Nonadiabatic travel time and time series synthesis in range dependent waveguides

2001 ◽  
Vol 110 (5) ◽  
pp. 2618-2618
Author(s):  
Kevin D. LePage
Keyword(s):  
Time Series ◽  
Travel Time

scholarly journals Road Short-Term Travel Time Prediction Method Based on Flow Spatial Distribution and the Relations

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Mingjun Deng ◽  
Shiru Qu
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Travel Time ◽  
Nonparametric Regression ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Short Term ◽  
Combination Model ◽  
The Road ◽  
Neighbor Search

There are many short-term road travel time forecasting studies based on time series, but indeed, road travel time not only relies on the historical travel time series, but also depends on the road and its adjacent sections history flow. However, few studies have considered that. This paper is based on the correlation of flow spatial distribution and the road travel time series, applying nearest neighbor and nonparametric regression method to build a forecasting model. In aspect of spatial nearest neighbor search, three different space distances are defined. In addition, two forecasting functions are introduced: one combines the forecasting value by mean weight and the other uses the reciprocal of nearest neighbors distance as combined weight. Three different distances are applied in nearest neighbor search, which apply to the two forecasting functions. For travel time series, the nearest neighbor and nonparametric regression are applied too. Then minimizing forecast error variance is utilized as an objective to establish the combination model. The empirical results show that the combination model can improve the forecast performance obviously. Besides, the experimental results of the evaluation for the computational complexity show that the proposed method can satisfy the real-time requirement.

scholarly journals INTEGRATION OF EXPONENTIAL SMOOTHING WITH STATE SPACE FORMULATION FOR BUS TRAVEL TIME AND ARRIVAL TIME PREDICTION

Transport ◽  
2015 ◽  
Vol 32 (4) ◽  
pp. 358-367 ◽  
Author(s):  
Selvaraj Vasantha Kumar ◽  
Krishna Chaitanya Dogiparthi ◽  
Lelitha Vanajakshi ◽  
Shankar Coimbatore Subramanian
Keyword(s):  
Time Series ◽  
State Space ◽  
Travel Time ◽  
Kalman Filtering ◽  
Arrival Time ◽  
Arima Models ◽  
Time Prediction ◽  
Space Formulation ◽  
Arrival Time Prediction ◽  
Bus Travel Time

In recent years, the problem of bus travel time prediction is becoming more important for applications such as informing passengers regarding the expected bus arrival time in order to make public transit more attractive to the urban commuters. One of the popular techniques reported for such prediction is the use of time series analysis. Most of the studies on the application of time series techniques for bus arrival time prediction used Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) models, which are presently not suited for real time implementation. This is mainly due to the necessity and dependence of ARIMA models on a time series modelling software to execute. Moreover, the ARIMA model building process is time consuming, making it difficult to use for real-time implementations. Alternatively, Exponential Smoothing (ES) methods can be used, as they are easy to understand and implement when compared to ARIMA models. The present study is an attempt in this direction, where the basic equation of ES is used, as the state equation with Kalman filtering to recursively update the travel time estimate as the new observation becomes available. The proposed algorithm of state space formulation of ES with Kalman filtering for bus travel time and arrival time prediction was field tested using 105 actual bus trips data along a particular bus route from Chennai, India. The results are promising and a comparison of the proposed algorithm with ES alone without state space formulation and Kalman filtering has also been performed. An information system based on a webpage for real-time display of bus arrival times has been designed and developed using the proposed algorithm.

scholarly journals Investigating bus travel time and predictive models: a time series-based approach

2020 ◽  
Vol 45 ◽  
pp. 692-699 ◽  
Author(s):  
Antonio Comi ◽  
Mykola Zhuk ◽  
Volodymyr Kovalyshyn ◽  
Volodymyr Hilevych
Keyword(s):  
Time Series ◽  
Travel Time ◽  
Predictive Models ◽  
Bus Travel Time

scholarly journals A real time forecasting tool for dynamic travel time from clustered time series

2017 ◽  
Vol 80 ◽  
pp. 216-238 ◽  
Author(s):  
A. Ladino ◽  
A.Y. Kibangou ◽  
C. Canudas de Wit ◽  
H. Fourati
Keyword(s):  
Time Series ◽  
Travel Time ◽  
Real Time
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