and the responses to them provide a window on the range of adaptations that are possible (and, given that we can actually observe people carrying them out, could be more widely acceptable) given the current configuration of the transport system. In other words, if we conceptualise the system as one in which disruptions are commonplace, then different policy choices become tractable. Policy change itself can also be seen as a positive disruption, which could open up a raft of new opportunities to align policy implementation with the capacity for change. However, when set against the current framing of stability and habit, disruption can also be a major political embarrassment. We conclude that rather than being inherently problematic, disruption are in fact an opportunity through which to construct a different approach to transport policy that might enable rather than frustrate significant, low carbon change.     

Planning for the unexpected: The value of reserve capacity for public transport network robustness

Public transport networks (PTN) are subject to recurring service disruptions. Most studies of the robustness of PTN have focused on network topology and considered vulnerability in terms of connectivity reliability. While these studies provide insights on general design principles, there is lack of knowledge concerning the effectiveness of different strategies to reduce the impacts of disruptions. This paper proposes and demonstrates a methodology for evaluating the effectiveness of a strategic increase in capacity on alternative PTN links to mitigate the impact of unexpected network disruptions. The evaluation approach consists of two stages: identifying a set of important links and then for each identified important link, a set of capacity enhancement schemes is evaluated. The proposed method integrates stochastic supply and demand models, dynamic route choice and limited operational capacity. This dynamic agent-based modelling of network performance enables to capture cascading network effects as well as the adaptive redistribution of passenger flows. An application for the rapid PTN of Stockholm, Sweden, demonstrates how the proposed method could be applied to sequentially designed scenarios based on their performance indicators. The method presented in this paper could support policy makers and operators in prioritizing measures to increase network robustness by improving system capacity to absorb unexpected disruptions.     

Managing port-related supply chain disruptions (PSCDs): a management model and empirical evidence

ABSTRACT

In the contemporary port business environment, port resilience is of paramount importance for supply chain continuity. A result of the increased integration of ports into supply chains is ports’ potential to supply chain disruptions. This paper introduces a PSCD management model that incorporates the application of risk management, business continuity management, and quality management theories with the purpose of increasing port resilience such that supply chain continuity is enhanced. To validate this model, primary data were collected from samples of port operators and port authorities and analyzed using structural equation modeling with AMOS 22.0. The results indicate that managing PSCD using the management model contributes positively to the identification of internal and external opportunities and through that to port’s resilience of internal operations. This, in turn, positively influences port’s financial health and market reputation. The management model has several managerial and academic implications as it offers meaningful insights on policy development as well as theory building.     

Disruption Management of an Inequality-Based Multi-Fleet Airline Schedule by a Multi-Objective Genetic Algorithm

Abstract

This paper presents a novel application of a Method of Inequality-based Multi-objective Genetic Algorithm (MMGA) to generate an efficient time-effective multi-fleet aircraft routing algorithm in response to the schedule disruption of short-haul flights. It attempts to optimize objective functions involving ground turn-around times, flight connections, flight swaps, total flight delay time and a 30-minute maximum delay time of original schedules. The MMGA approach, which combines a traditional Genetic Algorithm (GA) with a multi-objective optimization method, can address multiple objectives at the same time, then explore the optimal solution. The airline schedule disruption management problem is traditionally solved by Operations Research (OR) techniques that always require a precise mathematical model. However, airline operations involve too many factors that must be considered dynamically, making a precise mathematical model difficult to define. Experimental results based on a real airline flight schedule demonstrate that the proposed method, Multi-objective Optimization Airline Disruption Management by GA, can recover the perturbation efficiently within a very short time. Our results further demonstrate that the application can yield high quality solutions quickly and, consequently, has potential to be employed as a real-time decision support tool for practical complex airline operations.     

Understanding travel behaviour change during mega-events: Lessons from the London 2012 Games

This paper presents results from a longitudinal study of the travel behaviour change associated with the London 2012 Olympic and Paralympic Games (the ‘Games’). The research examines commuter travel behaviour through a panel approach enabling an understanding of individual behaviour across three waves (before, during and after), with the study utilising unique access to a Transport for London panel study (n = 1132). The findings indicate that a substantial amount of change occurred during the Games (54% made at least one change), with reducing or re-timing journeys being the most likely adaptations made. A key objective of this work was to advance the discussion about the theoretical constructs that are most applicable in the study of behaviour change associated with disruptive events, which was done through the application and critical evaluation of the Transtheoretical Model. The insights from the stages of change element of the model were relatively limited but the analysis shows significant differences in the underlying factors explaining change according to the type of change made (reduce, re-time, re-mode and re-route). Whilst the long-term behavioural impacts of events like the Games appear small, the study has uncovered a need to consider these behavioural choices as distinct rather than under the collective term of “travel behaviour change”, as is current practice.     

