16th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2016), ATMOS 2016, August 25, 2016, Aarhus, Denmark
ATMOS 2016
August 25, 2016
Aarhus, Denmark
Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems
ATMOS
http://atmos-workshop.org/
https://dblp.org/db/conf/atmos
Open Access Series in Informatics
OASIcs
https://www.dagstuhl.de/dagpub/2190-6807
https://dblp.org/db/series/oasics
2190-6807
Marc
Goerigk
Marc Goerigk
Renato F.
Werneck
Renato F. Werneck
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
54
2016
978-3-95977-021-7
https://www.dagstuhl.de/dagpub/978-3-95977-021-7
Front Matter, Table of Contents, Preface, Organization
Front Matter, Table of Contents, Preface, Organization
Front Matter
Table of Contents
Preface
Organization
0:i-0:x
Front Matter
Marc
Goerigk
Marc Goerigk
Renato F.
Werneck
Renato F. Werneck
10.4230/OASIcs.ATMOS.2016.0
Creative Commons Attribution 3.0 Unported license
https://creativecommons.org/licenses/by/3.0/legalcode
A Matching Approach for Periodic Timetabling
The periodic event scheduling problem (PESP) is a well studied problem known as intrinsically hard, but with important applications mainly for finding good timetables in public transportation. In this paper we consider PESP in public transportation, but in a reduced version (r-PESP) in which the driving and waiting times of the vehicles are fixed to their lower bounds. This results in a still NP-hard problem which has less variables, since only one variable determines the schedule for a whole line. We propose a formulation for r-PESP which is based on scheduling the lines. This enables us on the one hand to identify a finite candidate set and an exact solution approach. On the other hand, we use this formulation to derive a matching-based heuristic for solving PESP. Our experiments on close to real-world instances from LinTim show that our heuristic is able to compute competitive timetables in a very short runtime.
PESP
Timetabling
Public Transport
Matching
Finite Dominating Set
1:1-1:15
Regular Paper
Julius
Pätzold
Julius Pätzold
Anita
Schöbel
Anita Schöbel
10.4230/OASIcs.ATMOS.2016.1
M. Goerigk. Exact and heuristic approaches to the robust periodic event scheduling problem. Public Transport, 7(1):101-119, 2015.
M. Goerigk, M. Schachtebeck, and A. Schöbel. Evaluating line concepts using travel times and robustness: Simulations with the lintim toolbox. Public Transport, 5(3), 2013.
M. Goerigk and A. Schöbel. Improving the modulo simplex algorithm for large-scale periodic timetabling. Computers and Operations Research, 40(5):1363-1370, 2013.
P. Großmann, S. Hölldobler, N. Manthey, K. Nachtigall, J. Opitz, and P. Steinke. Solving periodic event scheduling problems with sat. In H. Jiang, W. Ding, M. Ali, and X. Wu, editors, Advanced Research in Applied Artificial Intelligence, volume 7345, pages 166-175. Springer, 2012.
J. Harbering, A. Schiewe, and A. Schöbel. LinTim - Integrated Optimization in Public Transportation. Homepage. see http://lintim.math.uni-goettingen.de/.
L. Kroon, G. Maróti, M. R. Helmrich, M. Vromans, and R. Dekker. Stochastic improvement of cyclic railway timetables. Transportation Research Part B: Methodological, 42(6):553 - 570, 2008.
L.G. Kroon, D. Huisman, E. Abbink, P.-J. Fioole, M. Fischetti, G. Maroti, A. Shrijver, A. Steenbeek, and R. Ybema. The new Dutch timetable: The OR Revolution. Interfaces, 39:6-17, 2009.
C. Liebchen. Periodic Timetable Optimization in Public Transport. dissertation.de - Verlag im Internet, Berlin, 2006.
C. Liebchen. The first optimized railway timetable in practice. Transportation Science, 42(4):420-435, 2008.
M. Michaelis and A. Schöbel. Integrating line planning, timetabling, and vehicle scheduling: A customer-oriented approach. Public Transport, 1(3):211-232, 2009.
K. Nachtigall. Periodic Network Optimization and Fixed Interval Timetables. PhD thesis, University of Hildesheim, 1998.
K. Nachtigall and J. Opitz. Solving periodic timetable optimisation problems by modulo simplex calculations. In Proc. ATMOS, 2008.
K. Nachtigall and S. Voget. A genetic approach to periodic railway synchronization. Computers Ops. Res., 23(5):453-463, 1996.
M. A. Odijk. A constraint generation algorithm for the construction of periodic railway timetables. Transportation Research, 30B:455-464, 1996.
J. Pätzold. Periodic timetabling with fixed driving and waiting times. Master’s thesis, Fakultät für Mathematik und Informatik, Georg August University Göttingen, 2016. (in German).
L. Peeters and L. Kroon. A cycle based optimization model for the cyclic railway timetabling problem. In S. Voß and J. Daduna, editors, Computer-Aided Transit Scheduling, volume 505 of Lecture Notes in Economics and Mathematical systems, pages 275-296. Springer, 2001.
P. Serafini and W. Ukovich. A mathematical model for periodic scheduling problems. SIAM Journal on Discrete Mathematic, 2:550-581, 1989.
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Sensitivity Analysis and Coupled Decisions in Passenger Flow-Based Train Dispatching
Frequent train delays make passenger-oriented train dispatching a task of high practical relevance. In case of delays, dispatchers have to decide whether trains should wait for one or several delayed feeder trains or should depart on time. To support dispatchers, we have recently introduced the train dispatching framework PANDA (CASPT 2015).
In this paper, we present and evaluate two enhancements which are also of general interest.
First, we study the sensitivity of waiting decisions with respect to the accuracy of passenger flow data. More specifically, we develop an integer linear programming formulation for the following optimization problem: Given a critical transfer, what is the minimum number of passengers we have to add or to subtract from the given passenger flow such that the decision would change from waiting to non-waiting or vice versa?
Based on experiments with realistic passenger flows and delay data from 2015 in Germany, an important empirical finding is that a significant fraction of all decisions is highly sensitive to small changes in passenger flow composition. Hence, very accurate passenger flows are needed in these cases.
Second, we investigate the practical value of more sophisticated simulations. A simple strategy evaluates the effect of a waiting decision of some critical transfer on passenger delay subject to the assumption that all subsequent decisions are taken according to standard waiting time rules, as usually employed by railway companies like Deutsche Bahn. Here we analyze the impact of a higher level of simulation where waiting decisions for a critical transfer are considered jointly with one or more other decisions for subsequent transfers. We learn that such "coupled decisions" lead to improved solution in about 6.3% of all considered cases.
train delays
event-activity model
multi-criteria decisions
passenger flows
sensitivity analysis
2:1-2:15
Regular Paper
Martin
Lemnian
Martin Lemnian
Matthias
Müller-Hannemann
Matthias Müller-Hannemann
Ralf
Rückert
Ralf Rückert
10.4230/OASIcs.ATMOS.2016.2
Reinhard Bauer and Anita Schöbel. Rules of thumb - practical online strategies for delay management. Public Transport, 6:85-105, 2014.
