Skip to main content

2020 | OriginalPaper | Buchkapitel

7. A New Multi-objective Solution Approach Using ModeFRONTIER and OpenTrack for Energy-Efficient Train Timetabling Problem

verfasst von : Giovanni Longo, Teresa Montrone, Carlo Poloni

Erschienen in: Computation and Big Data for Transport

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Trains move along the railway infrastructure according to specific timetables. The timetables are based on the running time calculation and they are usually calculated without considering explicitly energy consumption. Since green transportation is becoming more and more important from environmental perspectives, energy consumption minimization could be considered also in timetable calculation. In particular, the Energy-Efficient Train Timetabling Problem (EETTP) consists in the energy-efficient timetable calculation considering the trade-off between energy efficiency and running times. In this work, a solution approach to solve a multi-objective EETTP is described in which the two objectives are the minimization of both energy consumption and the total travel time. The approach finds the schedules to guarantee that the train speed profiles minimize the objectives. It is based on modeFRONTIER and OpenTrack that are integrated by using the OpenTrack Application Programming Interface in a modeFRONTIER workflow. In particular, the optimization is made by modeFRONTIER, while the calculation of the train speed profiles, energy consumption and total travel time is made by OpenTrack. The approach is used with Multi-objective Genetic Algorithm-II and the Non-dominating Sorting Genetic-II, which are two genetic algorithms available in modeFRONTIER. The solution approach is tested on a case study that represents a real situation of metro line in Turkey. For both algorithms, a Pareto Front of solution which are a good trade-off between the objectives are reported. The results show significant reduction of both energy consumption and total travel time with respect to the existing timetable.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Albrecht T, Oettich S (2002) Computers in Railways VII, chapter a new integrated approach to dynamic schedule synchronization and energy saving train control. WIT Press, Southampton, pp 847–856 Albrecht T, Oettich S (2002) Computers in Railways VII, chapter a new integrated approach to dynamic schedule synchronization and energy saving train control. WIT Press, Southampton, pp 847–856
2.
Zurück zum Zitat Brunger O, Dahlhaus E (2009) Railway timetable and traffic: analysis, modelling and simulation, chapter running time estimation (4). Hamburg, Germany, pp 58–82 Brunger O, Dahlhaus E (2009) Railway timetable and traffic: analysis, modelling and simulation, chapter running time estimation (4). Hamburg, Germany, pp 58–82
3.
Zurück zum Zitat Chevrier R, Pellegrini P, Rodriguez J (2013) Energy saving in railway timetabling: a bi-objective evolutionary approach for computing alternative running times. Transp Res Part C 37:20–41CrossRef Chevrier R, Pellegrini P, Rodriguez J (2013) Energy saving in railway timetabling: a bi-objective evolutionary approach for computing alternative running times. Transp Res Part C 37:20–41CrossRef
4.
Zurück zum Zitat Cucala AP, Fernández A, Sicre C, Dominguez M (2012) Fuzzy optimal schedule of high speed train operation to minimize energy consumption with uncertain delays and driver’s behavioral response. Engineering Applications of Artificial Intelligence 25(8):1548–1557CrossRef Cucala AP, Fernández A, Sicre C, Dominguez M (2012) Fuzzy optimal schedule of high speed train operation to minimize energy consumption with uncertain delays and driver’s behavioral response. Engineering Applications of Artificial Intelligence 25(8):1548–1557CrossRef
5.
Zurück zum Zitat Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRef Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRef
6.
