A PSO based transportation network design optimization of the mega city Dhaka

Akhand, M. A. H.; Shill, Pintu Chandra; Sabuj, Mir Shahriar; Afrin, Zakia; Rahman, M. M. Hafizur

doi:10.1109/iceeict.2015.7307459

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…Moreover, many researchers claimed that PSO is a viable method and its computational efficiency is better than GA and DE [14][15][16][17]. Also, there is a cluster of studies that used PSO in solving the BRT route optimization problem and the Transportation Network Design Problems (TNDP) in high-density urban areas [18][19][20][21]. Many studies focused on the bus stations' spacing in urban areas.…”

Section: Generating the New Population And Go Up For Evaluatingmentioning

confidence: 99%

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Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

et al. 2021

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In this research, a feasible mechanism is developed to determine the optimum number of bus rapid transit (BRT) stations as well as their respective locations along the service corridor. To accomplish this, a mathematical model is developed and optimized by using three different evolutionary algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), and the results are compared. The total cost function is composed of two main costs namely the operator’s cost, i.e., related to costs on service provider’s end, and the user’s cost, i.e., related to costs on commuters’ end. A functional numerical example with the commuters’ demand is worked out by minimizing the cost function, which demonstrates the applicability of the framework. In our case study, PSO outclassed GA and DE on the basis of convergence rate. Since our work has proved the robustness of PSO as compared to GA and DE, we conducted our sensitivity analysis keeping PSO as our benchmark algorithm to study the influence of various parameters on the optimal cost. The computational experiments reveal that the optimal cost is substantially affected by the variations in the commuters’ demand, commuters’ walking speed, and value of the users’ access and in-vehicle time. On the contrary, the acceleration/deceleration delays at a bus station, bus operating cost, and headway have an inconsiderable impact on the optimal cost.

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Section: Generating the New Population And Go Up For Evaluatingmentioning

confidence: 99%

“…Now, substituting Eqs. 17, (20) and (23) in Eq. 16provides with the total users' in-vehicle cost as Eq.…”

Section: Users' In-vehicle Cost E U-invmentioning

confidence: 99%

Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

et al. 2021

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“…Compared to other evolutionary algorithms based on population, PSO presents a superior dominance when dealing with difficult optimization problems and has shown good results in static, noisy, and continuously changing environments with little influence on the continuity of the objective function Song and Gu [32]. As a result, it has been widely applied in many transportation planning and optimization fields, Masdari et al [33]; Akhand et al [34]; Afkar and Babazadeh [35]; Xu et al [36]; Yan et al [37]. Because it has been proven to be effective in solving NP-hard problems, it was adopted in this paper to handle the proposed model.…”

Section: Solution Approachmentioning

confidence: 99%

Transportation Service Procurement Bid Construction Problem from Less Than Truckload Perspective

Yan

et al. 2018

Mathematical Problems in Engineering

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This paper presents mixed integer programming for a transportation service procurement bid construction problem from a less than full truckload perspective, in which the bidders (carriers) generate their best bid (package) using a bundled price to maximize their utility and increase the chance of winning the business. The models are developed from both the carriers and shippers perspectives to establish a relationship between the quoted price and the likelihood of winning to assist the carriers in balancing the potential benefits and the possibility of winning the bid. An intelligent algorithm based on Particle Swarm Optimization is then designed to solve the proposed model and hypothetical data sets are used to test the effectiveness and efficiency of the proposed model and algorithm.

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“…In general, TND concerns the optimal selection of a project from various alternatives, satisfying associated constraints to maximize cost-benefit. Various approaches have been applied to solve TND problems in the last decade, including different heuristic and bioinspired methods [11][12][13]. The major issue with the existing methods is optimizing network length to minimize construction costs.…”

Section: Introductionmentioning

confidence: 99%

“…Its ability to optimize its shape attracted the research community to solve complex optimization tasks, such as optimal network design [16]. The computational model of Physarum's network design technique is expected to solve complex design problems more effectively than the existing bioinspired and traditional methods [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Physarum-Inspired Bicycle Lane Network Design in a Congested Megacity

et al. 2021

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Improvement of mobility, especially environment-friendly green mobility, is challenging in existing megacities due to road network complexity and space constraints. Endorsing the bicycle lane network (BLN) in congested megacities is a promising option to foster green mobility. This research presents a novel bioinspired network design method that considers various constraints and preferences related to the megacity for designing an optimal BLN. The proposed method is inspired by natural Physarum polycephalum, a brainless, multi-headed single-celled organism, which is capable of developing a reticulated network of complex foraging behaviors in pursuit of food. The mathematical model of Physarum foraging behavior is adapted to maneuver various BLN constraints in megacity contexts in designing the optimal BLN. The Physarum-inspired BLN method is applied to two case studies on the megacity Dhaka for designing BLNs: the first one covers congested central city area, and the second one covers a broader area that includes major locations of the city. The obtained BLNs were evaluated comparing their available routes between different locations with the existing vehicle routes of the city in terms of distance and required travel times in different time periods, and the BLN routes were found to be suitable alternatives for avoiding congested main roads. The expected travel time using BLNs is shorter than other transport (e.g., car and public bus); additionally, at glance, the average travel speed on BLNs is almost double that of public buses in peak hours. Finally, the designed BLNs are promising for environment-friendly and healthy mobility.

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A PSO based transportation network design optimization of the mega city Dhaka

Cited by 4 publications

References 7 publications

Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

Transportation Service Procurement Bid Construction Problem from Less Than Truckload Perspective

Physarum-Inspired Bicycle Lane Network Design in a Congested Megacity

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