Annabell Berger, Christian Blaar, Andreas Gebhardt, Matthias Müller-Hannemann, and Mathias Schnee. Passenger flow-oriented train disposition. In C. Demetrescu and M. M. Halldórsson, editors, Proceedings of the 19th Annual European Symposium on Algorithms (ESA), volume 6942 of LNCS, pages 227-238. Springer, 2011.
Francesco Corman, Dario Pacciarelli, Andrea D'Ariano, and Marcella Samà. Railway traffic rescheduling with minimization of passengers' discomfort. In Computational Logistics, ICCL 2015, volume 9335 of LNCS, pages 602-616. Springer, 2015.
Twan Dollevoet and Dennis Huisman. Fast heuristics for delay management with passenger rerouting. Public Transport, 6:67-84, 2014.
Twan Dollevoet, Dennis Huisman, Marie Schmidt, and Anita Schöbel. Delay management with rerouting of passengers. Transportation Science, 46(1):74-89, 2012.
Satoshi Kanai, Koichi Shiina, Shingo Harada, and Norio Tomii. An optimal delay management algorithm from passengers' viewpoints considering the whole railway network. Journal of Rail Transport Planning &Management, 1:25 - 37, 2011.
Natalia Kliewer and Leena Suhl. A note on the online nature of the railway delay management problem. Networks, 57:28-37, 2011.
Leo G. Kroon, Gabor Maróti, and Lars K. Nielsen. Rescheduling of railway rolling stock with dynamic passenger flows. Transportation Science, 49:165-184, 2015.
Martin Lemnian, Ralf Rückert, Steffen Rechner, Christoph Blendinger, and Matthias Müller-Hannemann. Timing of train disposition: Towards early passenger rerouting in case of delays. In Stefan Funke and Matús Mihalák, editors, 14th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2014, volume 42 of OASICS, pages 122-137. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2014.
Matthias Müller-Hannemann and Mathias Schnee. Efficient timetable information in the presence of delays. In R. Ahuja, R.-H. Möhring, and C. Zaroliagis, editors, Robust and Online Large-Scale Optimization, volume 5868 of LNCS, pages 249-272. Springer, 2009.
Ralf Rückert, Martin Lemnian, Steffen Rechner, Christoph Blendinger, and Matthias Müller-Hannemann. PANDA: A software tool for improved train dispatching with focus on passenger flows. In Proceedings of CASPT 2015 (Conference on Advanced Systems in Public Transport), Rotterdam. 2015. URL: https://www.caspt.org/proceedings/paper90.pdf.
https://www.caspt.org/proceedings/paper90.pdf
Marie Schmidt. Simultaneous optimization of delay management decisions and passenger routes. Public Transport, 5:125-147, 2013.
Anita Schöbel. A model for the delay management problem based on mixed-integer programming. Electronic Notes in Theoretical Computer Science, 50(1), 2001.
Anita Schöbel. Integer programming approaches for solving the delay management problem. In F. Geraets, L. Kroon, A. Schoebel, D. Wagner, and C. Zaroliagis, editors, Algorithmic Methods for Railway Optimization, volume 4359 of LNCS, pages 145-170. Springer, 2007.
Lucas P. Veelenturf, Leo G. Kroon, and Gábor Maróti. Passenger oriented railway disruption management by adapting timetables and rolling stock schedules. In 10th International Conference of the Practice and Theory of Automated Timetabling (PATAT 2014), pages 11-34. 2014.
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Integrating Passengers' Routes in Periodic Timetabling: A SAT approach
The periodic event scheduling problem (PESP) is a well studied problem known as intrinsically hard. Its main application is for designing periodic timetables in public transportation. To this end, the passengers' paths are required as input data. This is a drawback since the final paths which are used by the passengers depend on the timetable to be designed. Including the passengers' routing in the PESP hence improves the quality of the resulting timetables. However, this makes PESP even harder.
Formulating the PESP as satisfiability problem and using SAT solvers for its solution has been shown to be a highly promising approach. The goal of this paper is to exploit if SAT solvers can also be used for the problem of integrated timetabling and passenger routing. In our model of the integrated problem we distribute origin-destination (OD) pairs temporally through the network by using time-slices in order to make the resulting model more realistic. We present a formulation of this integrated problem as integer program which we are able to transform to a satisfiability problem. We tested the latter formulation within numerical experiments, which are performed on Germany's long-distance passenger railway network. The computation's analysis in which we compare the integrated approach with the traditional one with fixed passengers' weights, show promising results for future scientific investigations.
PESP
Timetabling
Public Transport
Passengers' Routes
SAT
3:1-3:15
Regular Paper
Philine
Gattermann
Philine Gattermann
Peter
Großmann
Peter Großmann
Karl
Nachtigall
Karl Nachtigall
Anita
Schöbel
Anita Schöbel
10.4230/OASIcs.ATMOS.2016.3
R. Borndörfer, H. Hoppmann, and M. Karbstein. Timetabling and passenger routing in public transport. In Proceedings of the 13th Conference on Advanced Systems in Public Transport (CASPT) 2015, 2015.
M. Goerigk and A. Schöbel. Improving the modulo simplex algorithm for large-scale periodic timetabling. Computers and Operations Research, 40(5):1363-1370, 2013.
P. Großmann. Polynomial reduction from PESP to SAT. Technical Report 4, Technische Universität Dresden, Germany, October 2011.
P. Großmann, J. Opitz, R. Weiß, and M. Kümmling. On resolving infeasible periodic event networks. In Proceedings of the 13th Conference on Advanced Systems in Public Transport (CASPT) 2015. Erasmus University, 2015.
Peter Großmann, Steffen Hölldobler, Norbert Manthey, Karl Nachtigall, Jens Opitz, and Peter Steinke. Solving periodic event scheduling problems with SAT. In Advanced Research in Applied Artificial Intelligence, pages 166-175. Springer, 2012.
Z. Gu, E. Rothberg, and R. Bixby. Gurobi 6.0.3. Gurobi Optimization, Inc., Houston, TX, May 2015.
L.G. Kroon, D. Huisman, E. Abbink, P.-J. Fioole, M. Fischetti, G. Maroti, A. Shrijver, A. Steenbeek, and R. Ybema. The new Dutch timetable: The OR Revolution. Interfaces, 39:6-17, 2009.
M. Kümmling, P. Großmann, K. Nachtigall, J. Opitz, and R. Weiß. A state-of-the-art realization of cyclic railway timetable computation. Public Transport, 7(3):281-293, 2015.
M. Kümmling, J. Opitz, and P. Großmann. Combining cyclic timetable optimization and traffic assignment. In 20th Conference of the International Federation of Operational Research Societies (IFORS), Barcelona, Spain, presentation, 2014.
C. Liebchen. Finding short integral cycle bases for cyclic timetabling. In Proceedings of European Symposium on Algorithms (ESA) 2003, pages 715-726, 2003.
