Scipediacontent: Scipediacontent moved page Draft Content 302393880 to Dubey et al 2019a

2021-01-21T12:29:28Z

Scipediacontent moved page Draft Content 302393880 to Dubey et al 2019a

Scipediacontent: Created page with " == Abstract == Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures i..."

2021-01-21T12:29:25Z

Created page with " == Abstract == Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures i..."

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== Abstract ==

Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these,this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/1912.02574 http://arxiv.org/abs/1912.02574]

* [http://arxiv.org/pdf/1912.02574 http://arxiv.org/pdf/1912.02574]

* [http://link.springer.com/content/pdf/10.1007/978-3-030-37188-3_16 http://link.springer.com/content/pdf/10.1007/978-3-030-37188-3_16],
: [http://dx.doi.org/10.1007/978-3-030-37188-3_16 http://dx.doi.org/10.1007/978-3-030-37188-3_16] under the license http://www.springer.com/tdm

* [https://dblp.uni-trier.de/db/journals/corr/corr1912.html#abs-1912-02574 https://dblp.uni-trier.de/db/journals/corr/corr1912.html#abs-1912-02574],
: [https://link.springer.com/chapter/10.1007/978-3-030-37188-3_16 https://link.springer.com/chapter/10.1007/978-3-030-37188-3_16],
: [https://academic.microsoft.com/#/detail/2997842726 https://academic.microsoft.com/#/detail/2997842726]

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Dubey et al 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 302393880 to Dubey et al 2019a

Scipediacontent: Created page with " == Abstract == Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures i..."