Zurück zum Zitat Ding Y, Liu H, Bay Y, Zhou F (2011) A two-level optimization model and algorithm for energy-efficient urban train operation. J Transp Syst Eng Inf Technol 11(1):96–101CrossRef Ding Y, Liu H, Bay Y, Zhou F (2011) A two-level optimization model and algorithm for energy-efficient urban train operation. J Transp Syst Eng Inf Technol 11(1):96–101CrossRef
8.
Zurück zum Zitat Fabris D, Longo G, Medeossi G (2011) Automatic generation of railway timetables based on a mesoscopic infrastructure model. J Rail Transp Plan Manag 51:577–585 Fabris D, Longo G, Medeossi G (2011) Automatic generation of railway timetables based on a mesoscopic infrastructure model. J Rail Transp Plan Manag 51:577–585
9.
Zurück zum Zitat Fabris D, Longo G, Medeossi G, Pesenti R (2014) Automatic generation of railway timetables based on a mesoscopic infrastructure model. WIT Trans Model Simul 4(1):2–13 Fabris D, Longo G, Medeossi G, Pesenti R (2014) Automatic generation of railway timetables based on a mesoscopic infrastructure model. WIT Trans Model Simul 4(1):2–13
10.
Zurück zum Zitat Goverde RMP, Bešinović N, Binder A, Cacchiani V, Quaglietta E, Roberti R, Toth P (2016) A three-level framework for performance-based railway timetabling. Transp Res Part C: Emerg Technol 67:62–83CrossRef Goverde RMP, Bešinović N, Binder A, Cacchiani V, Quaglietta E, Roberti R, Toth P (2016) A three-level framework for performance-based railway timetabling. Transp Res Part C: Emerg Technol 67:62–83CrossRef
11.
Zurück zum Zitat Li X, Lo HK (2014a) An energy-efficient scheduling and speed control approach for metro rail operations. Transp Res Part B: Methodol 64:73–89CrossRef Li X, Lo HK (2014a) An energy-efficient scheduling and speed control approach for metro rail operations. Transp Res Part B: Methodol 64:73–89CrossRef
12.
Zurück zum Zitat Li X, Lo HK (2014b) Energy minimization in dynamic train scheduling and control for metro rail operations. Transp Res Part B: Methodol 70:269–284CrossRef Li X, Lo HK (2014b) Energy minimization in dynamic train scheduling and control for metro rail operations. Transp Res Part B: Methodol 70:269–284CrossRef
13.
Zurück zum Zitat Li X, Wang D, Li K, Gao Z (2013) A green train scheduling model and fuzzy multi-objective optimization algorithm. Appl Math Model 37(4):2063–2073MathSciNetCrossRef Li X, Wang D, Li K, Gao Z (2013) A green train scheduling model and fuzzy multi-objective optimization algorithm. Appl Math Model 37(4):2063–2073MathSciNetCrossRef
14.
Zurück zum Zitat Montrone T (2017) Energy consumption minimization in railway systems Montrone T (2017) Energy consumption minimization in railway systems
15.
Zurück zum Zitat Montrone T, Pellegrini P, Nobili P, Longo G (2016) Energy consumption minimization in railway planning. In: 16th IEEE International Conference on Environment and Electrical Engineering, Florence, Italy Montrone T, Pellegrini P, Nobili P, Longo G (2016) Energy consumption minimization in railway planning. In: 16th IEEE International Conference on Environment and Electrical Engineering, Florence, Italy
16.
Zurück zum Zitat ONTIME Consortium (2014) Methods and algorithms for the development of robust and resilient timetables. Optimal Networks for Train Integration Management across Europe D3(1):1–54 ONTIME Consortium (2014) Methods and algorithms for the development of robust and resilient timetables. Optimal Networks for Train Integration Management across Europe D3(1):1–54
18.
Zurück zum Zitat Poloni G, Pediroda V (1997) Ga coupled with computationally expensive simulations: tools to improve efficiency. Genetic algorithms and evolution strategies in engineering and computer science. pp 267–288 Poloni G, Pediroda V (1997) Ga coupled with computationally expensive simulations: tools to improve efficiency. Genetic algorithms and evolution strategies in engineering and computer science. pp 267–288
19.
Zurück zum Zitat Pontryagin L (1987) The mathematical theory of optimal processes. Routledge, LondonCrossRef Pontryagin L (1987) The mathematical theory of optimal processes. Routledge, LondonCrossRef
20.