C. Liebchen. Periodic Timetable Optimization in Public Transport. dissertation.de - Verlag im Internet, Berlin, 2006.
C. Liebchen. The first optimized railway timetable in practice. Transportation Science, 42(4):420-435, 2008.
C. Liebchen and R. Möhring. The modeling power of the periodic event scheduling problem: railway timetables - and beyond. In Algorithmic Methods for Railway Optimization, number 4359 in Lecture Notes on Computer Science, pages 3-40. Springer, 2007.
C. Liebchen and R. Rizzi. A greedy approach to compute a minimum cycle basis of a directed graph. Information Processing Letters, 94(3):107-112, 2005.
R. Martins, V. Manquinho, and I. Lynce. Open-WBO: A Modular MaxSAT Solver. In Carsten Sinz and Uwe Egly, editors, Theory and Applications of Satisfiability Testing - SAT 2014, volume 8561 of Lecture Notes in Computer Science, pages 438-445. Springer International Publishing, 2014.
K. Nachtigall. Periodic Network Optimization and Fixed Interval Timetables. PhD thesis, University of Hildesheim, 1998.
K. Nachtigall and J. Opitz. Solving periodic timetable optimisation problems by modulo simplex calculations. In Proc. ATMOS, 2008.
K. Nachtigall and S. Voget. A genetic approach to periodic railway synchronization. Computers Ops. Res., 23(5):453-463, 1996.
M. A. Odijk. A constraint generation algorithm for the construction of periodic railway timetables. Transportation Research, 30B:455-464, 1996.
L. Peeters. Cyclic Railway Timetabling Optimization. PhD thesis, ERIM, Rotterdam School of Management, 2003.
L. Peeters and L. Kroon. A cycle based optimization model for the cyclic railway timetabling problem. In S. Voß and J. Daduna, editors, Computer-Aided Transit Scheduling, volume 505 of Lecture Notes in Economics and Mathematical systems, pages 275-296. Springer, 2001.
L. Peeters and L. Kroon. A variable trip time model for cyclic railway timetabling. Transportation Science, 37(2):198-212, 2003.
M. Schmidt. Integrating Routing Decisions in Public Transportation Problems, volume 89 of Optimization and Its Applications. Springer, 2014.
M. Schmidt and A. Schöbel. Timetabling with passenger routing. OR Spectrum, 37:75-97, 2015.
A. Schöbel. Line planning in public transportation: models and methods. OR Spectrum, 34(3):491-510, 2012.
P. Serafini and W. Ukovich. A mathematical model for periodic scheduling problems. SIAM Journal on Discrete Mathematic, 2:550-581, 1989.
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Pricing Toll Roads under Uncertainty
We study the toll pricing problem when the non-toll costs on the network are not fixed and can vary over time. We assume that users who take their decisions, after the tolls are fixed, have full information of all costs before making their decision. Toll-setter, on the other hand, do not have any information of the future costs on the network. The only information toll-setter have is historical information (sample) of the network costs. In this work we study this problem on parallel networks and networks with few number of paths in single origin-destination setting. We formulate toll-setting problem in this setting as a distributionally robust optimization problem and propose a method to solve to it. We illustrate the usefulness of our approach by doing numerical experiments using a parallel network.
Conditional value at risk
robust optimization
toll pricing
4:1-4:14
Regular Paper
Trivikram
Dokka
Trivikram Dokka
Alain
Zemkoho
Alain Zemkoho
Sonali Sen
Gupta
Sonali Sen Gupta
Fabrice Talla
Nobibon
Fabrice Talla Nobibon
10.4230/OASIcs.ATMOS.2016.4
S.M. Alizadeh, P. Marcotte, and G. Savard. Two-stage stochastic bilevel programming over a transportation network. Transportation Research Part B, 58:92-105, 2013.
M. Bouhtou, G. Erbs, and M. Minoux. Joint optimization of pricing and resource allocation in competitive telecommunication networks. Networks, 50(1):37-49, 2007.
M. Bouhtou, S. van Hoesel, A. Van der Kraaij, and J. Lutton. Tarriff optimization in networks. Informs Journal of Computing, 19:458-469, 2007.
C. Brown. Financing transport infrastructure: For whom the road tolls. Australian Economic Review, 38:431-438, 2005.
J.-P. Coté, P. Marcotte, and G. Savard. A bilevel modeling approach to pricing and fare optimization in the airline industry. Journal of Revenue and Pricing Management, 1:23-36, 2003.
L.M. Gardner, A. Unnikrishnan, and S.T. Waller. Solution methods for robust pricing of transportation networks under uncertain demand. Transportation Research Part C, 18:656-667, 2010.
F. Gilbert, P. Marcotte, and G. Savard. A numerical study of the logit network pricing problem. Transportation Science, 49(3):706-719, 2015.
J. Goh and M. Sim. Distributionally robust optimization and its tractable approximations. Operations Research, 58(4):902-917, 2010.
V. Goyal and R. Ravi. An fptas for minimizing a class of quasi-concave functions over a convex set. Operations Research Letters, 41(2):191-196, 2013.
G. Heilporn, M. Labbé, P. Marcotte, and G. Savard. A parallel between two classes of pricing problems in transportation and marketing. Journal of Revenue and Pricing Management, 9:110-125, 2010.
G. Karakostas and SG. Kolliopoulos. Edge pricing of multicommodity networks for heterogenous selfish users. FOCS, 2004.
M. Labbé, P. Marcotte, and G. Savard. A bilevel model of taxation and its application to optimal highway pricing. Management Science, 44:1608-1622, 1998.
M. Labbé and A. Violin. Bilevel programming and price setting problems. 4OR, 11:1-30, 2013.
T.G.J. Myklebust, M.A. Sharpe, and L. Tuncel. Efficient heuristic algorithms for maximum utility product pricing problems. Computers and Operations Research, 69:25-39, 2016.
RT. Rockafeller and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2(3):21-42, 2000.
Iakovos Toumazis and Changhyun Kwon. Worst-case conditional value-at-risk minimization for hazardous materials transportation. Transportation Science, http://dx.doi.org/10.1287/trsc.2015.0639:1-14, 2015.
S. van Hoesel. An overview of stackelberg pricing in networks. European Journal of Operational Reseach, 189:1393-1492, 2008.
A. Violin. Mathematical programming approaches to pricing problems. PhD thesis, Universite Libre de Bruxelles, 2014.
Shushang Zhu and Masao Fukushima. Worst-case conditional value-at-risk with application to robust portfolio management. Operations Research, 57(5):1155-1168, 2009.
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Scheduling Autonomous Vehicle Platoons Through an Unregulated Intersection
We study various versions of the problem of scheduling platoons of autonomous vehicles through an unregulated intersection, where an algorithm must schedule which platoons should wait so that others can go through, so as to minimize the maximum delay for any vehicle. We provide polynomial-time algorithms for constructing such schedules for a k-way merge intersection, for constant k, and for a crossing intersection involving two-way traffic. We also show that the more general problem of scheduling autonomous platoons through an intersection that includes both a k-way merge, for non-constant k, and a crossing of two-way traffic is NP-complete.
autonomous vehicles
platoons
scheduling
5:1-5:14
Regular Paper
Juan José
Besa Vial
Juan José Besa Vial
William E.
Devanny
William E. Devanny
David
Eppstein
David Eppstein
Michael T.
