Delay Management with Re-Routing of Passengers

The question of delay management is whether trains should wait for a delayed feeder trainor should depart on time. In classical delay management models passengers always taketheir originally planned route. In this paper, we propose a model where re-routing ofpassengers is incorporated.To describe the problem we represent it as an event-activity network similar to the oneused in classical delay management, with some additional events to incorporate originand destination of the passengers. We present an integer programming formulation ofthis problem. Furthermore, we discuss the variant in which we assume fixed costs formaintaining connections and we present a polynomial algorithm for the special case ofonly one origin-destination pair. Finally, computational experiments based on real-worlddata from Netherlands Railways show that significant improvements can be obtained bytaking the re-routing of passengers into account in the model.public transportation;OD-pairs;delay management;re-routing

Delay Management with Re-Routing of Passengers

The question of delay management is whether trains should wait for a delayed feeder train or should depart on time. In classical delay management models passengers always take their originally planned route. In this paper, we propose a model where re-routing of passengers is incorporated. To describe the problem we represent it as an event-activity network similar to the one used in classical delay management, with some additional events to incorporate origin and destination of the passengers. We present an integer programming formulation of this problem. Furthermore, we discuss the variant in which we assume fixed costs for maintaining connections and we present a polynomial algorithm for the special case of only one origin-destination pair. Finally, computational experiments based on real-world data from Netherlands Railways show that significant improvements can be obtained by taking the re-routing of passengers into account in the model

Train-scheduling optimization model for railway networks with multiplatform stations

This paper focuses on optimizing the schedule of trains on railway networks composed of busy complex stations. A mathematical formulation of this problem is provided as a Mixed Integer Linear Program (MILP). However, the creation of an optimal new timetable is an NP-hard problem; therefore, the MILP can be solved for easy cases, computation time being impractical for more complex examples. In these cases, a heuristic approach is provided that makes use of genetic algorithms to find a good solution jointly with heuristic techniques to generate an initial population. The algorithm was applied to a number of problem instances producing feasible, though not optimal, solutions in several seconds on a laptop, and compared to other proposals. Some improvements are suggested to obtain better results and further improve computation time. Rail transport is recognized as a sustainable and energy-efficient means of transport. Moreover, each freight train can take a large number of trucks off the roads, making them safer. Studies in this field can help to make railways more attractive to travelers by reducing operative cost, and increasing the number of services and their punctuality. To improve the transit system and service, it is necessary to build optimal train scheduling. There is an interest from the industry in automating the scheduling process. Fast computerized train scheduling, moreover, can be used to explore the effects of alternative draft timetables, operating policies, station layouts, and random delays or failures.Postprint (published version

Railway timetabling from an operations research

In this paper we describe Operations Research (OR) models andtechniques that can be used for determining (cyclic) railwaytimetables. We discuss the two aspects of railway timetabling: ($i$)the determination of arrival and departure times of the trains atthe stations and other relevant locations such as junctions andbridges, and ($ii$) the assignment of each train to an appropriateplatform and corresponding inbound and outbound routes in everystation. Moreover, we discuss robustness aspects of bothsubproblems.

Towards Opportunistic Data Dissemination in Mobile Phone Sensor Networks

Recently, there has been a growing interest within the research community in developing opportunistic routing protocols. Many schemes have been proposed; however, they differ greatly in assumptions and in type of network for which they are evaluated. As a result, researchers have an ambiguous understanding of how these schemes compare against each other in their specific applications. To investigate the performance of existing opportunistic routing algorithms in realistic scenarios, we propose a heterogeneous architecture including fixed infrastructure, mobile infrastructure, and mobile nodes. The proposed architecture focuses on how to utilize the available, low cost short-range radios of mobile phones for data gathering and dissemination. We also propose a new realistic mobility model and metrics. Existing opportunistic routing protocols are simulated and evaluated with the proposed heterogeneous architecture, mobility models, and transmission interfaces. Results show that some protocols suffer long time-to-live (TTL), while others suffer short TTL. We show that heterogeneous sensor network architectures need heterogeneous routing algorithms, such as a combination of Epidemic and Spray and Wait

Agent-based modelling of air transport demand

Constraints such as opening hours or passenger capacities influence travel options that can be offered by an airport and by the connecting airlines. If infrastructure, policy or technological measures modify transport options, then the benefits do not only depend on the technology, but also on possibly heterogeneous user preferences such as desired arrival times or on the availability of alternative travel modes. This paper proposes an agent-based, iterative assignment procedure to model European air traffic and German passenger demand on a microscopic level, capturing individual passenger preferences. Air transport technology is simulated microscopically, i.e. each aircraft is represented as single unit with attached attributes such as departure time, flight duration or seat availability. Trip-chaining and delay propagation can be added. Microsimulation is used to verify and assess passengers’ choices of travel alternatives, where those choices improve over iterations until an agent-based stochastic user equilibrium is reached. This requires fast simulation models, thus, similar to other approaches in air traffic modelling a queue model is used. In contrast to those approaches, the queue model in this work is solved algorithmically. Overall, the approach is suited to analyze, forecast and evaluate the consequences of mid-distance transport measures