Estimation of Passenger Flow in a Bus Route using Kalman Filter

Vidya, Gayatri; Hari, V S; Shivasagaran, Suryakumar

doi:10.1109/icaccs48705.2020.9074363

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…When the bus can arrive at the appropriate time, it can reduce the waiting time of passengers. In the meantime, public transport companies can reasonably arrange vehicle shifts to reduce operating costs [7]. This kind of algorithm can express the prediction problem with a simplified model, which is interpretable and fast.…”

Section: Traditional Methodsmentioning

confidence: 99%

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MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection

Guo,

Zhu,

Wang

2024

Applied Sciences

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In recent years, the rapid growth of vehicles has imposed a significant burden on urban road resources. To alleviate urban traffic congestion in intelligent transportation systems (ITS), real-time and accurate traffic flow prediction has emerged as an effective approach. However, selecting relevant parameters from traffic flow information and adjusting hyperparameters in intelligent algorithms to achieve high prediction accuracy is a time-consuming process, posing practical challenges in dynamically changing traffic conditions. To address these challenges, this paper introduces a novel prediction architecture called Multiple Variables Heuristic Selection Long Short-Term Memory (MVHS-LSTM). The key innovation lies in its ability to select informative parameters, eliminating unnecessary factors to reduce computational costs while achieving a balance between prediction performance and computing efficiency. The MVHS-LSTM model employs the Ordinary Least Squares (OLS) method to intelligently reduce factors and optimize cost efficiency. Additionally, it dynamically selects hyperparameters through a heuristic iteration process involving epoch, learning rate, and window length, ensuring adaptability and improved accuracy. Extensive simulations were conducted using real traffic flow data from Shanghai to evaluate the enhanced performance of MVHS-LSTM. The prediction results were compared with those of the ARIMA, SVM, and PSO-LSTM models, demonstrating the innovative capabilities and advantages of the proposed model.

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Section: Traditional Methodsmentioning

confidence: 99%

“…The R-squared and test results are shown as the following Table 2. According to Equation (7), it is obvious that the correlation between the vehicle speed and traffic flow is much higher than the others, and the R-squared is less than 0.25. The highest is 0.1383, which is Speed+Lanes+Veh Distance.…”

Section: Performance Analysis Of Our Proposed Methodsmentioning

confidence: 99%

MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection

Guo,

Zhu,

Wang

2024

Applied Sciences

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“…Kalman Filters (KFs) are often combined with other models, especially for combining historical models with a real-time component [38][39][40]. The primary advantage of KF models is their noise robustness and little dependence on large training data sets.…”

Section: A Forecasting Models For Passenger Load Predictionmentioning

confidence: 99%

“…The primary advantage of KF models is their noise robustness and little dependence on large training data sets. For example, Vidya et al [39] estimated the passenger flow with a Geometric Brownian motion as an internal model and updated its prediction with a KF by applying currently observed passenger numbers, highlighting the KF model's ability to function with very few data points. On its own, a KF often lacks accuracy, but with other models, a KF can improve other models' accuracy by updating a prediction with observed values.…”

Section: A Forecasting Models For Passenger Load Predictionmentioning

confidence: 99%

Improving the Prediction of Passenger Numbers in Public Transit Networks by Combining Short-Term Forecasts With Real-Time Occupancy Data

Hoppe

Schwinger

Haeger

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Passengers of public transportation nowadays expect reliable and accurate travel information. The need for occupancy information is becoming more prevalent in intelligent public transport systems as people started avoiding overcrowded vehicles during the COVID-19 pandemic. Furthermore, public transportation companies require accurate occupancy forecasts to improve service quality. We present a novel approach to improve the prediction of passenger numbers that enhances a day-ahead prediction with real-time data. We first train a baseline predictor on historical automatic passenger counting data. Next, we train a realtime model on the deviations between baseline prediction and observed values, thus capturing events not addressed by the baseline. For the forecast, we attempt to detect emerging patterns in real time and adjust the baseline prediction with deviations from the patterns. Our experiments with data from Germany show that the proposed model improves the forecast of the baseline model and is only outperformed by artificial neural networks in some instances. If the training sets only cover a limited period of up to four months, our approach outperforms competing methods. For larger training sets, there are mixed results in the sense that for some test cases, certain types of neural networks yield slightly better results, but our method still performs well with less training effort, is explainable along the whole prediction process and can be applied to existing prediction methods.

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Forecasting the number of vehicle kilometers by applying the autoregression model, using Warsaw trams as an example

Grzelak

Wiejak

Świderski

2022

WJTE

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One of the biggest challenges of the 21st century is ecological responsibility. It also concerns the sustainable development of transport and the reduction of threats related to the negative impact of this phenomenon on the environment. A constant increase in transport congestion, atmospheric air pollution, and noise promotes the search for new solutions, especially in urban areas. One of the systematically implemented and improved ideas in this area is the development of urban transport systems. Their effectiveness and efficiency are evidenced by the level of meeting the transport needs of residents, with the optimal utilization of vehicles. The article analyses urban transport in Warsaw, focusing only on trams as the second most popular means of transport after wheeled vehicles. Two objectives of the study were adopted. The first was evaluating the current state and characteristics of the available options and indicating potential development directions, considering factors that determine it. The second goal was to select the appropriate model describing the number of vehicle kilometers accumulated by Warsaw trams in the years 2017-2019 and parametric identification of this model. The study allowed us to estimate and make a short-term forecast of transport services carried out by trams. The research has shown that the current situation regarding the performance of transport work by trams in Warsaw does not fit into the paradigm of sustainable transport development. This is due to the loss of vehicles from the records in the absence of new vehicle purchases. Additionally, the developed tool indicates a decrease in the number of vehicles-km performed in the following months and, thus, a reduction in the share of trams in transport in the Warsaw communication system. The identified problem (i.e., a downward trend in transport performance) is essential from the point of view of the quality of the system's operation and the ability to meet passengers' expectations. It also informs decision-makers about the need to implement changes leading to an increase in the share of tram transport, mainly in capacity and operating costs.

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Estimation of Passenger Flow in a Bus Route using Kalman Filter

Cited by 3 publications

References 12 publications

MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection

MVHS-LSTM: The Comprehensive Traffic Flow Prediction Based on Improved LSTM via Multiple Variables Heuristic Selection

Improving the Prediction of Passenger Numbers in Public Transit Networks by Combining Short-Term Forecasts With Real-Time Occupancy Data

Forecasting the number of vehicle kilometers by applying the autoregression model, using Warsaw trams as an example

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