Hybrid Genetic Algorithm for an On-Demand First Mile Transit System Using Electric Vehicles

Perera, Thilina; Prakash, Alok; Gamage, Chathura; Srikanthan, Thambipillai

doi:10.1007/978-3-319-93698-7_8

Cited by 12 publications

(11 citation statements)

References 24 publications

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“…They have been selected to show the robustness of the proposed methods for different scenarios. Table 10 gives the parameters of the GA. GA parameters specify the maximum number of iterations and the population size for both the proposed method and [5] used for comparison.…”

Section: Resultsmentioning

confidence: 99%

“…The performance of the proposed method in terms of VMT of the fleet and computation time of routes is assessed by comparing against [5]. In the experiments, each relaxed TC in Table 9 is repeated three times while randomly distributing passengers and EVs in the selected area.…”

Section: Resultsmentioning

confidence: 99%

“…The input contains clusters with a set of vehicles and passengers that need to be routed such that all constraints are satisfied while minimising the VMT. To this end, we use the GA proposed in our previous work [5]. After executing the algorithm in parallel for each cluster, passengers that are not picked‐up due to the violation of a constraint is added to the unallocated cluster created in Section 3.2.…”

Section: Methodsmentioning

confidence: 99%

“…ODPT systems have been specifically deployed to provide easy and convenient connections to rapid transit nodes [5]. In these systems, passengers request for rides indicating the pick‐up location and the system matches the passenger with available vehicles, which operate in shared‐ride mode.…”

Section: Introductionmentioning

confidence: 99%

“…In these systems, passengers request for rides indicating the pick‐up location and the system matches the passenger with available vehicles, which operate in shared‐ride mode. Thus, we use the setup proposed in our previous work [5] which studies an ODPT system consisting of a fleet of homogeneous, high‐capacity, electric vehicles (EVs) dispersed in a neighbourhood, that responds to the passenger demand in real‐time by picking passengers from the origin and dropping off at the nearest metro station (common destination). The system generated near‐optimal routes using a genetic algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

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Directionality‐centric bus transit network segmentation for on‐demand public transit

Perera

Wijesundera

Wijerathna

et al. 2020

IET intell. transp. syst

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The recent growth in real‐time, high‐capacity ride‐sharing has made on‐demand public transit (ODPT) a reality. ODPT systems serving passengers using a vehicle fleet that operates with flexible routes, strive to minimise fleet travel distance. Heuristic routing algorithms have been integrated in ODPT systems in order to improve responsiveness. However, route computation time in such algorithms depends on problem complexity and hence increases for large scale problems. Thus, network segmentation techniques that exploit parallel computing have been proposed in order to reduce route computation time. Even though computation time can be reduced using segmentation in existing techniques, it comes at the cost of degradation of route quality due to static demarcation of boundaries and disregarding real road network distances. Thus, this work proposes, a directionality‐centric bus transit network segmentation technique that exploits parallel computation capable of computing routes in near real‐time while providing high scalability. Additionally, a dynamic fleet allocation algorithm that exploits proximity and flexibility to minimise vehicle detours while maximising fleet utilisation is proposed. Experimental evaluations on a real road network confirm that the proposed method achieves notable speed‐up in flexible route computation without compromising route quality compared to a widely used unsupervised learning technique.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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