Goodrich
Michael T. Goodrich
10.4230/OASIcs.ATMOS.2016.5
H. Ahn, A. Colombo, and D. Del Vecchio. Supervisory control for intersection collision avoidance in the presence of uncontrolled vehicles. In 2014 American Control Conf., pages 867-873, 2014.
F. Altché, X. Qian, and A. de La Fortelle. Time-optimal coordination of mobile robots along specified paths. arXiv preprint arXiv:1603.04610, 2016.
G. Antonelli and S. Chiaverini. Kinematic control of platoons of autonomous vehicles. IEEE Trans. on Robotics, 22(6):1285-1292, 2006.
T. Au and P. Stone. Motion planning algorithms for autonomous intersection management. In AAAI Workshop on Bridging the Gap Between Task and Motion Planning, 2010.
J. Baber, J. Kolodko, T. Noel, M. Parent, and L. Vlacic. Cooperative autonomous driving: intelligent vehicles sharing city roads. IEEE Robotics Automation Magazine, 12(1):44-49, 2005.
F. Berger and R. Klein. A traveller’s problem. In 26th ACM Symp. on Computational Geometry (SoCG), pages 176-182, 2010.
D. Carlino, S. D. Boyles, and P. Stone. Auction-based autonomous intersection management. In 16th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), pages 529-534, 2013.
P. Dasler and D. Mount. On the complexity of an unregulated traffic crossing. In Frank Dehne, Jörg-Rüdiger Sack, and Ulrike Stege, editors, 14th Int. Symp. on Alg. and Data Struct. (WADS), pages 224-235, 2015. see also URL: http://arxiv.org/abs/1505.00874.
http://arxiv.org/abs/1505.00874
K. Dresner and P. Stone. Multiagent traffic management: A reservation-based intersection control mechanism. In 3rd Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), pages 530-537, 2004.
K. Dresner and P. Stone. Multiagent traffic management: An improved intersection control mechanism. In 4th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), pages 471-477, 2005.
K. Dresner and P. Stone. A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research, pages 591-656, 2008.
E. Frazzoli and F. Bullo. Decentralized algorithms for vehicle routing in a stochastic time-varying environment. In 43rd IEEE Conf. on Decision and Control (CDC), volume 4, pages 3357-3363 Vol.4, 2004.
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S. Ilgin Guler, M. Menendez, and L. Meier. Using connected vehicle technology to improve the efficiency of intersections. Transportation Research Part C: Emerging Technologies, 46:121-131, 2014.
W. Hatzack and B. Nebel. The operational traffic control problem: Computational complexity and solutions. In Sixth European Conference on Planning, pages 113-119, 2014.
R. A. Hearn and E. D. Demaine. PSPACE-completeness of sliding-block puzzles and other problems through the nondeterministic constraint logic model of computation. Theoretical Computer Science, 343(1):72-96, 2005.
J. Lee and B. Park. Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment. IEEE Transactions on Intelligent Transportation Systems, 13(1):81-90, 2012.
J. Levinson, J. Askeland, J. Becker, J. Dolson, D. Held, S. Kammel, J. Z. Kolter, D. Langer, O. Pink, V. Pratt, M. Sokolsky, G. Stanek, D. Stavens, A. Teichman, M. Werling, and S. Thrun. Towards fully autonomous driving: Systems and algorithms. In IEEE Intelligent Vehicles Symp. (IV), pages 163-168, 2011.
G. Lu, L. Li, Y. Wang, R. Zhang, Z. Bao, and H. Chen. A rule based control algorithm of connected vehicles in uncontrolled intersection. In 17th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), pages 115-120, 2014.
N. Megiddo. Applying parallel computation algorithms in the design of serial algorithms. J. ACM, 30(4):852-865, 1983.
D. Miculescu and S. Karaman. Polling-systems-based control of high-performance provably-safe autonomous intersections. In 53rd IEEE Conf. on Decision and Control, pages 1417-1423, 2014.
R. Naumann, R. Rasche, and J. Tacken. Managing autonomous vehicles at intersections. IEEE Intelligent Systems and their Applications, 13(3):82-86, 1998.
R. Rajamani and S. E. Shladover. An experimental comparative study of autonomous and co-operative vehicle-follower control systems. Transportation Research Part C: Emerging Technologies, 9(1):15-31, 2001.
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M. VanMiddlesworth, K. Dresner, and P. Stone. Replacing the stop sign: Unmanaged intersection control for autonomous vehicles. In 7th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), pages 1413-1416, 2008.
C. Wuthishuwong, A. Traechtler, and T. Bruns. Safe trajectory planning for autonomous intersection management by using vehicle to infrastructure communication. EURASIP Journal on Wireless Communications and Networking, 2015(1):1-12, 2015.
Creative Commons Attribution 3.0 Unported license
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Multi-Column Generation Model for the Locomotive Assignment Problem
We propose a new decomposition model and a multi-column generation algorithm for solving the Locomotive Assignment Problem (LAP). The decomposition scheme relies on consist configurations, where each configuration is made of a set of trains pulled by the same set of locomotives. We use the concept of conflict graphs in order to reduce the number of trains to be considered in each consist configuration generator problem: this contributes to significantly reduce the fraction of the computational times spent in generating new potential consists. In addition, we define a column generation problem for each set of variables, leading to a multi-column generation process, with different types of columns.
Numerical results, with different numbers of locomotives, are presented on adapted data sets coming from Canada Pacific Railway (CPR). They show that the newly proposed algorithm is able to solve exactly realistic data instances for a timeline spanning up to 6 weeks, in very reasonable computational times.
Railway optimization
Locomotive assignment
Column Generation
6:1-6:13
Regular Paper
Brigitte
Jaumard
Brigitte Jaumard
Huaining
Tian
Huaining Tian
10.4230/OASIcs.ATMOS.2016.6
R.K. Ahuja, J. Liu, J.B. Orlin, D. Sharma, and L.A. Shughart. Solving real-life locomotive-scheduling problems. Transportation Science, 39(4):503-517, 2005.
R.K. Ahuja, T.L. Magnanti, and J.B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice-Hall, 1993.
V. Cacchiani, A. Caprara, and P. Toth. Models and algorithms for the train unit assignment problem. In R. Mahjoub, V. Markakis, I. Milis, and V.T. Paschos, editors, Combinatorial Optimization, volume 7422 of Lecture Notes in Computer Science, pages 24-35. Springer, 2012.
S. Chen and Y. Shen. An improved column generation algorithm for crew scheduling problems. Journal of Information and Computational Science, 10(1):175-183, 2013.
V. Chvátal. Linear programming. A series of books in the mathematical sciences, 1983.
J.-F. Cordeau, F. Soumis, and J. Desrosiers. A benders decomposition approach for the locomotive and car assignment problem. Transportation science, 34(2):133-149, 2000.
G. Desaulniers, J. Desrosiers, and M. M. Solomon. Accelerating strategies in column generation methods for vehicle routing and crew scheduling problems. Springer, 2002.
A. Fügenschuh, H. Homfeld, A. Huck, A. Martin, and Z. Yuan. Scheduling locomotives and car transfers in freight transport. Transportation Science, 42(4):478-491, 2008.
