Accepted papers

Accepted Papers

Ben Strasser: Dynamic Time-Dependent Routing in Road Networks through Sampling

We study the earliest arrival and profile problems in road networks with time-dependent functions as arc weights and dynamic updates. We present and experimentally evaluate simple, sampling-based, heuristic algorithms. Our evaluation is performed on large, current, production-grade road graph data with time-dependent arc weights. It clearly shows that the proposed algorithms are fast and compute paths with a sufficiently small error for most practical applications. We experimentally compare our algorithm against the current state-of-the-art. Our experiments reveal, that the memory consumption of existing algorithms is prohibitive on large instances. Our approach does not suffer from this limitation. Further, our algorithm is the only competitor able to answer profile queries on all test instances below 50ms. As our algorithm is simple to implement, we believe that it is a good fit for many realworld applications.

Tatsuki Yamauchi, Mizuyo Takamatsu and Shinji Imahori: Optimizing Train Stopping Patterns for Congestion Management

In this paper, we optimize train stopping patterns during morning rush hour in Japan. Since trains are extremely crowded, we need to determine stopping patterns based not only on travel time but also on congestion rates of trains. We exploit a Wardrop equilibrium model to compute passenger flows subject to congestion phenomena and present an efficient local search algorithm to optimize stopping patterns which iteratively computes a Wardrop equilibrium. We apply our algorithm to railway lines in Tokyo including Keio Line with six types of trains and succeed in relaxing congestion with a small effect on travel time.

Fahimeh Khoshniyat and Johanna Törnquist Krasemann: Analysis of Strengths and Weaknesses of a MILP Model for Railway Traffic Timetabling

A railway timetable is typically planned one year in advance, but may be revised several times prior to the time of operation in order to accommodate on-demand slot requests for inserting additional trains and network maintenance. Revising timetables is a computationally demanding task, given the many dependencies and details to consider. In this paper, we focus on the potential of using optimization-based scheduling approach for revising train timetables during short term planning, from one week to few hours before the actual operation. The approach relies on a MILP (Mixed Integer Linear Program) model which is solved by using the commercial solver Gurobi.
In a previous experimental study, the MILP approach was used to revise a significant part of the annual timetable for a sub-network in Southern Sweden to insert additional trains and allocate time slots for urgent maintenance. The results showed that the proposed MILP approach in many cases generates feasible, good solutions rather fast. However, proving optimality was in several cases time-consuming, especially for larger problems. Thus, there is a need to investigate and develop strategies to improve the computational performance. In this paper, we present results from a study, where a number of valid inequalities has been selected and applied to the MILP model with the aim to reduce the computation time. The experimental evaluation of the selected valid inequalities showed that although they can provide a slight improvement with respect to computation time, they are also weakening the LP relaxation of the model.

Robert Scheffler and Martin Strehler: Optimizing traffic signal settings for public transport priority

In order to promote public transport many municipalities use traffic signal control with a priority for buses or trams. In this paper, we address the problem of finding optimal passive transit signal priority settings. Building on a cyclically time-expanded network model for the combined traffic assignment traffic signal coordination problem, we introduce a suitable queuing model and several modifications to model public transport vehicles appropriately. We evaluate the applicability of this approach by computing and analyzing optimal solutions for several instances of a real-world scenario.

Alexander Schiewe, Anita Schöbel, Markus Friedrich and Maximilian Hartl: Integrating Passengers’ Assignment in Cost-Optimal Line Planning

Finding a line plan with corresponding frequencies is an mportant stage of planning a public transport system. A line plan should permit all passengers to travel with an appropriate quality at appropriate costs for the public transport operator. Traditional line planning procedures proceed sequentially: In a first step a traffic assignment allocates passengers to routes in the network, often by means of a shortest path assignment. The resulting traffic loads are used in a second step to determine a cost-optimal line concept. It is well known that travel time of the resulting line concept depends on the traffic assignment. In this paper we investigate the impact of the assignment on the operating costs of the line concept.
We show that the traffic assignment has significant influence on the costs even if all passengers are routed on shortest paths. We formulate an integrated model and analyze the error we can make by using the traditional approach and solve it sequentially. We give bounds on the error in special cases. We furthermore investigate and enhance three heuristics for finding an initial passengers’ assignment and compare the resulting line concepts in terms of operating costs and passengers’ travel time. It turns out that the costs of a line concept can be reduced significantly if passengers are not necessarily routed on shortest paths and that it is beneficial for the travel time and the costs to include knowledge on the line pool already in the assignment step.

