Hybrid time series forecasting methods for travel time prediction

Seri̇n, Faruk; ALİŞAN, Yiğit; Kece, Adnan

doi:10.1016/j.physa.2021.126134

Cited by 19 publications

(6 citation statements)

References 27 publications

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“…Others have developed travel time prediction models, including parametric methods such as linear regression [43], Bayesian Nets [44], and Time Series models [45]. Additionally, there are non-parametric models like Artificial Neural Network models [46] and machine learning methods like K-Nearest Neighbours [47], Support Vector Regression [48], and Random Forest Regression [49].…”

Section: Travel Timementioning

confidence: 99%

Route Planning under Mobility Restrictions in the Palestinian Territories

Aburas,

Shahrour,

Giglio

2024

Sustainability

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This study aims to enhance people’s mobility in the context of mobility restrictions in the Palestinian territories, West Bank. It aims to develop a comprehensive route planning model that prioritises safety and optimises travel time while also considering sustainability issues. Unlike previous research, which has often focused solely on traffic crashes and physical road considerations in safety route planning, this study addresses the gap by developing a comprehensive model that integrates new risk criteria including mobility restrictions and violent events. The methodology involves historical and real-time data collection and processing, machine learning-based travel time prediction, and route optimisation using Dijkstra’s algorithm. The results highlight the significant impact of violent incidents on comprehensive risk scores, offering insights for proactive, sustainable measures. The waiting time prediction model performs strongly, with (R-squared) R2 values ranging from 80% to 92%. The developed route planning model provides three categorised routes under mobility restrictions—safest, fastest, and shortest—offering travellers sustainable and tailored options.

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Section: Travel Timementioning

confidence: 99%

Route Planning under Mobility Restrictions in the Palestinian Territories

Aburas,

Shahrour,

Giglio

2024

Sustainability

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“…Time series are frequently used in decision-making mechanisms as they make it easier for us to make predictions for the future on management issues such as investment, planning and optimization. In the transportation sector, models have been developed using time series for calculating the transportation times of vehicles, especially in metropolitans that have transportation problems due to traffic density, for planning urban transportation vehicles [5] [6]. There are lots of studies on changes and fluctuations on time series data sets, especially in areas such as seasonal electricity consumption [4], [7], natural gas consumption [8], [9], [10] economy [11] and food [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Time Series Cleaning Methods for Hospital Emergency Admissions

ALİŞAN

Olcay

2022

Journal of Soft Computing and Artificial Intelligence

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Due to the nature of hospital emergency services, density cannot be easily estimated. It is one of the important issues that should be planned for emergency service managers to have sufficient resources continuously in services that develop suddenly, and emergency interventions are made for human life. Effective and efficient management and planning of limited resources are important not only for hospital administrators but also for people who will receive service from emergency services. In this situation, estimating the number of people who will request service in the emergency service with the least error is of great importance in terms of resource management and the operations carried out in the emergency services. The density of patients coming to the emergency department may vary according to the season, special dates, and even time zones during the day. The aim of the study is to show that more successful results will be obtained because of processing the time series by considering the country and area-specific features instead of the traditional approach. In this paper, the patient admission dataset of the public hospital emergency service in Turkey was used. Data cleaning and arranging operations were carried out by considering the official and religious special days of Turkey and the time periods during the day. The data set is first handled holistically, and its performances are measured by making predictions with the LSTM (Long Short Term Memory) model. Then, to examine the effect of time zones, performance values were calculated separately by dividing each day into 3 equal time zones. Finally, to investigate the effect of triage areas on the total density, the model performance was measured by dividing the data forming each time zone into 3 different triage areas in 3 equal time periods. Three stages were applied both on the raw data set and on the data created by extracting the official, religious holidays, and weekend data specific to Turkey. According to the MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) results, more successful results are obtained thanks to the cleaning and editing processes. Thanks to the study, it is thought that the data sets used for demand forecasting studies in the health sector will produce results closer to reality by determining and standardizing the purification criteria in this way.

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“…Deep learning models can map complex relations between the input factors and bus travel time without an explicit function form [21], which can be classified into Support Vector Machine (SVM) models, Kalman Filtering (KF) models, and Artificial Neural Network (ANN) models.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach

Jiang

Chien

et al. 2022

PROMTT

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The application of predicting bus travel time with real-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effective to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genetic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model performance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various combinations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC.

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Hybrid time series forecasting methods for travel time prediction

Cited by 19 publications

References 27 publications

Route Planning under Mobility Restrictions in the Palestinian Territories

Route Planning under Mobility Restrictions in the Palestinian Territories

Time Series Cleaning Methods for Hospital Emergency Admissions

Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach

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