Multi-output Deep Learning for Bus Arrival Time Predictions

Petersen, Niklas; Rodrigues, Filipe; Pereira, Francisco C.

doi:10.1016/j.trpro.2019.09.025

Cited by 14 publications

(14 citation statements)

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“…Also, the next bus's waiting time at transfer point takes only historical average bus coming time. Petersen and et al, [31] has presented a multi-model deep neural network prediction framework for bus arrival time using Convolutional and Long short-term memory. This paper provides link travel time prediction as in our research.…”

Section: Related Work and Discussionmentioning

confidence: 99%

The bus arrival time prediction using LSTM neural network and location analysis

2020

J. adv. tec. eng. res.

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The accurate bus arrival time information is crucial to passengers for reducing waiting times at the bus stop and improve the attractiveness of public transport. GPS-equipped buses can be considered as mobile sensors showing traf ic lows on road surfaces. In this paper, we present an approach that predicts bus arrival time using historical bus GPS information and real-time situation on the road. In this study, we divide bus arrival time into bus dwelling time at bus stops and bus travel time between stations and predict each of them separately. The clustering approach used to predict the travel time between stations, and then for each cluster, we apply LSTM NN to predict walking time between stations. The latency at each bus stop we evaluate by historical dwelling time and using location analysis to ind the importance of the bus stop as a point of interest during prediction time. The study is trained and tested on GPS data collected from 1200 buses in a period of 3 months. According to tests results our method show small mean absolute error for buses that not far from departure station. The outcomes of this work can be used as an additional information for bus passengers to know possible bus coming time and to estimate possible travel time in bus journey. The method for arrival time prediction proposed in this research has several advantages. It considers historical bus travel time information, real time information, bus dwelling time, riding time, traf ic lights and city facilities.

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Section: Related Work and Discussionmentioning

confidence: 99%

The bus arrival time prediction using LSTM neural network and location analysis

2020

J. adv. tec. eng. res.

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“…In all, schedule adherence highly affects driver's behavior, dwell time at stops and link travel time. Therefore schedule adherence is an important input to be considered for BAT prediction. Link Travel TimeTo predict BAT, various studies have been conducted which have incorporated link travel time at previous stops, at the current stop of preceding bus or combination of both as input to their models and have shown significant improvement in the performance of concerned models (Lin & Bertini, 2004; Pang et al, 2019; Petersen et al, 2019; Yu et al, 2011). Yin et al (2017) considered the travel time of preceding bus on current study segment as one of the input to predict travel time on current segment and showed that irrespective of model considered, preceding BTT improves the accuracy of a model.…”

Section: Understanding the Bus Arrival Time Prediction Problem And As...mentioning

confidence: 99%

“…The derived results show that the CNN managed to predict the traffic more accurately in some scenarios and trains faster than other approaches. Some other works have combined CNN with LSTM (Petersen et al, 2019; Xie et al, 2021) to form hybrid models which have improved the overall accuracy. Hybrid models have been discussed in Section 7.1.…”

Section: Artificial Intelligence Based Modelsmentioning

confidence: 99%

A review of bus arrival time prediction using artificial intelligence

Singh

Kumar

2022

WIREs Data Min & Knowl

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Buses are one of the important parts of public transport system. To provide accurate information about bus arrival and departure times at bus stops is one of the main parameters of good quality public transport. Accurate arrival and departure times information is important for a public transport mode since it enhances ridership as well as satisfaction of travelers. With accurate arrival-time and departure time information, travelers can make informed decisions about their journey. The application of artificial intelligence (AI) based methods/algorithms to predict the bus arrival time (BAT) is reviewed in detail. Systematic survey of existing research conducted by various researchers by applying the different branches of AI has been done. Prediction models have been segregated and are accumulated under respective branches of AI. Thorough discussion is presented to elaborate different branches of AI that have been applied for several aspects of BAT prediction. Research gaps and possible future directions for further research work are summarized.

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“…For instance, a bus route map can be constructed and segmented, based on real-time bus GPS data to derive a path-based waiting time prediction [14]. A similar idea is used by applying three models to predict upstream bus routes separately to provide short-term forecasts for public transportation [15]. Large GPS trajectory data are empirically processed to derive taxi waiting time probabilities at some given locations and times [16].…”

Section: Related Workmentioning

confidence: 99%

A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction

You

Guan

et al. 2021

IJGI

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Taxi waiting times is an important criterion for taxi passengers to choose appropriate pick-up locations in urban environments. How to predict the taxi waiting time accurately at a certain time and location is the key solution for the imbalance between the taxis’ supplies and demands. Considering the life schedule of urban residents and the different functions of geogrid regions, the research developed in this paper introduces a spatio-temporal schedule-based neural network for urban taxi waiting time prediction. The approach integrates a series of multi-source data from taxi trajectories to city points of interest, different time frames and human behaviors in the city. We apply a grid-based and functional structuration of an urban space that provides a lower-level data representation. Overall, the neural network model can dynamically predict the waiting time of taxi passengers in real time under some given spatio-temporal constraints. The experimental results show that the granular-based grids and spatio-temporal neural network can effectively predict and optimize the accuracy of taxi waiting times. This work provides a decision support for intelligent travel predictions of taxi waiting time in a smart city.

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Multi-output Deep Learning for Bus Arrival Time Predictions

Cited by 14 publications

References 14 publications

The bus arrival time prediction using LSTM neural network and location analysis

The bus arrival time prediction using LSTM neural network and location analysis

A review of bus arrival time prediction using artificial intelligence

A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction

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