Frank Fischer and Thomas Schlechte: Strong Relaxations for the Train Timetabling Problem using Connected Configurations

The task of the train timetabling problem or track allocation problem is to find conflict free schedules for a set of trains with predefined routes in a railway network. Especially for non-periodic instances models based on time expanded networks are often used. Unfortunately, the linear programming relaxation of these models is often extremely weak because these models do not describe combinatorial relations like overtaking possibilities very well. In this paper we extend the model by so called connected configuration subproblems. These subproblems perfectly describe feasible schedules of a small subset of trains (2-3) on consecutive track segments. In a Lagrangian relaxation approach we solve several of these subproblems together in order to produce solutions which consist of combinatorially compatible schedules along the track segments. The computational results on a mostly single track corridor taken from the INFORMS RAS Problem Solving Competition 2012 data indicate that our new solution approach is rather strong. Indeed, for this instance the solution of the Lagrangian relaxation is already integral.

Trivikram Dokka and Marc Goerigk :An Experimental Comparison of Uncertainty Sets for Robust Shortest Path Problems

Through the development of efficient algorithms, data structures and preprocessing techniques, real-world shortest path problems in street networks are now very fast to solve. But in reality, the exact travel times along each arc in the network may not be known. This led to the development of robust shortest path problems, where all possible arc travel times are contained in a so-called uncertainty set of possible outcomes.
Research in robust shortest path problems typically assumes this set to be given, and provides complexity results as well as algorithms depending on its shape. However, what can actually be observed in real-world problems are only discrete raw data points. The shape of the uncertainty is already a modelling assumption. In this paper we test several of the most widely used assumptions on the uncertainty set using real-world traffic measurements provided by the City of Chicago. We calculate the resulting different robust solutions, and evaluate which uncertainty approach is actually reasonable for our data. This anchors theoretical research in a real-world application and gives an indicator which robust models should be the future focus of algorithmic development.

Casper Kehlet Jensen, Marco Chiarandini and Kim S. Larsen: Flight planning in free route airspaces

We consider the problem of finding cheapest flight routes through free route airspaces in a 2D setting. We subdivide the airspace into regions determined by a Voronoi subdivision around the points from a weather forecast. This gives rise to a regular grid of rectangular regions (quads) with every quad having an associated vector-weight that represents the wind magnitude and direction. Finding a cheapest path in this setting corresponds to finding a piece-wise linear path determined by points on the boundaries of the quads. In our solution approach, we discretize such boundaries by introducing border points and only consider segments connecting border points belonging to the same quad. While classic shortest path graph algorithms are available and applicable to the graphs originating from these border points, we design an algorithm that exploits the geometric structure of our scenario and show that this algorithm is more efficient in practice than classic graph-based algorithms. In particular, it scales better with the number of quads in the subdivision of the airspace, making it possible to find more accurate routes or to solve larger problems.

Andreas Bärtschi, Daniel Graf and Paolo Penna: Truthful Mechanisms for Delivery with Agents

We study the game-theoretic task of selecting mobile agents to deliver multiple items on a network. An instance is given by m packages (physical objects) which have to be transported between specified source-target pairs in an undirected graph, and k mobile heterogeneous agents, each being able to transport one package at a time. Following a recent model by Bärtschi et al., each agent i has a different rate of energy consumption per unit distance traveled, i.e., its weight. We are interested in optimizing or approximating the total energy consumption over all selected agents.
Unlike previous research, we assume the weights to be private values known only to the respective agents. We present three different mechanisms which select, route and pay the agents in a truthful way that guarantees voluntary participation of the agents, while approximating the optimum energy consumption by a constant factor. To this end, we analyze a previous structural result and an approximation algorithm given in Bärtschi et al. Finally, we show that for some instances in the case of a single package, the sum of the payments can be bounded in terms of the optimum.

Spyros Kontogiannis, Andreas Paraskevopoulos, Georgia Papastavrou, Dorothea Wagner and Christos Zaroliagis: Improved Oracles for Time-Dependent Road Networks

A novel landmark-based oracle (CFLAT) is presented, which provides earliest-arrival-time route plans in time-dependent road networks. To our knowledge, this is the first oracle that preprocesses combinatorial structures (collections of time-stamped min-travel-time-path trees) rather than travel-time functions. The preprocessed data structure is exploited by a new query algorithm (CFCA) which also computes (and pays for it) the actual connecting path that preserves the theoretical approximation guarantees. To make it practical and tackle the main burden of landmark-based oracles (the large preprocessing requirements), CFLAT is extensively engineered. A thorough experimental evaluation on two real-world benchmark instances shows that CFLAT achieves a significant improvement on preprocessing, approximation guarantees and query-times, in comparison to previous landmark-based oracles. It also achieves competitive query-time performance compared to state-of-art speedup heuristics for time-dependent road networks, whose query-times in most cases do not account for path construction.

Ananya Christman, Christine Chung, Nicholas Jaczko, Marina Milan, Anna Vasilchenko, and Scott Westvold: Revenue Maximization in Online Dial-a-ride

We study a variation of the Online-Dial-a-Ride Problem where each request comes with not only a source, destination and release time, but also has an associated revenue. The server’s goal is to maximize its total revenue within a given time limit, T. We show that the competitive ratio is unbounded for any deterministic online algorithm for the problem. We then provide a 3-competitive algorithm for the problem in a uniform metric space and a 6-competitive algorithm for the general case of weighted graphs (under reasonable assumptions about the input instance). We conclude with an experimental evaluation of our algorithm in simulated settings inspired by real-world Dial-a-Ride data. Experimental results show that our algorithm performs well when compared to an offline version of the algorithm and a greedy algorithm.

