Performance Comparison between the Multi-Colony and Multi-Pheromone ACO Algorithms for Solving the Multi-objective Routing Problem in a Public Transportation Network

Faroqi, Hamed; Mesgari, Mehran

doi:10.1017/s0373463315000594

Cited by 8 publications

(1 citation statement)

References 21 publications

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“…Outcomes of the model are validated for two subsequent weekdays. Analyses show that the proposed model can reduce the length of the rebalancing tour by 30%.Bike sharing systems are one of the effective and environmentally friendly solutions to the first/last mile problem; they notably reduce motorized trips, air pollution, fuel consumption and needs for parking spaces [1][2][3][4][5][6][7]. Since the last decade, these systems have become one of the main complementary modes of public transit systems in many urban areas.…”

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Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

et al. 2019

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Bike sharing systems, as one of the complementary modes for public transit networks, are designed to help travelers in traversing the first/last mile of their trips. Different factors such as accessibility, availability, and fares influence these systems. The availability of bikes at certain times and locations is studied under rebalancing problem. The paper proposes a bottom-up cluster-based model to solve the static rebalancing problem in bike sharing systems. First, the spatial and temporal patterns of bike sharing trips in the network are investigated. Second, a similarity measure based on the trips between stations is defined to discover groups of correlated stations, using a hierarchical agglomerative clustering method. Third, two levels for rebalancing are assumed as intra-clusters and inter-clusters with the aim of keeping the balance of the network at the beginning of days. The intra-cluster level keeps the balance of bike distribution inside each cluster, and the inter-cluster level connects different clusters in order to keep the balance between the clusters. Finally, rebalancing tours are optimized according to the positive or negative balance at both levels of the intra-clusters and inter-clusters using a single objective genetic algorithm. The rebalancing problem is modeled as an optimization problem, which aims to minimize the tour length. The proposed model is implemented in one week of bike sharing trip data set in Chicago, USA. Outcomes of the model are validated for two subsequent weekdays. Analyses show that the proposed model can reduce the length of the rebalancing tour by 30%.Bike sharing systems are one of the effective and environmentally friendly solutions to the first/last mile problem; they notably reduce motorized trips, air pollution, fuel consumption and needs for parking spaces [1][2][3][4][5][6][7]. Since the last decade, these systems have become one of the main complementary modes of public transit systems in many urban areas. Different factors such as accessibility to stations, availability of bikes and vacant docks, and fares influence the effectiveness of these systems, among which the availability of bikes during the day is one of the most challenging issues in the designing, operating, and management of these services.To a large extent, bike sharing systems are used for one-way trips, and such a trend leads to inappropriate bike distribution in time and space [7,8]. As a result, unmet demand and consequently user dissatisfaction occur in the system. To minimize the demand dissatisfaction (i.e., maximize usage and revenue), the redistribution of bikes over stations is required. This redistribution, also called rebalancing, is implemented by employing vehicles (usually trucks) to collect surplus bikes from overloaded stations and deliver to bike deficient stations [9]. The efficiency of rebalancing operations is important to the authorities to ensure that the number of bikes and vacant docks at each station are periodically restored to predefined target values [8].Rebalancing...

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Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

et al. 2019

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Multi-modal Routing in Urban Transportation Network Using Multi-objective Quantum Particle Swarm Optimization

Farahmand-Tabar,

Afrasyabi

2024

Springer Tracts in Nature-Inspired Computing

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GIS-based ride-sharing and DRT in Tehran city

Faroqi

Sadeghi‐Niaraki

2016

Public Transp

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In this paper, due to the inefficiency of implemented ride-sharing (RS) and demand responsive transport (DRT) systems in recent decades, these two systems are integrated in order to design a spatial system to manage numbers of single-passenger vehicles and, also, efficiently use public fleet capacity. In the designed system, after preparing and pre-processing on data, in the RS stage, the proposed system searches for fellow travellers for private vehicle users with priority given firstly to the applicants owning a private vehicle. And then, in the DRT stage, the remaining applicants are formed into DRT fellow traveller groups in which each DRT fellow traveller group is assigned an appropriate public vehicle and a parking lot. The proposed system is implemented based on real commuting trips taken in Tehran. The effectiveness of the system is mainly evaluated by comparing the decrease of mileage, gas consumption, and total travel time per vehicle.

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Performance Comparison between the Multi-Colony and Multi-Pheromone ACO Algorithms for Solving the Multi-objective Routing Problem in a Public Transportation Network

Cited by 8 publications

References 21 publications

Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

Multi-modal Routing in Urban Transportation Network Using Multi-objective Quantum Particle Swarm Optimization

GIS-based ride-sharing and DRT in Tehran city

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