Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model
Ahmad Reza Jafarian-Moghaddam
Urban Rail Transit ›› 2021, Vol. 7 ›› Issue (3) : 191 -208.
Speed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.
Transportation / Scheduling / Economical speed / Fuzzy multi-objective train scheduling model / Energy consumption
| [1] |
|
| [2] |
Jin B, Feng X, Wang Q, Sun P, Fang Q Train. Scheduling method to reduce substation energy consumption and peak power of metro transit systems. Transp Res Rec, 0361198120974677 |
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
Pavlides A, Chow AH (2018) Multi-objective optimization of train timetable with consideration of customer satisfaction. Transp Res Rec, 0361198118777629 |
| [25] |
UIC (2012) Moving towards Sustainable Mobility: European Rail Sector Strategy 2030 and beyond. International Union of Railways (UIC) and Community of European Railway and Infrastructure Companies (CER), Paris, France |
| [26] |
UIC (2018) Sustainable Development: Making railways greener, quieter and more energy efficient. International Union of Railways (UIC), Paris, France |
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
|
| [41] |
|
| [42] |
Peeters L (2003) Cyclic railway timetable optimization, vol EPS-2003-022-LIS |
| [43] |
|
| [44] |
|
| [45] |
|
| [46] |
|
| [47] |
|
| [48] |
|
| [49] |
Davis WJ (1926) The tractive resistance of electric locomotives and cars. General Electric |
| [50] |
AREA (1995) Manual for Railway Engineering 1995. American Railway Engineering Association |
| [51] |
|
| [52] |
|
| [53] |
|
| [54] |
|
| [55] |
Miettinen K (2008) Introduction to multiobjective optimization: noninteractive approaches. In: Multiobjective optimization. Springer, pp 1–26 |
| [56] |
|
| [57] |
|
| [58] |
|
| [59] |
|
| [60] |
|
| [61] |
|
| [62] |
Lai Y-J, Hwang C-L (1994) Fuzzy multiple objective decision making. In: Fuzzy multiple objective decision making. Springer, pp 139–262 |
| [63] |
|
| [64] |
|
| [65] |
Islamic Republic of Iran Railways (2016) Iranian Railways Statistical Yearbook. |
| [66] |
European Environment Agency (2016) EMEP/EEA air pollutant emission inventory guidebook. 1.A.3.c. Luxembourg |
/
| 〈 |
|
〉 |