A simulation model for public bike-sharing systems

Soriguera, Francesc; Casado, Víctor; Jiménez, Enrique

doi:10.1016/j.trpro.2018.10.086

Cited by 26 publications

(20 citation statements)

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“…Urban Mobility Systems Simulation. Building simulation environments to study urban mobility systems has become a popular approach in existing work [11], [12], [13]. However, most of those solutions rely on oversimplified assumptions, while only considering the historical data of the mobility system for training and testing.…”

Section: Related Workmentioning

confidence: 99%

Deployment Optimization for Shared e-Mobility Systems With Multi-Agent Deep Neural Search

Luo

Klemmer

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Section: Related Workmentioning

confidence: 99%

Deployment Optimization for Shared e-Mobility Systems With Multi-Agent Deep Neural Search

Luo

Klemmer

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…No max T c (Total capacity) restriction has been set for the sake of clarity. Let us see an example: being two candidates of a city with eight stations and 100 bikes: [15,20,30,4,3,30] and C 2 = [15,2,4,40,25,20], the crossover will produce the following results: random positions p1 = 2 , p2 = 3. O f f spring1 = [15,2,4,4,3,30].…”

Section: Variable Neighborhood Local Searchmentioning

confidence: 99%

“…Let us see an example: being two candidates of a city with eight stations and 100 bikes: [15,20,30,4,3,30] and C 2 = [15,2,4,40,25,20], the crossover will produce the following results: random positions p1 = 2 , p2 = 3. O f f spring1 = [15,2,4,4,3,30]. This offspring is invalid due to the total capacity being lower than the number of bikes (58).…”

Section: Variable Neighborhood Local Searchmentioning

confidence: 99%

“…This offspring is invalid due to the total capacity being lower than the number of bikes (58). The algorithm will then extend the copied positions until it is valid on position 5, being the result Offspring1 = [15,2,4,40,25,30]. The number of individuals in the population is fixed, therefore these two solutions will replace some other one using a steady state population type [32].…”

Section: Variable Neighborhood Local Searchmentioning

confidence: 99%

“…Bikes usage in cities has seen a dramatic increase in popularity, both in big and medium size cities, with more than 2000 cities with a bike share program [1] and more than 3.5 million bikes [2]. Some of the main concerns in big cities are contamination and traffic jams; in 2016, bike sharing in Shanghai saved 8358 tonnes of petrol and decreased CO 2 and NOX emissions by 25,240 and 64 tonnes, respectively [3].…”

Section: Introductionmentioning

confidence: 99%

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Genetic Hybrid Optimization of a Real Bike Sharing System

et al. 2021

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In recent years there has been a growing interest in resource sharing systems as one of the possible ways to support sustainability. The use of resource pools, where people can drop a resource to be used by others in a local context, is highly dependent on the distribution of those resources on a map or graph. The optimization of these systems is an NP-Hard problem given its combinatorial nature and the inherent computational load required to simulate the use of a system. Furthermore, it is difficult to determine system overhead or unused resources without building the real system and test it in real conditions. Nevertheless, algorithms based on a candidate solution allow measuring hypothetical situations without the inconvenience of a physical implementation. In particular, this work focuses on obtaining the past usage of bike loan network infrastructures to optimize the station’s capacity distribution. Bike sharing systems are a good model for resource sharing systems since they contain common characteristics, such as capacity, distance, and temporary restrictions, which are present in most geographically distributed resources systems. To achieve this target, we propose a new approach based on evolutionary algorithms whose evaluation function will consider the cost of non-used bike places as well as the additional kilometers users would have to travel in the new distribution. To estimate its value, we will consider the geographical proximity and the trend in the areas to infer the behavior of users. This approach, which improves user satisfaction considering the past usage of the former infrastructure, as far as we know, has not been applied to this type of problem and can be generalized to other resource sharing problems with usage data.

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