Prediction of Bus Travel Time Using Artificial Neural Network

Johar, Amita; Singh, Jain Sukhvir; Kumar, Garg Pradeep

doi:10.7708/ijtte.2015.5(4).06

Cited by 30 publications

(15 citation statements)

References 17 publications

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“…Many interesting neural network projects have been carried out ever since its introduction, for instance, the famous DeepMind by Google to play board games against professionals has gained huge success from it. ANN consists of a few layers, generally the input layer, hidden layer (there can be multiple hidden layers depending on the complexity of the problem) and output layer [22]. All the layers are interconnected, where the input layer consists of all the input variables, and all the input layers are connected to the hidden layers, and the hidden layer is connected to the output layer.…”

Section: Machine Learning Modelmentioning

confidence: 99%

Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

Noor¹,

N²,

Kolandaisamy³

et al. 2020

Preprint

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The impact of the accurate estimated time of arrival (ETA) is often overlooked by bus operators. By providing accurate ETA to riders, it gives them the impression of bus services is efficient and reliable and this promotes higher ridership in the long run. This research project aims to predict bus arrival time by using the Support Vector Regression (SVR) model which is based on the same theory as the Support Vector Machine (SVM). Urban City Bus data covering part of the Petaling Jaya area (route name PJ03) is used in this research work. Features related to traffic such as travel duration, a distance of the road, weather and operation at peak or non-peak hour have been used as input in the training of the SVR model. By using kernel trick and specifying optimum parameters, all the features in higher dimensions are efficiently calculated and the SVR model achieves convergence. The model is evaluated with the test set of data split from the original dataset. The experimental result indicates the SVR model displays good prediction ability with its low average error on the prediction result. However, weather data has not been influential to the prediction model as the results of the model trained with and without weather data show a negligible difference.

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Section: Machine Learning Modelmentioning

confidence: 99%

Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

Noor¹,

N²,

Kolandaisamy³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Results indicated that the model performed well in terms of reducing relative mean errors and root-mean-squared errors in relation to other models. Amita et al (2015) employed Artificial Neural Network to develop a bus travel time prediction model and when the model used to predict bus travel time underperformed compared with other models in terms of accuracy and robustness. In general, factors that influence urban travel time are intercorrelated with each other, which limits the applicability of the regression models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Artificial Neural Network (ANN) models have been developed to complement the weakness found in the regression model. Many researchers have opted to use the model to predict urban travel time because of their ability to solve complicated nonlinear relationships in traffic f low prediction (Amita et al, 2015;Bai et al, 2015;Fan and Gurmu, 2015;Chien et al, 2002). Zheng and Van Zuylen (2013) applied an Artificial Neural Network (ANN) model to estimate urban link travel time using speed, position and time-stamped from the probe vehicle as input data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Zheng and Van Zuylen (2013) applied an Artificial Neural Network (ANN) model to estimate urban link travel time using speed, position and time-stamped from the probe vehicle as input data. Amita et al (2015) employed an Artificial Neural Network to predict bus travel time and the model outperformed in terms of accuracy and robustness. However, Li et al (2017) reported that the Artificial Neural Network model shall be only applied to predict dynamic travel time when online information is available.…”

Section: Literature Reviewmentioning

confidence: 99%

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Comparative Assessment of Dynamic Travel Time Prediction Models in the Developing Countries Cities

2020

IJTTE

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Providing real and accurate travel time information usually, assists road users to plan their trips and choose the appropriate mode of transport. However, accurate prediction of travel time is a challenging problem, especially in developing countries where heterogeneous flow conditions exist and there are no records of information about the travel time for travelers. Most of the dynamic travel-time prediction models developed emphasize on link travel time without taking into account delay time at the intersections and waiting time at the bus stops. The objective of this study was to compare Multi-Linear Regression and Artificial Neural Network models to obtain a suitable model for developing a dynamic travel-time prediction model using waiting time at the bus stop, intersection delay time, link distance, traffic volume, link travel time, peak hours and off-peak hours as model inputs. Link travel time was modeled by a well-trained Neural Network and Kalman filtering dynamic algorithm using field survey data collected by employing public buses in Dar es Salaam city. The model was validated by using data collected in five main routes in Dar es Salaam City. The Root Mean Square Error and Mean Absolute Percent Error were used to evaluate the performance of the model by comparing it with other prediction models. Findings indicate that the integration of the Artificial Neural Network and Kalman Filter algorithm model (ANN-KF) promised to be a reasonable model for predicting dynamic travel time in Dar es Salaam city.

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“…A link based ANN model for travel time prediction was proposed using a feed-forward network for training the network and logistic sigmoid function as activation function (Nahar and Sultana, 2014). A bus travel time prediction model was developed using Artificial Neural Network (ANN) model by taking dwell time, delays and distance between the bus stops as inputs (Amita et al, 2015). Model was developed, validated and tested using GPS (Global Positioning System) data collected from field study.…”

Section: Reviewmentioning

confidence: 99%

Travel Time Prediction Modelling in Mixed Traffic Conditions

2018

IJTTE

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Abstract:Over the past few decades, exponential increase in vehicle ownership resulted in issues of traffic control and management. Intelligent Transportation System (ITS) is one of the solutions for the intelligent management of traffic. ITS applications like Advanced Traveler Information Systems and Advanced Traffic Management Systems need travel time as a major input. The estimation of travel time in urban network became more complicated because of the rapid change in the system and traffic. This study is done in order to assess the impact of different travel modes on travel time. Data has been collected on a stretch of 14 km length in Warangal city, India using GPS probe vehicle along with video camera. Different private modes of transportation such as 2 wheeler, passenger car and 3 wheeler have been used as test vehicles for the collection of data in different traffic flow scenarios. Artificial Neural Network and a multi linear regression model have been developed to compare the estimated travel times with the field data. Two combinations of ANN model using single hidden layer, different numbers of neurons and epochs have been compared. The travel time of different modes has been compared and the effect of vehicle composition on travel time has been analyzed. The ANN model perform better than the regression model.

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Prediction of Bus Travel Time Using Artificial Neural Network

Cited by 30 publications

References 17 publications

Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

Comparative Assessment of Dynamic Travel Time Prediction Models in the Developing Countries Cities

Travel Time Prediction Modelling in Mixed Traffic Conditions

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