Dorothea Wagner and Tobias Zündorf. Public Transit Routing with Unrestricted Walking

We study the problem of answering profile queries in public transportation networks that allow unrestricted walking. That is, finding all Pareto-optimal journeys regarding travel time and number of transfers in a given time interval. We introduce a novel algorithm that, unlike most state-of-the-art algorithms, can compute profiles efficiently in a setting that allows arbitrary walking. Using our algorithm, we show in an extensive experimental study that allowing unrestricted walking, significantly reduces travel times, compared to settings where walking is restricted. Beyond that, we publish the transportation networks of Switzerland that we used in our study, in order to encourage further research on this topic.

Daniel Delling, Julian Dibbelt, Thomas Pajor and Tobias Zündorf. Faster Transit Routing by Hyper Partitioning

We present a preprocessing-based acceleration technique for computing bi-criteria Pareto-optimal journeys in public transit networks, based on the well-known RAPTOR algorithm. Our key idea is to first partition a hypergraph into cells, in which vertices correspond to routes (e.g., bus lines) and hyperedges to stops, and to then mark routes sufficient for optimal travel across cells. The query can then be restricted to marked routes and those in the source and target cells. This results in a practical approach, suitable for networks that are too large to be efficiently handled by the basic RAPTOR algorithm.

Marc Goerigk and Christian Liebchen. An Improved Algorithm for the Periodic Timetabling Problem

We consider the computation of periodic timetables, which is a key task in the service design process of public transportation companies. We propose a new approach for solving the periodic timetable optimisation problem. It consists of a (partially) heuristic network aggregation to reduce the problem size and make it accessible to standard mixed-integer programming (MIP) solvers. We alternate the invocation of a MIP solver with the well-known problem specific modulo network simplex heuristic (ModSim). This iterative approach helps the ModSim-method to overcome local minima efficiently, and provides the MIP solver with better initial solutions.
Our computational experiments are based on the 16 railway instances of the PESPlib, which is the only currently available collection of periodic event scheduling problem instances. For each of these instances, we are able to reduce the objective values of previously best known solutions by at least 10.0%, and up to 22.8% with our iterative combined method.

Julius Pätzold, Alexander Schiewe, Philine Schiewe and Anita Schöbel: Look-Ahead Approaches for Integrated Planning in Public Transportation

In this paper we deal with three consecutive planning stages in public transportation: Line planning (including line pool generation), timetabling, and vehicle scheduling. These three steps are traditionally performed one after another in a sequential way often leading to high costs in the (last) vehicle scheduling stage. In this paper we propose three different ways to “look ahead”, i.e., to include aspects of vehicle scheduling already earlier in the sequential process: an adapted line pool generation algorithm, a new cost structure for line planning, and a reordering of the sequential planning stages. We analyze these enhancements experimentally and show that they can be used to decrease the costs significantly.

Marco Blanco, Ralf Borndoerfer, Nam Dũng Hoàng, Anton Kaier, Pedro M. Casas, Thomas Schlechte and Swen Schlobach: Cost Projection Methods for the Shortest Path Problem with Crossing Costs

Real world routing problems, e.g., in the airline industry or in public and rail transit, can feature complex non-linear cost functions. An important case are costs for crossing regions, such as countries or fare zones. We introduce the shortest path problem with crossing costs (SPPCCC) to address such situations; it generalizes the classical shortest path problem and variants such as the resource constrained shortest path problem and the minimum label path problem.
Motivated by an application in flight trajectory optimization with overflight costs, we focus on the case in which the crossing costs of a region depend only on the nodes used to enter or exit it. We propose an exact Two-Layer-Dijkstra Algorithm as well as a novel cost-projection linearization technique that transforms crossing costs into shadow costs on individual arcs, thus approximating the (SPPCC) by a standard shortest path problem. We evaluate all algorithms’ performance on real-world flight trajectory optimization instances, obtaining very good à posteriori error bounds.

Markus Friedrich, Matthias Müller-Hannemann, Ralf Rückert, Alexander Schiewe and Anita Schöbel: Robustness Tests for Public Transport Planning

The classical planning process in public transport planning focuses on the two criteria operating costs and quality for passengers. Quality mostly considers quantities like average travel time and number of transfers. Since public transport often suffers from delays caused by random disturbances, we are interested in adding a third dimension: robustness. We propose passenger-oriented robustness indicators for public transport networks and timetables. These robustness indicators are evaluated for several public transport plans which have been created for an artificial urban network with the same demand. The study shows that these indicators are suitable to measure the robustness of a line plan and a timetable. We explore different trade-offs between operating costs, quality (average travel time of passengers), and robustness against delays. Our results show that the proposed robustness indicators give reasonable results.