Zurück zum Zitat Scheepmaker G, Goverde RMP, Kroon LG (2017) Review of energy-efficient train control and timetabling. Eur J Oper Res 257:355–376MathSciNetCrossRef Scheepmaker G, Goverde RMP, Kroon LG (2017) Review of energy-efficient train control and timetabling. Eur J Oper Res 257:355–376MathSciNetCrossRef
21.
Zurück zum Zitat Sicre C, Cucala AP, Fernández A, Jiménez JA, Ribera I, Serrano A (2010) A method to optimise train energy consumption combining manual energy efficient driving and scheduling. WIT Trans Built Environ 114:549–560 Sicre C, Cucala AP, Fernández A, Jiménez JA, Ribera I, Serrano A (2010) A method to optimise train energy consumption combining manual energy efficient driving and scheduling. WIT Trans Built Environ 114:549–560
22.
Zurück zum Zitat Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithms Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithms
23.
Zurück zum Zitat Su S, Li X, Tang T, Gao Z (2013) A subway train timetable optimization approach based on energy-efficient operation strategy. IEEE Trans Intell Transp Syst 14(2):883–893CrossRef Su S, Li X, Tang T, Gao Z (2013) A subway train timetable optimization approach based on energy-efficient operation strategy. IEEE Trans Intell Transp Syst 14(2):883–893CrossRef
24.
Zurück zum Zitat Su S, Tang T, Li X, Gao Z (2014) Optimization of multitrain operations in a subway system. IEEE Trans Intell Transp Syst 15(2):673–684 Su S, Tang T, Li X, Gao Z (2014) Optimization of multitrain operations in a subway system. IEEE Trans Intell Transp Syst 15(2):673–684
26.
Zurück zum Zitat Vinter R (2000) Optimal Control. Birkhauser, Boston Vinter R (2000) Optimal Control. Birkhauser, Boston
27.
Zurück zum Zitat Wang P, Goverde RMP (2019) Multi-train trajectory optimization for energy-efficient timetabling. Eur J Oper Res 272(2):621–635MathSciNetCrossRef Wang P, Goverde RMP (2019) Multi-train trajectory optimization for energy-efficient timetabling. Eur J Oper Res 272(2):621–635MathSciNetCrossRef
28.
Zurück zum Zitat Xu Y, Jia B, Ghiasi A, Li X, Li M (2018) An integrated micro–macro approach for high-speed railway energy-efficient timetabling problem. Technical report Xu Y, Jia B, Ghiasi A, Li X, Li M (2018) An integrated micro–macro approach for high-speed railway energy-efficient timetabling problem. Technical report
29.
Zurück zum Zitat Yang X, Chen A, Li X, Ning B, Tang T (2015) An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems. Transp Res Part C: Emerg Technol 57:13–29CrossRef Yang X, Chen A, Li X, Ning B, Tang T (2015) An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems. Transp Res Part C: Emerg Technol 57:13–29CrossRef
30.
Zurück zum Zitat Yang X, Li X, Gao Z, Wang H, Tang T (2013) A cooperative scheduling model for timetable optimization in subway systems. IEEE Trans Intell Transp Syst 14(1):438–447CrossRef Yang X, Li X, Gao Z, Wang H, Tang T (2013) A cooperative scheduling model for timetable optimization in subway systems. IEEE Trans Intell Transp Syst 14(1):438–447CrossRef
31.
Zurück zum Zitat Yang X, Ning B, Li X, Tang T (2014) A two-objective timetable optimization model in subway systems. IEEE Trans Intell Transp Syst 15(5):1913–1921CrossRef Yang X, Ning B, Li X, Tang T (2014) A two-objective timetable optimization model in subway systems. IEEE Trans Intell Transp Syst 15(5):1913–1921CrossRef
32.
Zurück zum Zitat Zhang H, Jia L, Wang L, Xu X (2019) Energy consumption optimization of train operation for railway systems: algorithm development and real-world case study. J Clean Prod 214:1024–1037CrossRef Zhang H, Jia L, Wang L, Xu X (2019) Energy consumption optimization of train operation for railway systems: algorithm development and real-world case study. J Clean Prod 214:1024–1037CrossRef
Metadaten
Titel
A New Multi-objective Solution Approach Using ModeFRONTIER and OpenTrack for Energy-Efficient Train Timetabling Problem
verfasst von
Giovanni Longo
Teresa Montrone
Carlo Poloni
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-37752-6_7

    Premium Partner