Development of an Algorithm for Optimal Demand Responsive Relocatable Feeder Transit Networks Serving Multiple Trains and Stations
Young-Jae Lee , Mana Meskar , Amirreza Nickkar , Sina Sahebi
Urban Rail Transit ›› 2019, Vol. 5 ›› Issue (3) : 186 -201.
Development of an Algorithm for Optimal Demand Responsive Relocatable Feeder Transit Networks Serving Multiple Trains and Stations
Although demand responsive feeder bus operation is possible with human-driven vehicles, it has not been very popular and is mostly available as a special service because of its high operating costs due to intensive labor costs as well as requirement for advanced real-time information technology and complicated operation. However, once automated vehicles become available, small-sized flexible door-to-door feeder bus operation will become more realistic, so preparing for such automated flexible feeder services is necessary to take advantage of the rapid improvement of automated vehicle technology. Therefore, in this research, an algorithm for optimal flexible feeder bus routing, which considers relocation of buses for multiple stations and trains, was developed using a simulated annealing algorithm for future automated vehicle operation. An example was developed and tested to demonstrate the developed algorithm. The algorithm successfully handled relocation of buses when the optimal bus routings were not feasible using the buses available at certain stations. Furthermore, the developed algorithm limited the maximum degree of circuity for each passenger while minimizing the total cost, including total vehicle operating costs and total passenger in-vehicle travel time costs.
Automated transit / Vehicle routing / Feeder bus / Simulated annealing / Demand responsive transit / First and last mile service
| [1] |
|
| [2] |
|
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
|
| [24] |
|
| [25] |
|
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
|
| [37] |
|
| [38] |
|
| [39] |
|
| [40] |
Zidi I, Zidi K, Mesghouni K, Ghedira K (2011) A multi-agent system based on the multi-objective simulated annealing algorithm for the static dial a ride problem. In: Paper presented at the 18th World Congress of the international federation of automatic control (IFAC), Milan (Italy) |
| [41] |
Belhaiza S (2017). A data driven hybrid heuristic for the dial-a-ride problem with time windows. In: Paper presented at the 2017 IEEE symposium series on computational intelligence (SSCI) |
| [42] |
|
| [43] |
|
| [44] |
|
| [45] |
|
| [46] |
|
| [47] |
|
| [48] |
|
| [49] |
van Engelen M, Cats O, Post H, Aardal K (2018) Demand-anticipatory flexible public transport service. Transportation Research Board 97th Annual Meeting, Washington DC, United States |
| [50] |
|
| [51] |
|
| [52] |
|
| [53] |
|
| [54] |
|
| [55] |
|
| [56] |
Van Breedam A (1996) An analysis of the effect of local improvement operators in genetic algorithms and simulated annealing for the vehicle routing problem. 1996, RUCA working paper 96/14: University of Antwerp, Belgium |
| [57] |
|
| [58] |
|
| [59] |
|
| [60] |
|
| [61] |
|
| [62] |
|
| [63] |
|
| [64] |
|
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|
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