Optimization of Parking Public Transport Vehicles in Ostrava

Teichmann, Dusan; Dorda, Michal; Bínová, Helena; Ludvík, Martin

doi:10.7307/ptt.v27i1.1593

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…There is a need to improve parking spaces to reduce traffic delay due to the bad habit of street parking on the roadside (Munzir et al, 2010). Teichmann et al (2015) said that mathematics programming offers an effective method to solve problems regarding transportation. A study to solve the problem of parking availability inside the campus has been conducted to find the main factor and solutions to the problem faced by the university community.…”

Section: Related Work I Parking Problemsmentioning

confidence: 99%

Optimization of Parking Capacities at Railway Station Using Firefly Algorithm

Shariffuddin

Nor

Moktar

2019

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In present years, many cities face way more challenges than before especially those related to themobility of people and land usage. Parking management which links transportation and land use is one ofthe most challenging problems to encounter. Parking can be a nuisance when there is a shortage ofavailable parking spaces, especially during peak hours. The main challenge is the scarcity andmanagement of parking spaces. This is because of the unmet demand due to lack of parking spaces andineffective use of available facilities. It is a common issue in overcrowding area where drivers need toface this critical situation as the parking spaces provided are not enough to support the increasingnumber of the automobile on roads due to economic growth. KTMB Ipoh faced the same situationespecially during peak hours where the parking area is always congested with drivers who drive incircles to find a parking spot. The average time spent by drivers to find the parking spot is estimated to behalf an hour. Thus, this research is done to help the parking management team to improvise the efficiencyof parking spaces provided to optimize the parking area. Furthermore, the purpose of this study is tomaximize the number of parking. A firefly algorithm method is used to find the maximum number ofparking lot that can optimize the parking area. It is shown that with the current parking area, the numberof parking spaces can be increased by 48 lanes which can optimize the parking spaces to the fullest. Keywords: firefly algorithm, parking spaces, optimization.

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Section: Related Work I Parking Problemsmentioning

confidence: 99%

Optimization of Parking Capacities at Railway Station Using Firefly Algorithm

Shariffuddin

Nor

Moktar

2019

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“…These optimization techniques find widespread applications in various real-world engineering scenarios, including airspace surveillance systems [2], image processing in embedded systems [3], and automatic parking systems [4]. These methods not only result in superior outcomes but also curtail the complexity, processing time, and memory requirements of operating systems.…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of Nature-Inspired Optimization Techniques for Contrast Enhancement in Images: A Novel Software Tool

Ilhan,

Elbir,

Serbes

et al. 2023

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This study is rooted in the direct correlation between the performance of multivariate techniques and the selection of parameters. The complexity and time-consuming nature of parameter selection, due to the need for exhaustive testing of all available parameters for optimal results, is acknowledged. To mitigate this issue, a novel software tool, integrating nine nature-inspired optimization methods (Differential Evolution, Artificial Bee Colony, Particle Swarm, Cat Swarm, Dragonfly, Black Hole, Bacterial Foraging, Genetic Algorithms, and Simulated Annealing), is proposed. These methods are employed in histogram stretching, a parameter-dependent contrast enhancement technique, with multiplication, addition, and root extraction operations as the target parameters for optimization. In addition to this, histogram equalization, a parameter-independent contrast enhancement technique, is included for the purpose of comparative performance analysis. The software tool, publicly available, provides four performance metrics namely, Mean Square Error, Peak Signal-to-noise Ratio, Structural Similarity Index, and processing times. A rigorous evaluation using the widely recognized Tampere Image dataset indicates that Differential Evolution emerged as the most efficient technique, scoring highest for Structural Similarity Index (0.948) and second best for Mean Square Error (278.05) and Peak Signal to Noise Ratio (26.962). Furthermore, Particle Swarm Optimization demonstrated the fastest time complexity, requiring merely 0.6 sec per image for parameter definition. Notably, it was observed that while histogram equalization tends to degrade original images, the adaptive nature of optimized histogram stretching remains preserved, thereby leaving the image quality unaffected. Such findings highlight the efficacy of the proposed software tool in the optimization and evaluation of contrast enhancement techniques.

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