J.-L. Goffin and J.-P. Vial. Multiple cuts in the analytic center cutting plane method. SIAM Journal on Optimization, 11(1):266-288, 2000.
B. Jaumard, H. Tian, and P. Finnie. Locomotive assignment under consist busting and maintenance constraints. Submitted, 2014. URL: https://www.gerad.ca/en/papers/G-2014-54.
https://www.gerad.ca/en/papers/G-2014-54
B. Jaumard, H. Tian, and P. Finnie. Best compromise in deadheading and locomotive fleet size in locomotive assignment. In 2015 Joint Rail Conference, pages V001T04A003-V001T04A003. American Society of Mechanical Engineers, 2015.
A Mingozzi, M. A. Boschetti, S. Ricciardelli, and L. Bianco. A set partitioning approach to the crew scheduling problem. Operations Research, 47(6):873-888, 1999.
F. Piu and M. G. Speranza. The locomotive assignment problem: a survey on optimization models. International Transactions in Operational Research, 21(3):327-352, 2013.
S. Rouillon, G. Desaulniers, and F. Soumis. An extended branch-and-bound method for locomotive assignment. Transportation Research. Part B, Methodological, 40(5):404-423, June 2006.
M. Saddoune, G. Desaulniers, I. Elhallaoui, and F. Soumis. Integrated airline crew pairing and crew assignment by dynamic constraint aggregation. Transportation Science, 46(1):39-55, 2012.
R. Sadykov, F. Vanderbeck, A. Pessoa, and E. Uchoa. Column generation based heuristic for the generalized assignment problem. XLVII Simpósio Brasileiro de Pesquisa Operacional, Porto de Galinhas, Brazil, 2015.
C. Surapholchai, G. Reinelt, and H. G. Bock. Solving city bus scheduling problems in bangkok by eligen-algorithm. In Modeling, Simulation and Optimization of Complex Processes, pages 557-564. Springer, 2008.
B. Vaidyanathan, R.K. Ahuja, J. Liu, and L.A. Shughart. Real-life locomotive planning: new formulations and computational results. Transportation Research, Part B 42:147-168, 2008.
K. Ziarati, F. Soumis, J. Desrosiers, S. Gelinas, and A. Saintonge. Locomotive assignment with heterogeneous consists at CN North America. European Journal of Operational Research, 97(2):281-292, 1997.
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The Maximum Flow Problem for Oriented Flows
In several applications of network flows, additional constraints have to be considered. In this paper, we study flows, where the flow particles have an orientation. For example, cargo containers with doors only on one side and train coaches with 1st and 2nd class compartments have such an orientation. If the end position has a mandatory orientation, not every path from source to sink is feasible for routing or additional transposition maneuvers have to be made. As a result, a source-sink path may visit a certain vertex several times. We describe structural properties of optimal solutions, determine the computational complexity, and present an approach for approximating such flows.
network flow with orientation
graph expansion
approximation
container logistics
train routing
7:1-7:13
Regular Paper
Stanley
Schade
Stanley Schade
Martin
Strehler
Martin Strehler
10.4230/OASIcs.ATMOS.2016.7
Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, Upper Saddle River, New Jersey, 1993.
Georg Baier, Thomas Erlebach, Alexander Hall, Ekkehard Köhler, Petr Kolman, Ondřej Pangrác, Heiko Schilling, and Martin Skutella. Length-bounded cuts and flows. ACM Trans. Algorithms, 7(1):4:1-4:27, 2010.
Ralf Borndörfer, Marika Karbstein, Julika Mehrgahrdt, Markus Reuther, and Thomas Schlechte. The cycle embedding problem. In Operations Research Proceedings 2014, pages 465 - 472, 2016.
Daniel Dressler and Martin Strehler. Polynomial-time algorithms for special cases of the maximum confluent flow problem. Discrete Applied Mathematics, 163:142-154, 2014.
Shimon Even, Alon Itai, and Adi Shamir. On the Complexity of Timetable and Multi-Commodity Flow Problems. In FOCS, pages 184-193. IEEE Computer Society, 1975.
Lester R. Ford and Delbert R. Fulkerson. Maximal flow through a network. Canadian Journal of Mathematics, 8(3):399-404, 1956.
Naveen Garg and Jochen Koenemann. Faster and simpler algorithms for multicommodity flow and other fractional packing problems. SIAM Journal on Computing, 37(2):630-652, 2007.
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Optimizing Traffic Signal Timings for Mega Events
Most approaches for optimizing traffic signal timings deal with the daily traffic. However, there are a few occasional events like football matches or concerts of musicians that lead to exceptional traffic situations. Still, such events occur more or less regularly and place and time are known in advance. Hence, it is possible to anticipate such events with special signal timings. In this paper, we present an extension of a cyclically time-expanded network flow model and a corresponding mixed-integer linear programming formulation for simultaneously optimizing traffic signal timings and traffic assignment for such events. Besides the mathematical analysis of this approach, we demonstrate its capabilities by computing signal timings for a real world scenario.
traffic flow
traffic signal timings
cyclically time-expanded network
mega event
exceptional traffic
8:1-8:16
Regular Paper
Robert
Scheffler
Robert Scheffler
Martin
Strehler
Martin Strehler
10.4230/OASIcs.ATMOS.2016.8
R. E. Allsop. Selection of offsets to minimize delay to traffic in a network controlled by fixed-time signals. Transportation Science, pages 1-13, 1968.
R. E. Allsop and J. A. Charlesworth. Traffic in a signal-controlled road network: An example of different signal timings inducing different routeings. Traffic Eng. Control, 18(5):262-265, 1977.
S.-W. Chiou. Joint optimization for area traffic control and network flow. Computers and Operations Research, 32:2821-2841, 2005.
L. R. Ford and D. R. Fulkerson. Maximal flow through a network. Canadian Journal of Mathematics, 8:399-404, 1956.
L. R. Ford and D. R. Fulkerson. Flow in Networks. Princeton University Press, Princeton, 1962.
D. Grether. Extension of a multi-agent transport simulation for traffic signal control and air transport systems. Phd thesis, TU Berlin transport engineering, 2014.
D. Grether, J. Bischoff, and K. Nagel. Traffic-actuated signal control: Simulation of the user benefits in a big event real-world scenario. In Proceedings of the 2nd International Conference on Models and Technologies for Intelligent Transportation Systems, Leuven, Belgium, 2011.
E. Köhler and M. Strehler. Traffic signal optimization using cyclically expanded networks. In T. Erlebach and M. Lübbecke, editors, Proceedings of the 10th ATMOS, OpenAccess Series in Informatics (OASIcs), 2010.
E. Köhler and M. Strehler. Combining static and dynamic models for traffic signal optimization - inherent load-dependent travel times in a cyclically time-expanded network model. Procedia - Social and Behavioral Sciences, 54(0):1125 - 1134, 2012.
