A space–time diurnal method for short-term freeway travel time prediction

Zou, Yajie; Zhu, Xinxin; Zhang, Yunlong; Zeng, Xiaosi

doi:10.1016/j.trc.2013.10.007

Cited by 74 publications

(35 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The bus travel time during weekdays is periodic and recurs every days. This cyclical pattern of bus travel time in Yichun is quite similar to that of speed and volume in freeways in previous studies (Zou et al 2014;Zhang et al 2014;Xia et al 2011). Note that the cyclical travel time patterns are also observed at other 10 segments.…”

Section: Data Collectionsupporting

confidence: 87%

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

Hua

Wang

et al. 2017

Transport

View full text Add to dashboard Cite

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.

show abstract

Section: Data Collectionsupporting

confidence: 87%

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

Hua

Wang

et al. 2017

Transport

View full text Add to dashboard Cite

show abstract

“…The most recently developed deep learning methods, such as Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), have shown great promises in other prediction applications, but they have not been explored in parking prediction yet. On the other hand, although numerous studies in the literature incorporate the spatio-temporal correlations of features of traffic conditions into their traffic prediction models (Kamarianakis and Prastacos, 2003;Kamarianakis et al, 2012;Zou et al, 2014), parking occupancy prediction that also carries spatio-temporal features is overlooked yet. In fact, parking demand can exhibit certain spatio-temporal characteristics related to measures of traffic flow, incidents and weather.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources

Yang

et al. 2019

Transportation Research Part C: Emerging Technologies

152

View full text Add to dashboard Cite

A deep learning model is applied for predicting block-level parking occupancy in real time. The model leverages Graph-Convolutional Neural Networks (GCNN) to extract the spatial relations of traffic flow in large-scale networks, and utilizes Recurrent Neural Networks (RNN) with Long-Short Term Memory (LSTM) to capture the temporal features. In addition, the model is capable of taking multiple heterogeneously structured traffic data sources as input, such as parking meter transactions, traffic speed, and weather conditions. The model performance is evaluated through a case study in Pittsburgh downtown area. The proposed model outperforms other baseline methods including multi-layer LSTM and Lasso with an average testing MAPE of 10.6% when predicting block-level parking occupancies 30 minutes in advance. The case study also shows that, in generally, the prediction model works better for business areas than for recreational locations. We found that incorporating traffic speed and weather information can significantly improve the prediction performance. Weather data is particularly useful for improving predicting accuracy in recreational areas.

show abstract

“…In Figure 4, the speed distribution of station C during ve continuous representative weekdays is shown. It has been shown that the period pattern exists in the tra c parameters [49,56,57]. As shown in Figure 4, the speed data show a periodic pattern every 24 hours.…”

Section: Data Description and Preliminary Data Analysismentioning

confidence: 91%

“…ST model, modeling via the proper probability distribution of data, is introduced to tra c ow and tra c speed prediction [16,57]. e model contains two kinds of prediction values: point prediction and interval prediction.…”

Section: St Modelmentioning

confidence: 99%

Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models

Yang

Zou

Tang

et al. 2020

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.

show abstract

A space–time diurnal method for short-term freeway travel time prediction

Cited by 74 publications

References 39 publications

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

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

A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources

Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models

Contact Info

Product

Resources

About