Historical data based real time prediction of vehicle arrival time

Maiti, Santa; Pal, Arpan; Pal, Arindam; Chattopadhyay, Tanushyam; Mukherjee, Arijit

doi:10.1109/itsc.2014.6957960

Cited by 29 publications

(12 citation statements)

References 8 publications

(7 reference statements)

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“…One of the easiest ways to estimate the arrival time of a bus is to use the historical data of each route mentioned in [23][24][25]. In this method, the average historical data for each path is considered as the estimated value for that path.…”

Section: Literature Reviewmentioning

confidence: 99%

A new low complexity bus travel time estimator for fleet management system

Alizade

Hendessi

et al. 2021

Preprint

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Improving the experience of using the public transportation system can be done by estimating the arrival time of the bus and notifying the passengers. Consequently, the accuracy of the estimation affects this experience. As the number of buses, stations, and service areas increases, so does the data collected in the cloud, making travel time estimation-related data processing more challenging. Despite this challenge, a distributed method for estimating the arrival time of the bus is considered in this paper. Also, we present a way to decentralize data processing and distribute it on each bus. Besides, using the Kalman filter and updating the estimated values at short intervals improves the estimation error. Examination of the degree of complexity shows that the proposed method has significantly reduced the complexity in the cloud, which makes the proposed method can be implemented in metropolitan areas. The results of implementation on a dataset, show that the proposed method has a good performance in terms of mean square error and root mean square.

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Section: Literature Reviewmentioning

confidence: 99%

A new low complexity bus travel time estimator for fleet management system

Alizade

Hendessi

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…ITSs-defined as a group of technologies, systems, and services for efficient and secure transport services-cover a wide domain that includes both private and commercial transportation systems. Benefitting from intelligent technology integration, ITSs have already provided many new opportunities for building safe, reliable, and scalable service infrastructures for transport [22][23][24]. Consequently, ITSs have become a key element of smart cities and connected digital ecosystems [25].…”

Section: Intelligent Transport Management Systemsmentioning

confidence: 99%

“…Combined with machine learning techniques, such data allow forecasting about the network's status, even when there is no information available about external factors [23,[40][41][42][43]. Existing systems have exploited neural networks and regression and clustering techniques to predict bus arrival times [43][44][45][46][47], while others have performed route prediction using GPS data observation [48]. Overall, conversion of data into knowledge by the application of smart analytics techniques supports strategic decision-making and system automation, delivering improved operations and services.…”

Section: Transport Data Analyticsmentioning

confidence: 99%

Scalable System for Smart Urban Transport Management

Khan

Nebel

Khaddaj

et al. 2020

Journal of Advanced Transportation

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Efficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand-side transport management is particularly challenging due to the increasing flow of transmitted sensor data. In this work, a novel smart service-driven, adaptable middleware architecture is proposed to acquire, store, manipulate, and integrate information from heterogeneous data sources in order to deliver smart analytics aimed at supporting strategic decision-making. The architecture offers adaptive and scalable data integration services for acquiring and processing dynamic data, delivering fast response time, and offering data mining and machine learning models for real-time prediction, combined with advanced visualisation techniques. The proposed solution has been implemented and validated, demonstrating its ability to provide real-time performance on the existing, operational, and large-scale bus network of a European capital city.

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“…Other methods including NPR and LR are not so popular, but they are quite simple in calibration and calculation (Park et al 2007;Chang et al 2010;Maiti et at. 2014).…”

Section: Literaturementioning

confidence: 99%

Bus arrival time prediction using mixed multi-route arrival time data at previous stop

Hua

Wang

et al. 2017

Transport

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Abstract. The primary objective of this paper is to develop models to predict bus arrival time at a target stop using actual multi-route bus arrival time data from previous stop as inputs. In order to mix and fully utilize the multiple routes bus arrival time data, the weighted average travel time and three Forgetting Factor Functions (FFFs) -F1, F2 and F3 -are introduced. Based on different combinations of input variables, five prediction models are proposed. Three widely used algorithms, i.e. Support Vector Machine (SVM), Artificial Neutral Network (ANN) and Linear Regression (LR), are tested to find the best for arrival time prediction. Bus location data of 11 road segments from Yichun (China), covering 12 bus stops and 16 routes, are collected to evaluate the performance of the proposed approaches. The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy: the prediction errors reduce by around 20%. The algorithm comparison demonstrates that the SVM and ANN outperform the LR. The FFFs can also affect the performance errors: F1 is more suitable for ANN algorithm, while F3 is better for SVM and LR algorithms. Besides, the virtual road concept in this paper can slightly improve the prediction accuracy and halve the time cost of predicted arrival time calculation.

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Historical data based real time prediction of vehicle arrival time

Cited by 29 publications

References 8 publications

A new low complexity bus travel time estimator for fleet management system

A new low complexity bus travel time estimator for fleet management system

Scalable System for Smart Urban Transport Management

Bus arrival time prediction using mixed multi-route arrival time data at previous stop

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