E. Köhler and M. Strehler. Traffic signal optimization using cyclically expanded networks. Networks, 65(3):244-261, 2015. URL: http://dx.doi.org/10.1002/net.21601.
http://dx.doi.org/10.1002/net.21601
P. Koonce, L. Rodegerdts, K. Lee, S. Quayle, S. Beaird, C. Braud, J. Bonneson, P. Tarnoff, and T. Urbanik. Traffic Signal Timing Manual. Report Number FHWA-HOP-08-024. Federal Highway Administration, 2008.
S. Lämmer. Reglerentwurf zur dezentralen Online-Steuerung von Lichtsignalanlagen in Straßennetzwerken. PhD thesis, Technische Universität Dresden, 2007. (In German).
S. Lämmer. Stabilitätsprobleme vollverkehrsabhängiger Lichtsignalsteuerungen. Technical report, Technische Universität Dresden, 2009. (In German).
P. Li, P. Mirchandani, and X. Zhou. Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork. Transportation Research Part B, 81(1):103-130, 2015.
J. D. C. Little. The synchronizing of traffic signals by mixed-integer linear programming. Operations Research 14, pages 568-594, 1966.
J. T. Morgan and J. D. C. Little. Synchronizing traffic signals for maximal bandwidth. Journal of the Operations Research Society of America, 12(6):896-912, 1964.
D. I. Robertson. TRANSYT method for area traffic control. Traffic Engineering &Control, 10:276 - 281, 1969.
M. Rubert and L. da Silva Portugal. Strategies for transport during sports mega events and their degree of importance. In Proceedings of the XVI Pan-American Conference of Traffic and Transportation Engineering and Logistics, Lisbon, 2010.
M. Smith. Bilevel optimisation of prices and signals in transportation models. In Mathematical and Computational Models for Congestion Charging, pages 159-200. Springer Science+Business Media, 2006.
M. van den Berg, B. D. Schutter, J. Hellendoorn, and A. Hegyi. Influencing route choice in traffic networks: a model predictive control approach based on mixed-integer linear programming. In 17th IEEE International Conference on Control Applications, pages 299-304, 2008.
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Automatic Design of Aircraft Arrival Routes with Limited Turning Angle
We present an application of Integer Programming to the design of arrival routes for aircraft in a Terminal Maneuvering Area (TMA). We generate operationally feasible merge trees of curvature-constrained routes, using two optimization criteria: (1) total length of the tree, and (2) distance flown along the tree paths. The output routes guarantee that the overall traffic pattern in the TMA can be monitored by air traffic controllers; in particular, we keep merge points for arriving aircraft well separated, and we exclude conflicts between arriving and departing aircraft.
We demonstrate the feasibility of our method by experimenting with arrival routes for a runway at Arlanda airport in the Stockholm TMA.
Our approach can easily be extended in several ways, e.g., to ensure that the routes avoid no-fly zones.
Air Traffic Management
Standard Terminal Arrival Routes
Standard Instrument Departures
Integer programming
Turn constraints
9:1-9:13
Regular Paper
Tobias Andersson
Granberg
Tobias Andersson Granberg
Tatiana
Polishchuk
Tatiana Polishchuk
Valentin
Polishchuk
Valentin Polishchuk
Christiane
Schmidt
Christiane Schmidt
10.4230/OASIcs.ATMOS.2016.9
IATA air passenger forecast shows dip in long-term demand. http://www.iata.org/pressroom/pr/Pages/2015-11-26-01.aspx, November 2015. accessed on June 2, 2016.
S. Choi, J. E. Robinson, D. G. Mulfinger, and B. J. Capozzi. Design of an optimal route structure using heuristics-based stochastic schedulers. In Digital Avionics Systems Conference (DASC), 2010 IEEE/AIAA 29th, pages 2.A.5-1-2.A.5-17, Oct 2010.
EUROCONTROL. SAFMAP analysis. URL: https://docs.google.com/presentation/d/1Clq33HyI_PNoeRuzoCmPtfLqtoK45DFrZqOvWXKXXEk/edit#slide=id.g13444e53d7_0_0.
https://docs.google.com/presentation/d/1Clq33HyI_PNoeRuzoCmPtfLqtoK45DFrZqOvWXKXXEk/edit#slide=id.g13444e53d7_0_0
EUROCONTROL. European airspace concept handbook for PBN implementation edition 3.0. http://www.eurocontrol.int/publications/airspace-concept-handbook-implementation-performance-based-navigation-pbn, 2013.
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J Krozel, JSB Mitchell, V Polishchuk, and J Prete. Airspace capacity estimation with convective weather constraints. AIAA Guidance, Navigation, and Control Conference, 2007.
Jimmy Krozel, Changkil Lee, and Joseph S.B. Mitchell. Turn-constrained route planning for avoiding hazardous weather. Air Traffic Control Quarterly, 14(2), 2006.
Matthew Micallef, David Zammit-Mangion, Kenneth Chircop, and Alan Muscat. A proposal for revised approaches and procedures to Malta international airport. In 28th Congress of the International Council of the Aeronautical Sciences, 23 - 28 September 2012, Brisbane, Australia, 2012.
Diana Michalek Pfeil. Optimization of airport terminal-area air traffic operations under uncertain weather conditions. PhD thesis, Massachusetts Institute of Technology. Operations Research Center, 2011.
Valentin Polishchuk. Generating arrival routes with radius-to-fix functionalities. In ICRAT 2016, 2016.
Joseph Prete, Jimmy Krozel, Joseph Mitchell, Joondong Kim, and Jason Zou. Flexible, Performance-Based Route Planning for Super-Dense Operations. In AIAA Guidance, Navigation and Control Conference and Exhibit, Guidance, Navigation, and Control and Co-located Conferences. American Institute of Aeronautics and Astronautics, aug 2008.
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Jun Zhou, Sonia Cafieri, Daniel Delahaye, and Mohammed Sbihi. Optimization of Arrival and Departure Routes in Terminal Maneuvering Area. In ICRAT 2014, 6th International Conference on Research in Air Transportation, Istanbul, Turkey, May 2014.
Jun Zhou, Sonia Cafieri, Daniel Delahaye, and Mohammed Sbihi. Optimizing the design of a route in Terminal Maneuvering Area using Branch and Bound. In EIWAC 2015, ENRI International Workshop on ATM/CNS, Tokyo, Japan, November 2015. ENRI.
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Trip-Based Public Transit Routing Using Condensed Search Trees
We study the problem of planning Pareto-optimal journeys in public transit networks.
Most existing algorithms and speed-up techniques work by computing subjourneys to intermediary stops until the destination is reached.
In contrast, the trip-based model focuses on trips and transfers between them, constructing journeys as a sequence of trips.
In this paper, we develop a speed-up technique for this model inspired by principles behind existing state-of-the-art speed-up techniques, Transfer Patterns and Hub Labelling.
The resulting algorithm allows us to compute Pareto-optimal (with respect to arrival time and number of transfers) 24-hour profiles on very large real-world networks in less than half a millisecond.
Compared to the current state of the art for bicriteria queries on public transit networks, this is up to two orders of magnitude faster, while increasing preprocessing overhead by at most one order of magnitude.
