Improving the robustness of the railway system in Brussels

In order to improve the robustness of a railway system in station areas, this paper introduces an iterative approach to successively solve the route choice problem in station areas to optimality and to improve this solution by applying some changes to the timetable in a tabu search environment. Using a discrete event simulation model, the performance of our algorithms is evaluated based on a case study for the Brussels' area. The railway network of the Brussels' area is introduced and its relevance is emphasized. Computational results indicate an improvement in robustness of about 10%, a decrease in knock-on delay of more than 15%, and a 25% reduction in the number of trains that are confronted with conflicts

Improved methods for the travelling salesperson problem with hotel selection

In this talk, a new formulation and a new metaheuristic solution procedure for the travelling salesperson problem with hotel selection (TSPHS) is presented. The metaheuristic is a multi-start procedure that outperforms existing heuristics on all benchmark instances. We also provide a number of new optimal solutions found by a commercial solver extended with a dedicated cutting plane procedure, as well as new best known solutions for most benchmark instances

Effect of simplicity and attractiveness on route selection for different journey types

This study investigated the effects of six attributes, associated with simplicity or attractiveness, on route preference for three pedestrian journey types (everyday, leisure and tourist). Using stated choice preference experiments with computer generated scenes, participants were asked to choose one of a pair of routes showing either two levels of the same attribute (experiment 1) or different attributes (experiment 2). Contrary to predictions, vegetation was the most influential for both everyday and leisure journeys, and land use ranked much lower than expected in both cases. Turns ranked higher than decision points for everyday journeys as predicted, but the positions of both were lowered by initially unranked attributes. As anticipated, points of interest were most important for tourist trips, with the initially unranked attributes having less influence. This is the first time so many attributes have been compared directly, providing new information about the importance of the attributes for different journeys. © 2014 Springer International Publishing

Susceptibility of optimal train schedules to stochastic disturbances of process times

This work focuses on the stochastic evaluation of train schedules computed by a microscopic scheduler of railway operations based on deterministic information. The research question is to assess the degree of sensitivity of various rescheduling algorithms to variations in process times (running and dwell times). In fact, the objective of railway traffic management is to reduce delay propagation and to increase disturbance robustness of train schedules at a network scale. We present a quantitative study of traffic disturbances and their effects on the schedules computed by simple and advanced rescheduling algorithms. Computational results are based on a complex and densely occupied Dutch railway area; train delays are computed based on accepted statistical distributions, and dwell and running times of trains are subject to additional stochastic variations. From the results obtained on a real case study, an advanced branch and bound algorithm, on average, outperforms a First In First Out scheduling rule both in deterministic and stochastic traffic scenarios. However, the characteristic of the stochastic processes and the way a stochastic instance is handled turn out to have a serious impact on the scheduler performance

Personalized Tourist Route Generation

When tourists are at a destination, they typically search for information in the Local Tourist Organizations. There, the staff determines the profile of the tourists and their restrictions. Combining this information with their up-to-date knowledge about the local attractions and public transportation, they suggest a personalized route for the tourist agenda. Finally, they fine tune up this route to better fit tourists' needs. We present an intelligent routing system to fulfil the same task. We divide this process in three steps: recommendation, route generation and route customization. We focus on the last two steps and analyze them. We model the tourist planning problem, integrating public transportation, as the Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) and we present an heuristic able to solve it on real-time. Finally, we show the prototype which generates and customizes routes in real-time