INTEGRATION OF EXPONENTIAL SMOOTHING WITH STATE SPACE FORMULATION FOR BUS TRAVEL TIME AND ARRIVAL TIME PREDICTION

In recent years, the problem of bus travel time prediction is becoming more important for applications such as informing passengers regarding the expected bus arrival time in order to make public transit more attractive to the urban commuters. One of the popular techniques reported for such prediction is the use of time series analysis. Most of the studies on the application of time series techniques for bus arrival time prediction used Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) models, which are presently not suited for real time implementation. This is mainly due to the necessity and dependence of ARIMA models on a time series modelling software to execute. Moreover, the ARIMA model building process is time consuming, making it difficult to use for real-time implementations. Alternatively, Exponential Smoothing (ES) methods can be used, as they are easy to understand and implement when compared to ARIMA models. The present study is an attempt in this direction, where the basic equation of ES is used, as the state equation with Kalman filtering to recursively update the travel time estimate as the new observation becomes available. The proposed algorithm of state space formulation of ES with Kalman filtering for bus travel time and arrival time prediction was field tested using 105 actual bus trips data along a particular bus route from Chennai, India. The results are promising and a comparison of the proposed algorithm with ES alone without state space formulation and Kalman filtering has also been performed. An information system based on a webpage for real-time display of bus arrival times has been designed and developed using the proposed algorithm.

Download Full-text

Bus Arrival Time Modeling Based on Auckland Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119840620 ◽

2019 ◽

Vol 2673 (6) ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Prakash Ranjitkar ◽

Li-Sian Tey ◽

Enakshi Chakravorty ◽

Kirsten L. Hurley

Keyword(s):

Time Series ◽

Arrival Time ◽

Gene Expression Programming ◽

Time Prediction ◽

Time Modeling ◽

Regression Techniques ◽

Artificial Neural ◽

Arrival Time Prediction ◽

Bus Arrival Time Prediction ◽

Bus Travel Time

Inaccurate bus arrival time predictions are counterproductive to changing transport habits and promoting public transport use. This research sought to improve the bus passenger experience in terms of bus arrival time prediction by investigating various time series and regression-based techniques suitable for bus arrival time modeling. The models developed in the current study included: random walk with drift, multivariate linear regression, decision tree, artificial neural networks, and gene expression programming models. Historic automatic vehicle location and passenger flow data obtained for four bus routes spanning Auckland city, in both travel directions, were used as model inputs. Specifically, 10 independent variables were incorporated in the regression models, with distance between bus stops being the most significant predictor for bus travel time. Research results indicated that time series models outperformed regression techniques, with the time series artificial neural network being the most successful of the seven models developed. Moreover, the alternative models all performed significantly better than the prediction engine currently utilized by an Auckland bus company for arrival time prediction. However, these results require corroboration with manually collected field data, on account of the quality concerns afflicting the raw data reported by the transport company.

Download Full-text

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

Computational Intelligence and Neuroscience ◽

10.1155/2015/432389 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 31

Author(s):

Cong Bai ◽

Zhong-Ren Peng ◽

Qing-Chang Lu ◽

Jian Sun

Keyword(s):

Travel Time ◽

Dynamic Model ◽

Kalman Filtering ◽

Prediction Models ◽

Support Vector ◽

Travel Times ◽

Travel Time Prediction ◽

Real World Data ◽

Time Prediction ◽

Bus Travel Time

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Download Full-text

Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework

Remote Sensing ◽

10.3390/rs13091738 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1738

Author(s):

Huiyuan Fu ◽

Yuchao Zheng ◽

Yudong Ye ◽

Xueshang Feng ◽

Chaoxu Liu ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Arrival Time ◽

Optical Observations ◽

Feature Maps ◽

Feature Map ◽

Learning Framework ◽

Time Prediction ◽

Arrival Time Prediction ◽

Better Than

Fast and accurate prediction of the geoeffectiveness of coronal mass ejections (CMEs) and the arrival time of the geoeffective CMEs is urgent, to reduce the harm caused by CMEs. In this paper, we present a new deep learning framework based on time series of satellites’ optical observations that can give both the geoeffectiveness and the arrival time prediction of the CME events. It is the first time combining these two demands in a unified deep learning framework with no requirement of manually feature selection and get results immediately. The only input of the deep learning framework is the time series images from synchronized solar white-light and EUV observations. Our framework first uses the deep residual network embedded with the attention mechanism to extract feature maps for each observation image, then fuses the feature map of each image by the feature map fusion module and determines the geoeffectiveness of CME events. For the geoeffective CME events, we further predict its arrival time by the deep residual regression network based on group convolution. In order to train and evaluate our proposed framework, we collect 2400 partial-/full-halo CME events and its corresponding images from 1996 to 2018. The F1 score and Accuracy of the geoeffectiveness prediction can reach 0.270% and 75.1%, respectively, and the mean absolute error of the arrival time prediction is only 5.8 h, which are both significantly better than well-known deep learning methods and can be comparable to, or even better than, the best performance of traditional methods.

Download Full-text

A Real-Time Bus Travel-Time Prediction Model Based on Kalman Filtering Theory

Applications of Advanced Technologies in Transportation (2002) ◽

10.1061/40632(245)115 ◽

2002 ◽

Cited By ~ 1

Author(s):

Sheng Li

Keyword(s):

Prediction Model ◽

Travel Time ◽

Real Time ◽

Kalman Filtering ◽

Travel Time Prediction ◽

Filtering Theory ◽

Time Prediction ◽

Model Based ◽

Bus Travel Time

Download Full-text

Bus arrival time prediction using mixed multi-route arrival time data at previous stop

Transport ◽

10.3846/16484142.2017.1298055 ◽

2017 ◽

Vol 33 (2) ◽

pp. 543-554 ◽

Cited By ~ 7

Author(s):

Xuedong Hua ◽

Wei Wang ◽

Yinhai Wang ◽

Min Ren

Keyword(s):

Travel Time ◽

Prediction Accuracy ◽

Arrival Time ◽

Weighted Average ◽

Support Vector ◽

Time Data ◽

Time Prediction ◽

Algorithm Comparison ◽

Average Travel Time ◽

Arrival Time Prediction

The primary objective of this paper is to develop models to predict bus arrival time at a target stop using actual multi-route bus arrival time data from previous stop as inputs. In order to mix and fully utilize the multiple routes bus arrival time data, the weighted average travel time and three Forgetting Factor Functions (FFFs) – F1, F2 and F3 – are introduced. Based on different combinations of input variables, five prediction models are proposed. Three widely used algorithms, i.e. Support Vector Machine (SVM), Artificial Neutral Network (ANN) and Linear Regression (LR), are tested to find the best for arrival time prediction. Bus location data of 11 road segments from Yichun (China), covering 12 bus stops and 16 routes, are collected to evaluate the performance of the proposed approaches. The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy: the prediction errors reduce by around 20%. The algorithm comparison demonstrates that the SVM and ANN outperform the LR. The FFFs can also affect the performance errors: F1 is more suitable for ANN algorithm, while F3 is better for SVM and LR algorithms. Besides, the virtual road concept in this paper can slightly improve the prediction accuracy and halve the time cost of predicted arrival time calculation.

Download Full-text