Method of predicting bus arrival time based on MapReduce combining clustering with neural network

Zhang, Jian; Gu, Jun; Guan, Lei; Zhang, Suqi

doi:10.1109/icbda.2017.8078828

Cited by 13 publications

(11 citation statements)

References 10 publications

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“…This works by dividing these data to subsets and analyze each set for a parallel computation. While [152] use map-reduce to predict bus arrival time. They use Clustering K-Mean algorithm to divide the running time of a bus.…”

Section: Computation Complexity Of Ai Algorithmsmentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

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Section: Computation Complexity Of Ai Algorithmsmentioning

confidence: 99%

Applications of Artificial Intelligence in Transport: An Overview

et al. 2019

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“…Pang et al [12] proposed to exploit the long-range dependencies among the multiple time steps for bus arrival prediction via a recurrent neural network. Zhang et al [13] proposed a model based on MapReduce combining clustering with the neural network. Yang et al [14] proposed a novel stacked autoencoder Levenberg-Marquardt model, a type of deep architecture of neural network approach that aimed to improve forecasting accuracy.…”

Section: B Neural Networkmentioning

confidence: 99%

Travel time prediction of urban public transportation based on detection of single routes

et al. 2022

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Improving travel time prediction for public transit effectively enhances service reliability, optimizes travel structure, and alleviates traffic problems. Its greater time-variance and uncertainty make predictions for short travel times (≤35min) more subject to be influenced by random factors. It requires higher precision and is more complicated than long-term predictions. Effectively extracting and mining real-time, accurate, reliable, and low-cost multi-source data such as GPS, AFC, and IC can provide data support for travel time prediction. Kalman filter model has high accuracy in one-step prediction and can be used to calculate a large amount of data. This paper adopts the Kalman filter as a travel time prediction model for a single bus based on single-line detection: including the travel time prediction model of route (RTM) and the stop dwell time prediction model (DTM); the evaluation criteria and indexes of the models are given. The error analysis of the prediction results is carried out based on AVL data by case study. Results show that under the precondition of multi-source data, the public transportation prediction model can meet the accuracy requirement for travel time prediction and the prediction effect of the whole route is superior to that of the route segment between stops.

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“…Various types of neural networks have been proposed for bus arrival time predictions, such as feedforward neural networks (e.g.,. ( [27], [28]), recurrent neural networks (e.g., [26], [29]- [31]), or convolutional neural networks (e.g., [17]).…”

Section: B Transit Operationsmentioning

confidence: 99%

What Do We Know When? Modeling Predictability of Transit Operations

Büchel

Corman

2022

IEEE Trans. Intell. Transport. Syst.

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Predictions of transit delays are crucial to passengers and operators. Passengers utilize the predictions to decide on departure time, route choice, and mode choice, whereas operators decide on schedules, timetables, rolling stock allocation, and control actions. We introduce the concept of predictability of transit travel times as the study of the reduction of the predicted variability with the temporal approaching of a predicted event.We evaluate predictability on a real-life test case in Zurich, Switzerland, spanning multiple transit lines over one year of operations. The concept is shown based on predictions obtained by a state-of-the-art Bayesian network approach, where we show how predictability (in general) can be modeled as an exponential decay phenomenon. The study of predictability of transit operations leads to additional insights for control actions and system analysis compared to other complementary concepts such as punctuality or regularity, for instance, concerning bunching, identification of bottlenecks, and passenger routing.

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Method of predicting bus arrival time based on MapReduce combining clustering with neural network

Cited by 13 publications

References 10 publications

Applications of Artificial Intelligence in Transport: An Overview

Applications of Artificial Intelligence in Transport: An Overview

Travel time prediction of urban public transportation based on detection of single routes

What Do We Know When? Modeling Predictability of Transit Operations

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