Evaluating citywide bus service reliability using noisy GPS data

Wang, Shen; Namee, Brian Mac

doi:10.1109/isc2.2017.8090843

Cited by 3 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kim et al [ 34] introduced a road marking mapping approach using an optimal method considering probe vehicle loaded advance precision sensor for locating an autonomous vehicle in an area experiencing GPS interruption. Wang and Namee [35] have presented a study towards the reliability of navigational data obtained from a noisy signal. Decomposition of empirical values towards denoising principle is carried out by Baykut et al [36].…”

Section: Related Workmentioning

confidence: 99%

PPT-NS: Prioritized Progressive Trend Based Navigational System on GPS data with Artifacts

K G*

2019

IJITEE

View full text Add to dashboard Cite

The Global Positioning System is extensively used in the various context and location service-based applications. Any kind of abnormalities requires an efficient and suitable pre-processing algorithm to be implemented on the data which provides accurate results when used in the application synchronizations. This paper illustrates a framework for various pre-processing techniques applied to the real-time GPS data and its effect on trajectory mapping. The technique used includes Prioritized pattern-based, Savitzky-Golay filtering, outlier elimination, de-trending, and coefficient correlation. The performance assessment of methods discussed in this study is calculated in terms of accuracy with the original and re-created trajectory after the pre-processing and found that the best result is given by moving window method.

show abstract

Section: Related Workmentioning

confidence: 99%

PPT-NS: Prioritized Progressive Trend Based Navigational System on GPS data with Artifacts

K G*

2019

IJITEE

View full text Add to dashboard Cite

show abstract

BusTime: Which is the Right Prediction Model for My Bus Arrival Time?

Liu

Sun

Wang

2020

2020 5th IEEE International Conference on Big Data Analytics (ICBDA)

View full text Add to dashboard Cite

BusTime: Which is the Right Prediction Model for My Bus Arrival Time?

Liu¹,

Sun²,

Shen³

2020

Preprint

View full text Add to dashboard Cite

With the rise of big data technologies, many smart transportation applications have been rapidly developed in recent years including bus arrival time predictions. This type of applications help passengers to plan trips more efficiently without wasting unpredictable amount of waiting time at bus stops. Many studies focus on improving the prediction accuracy of various machine learning and statistical models, while much less work demonstrate their applicability of being deployed and used in realistic urban settings. This paper tries to fill this gap by proposing a general and practical evaluation framework for analysing various widely used prediction models (i.e. delay, knearest-neighbor, kernel regression, additive model, and recurrent neural network using long short term memory) for bus arrival time. In particular, this framework contains a raw bus GPS data pre-processing method that needs much less number of input data points while still maintain satisfactory prediction results. This pre-processing method enables various models to predict arrival time at bus stops only, by using a kd-tree based nearest point search method. Based on this framework, using raw bus GPS dataset in different scales from the city of Dublin, Ireland, we also present preliminary results for city managers by analysing the practical strengths and weaknesses in both training and predicting stages of commonly used prediction models.

show abstract

Evaluating citywide bus service reliability using noisy GPS data

Cited by 3 publications

References 8 publications

PPT-NS: Prioritized Progressive Trend Based Navigational System on GPS data with Artifacts

PPT-NS: Prioritized Progressive Trend Based Navigational System on GPS data with Artifacts

BusTime: Which is the Right Prediction Model for My Bus Arrival Time?

BusTime: Which is the Right Prediction Model for My Bus Arrival Time?

Contact Info

Product

Resources

About