Transit network design by Bee Colony Optimization

Nikolić, Miloš; Teodorović, Dušan

doi:10.1016/j.eswa.2013.05.002

Cited by 156 publications

(122 citation statements)

References 43 publications

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“…Moreover, the authors also implemented an alternative algorithm that increases the population size after each iteration. The later study present a comparison between the proposed algorithms and the ones by Baaj and Mahmassani (1991), Chakroborty and Dwivedi (2002), Fan and Machemehl (2008), Nikolić and Teodorović (2013). Numerical results show that the GA with elitism outperforms the other approaches compared in the study in all three criteria in most of the tested instances.…”

Section: Metaheuristic Algorithms For the Transit Network Designmentioning

confidence: 86%

“…Improvements are obtained also for the average travel time. Later, Nikolić and Teodorović (2014) extend their previous approach outlined by Nikolić and Teodorović (2013) considering elastic demand and the minimization of the weighted sum of the total number of unsatisfied passengers, the total travel time of all passengers, and the fleet size. Nayeem et al (2014) develop two versions of a GA with elitism to solve the TND with static demand which minimizes the weighted sum of unsatisfied passengers, the total number of transfers, and the total travel time of all served passengers.…”

Section: Metaheuristic Algorithms For the Transit Network Designmentioning

confidence: 97%

“…However, the study lacks any comparison with other algorithms in the literature. Another ABC is implemented by Nikolić and Teodorović (2013) to solve the TND with static demand that minimizes the number of transfers and the total travel time. The authors define a passenger as satisfied if two or less transfers are needed in his/her trip.…”

Section: Metaheuristic Algorithms For the Transit Network Designmentioning

confidence: 99%

See 2 more Smart Citations

Planning, operation, and control of bus transport systems: A literature review

Ibarra-Rojas

Delgado

Giesen

et al. 2015

Transportation Research Part B: Methodological

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Section: Metaheuristic Algorithms For the Transit Network Designmentioning

confidence: 86%

Section: Metaheuristic Algorithms For the Transit Network Designmentioning

confidence: 97%

Section: Metaheuristic Algorithms For the Transit Network Designmentioning

confidence: 99%

See 1 more Smart Citation

Planning, operation, and control of bus transport systems: A literature review

Ibarra-Rojas

Delgado

Giesen

et al. 2015

Transportation Research Part B: Methodological

587

243

View full text Add to dashboard Cite

“…Greedy based methods use a node as start node, then they begin to construct route by connecting a node adjacent to the considered node. This process is repeated, until the route construction is completed, as shown in Fig 3. The next node to visit is selected according to a strategy [35][36][37].…”

Section: Fig 2: the General Framework Of The Tndpmentioning

confidence: 99%

Issues Related to Transit Network Design Problem

Owais¹

2015

IJCA

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Public Transit (P.T) is very important means to reduce traffic congestions, to improve urban environmental conditions and consequently affects people social lives. Planning, designing and management of P.T are the key issues for offering a competitive mode that can compete with the private transportation. These transportation planning, designing and management issues are addressed in the Transit Network Design Problem (TNDP). It deals with a complete hierarchy of decision making process. It includes strategic, tactical and operational decisions. The main body of TNDP is two stages, namely; route design stage and frequency setting. The TNDP is extensively studied in the last five decades; however the research gate is still widely open due to its many practical and modeling challenges. In this paper, a comprehensive background is given to illustrate the issues and challenges related to the TNDP to help in directing the incoming researches towards the untouched areas of the problem.

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“…Transit deserts are defined as areas where the transit demand is significantly greater than the supply. Compared to previous transit planning methods based on complicated network modeling (Schöbel, 2011;Nayeem et al, 2014;Nikolić and Teodorović, 2013), this research presents a straightforward GIS method to quickly measure the transit demand and supply at the city block group level and professionally presents the results.…”

Section: Introductionmentioning

confidence: 99%

Identifying transit deserts in major Texas cities where the supplies missed the demands

Jiao

2017

JTLU

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Coined by the author, the concept “transit desert” is developed from the now common concept of a “food desert,” which is an area where there is limited or no access to fresh food (Clark et al. 2002; Jiao et al. 2012; Whelan et al. 2002; Wrigley 1993; Wrigley et al. 2002). The food desert concept has received a lot of attention and influenced planning policies and practices. By applying the same idea to transit systems within urban areas, geographic areas can be identified where there is a lack of transit service. This involves identifying the transit dependent populations as a measure of transit demand, calculating the transit supply, and then subtracting the supply from the demand to measure the gap (Jiao & Dillivan 2013). In detail, transit dependent populations are those who might require transit service to get around more than other people. The transit supply is measured by aggregating a number of criteria that contribute to better transit access and measured within a designated geographic area. Transit deserts are defined as areas where the transit demand is significantly greater than the supply.

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Transit network design by Bee Colony Optimization

Cited by 156 publications

References 43 publications

Planning, operation, and control of bus transport systems: A literature review

Planning, operation, and control of bus transport systems: A literature review

Issues Related to Transit Network Design Problem

Identifying transit deserts in major Texas cities where the supplies missed the demands

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