A Prediction Model for Bus Arrival Time at Bus Stop Considering Signal Control and Surrounding Traffic Flow

Zhang, Hu; Liang, Shidong; Han, Yin-Yi; Ma, Minghui; Leng, Rongmeng

doi:10.1109/access.2020.3004856

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…The regression analysis method is to analyze the relationship between multiple variables and establish a mathematical function formed by independent variables. According to the linear speed change shown by the relationship between bus speed and traffic density, Zhang et al proposed a bus arrival time prediction model considering upstream signalized intersection and surrounding traffic flow [3]. Zhou et al used the Kalman Filter algorithm to establish a prediction model of bus travel time [4].…”

Section: Statistical Prediction Methodmentioning

confidence: 99%

XGBoost-Based Travel Time Prediction between Bus Stations and Analysis of Influencing Factors

Zhu

Zou

2022

Wireless Communications and Mobile Computing

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Real-time and accurate travel time information between bus stations is critical for passengers to make suitable travel plans to reduce waiting time at the stops. By mining and analyzing bus operational data, it can be obtained that factors such as the variation of vehicle speed in adjacent sections and the proportion of bus lanes between stations have affected the travel time between bus stations. Therefore, considering the temporal feature, spatial feature, and weather feature as the prediction model’s input, travel time between bus stations prediction model based on eXtreme Gradient Boosting (XGBoost) was trained and established. The 28-day bus operation data of a certain bus line in Guangzhou was used for training and verification, and they were compared with the prediction models based on K -Nearest Neighbors (KNN), BP neural network, and Light Gradient Boosting Machine (LightGBM). In comparison with other models, the lowest MAPE of 11.96% was found for the XGBoost prediction model, which is 9.30% lower than other models on average. The sensitivity analysis of the proposed prediction model was further conducted: temporally, the accuracy of the prediction model was best during the flat peak hours; spatially, the MAPE of the model gradually decreased as the number of line units increased, and when the number of line units exceeded 18, the accuracy of the prediction model stabilized and was lower than 7%. The results confirm that the XGBoost model outperforms the KNN, BP, and LightGBM in terms of fitting, accuracy, and stability.

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Section: Statistical Prediction Methodmentioning

confidence: 99%

XGBoost-Based Travel Time Prediction between Bus Stations and Analysis of Influencing Factors

Zhu

Zou

2022

Wireless Communications and Mobile Computing

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“…According to Zhang et al [5] bus arrival time is commonly forecasted to assess future bus operation conditions (e.g., bus bunching and transit service reliability) and can then be used to inform present control actions, particularly for real-time control systems. The time of travel and traffic scenario are the most important components in forecasting, yet most previous techniques have only looked at the impact of signal control on delay in travel time.…”

Section: Related Workmentioning

confidence: 99%

Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Jalaney¹,

Ganesh²

2023

Intelligent Automation &Amp; Soft Computing

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Due to fast-growing urbanization, the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where information regarding all the buses connecting in a city will be gathered, processed and accurate bus arrival time prediction will be presented to the user. Various linear and time-varying parameters such as distance, waiting time at stops, red signal duration at a traffic signal, traffic density, turning density, rush hours, weather conditions, number of passengers on the bus, type of day, road type, average vehicle speed limit, current vehicle speed affecting traffic are used for the analysis. The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area. Multiple ELMs (MELM) for explicitly training dynamic, road and trajectory information are used in the proposed approach. A large-scale dataset (historical data) obtained from Kerala State Road Transport Corporation is used for training. Simulations are carried out by using MATLAB R2021a. The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week. It can manage huge volumes of data with less human intervention at greater learning speeds. It is found MELM yields prediction with accuracy in the range of 96.7% to 99.08%. The MAE value is between 0.28 to 1.74 minutes with the proposed approach. The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy. The research has proved that MELM is superior for arrival time predictions in terms of accuracy and error, compared with other approaches.

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“…The proposed model was tested on two signalized intersections, and it was found that the proposed model outperformed existing models in the literature. Zhang, et al [28] proposed a prediction model for bus arrival time considering signalized control and surrounding traffic flow. The authors adopted the speed-flow-density relationship to derive the travel speed.…”

Section: ) Bus Speed/travel Time Predictionmentioning

confidence: 99%

Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach

Cui

Xie

Hu³

et al. 2021

IEEE Access

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This study proposes a novel Bayesian hierarchical approach for online bus speed prediction by explicitly accounting for the spatiotemporal interaction (STI) of speed observations. The use of Laplace approximation can expedite the estimation of Bayesian models and enable the implementation of online prediction. Large numbers of trials are carried out to identify significant predictors and the optimal length of the look-back time window to achieve the highest prediction accuracy. The spatiotemporal interacting patterns are also explored, and results show that the Type IV model assuming the structured spatial effect interacts with the structured temporal effect can best accommodate the bus speed data. Besides, prediction errors of the Type IV model randomly distribute over time and space. The proposed model can achieve high prediction accuracy and computational efficiency without compromising the interpretability of the contributing factors and the unobserved spatiotemporal heterogeneity. The proposed model can be used to assist public transit operation and management, such as bus scheduling, congestion warning, and the development of proactive measures to mitigate bus delays.

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A Prediction Model for Bus Arrival Time at Bus Stop Considering Signal Control and Surrounding Traffic Flow

Cited by 14 publications

References 19 publications

XGBoost-Based Travel Time Prediction between Bus Stations and Analysis of Influencing Factors

XGBoost-Based Travel Time Prediction between Bus Stations and Analysis of Influencing Factors

Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach

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