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

Noor, Rafidah Md; N, Seong Yik; Kolandaisamy, Raenu; Ahmedy, Ismail; Ma, Hui; K, Alvin Yau; Shah, Wahidah Md; Nandy, Tarak

doi:10.20944/preprints202002.0197.v1

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…Time variant distributions of the travel time of path sections have been visualized through the clustering algorithm. Rafidah Md Noor et al [ 22 , 23 ] have used SVR for predicting bus arrival time. Attributes include distance of the road, peak or nonpeak hour, travel duration and weather.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic congestion prediction based on Estimated Time of Arrival

Zafar

Haq

2020

PLoS ONE

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With the rapid expansion of sensor technologies and wireless network infrastructure, research and development of traffic associated applications, such as real-time traffic maps, on-demand travel route reference and traffic forecasting are gaining much more attention than ever before. In this paper, we elaborate on our traffic prediction application, which is based on traffic data collected through Google Map API. Our application is a desktop-based application that predicts traffic congestion state using Estimated Time of Arrival (ETA). In addition to ETA, the prediction system takes into account various features such as weather, time period, special conditions, holidays, etc. The label of the classifier is identified as one of the five traffic states i.e. smooth, slightly congested, congested, highly congested or blockage. The results demonstrate that the random forest classification algorithm has the highest prediction accuracy of 92 percent followed by XGBoost and KNN respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Traffic congestion prediction based on Estimated Time of Arrival

Zafar

Haq

2020

PLoS ONE

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“…A similar problem was considered by Chen et al; however, they did not investigate the improvement resulting from the addition of the precipitation data explicitly (29). A more recent study by Noor et al, similar to Patnaik et al, has not been able to significantly improve the bus arrival time prediction model by incorporating the weather data (28,30). They suggested that the small sample size could have been the cause.…”

Section: Related Workmentioning

confidence: 99%

Estimating the Impact of High-Fidelity Rainfall Data on Traffic Conditions and Traffic Prediction

Prokhorchuk

Mitrović

Usman³

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Accurate prediction of network-level traffic parameters during inclement weather conditions can greatly help in many transportation applications. Rainfall tends to have a quantifiable impact on driving behavior and traffic network performance. This impact is often studied for low-resolution rainfall data on small road networks, whereas this study investigates it in the context of a large traffic network and high-resolution rainfall radar images. First, the impact of rainfall intensity on traffic performance throughout the day and for different road categories is analyzed. Next, it is investigated whether including rainfall information can improve the predictive accuracy of the state-of-the-art traffic forecasting methods. Numerical results show that the impact of rainfall on traffic varies for different rainfall intensities as well as for different times of the day and days of the week. The results also show that incorporating rainfall data into prediction models improves their overall performance. The average reduction in mean absolute percentage error (MAPE) for models with rainfall data is 4.5%. Experiments with downsampled rainfall data were also performed, and it was concluded that incorporating higher resolution weather data does indeed lead to an increase in performance of traffic prediction models.

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“…This section illustrates the scalability comparison of our model and SVR for this dataset. The reason for making this comparison is that SVR is among the other machine learning approaches which are popular for bus arrival time prediction problems and a lot of researchers used SVR with the Radial Basis Function (RBF) kernel for bus arrival time prediction [21].…”

Section: ) Scalability Comparison Between Our Model and Svrmentioning

confidence: 99%

A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On-Demand Public Transit

Rashvand¹,

Hosseini²,

Azarbayjani³

et al. 2023

Preprint

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Among the major public transportation systems in cities, bus transit has its problems, including more accuracy and reliability when estimating the bus arrival time for riders. This can lead to delays and decreased ridership, especially in cities where public transportation is heavily relied upon. A common issue is that the arrival times of buses do not match the schedules, resulting in latency for fixed schedules. According to the study in this paper on New York City bus data, there is an average delay of around eight minutes or 491 seconds mismatch between the bus arrivals and the actual scheduled time. This research paper presents a novel AI-based data-driven approach for estimating the arrival times of buses at each transit point (station). Our approach is based on a fully connected neural network and can predict the arrival time collectively across all bus lines in large metropolitan areas. Our neural-net data-driven approach provides a new way to estimate the arrival time of the buses, which can lead to a more efficient and smarter way to bring the bus transit to the general public. Our evaluation of the network bus system with more than 200 bus lines, and 2 million data points, demonstrates less than 40 seconds of estimated error for arrival times. The inference time per each validation set data point is less than 0.006 ms.

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

Cited by 8 publications

References 12 publications

Traffic congestion prediction based on Estimated Time of Arrival

Traffic congestion prediction based on Estimated Time of Arrival

Estimating the Impact of High-Fidelity Rainfall Data on Traffic Conditions and Traffic Prediction

A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On-Demand Public Transit

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