U.S. import/export container flow modeling and disruption analysis

International containerized freight movement is a vital part of the supply chain for many companies, and a critical element of moving consumer goods to points of retail sale within the U.S. Containerized imports also present a clear security concern (e.g., terrorists attempting to ship “dirty bombs,” chemical, biological or even nuclear weapons, into the U.S. in a shipping container). The goal of the research presented here is to create a modeling tool for analyzing flows of U.S. imports and exports of containerized freight, and the potential changes in those flows under a variety of conditions (e.g., port disruptions, extensive security-related delays, etc.). Our focus is on movements through maritime container ports, and not overland movements between the U.S. and Canada or Mexico.The network model, referred to as the System for Import/Export Routing and Recovery Analysis (SIERRA), represents container movements between the U.S. and 46 other countries that account for the vast majority of U.S. imports and exports. The SIERRA model is a network equilibrium model that predicts flows between foreign countries and North American ports, the total volumes handled (import and export) by each port, the modal volumes (truck and rail) moving domestically into and out of each port, and volumes between each port and a set of transportation analysis zones within the U.S.     

Joint inventory-location problem under the risk of probabilistic facility disruptions

This paper studies a reliable joint inventory-location problem that optimizes facility locations, customer allocations, and inventory management decisions when facilities are subject to disruption risks (e.g., due to natural or man-made hazards). When a facility fails, its customers may be reassigned to other operational facilities in order to avoid the high penalty costs associated with losing service. We propose an integer programming model that minimizes the sum of facility construction costs, expected inventory holding costs and expected customer costs under normal and failure scenarios. We develop a Lagrangian relaxation solution framework for this problem, including a polynomial-time exact algorithm for the relaxed nonlinear subproblems. Numerical experiment results show that this proposed model is capable of providing a near-optimum solution within a short computation time. Managerial insights on the optimal facility deployment, inventory control strategies, and the corresponding cost constitutions are drawn.     

Robust single-track train dispatching model under a dynamic and stochastic environment: A scenario-based rolling horizon solution approach

After a major service disruption on a single-track rail line, dispatchers need to generate a series of train meet-pass plans at different decision times of the rescheduling stage. The task is to recover the impacted train schedule from the current and future disturbances and minimize the expected additional delay under different forecasted operational conditions. Based on a stochastic programming with recourse framework, this paper incorporates different probabilistic scenarios in the rolling horizon decision process to recognize (1) the input data uncertainty associated with predicted segment running times and segment recovery times and (2) the possibilities of rescheduling decisions after receiving status updates. The proposed model periodically optimizes schedules for a relatively long rolling horizon, while selecting and disseminating a robust meet-pass plan for every roll period. A multi-layer branching solution procedure is developed to systematically generate and select meet-pass plans under different stochastic scenarios. Illustrative examples and numerical experiments are used to demonstrate the importance of robust disruption handling under a dynamic and stochastic environment. In terms of expected total train delay time, our experimental results show that the robust solutions are better than the expected value-based solutions by a range of 10-30%.     

Tweeting Transit: An examination of social media strategies for transport information management during a large event

Social media platforms are seeing increasing adoption by public transport agencies, as they provide a cost-effective, reliable, and timely mechanism for sharing information with passengers and other travellers. In this paper, we use a case study of the @GamesTravel2014 Twitter account to evaluate how this social media platform was used over the course of the 2014 Commonwealth Games in Glasgow, Scotland to provide and share transport-related information and respond to information requests. The case study provides an exemplar for the public co-ordination of information from multiple partners in a complex environment during a time of transport disruption. We evaluate both the structure and intent of the @GamesTravel2014 social media strategy via interviews with involved parties and an analysis of Tweets related to the account. Findings indicate the potential for future applications of social media by transport operators and authorities in producing a more effective network of communication with passengers.