Book ChapterDOI
Bus Arrival Time Prediction Using a Modified Amalgamation of Fuzzy Clustering and Neural Network on Spatio-Temporal Data
Sonia Khetarpaul,S. K. Gupta,Shikhar Malhotra,L. Venkata Subramaniam +3 more
- pp 142-154
TLDR
A dynamic model that can provide prediction for the estimated arrival time of a bus at a given bus stop using Global Positioning System (GPS) data is presented and it is shown that the method is effective in stated conditions.Abstract:
This paper presents a dynamic model that can provide prediction for the estimated arrival time of a bus at a given bus stop using Global Positioning System (GPS) data. The proposed model is a hybrid intelligent system combining Fuzzy Logic and Neural Networks. While Neural Networks are good at recognizing patterns and predicting, they are not good at explaining how they decide their input parameters. Fuzzy Logic systems, on the other hand, can reason with imprecise information, but require linguistic rules to explain their fuzzy outputs. Thus combining both helps in countering each other’s limitations and a reliable and effective prediction system can be developed. Experiments are performed on a real-world dataset and show that our method is effective in stated conditions. The accuracy of result is 86.293% obtainedread more
Citations
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Journal ArticleDOI
Bus arrival time prediction using mixed multi-route arrival time data at previous stop
TL;DR: The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy and halve the time cost of predicted arrival time calculation.
Journal ArticleDOI
A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM
TL;DR: A GPS calibration method is put forward, while projection rules of specific road shapes are discussed, and a hybrid dynamic BAT prediction factor, which achieves accuracy enhancement by taking into account traffic flow evaluation results and GPS position calibration, is defined.
Journal ArticleDOI
Predicting Bus Arrival Time Using BP Neural Network and Dynamic Transfer
TL;DR: Considering real-time bus operation data and transfer schemes, a fusion scheme to predict bus arrival time and improve prediction accuracy is proposed and a dynamic transfer scheme based on the shortest time priority is designed according to the prediction of bus arrivals.
Journal ArticleDOI
A review of bus arrival time prediction using artificial intelligence
Nisha Singh,Kranti Kumar +1 more
TL;DR: The application of artificial intelligence (AI) based methods/algorithms to predict the bus arrival time (BAT) is reviewed in detail and thorough discussion is presented to elaborate different branches of AI that have been applied for several aspects of BAT prediction.
Book ChapterDOI
Alternatives for Reliable Trip Planning
TL;DR: This paper surveys advances in trip-planning techniques in multimodal transit systems using the example of Mumbai city transit and highlights the relative strengths of different approaches.
References
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Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Journal ArticleDOI
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
TL;DR: Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space; in both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squarederror criterion function.
A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.
Journal ArticleDOI
Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
Billy M. Williams,Lester A Hoel +1 more
TL;DR: The theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes as well as empirical results using actual intelligent transportation system data are presented and found to be consistent with the theoretical hypothesis.
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