Data-Driven Optimization of Public Transit Schedule

Basak, Sanchita; Sun, Fangzhou; Sengupta, Saptarshi; Dubey, Abhishek

doi:10.1007/978-3-030-37188-3_16

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…When the bus arrives at the bus stop earlier, passengers might miss the bus, and also, for late buses, public transportation infrastructure suffers from the delay. Any of these two arrival time variations impact commuters' satisfaction significantly [23]. Our study found that the average delay and mismatch time across all lines of this dataset is around 8 minutes (491 seconds) and 6 minutes, respectively.…”

Section: Exvolqhv Dyhudjhghod\iruhdfk Exvolqhvhfrqgmentioning

confidence: 60%

“…S. Basak, F. Sun, S. Sengupta, and A. Dubey have conducted a similar study [23], using unsupervised clustering mechanisms to optimize transit on-time performance. As a local case study, they analyzed the monthly and seasonal delays of the Nashville metro region and clustered months with similar patterns.…”

Section: Related Workmentioning

confidence: 99%

“…As a local case study, they analyzed the monthly and seasonal delays of the Nashville metro region and clustered months with similar patterns. In this paper, they presented a stochastic optimization toolchain along with sensitivity analyses for choosing the optimal hyperparameters, and they solved the optimization problem by using a single-objective optimization task as well as a greedy algorithm, a genetic algorithm (GA), and a particle swarm optimization (PSO) algorithm [23].…”

Section: Related Workmentioning

confidence: 99%

“…On-time performance at stops is an essential factor. The unpredictable nature of delay has been selected as the top reason people avoid bus transit systems in many cities [23].…”

Section: Analysis Of the Datamentioning

confidence: 99%

See 3 more Smart Citations

A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On-Demand Public Transit

Rashvand¹,

Hosseini²,

Azarbayjani³

et al. 2023

Preprint

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Among the major public transportation systems in cities, bus transit has its problems, including more accuracy and reliability when estimating the bus arrival time for riders. This can lead to delays and decreased ridership, especially in cities where public transportation is heavily relied upon. A common issue is that the arrival times of buses do not match the schedules, resulting in latency for fixed schedules. According to the study in this paper on New York City bus data, there is an average delay of around eight minutes or 491 seconds mismatch between the bus arrivals and the actual scheduled time. This research paper presents a novel AI-based data-driven approach for estimating the arrival times of buses at each transit point (station). Our approach is based on a fully connected neural network and can predict the arrival time collectively across all bus lines in large metropolitan areas. Our neural-net data-driven approach provides a new way to estimate the arrival time of the buses, which can lead to a more efficient and smarter way to bring the bus transit to the general public. Our evaluation of the network bus system with more than 200 bus lines, and 2 million data points, demonstrates less than 40 seconds of estimated error for arrival times. The inference time per each validation set data point is less than 0.006 ms.

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Section: Exvolqhv Dyhudjhghod\iruhdfk Exvolqhvhfrqgmentioning

confidence: 60%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…On-time performance at stops is an essential factor. The unpredictable nature of delay has been selected as the top reason people avoid bus transit systems in many cities [23].…”

Section: Analysis Of the Datamentioning

confidence: 99%

See 2 more Smart Citations

A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On-Demand Public Transit

Rashvand¹,

Hosseini²,

Azarbayjani³

et al. 2023

Preprint

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“…The K-Means algorithm is a well-known clustering algorithm that has been widely applied in many different computational problems [5,22,23]. It is an iterative algorithm, which means that it runs continuously from a given starting point and generates new approximate solutions by adding improvements to the results from previous runs.…”

Section: K-means Algorithmmentioning

confidence: 99%

Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks

Pickens

Sengupta

2021

MAKE

Self Cite

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Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics, and sentiment analysis, to name a few. It is therefore important to verify the effectiveness of the clustering algorithm in question and to make reasonably strong arguments for the acceptance of the end results generated by the validity indices that measure the compactness and separability of clusters. This work aims to explore the successes and limitations of two popular clustering mechanisms by comparing their performance over publicly available benchmarking data sets that capture a variety of data point distributions as well as the number of attributes, especially from a computational point of view by incorporating techniques that alleviate some of the issues that plague these algorithms. Sensitivity to initialization conditions and stagnation to local minima are explored. Further, an implementation of a feedforward neural network utilizing a fully connected topology in particle swarm optimization is introduced. This serves to be a guided random search technique for the neural network weight optimization. The algorithms utilized here are studied and compared, from which their applications are explored. The study aims to provide a handy reference for practitioners to both learn about and verify benchmarking results on commonly used real-world data sets from both a supervised and unsupervised point of view before application in more tailored, complex problems.

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Addressing APC Data Sparsity in Predicting Occupancy and Delay of Transit Buses: A Multitask Learning Approach

Bin Zulqarnain,

Gupta,

Talusan

et al. 2023

2023 IEEE International Conference on Smart Computing (SMARTCOMP)

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Data-Driven Optimization of Public Transit Schedule

Cited by 5 publications

References 23 publications

A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On-Demand Public Transit

A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On-Demand Public Transit

Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks

Addressing APC Data Sparsity in Predicting Occupancy and Delay of Transit Buses: A Multitask Learning Approach

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