Public Transit
Routing
Public Transport
Route Planning
10:1-10:12
Regular Paper
Sascha
Witt
Sascha Witt
10.4230/OASIcs.ATMOS.2016.10
Hannah Bast, Erik Carlsson, Arno Eigenwillig, Robert Geisberger, Chris Harrelson, Veselin Raychev, and Fabien Viger. Fast Routing in Very Large Public Transportation Networks Using Transfer Patterns. In European Symposium on Algorithms (ESA), volume 6346, pages 290-301, 2010.
Hannah Bast, Daniel Delling, Andrew Goldberg, Matthias Müller-Hannemann, Thomas Pajor, Peter Sanders, Dorothea Wagner, and Renato F. Werneck. Route Planning in Transportation Networks. ArXiv e-prints, April 2015. URL: http://arxiv.org/abs/1504.05140.
http://arxiv.org/abs/1504.05140
Hannah Bast, Matthias Hertel, and Sabine Storandt. Scalable Transfer Patterns. In Algorithm Engineering and Experiments (ALENEX), pages 15-29, 2016. URL: http://dx.doi.org/10.1137/1.9781611974317.2.
http://dx.doi.org/10.1137/1.9781611974317.2
Hannah Bast and Sabine Storandt. Frequency-Based Search for Public Transit. In ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 13-22. ACM Press, November 2014. URL: http://dx.doi.org/10.1145/2666310.2666405.
http://dx.doi.org/10.1145/2666310.2666405
Annabell Berger, Martin Grimmer, and Matthias Müller-Hannemann. Fully Dynamic Speed-Up Techniques for Multi-criteria Shortest Path Searches in Time-Dependent Networks. In Symposium on Experimental Algorithms (SEA), pages 35-46. Springer Berlin Heidelberg, 2010. URL: http://dx.doi.org/10.1007/978-3-642-13193-6_4.
http://dx.doi.org/10.1007/978-3-642-13193-6_4
J.C. Bermond, M.C. Heydemann, and D. Sotteau. Line graphs of hypergraphs I. Discrete Mathematics, 18(3):235-241, 1977. URL: http://dx.doi.org/10.1016/0012-365X(77)90127-3.
http://dx.doi.org/10.1016/0012-365X(77)90127-3
Ulrik Brandes. A Faster Algorithm for Betweenness Centrality. Journal of Mathematical Sociology, 25(2):163-177, 2001.
Ulrik Brandes and Christian Pich. Centrality Estimation in Large Networks. International Journal of Bifurcation and Chaos, 17(07):2303-2318, 2007.
Edith Cohen, Eran Halperin, Haim Kaplan, and Uri Zwick. Reachability and distance queries via 2-hop labels. SIAM Journal on Computing, 32(5):1338-1355, 2003.
Rene De La Briandais. File Searching Using Variable Length Keys. In Western Joint Computer Conference 1959, IRE-AIEE-ACM '59 (Western), pages 295-298, New York, NY, USA, 1959. ACM. URL: http://dx.doi.org/10.1145/1457838.1457895.
http://dx.doi.org/10.1145/1457838.1457895
Daniel Delling, Julian Dibbelt, Thomas Pajor, Dorothea Wagner, and Renato F Werneck. Computing Multimodal Journeys in Practice. In Experimental Algorithms, pages 260-271. Springer, 2013.
Daniel Delling, Julian Dibbelt, Thomas Pajor, and Renato F. Werneck. Public Transit Labeling. In Experimental Algorithms, volume 9125 of Lecture Notes in Computer Science (LNCS), pages 273-285. Springer, 2015.
Daniel Delling, Bastian Katz, and Thomas Pajor. Parallel computation of best connections in public transportation networks. Journal of Experimental Algorithmics (JEA), 17, 2012. URL: http://dx.doi.org/10.1145/2133803.2345678.
http://dx.doi.org/10.1145/2133803.2345678
Daniel Delling, Thomas Pajor, and Renato F. Werneck. Round-Based Public Transit Routing. Transportation Science, 49(3):591-604, 2015. URL: http://dx.doi.org/10.1287/trsc.2014.0534.
http://dx.doi.org/10.1287/trsc.2014.0534
Julian Dibbelt, Thomas Pajor, Ben Strasser, and Dorothea Wagner. Intriguingly Simple and Fast Transit Routing. In Experimental Algorithms, volume 7933 of Lecture Notes in Computer Science (LNCS), pages 43-54. Springer, Heidelberg, 2013.
Yann Disser, Matthias Müller-Hannemann, and Mathias Schnee. Multi-criteria Shortest Paths in Time-Dependent Train Networks. In Workshop on Experimental Algorithms (WEA), pages 347-361. Springer Berlin Heidelberg, 2008. URL: http://dx.doi.org/10.1007/978-3-540-68552-4_26.
http://dx.doi.org/10.1007/978-3-540-68552-4_26
Alexandros Efentakis. Scalable Public Transportation Queries on the Database. In International Conference on Extending Databse Technology (EDBT), pages 527-538. OpenProceedings.org, 2016. URL: http://dx.doi.org/10.5441/002/edbt.2016.50.
http://dx.doi.org/10.5441/002/edbt.2016.50
Marco Farina and Paolo Amato. A Fuzzy Definition of "Optimality" for Many-Criteria Optimization Problems. Systems, Man and Cybernetics, Part A: Systems and Humans, 34(3):315-326, 2004.
Linton C. Freeman. A Set of Measures of Centrality Based on Betweenness. Sociometry, 40(1):35-41, 1977.
Robert Geisberger. Contraction of Timetable Networks with Realistic Transfers. In Experimental Algorithms, volume 6049 of Lecture Notes in Computer Science (LNCS), pages 71-82. Springer, Heidelberg, 2010.
Rolf H. Möhring, Heiko Schilling, Birk Schütz, Dorothea Wagner, and Thomas Willhalm. Partitioning Graphs to Speedup Dijkstra’s Algorithm. J. Exp. Algorithmics, 11, February 2007. URL: http://dx.doi.org/10.1145/1187436.1216585.
http://dx.doi.org/10.1145/1187436.1216585
Matthias Müller-Hannemann and Karsten Weihe. On the cardinality of the Pareto set in bicriteria shortest path problems. Annals of Operations Research, 147(1):269-286, 2006.
