Short-Term Travel Time Prediction considering the Effects of Weather

Qiao, Wenxin; Haghani, Ali; Hamedi, Masoud

doi:10.3141/2308-07

Cited by 39 publications

(16 citation statements)

References 30 publications

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“…Autocorrelation analysis describes the correlation degree between two times i, j of time series X, which is in the range of [21,1]. The random time series with a total of J observations is divided into J 2 1 pairs, that is, (…”

Section: Choice Of State Vectormentioning

confidence: 99%

Long-term forecasting oriented to urban expressway traffic situation

Dong

Jia

et al. 2016

Advances in Mechanical Engineering

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Long-term traffic forecasting has become a basic and critical work in the research on road traffic congestion. It plays an important role in alleviating road traffic congestion and improving traffic management quality. According to the problem that long-term traffic forecasting is short of systematic and effective methods, a long-term traffic situation forecasting model is proposed in this article based on functional nonparametric regression. In the functional nonparametric regression framework, autocorrelation analysis (ACF) is introduced to analyze the autocorrelation coefficient of traffic flow for selecting the state vector, and the functional principal component analysis is also used as distance function for computing proximities between different traffic flow time series. The experiments based on the traffic flow data in Beijing expressway prove that the functional nonparametric regression model outperforms forecast methods in accuracy and effectiveness.

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Section: Choice Of State Vectormentioning

confidence: 99%

Long-term forecasting oriented to urban expressway traffic situation

Dong

Jia

et al. 2016

Advances in Mechanical Engineering

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“…K-Nearest Neighbour (KNN) was used to predict travel times from two sources of data (vehicle detectors and toll collection systems) with an improvement of the matching process [10]. KNN could be also used to predict travel times under different weathers [11]. In general, those methods can be tailored to specific sites of interest, but may require long training processes.…”

Section: Literature Reviewmentioning

confidence: 99%

A three-stage framework for motorway travel time prediction

Xiong

Rey

Mao

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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This paper presents a framework for short-term travel time prediction in a motorway with a three-stage architecture: traffic flow forecasting, traffic flow generation and travel time extraction. Traffic flow forecasting reads the historical traffic data and utilizes a forecasting model -Autoregressive Integrated Moving Average (ARIMA) to predict short-term traffic flow. The traffic flow generation utilizes the Cell Transmission Model (CTM) to generate outgoing flow of a road of interest based on the predicted incoming flow from ARIMA. Predicted short term travel times can then be obtained through N-Curve Analysis. Compared to most studies, this paper presents a historical data-driven framework for travel time prediction that can be trained based on specific profiles of routes and cities. The motorway M4 in Sydney, Australia was used to test this framework. It is shown that the predicted travel times can be used to anticipate congestion episodes at the network level.

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“…In general, to the best of the authors knowledge, most of studies in this area have 30 considered only traffic related variables in order to develop a model for travel time prediction. 31 However, other variables such as weather condition could have significant impact on the travel 32 time (Qiao et al, 2012;Rakha et al, 2012), which is infrequently investigated in travel time 33 prediction studies. Nookala showed that inclement weather condition could increase the traffic 34 congestion and travel time, since the capacity of highway decreased in inclement weather, while 35 the traffic demand does not change (Nookala, 2006).…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Techniques in Short-term Travel Time Prediction Using Multiple Data Sources

Taghipour¹,

Parsa²,

Mohammadian³

2020

Preprint

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10Having access to accurate travel time is of great importance for both highway network users and 11 traffic engineers. The travel time which is currently reported on several highways is estimated by 12 employing naïve methods and using limited sources of data. This results in unreliable and 13 inaccurate travel time prediction and could impose delay on travelers. Therefore, the main 14 objective of this study is short-term prediction of travel time for highways using multiple data 15 sources including loop detectors, probe vehicles, weather condition, network, accidents, road 16 works, and special events in order to consider the effect of different factors on travel time. To this 17 end, two machine learning methods, K-Nearest Neighbors and Random Forest, are employed. 18 After applying data cleaning process on datasets and combining them, the models are trained to 19 predict and compare short-term harmonic average speed as a representative of travel time for 5-20 minute prediction horizons in one hour ahead. The travel time is calculated as the ratio of the length 21 of each link and the harmonic average speed for all reporting vehicles. Hence, a model is trained 22 for each technique to predict travel time 5 minutes ahead, 10 minutes ahead, and all the way down 23 to 60 minutes ahead. The results confirm satisfying performance of both models in short-term 24 travel time prediction with slightly outperformance of Random Forest model. A feature importance 25 and sensitivity analysis also applied for the Random Forest model, and traffic variables are found 26 as the most effective variables in predicting the travel time. 27 28

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Short-Term Travel Time Prediction considering the Effects of Weather

Cited by 39 publications

References 30 publications

Long-term forecasting oriented to urban expressway traffic situation

Long-term forecasting oriented to urban expressway traffic situation

A three-stage framework for motorway travel time prediction

Application of Machine Learning Techniques in Short-term Travel Time Prediction Using Multiple Data Sources

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