Towards Improved Vehicle Arrival Time Prediction in Public Transportation: Integrating SUMO and Kalman Filter Models

Abidin, Ahmad Faisal Amri; Kolberg, Mario

doi:10.1109/uksim.2015.63

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…Wu et al [4], MatiurRahman et al [5] presented reviews about several common methods of location prediction based on trajectory data. Technically, these methods can be divided into five categories: Support Vector Machines (SVM) [6]- [11] based, Kalman Filter (KF) [12], [13], [14] based, Global Positioning System (GPS) [15], [16] based, Particle Filtering (PF) [17], [18] based, and Neural Network [19]- [31] based.…”

Section: Related Workmentioning

confidence: 99%

“…KF has been widely applied to this task. Abidin et al considered the effect of utilizing information acquired from social networks in the Kalman Filter model [12]. Li et al considered KF combined with other methods, and proposed a three-stage mixed model which includes K-means, real-time adjusted Kalman filter, Markov historical transfer model [13].…”

Section: B Kalman Filteringmentioning

confidence: 99%

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Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector

Liu

et al. 2020

IEEE Access

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Bus arrival prediction has important implications for public travel, urban dispatch, and mitigation of traffic congestion. The factors affecting urban traffic conditions are complex and changeable. As the predicted distance increases, the difficulty of traffic prediction becomes more difficult. Forecast based on historical data responds quite slowly for changes under the short-term conditions, and vehicle prediction based on real-time speed is not sufficient to predict under long-term conditions. Therefore, an arrival prediction method based on temporal vector and another arrival prediction method based on spatial vector is proposed to solve the problems of remote dependence of bus arrival and road incidents, respectively. In this paper, combining the advantages of the two prediction models, this paper proposes a long short-term memory (LSTM) and Artificial neural networks (ANN) comprehensive prediction model based on spatialtemporal features vectors. The long-distance arrival-to-station prediction is realized from the dimension of time feature, and the short-distance arrival-to-station prediction is realized from the dimension of spatial feature, thereby realizing the bus-to-station prediction. Besides, experiments were conducted and tested based on the entity dataset, and the result shows that the proposed method has high accuracy among bus arrival prediction problems. INDEX TERMS Artificial neural networks, bus arrival prediction, LSTM, spatial-temporal feature vector.

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Section: Related Workmentioning

confidence: 99%

Section: B Kalman Filteringmentioning

confidence: 99%

Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector

Liu

et al. 2020

IEEE Access

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show abstract

“…Among the many prediction models available in the literature, Mir et al [10] consider that those based on Kalman filters are able to optimize speed predictions by minimizing the variance between the measured real-time speed and its estimation. Similarly, Abidin et al [11] used a Kalman filter model to predict the arrival time of public transport vehicles using information captured from social networks. However, making Kalman filter-based estimations is often complex, and requires a high computational power to predict traffic, in conjunction with specialized time series analysis software [12].…”

Section: Related Workmentioning

confidence: 99%

A Centralized Route-Management Solution for Autonomous Vehicles in Urban Areas

et al. 2019

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Currently, one of the main challenges that large metropolitan areas must face is traffic congestion. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, environmental pollution. By properly analyzing traffic demand, it is possible to predict future traffic conditions, using this information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution, thereby improving the traffic flow in a city in a fully centralized manner. This paper represents a step forward towards this novel traffic management paradigm by proposing a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. We perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed traffic prediction equation, combined with frequent updating of traffic conditions on the route server, can achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity.

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“…This is read by the microcontroller which invokes a PHP script on the server which uses a toggle to change the status of the student to either onboard or off board, finds the device id of the corresponding parent and pushes a notification message including the location and time from the status data table to the smart phones of the respective parent via an android application, using the Firebase Cloud Messaging service. The front-end mobile application for the proposed model works on the Android operating system [7]. The application can accommodate three types of users, administration, parents and drivers.…”

Section: Existing Systemmentioning

confidence: 99%

Identification and Transportation Tracking System for School Children using IOT

Inbaraj¹

2019

IJRASET

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The objective of this project is to monitor and track the school children's using Global Positioning System and RFID, IOT. In this paper a system for increasing children's safety is proposed. The focus is on the daily route from home to school and vice versa, assuming the use of school buses. IoT paradigm is exploited together with different localization techniques i.e. RFID and GPS, in order to design a solution for parents willing to make certain of their child's following the main steps to school or home, i.e. taking the school bus and entering school or leaving school and entering the school bus. In this paper the applicability of RFID technology efficient tracking capabilities is tested in children's tracking and monitoring during their trip to and from school by school buses. The proposed solution is discussed in terms of technologies and architecture and the first prototype is presented. Finally a test phase is planned to verify the correct operation of the system.

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Towards Improved Vehicle Arrival Time Prediction in Public Transportation: Integrating SUMO and Kalman Filter Models

Cited by 12 publications

References 10 publications

Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector

Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector

A Centralized Route-Management Solution for Autonomous Vehicles in Urban Areas

Identification and Transportation Tracking System for School Children using IOT

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