Evangelia Pyrga, Frank Schulz, Dorothea Wagner, and Christos Zaroliagis. Efficient Models for Timetable Information in Public Transportation Systems. Journal of Experimental Algorithmics, 12:1, 2008. URL: http://dx.doi.org/10.1145/1227161.1227166.
http://dx.doi.org/10.1145/1227161.1227166
Ben Strasser and Dorothea Wagner. Connection Scan Accelerated. In Algorithm Engineering and Experiments (ALENEX), 2014. URL: http://dx.doi.org/10.1137/1.9781611973198.12.
http://dx.doi.org/10.1137/1.9781611973198.12
Sibo Wang, Wenqing Lin, Yi Yang, Xiaokui Xiao, and Shuigeng Zhou. Efficient Route Planning on Public Transportation Networks: A Labelling Approach. In ACM SIGMOD International Conference on Management of Data, SIGMOD '15, pages 967-982, New York, NY, USA, 2015. ACM. URL: http://dx.doi.org/10.1145/2723372.2749456.
http://dx.doi.org/10.1145/2723372.2749456
Sascha Witt. Trip-Based Public Transit Routing. In European Symposium on Algorithms (ESA), pages 1025-1036. Springer Berlin Heidelberg, 2015. URL: http://dx.doi.org/10.1007/978-3-662-48350-3_85.
http://dx.doi.org/10.1007/978-3-662-48350-3_85
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Time-Dependent Bi-Objective Itinerary Planning Algorithm: Application in Sea Transportation
A special case of the Time-Dependent Shortest Path Problem (TDSPP) is the itinerary planning problem where the objective is to find the shortest path between a source and a destination node which passes through a fixed sequence of intermediate nodes. In this paper, we deviate from the common approach for solving this problem, that is, finding first the shortest paths between successive nodes in the above sequence and then synthesizing the final solution from the solutions of these sub-problems. We propose a more direct approach and solve the problem by a label-setting approach which is able to early prune a lot of partial paths that cannot be part of the optimal solution. In addition, we study a different version of the main problem where it is only required that the solution path should pass through a set of specific nodes irrespectively of the particular order in which these nodes are included in the path. As a case study, we have applied the proposed techniques for solving the itinerary planning of a ship with respect to two conflicting criteria, in the area of the Aegean Sea, Greece. Moreover, the algorithm handles the case that the ship speed is not constant throughout the whole voyage. Specifically, it can be set at a different level each time the ship departs from an intermediate port in order to obtain low cost solutions for the itinerary planning. The experimental results confirm the high performance of the proposed algorithms.
Multi-criteria optimization
Label setting algorithm
Time dependent networks
Travel planning
Itinerary planning
Sea transportation
11:1-11:14
Regular Paper
Aphrodite
Veneti
Aphrodite Veneti
Charalampos
Konstantopoulos
Charalampos Konstantopoulos
Grammati
Pantziou
Grammati Pantziou
10.4230/OASIcs.ATMOS.2016.11
Konstantinos N Androutsopoulos and Konstantinos G Zografos. Solving the multi-criteria time-dependent routing and scheduling problem in a multimodal fixed scheduled network. European Journal of Operational Research, 192(1):18-28, 2009.
Kyriakos Avgouleas. Optimal ship routing. PhD thesis, Massachusetts Institute of Technology, 2008.
Jean-François Bérubé, Jean-Yves Potvin, and Jean Vaucher. Time-dependent shortest paths through a fixed sequence of nodes: application to a travel planning problem. Computers &operations research, 33(6):1838-1856, 2006.
Ismail Chabini. Discrete dynamic shortest path problems in transportation applications: Complexity and algorithms with optimal run time. Transportation Research Record: Journal of the Transportation Research Board, 1645(1):170-175, 1998.
Camila F Costa, Mario A Nascimento, José AF Macêdo, Yannis Theodoridis, Nikos Pelekis, and Javam Machado. Optimal time-dependent sequenced route queries in road networks. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, page 56. ACM, 2015.
Rafael Castro de Andrade. New formulations for the elementary shortest-path problem visiting a given set of nodes. European Journal of Operational Research, 254(3):755-768, 2016.
Theodoros Giannakopoulos, Ioannis A Vetsikas, Ioanna Koromila, Vangelis Karkaletsis, and Stavros Perantonis. Aminess: a platform for environmentally safe shipping. In Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, page 45. ACM, 2014.
Konstantinos G Gkonis and Harilaos N Psaraftis. Some key variables affecting liner shipping costs. Laboratory for Maritime Transport, National Technical University of Athens, 2010.
Jörn Hinnenthal and Günther Clauss. Robust pareto-optimum routing of ships utilising deterministic and ensemble weather forecasts. Ships and Offshore Structures, 5(2):105-114, 2010.
Lars Magnus Hvattum, Inge Norstad, Kjetil Fagerholt, and Gilbert Laporte. Analysis of an exact algorithm for the vessel speed optimization problem. Networks, 62(2):132-135, 2013.
MSC IMO. 1/circ. 1228. Revised guidance to the master for avoiding dangerous situations in adverse weather and sea conditions, adopted 11th January, 2007.
David E Kaufman and Robert L Smith. Fastest paths in time-dependent networks for intelligent vehicle-highway systems application. Journal of Intelligent Transportation Systems, 1(1):1-11, 1993.
Ioanna Koromila, Zoe Nivolianitou, and Theodoros Giannakopoulos. Bayesian network to predict environmental risk of a possible ship accident. In Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, page 44. ACM, 2014.
Feifei Li, Dihan Cheng, Marios Hadjieleftheriou, George Kollios, and Shang-Hua Teng. On trip planning queries in spatial databases. In Advances in Spatial and Temporal Databases, pages 273-290. Springer, 2005.
John J Liu. Supply chain management and transport logistics. Routledge, 2011.
G Mannarini, G Coppini, P Oddo, and N Pinardi. A prototype of ship routing decision support system for an operational oceanographic service. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 7(1):53-59, 2013.
Stéphane Marie, Eric Courteille, et al. Multi-objective optimization of motor vessel route. In Proceedings of the Int. Symp. TransNav, volume 9, pages 411-418, 2009.
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Mehdi Sharifzadeh, Mohammad Kolahdouzan, and Cyrus Shahabi. The optimal sequenced route query. The VLDB journal, 17(4):765-787, 2008.
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Solving Time Dependent Shortest Path Problems on Airway Networks Using Super-Optimal Wind
We study the Flight Planning Problem for a single aircraft, which deals with finding a path of minimal travel time in an airway network. Flight time along arcs is affected by wind speed and direction, which are functions of time. We consider three variants of the problem, which can be modeled as, respectively, a classical shortest path problem in a metric space, a time-dependent shortest path problem with piecewise linear travel time functions, and a time-dependent shortest path problem with piecewise differentiable travel time functions.
The shortest path problem and its time-dependent variant have been extensively studied, in particular, for road networks. Airway networks, however, have different characteristics: the average node degree is higher and shortest paths usually have only few arcs.
We propose A* algorithms for each of the problem variants. In particular, for the third problem, we introduce an application-specific "super-optimal wind" potential function that overestimates optimal wind conditions on each arc, and establish a linear error bound. We compare the performance of our methods with the standard Dijkstra algorithm and the Contraction Hierarchies (CHs)
algorithm. Our computational results on real world instances show that CHs do not perform as well as on road networks. On the other hand, A* guided by our potentials yields very good results. In particular, for the case of piecewise linear travel time functions, we achieve query times about 15 times shorter than CHs.
shortest path problem
A*
flight trajectory optimization
preprocessing
contraction hierarchies
time-dependent shortest path problem
12:1-12:15
Regular Paper
Marco
Blanco
Marco Blanco
Ralf
Borndörfer
Ralf Borndörfer
Nam-Dung
Hoang
Nam-Dung Hoang
Anton
Kaier
Anton Kaier
Adam
Schienle
Adam Schienle
Thomas
Schlechte
Thomas Schlechte
Swen
Schlobach
Swen Schlobach
10.4230/OASIcs.ATMOS.2016.12
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