Real-Time Freeway-Experienced Travel Time Prediction Using N-Curve and k Nearest Neighbor Methods

2011 ◽  
Vol 2243 (1) ◽  
pp. 127-137 ◽  
Author(s):  
Brenda I. Bustillos ◽  
Yi-Chang Chiu
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Travel Time Prediction ◽  
Time Prediction

scholarly journals Travel-time Prediction Using K-nearest Neighbor Method with Distance Metric of Correlation Coefficient

2019 ◽  
Vol 13 (1) ◽  
pp. 141-150
Author(s):  
Jinhwan Jang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Correlation Coefficient ◽  
Nearest Neighbor ◽  
Modern Society ◽  
K Nearest Neighbor ◽  
Travel Time Prediction ◽  
Significance Level ◽  
Dedicated Short Range Communications ◽  
Road Users

Background: Real-time Travel Time (TT) information has become an essential component of daily life in modern society. With reliable TT information, road users can increase their productivity by choosing less congested routes or adjusting their trip schedules. Drivers normally prefer departure time-based TT, but most agencies in Korea still provide arrival time-based TT with probe data from Dedicated Short-Range Communications (DSRC) scanners due to a lack of robust prediction techniques. Recently, interest has focused on the conventional k-nearest neighbor (k-NN) method that uses the Euclidean distance for real-time TT prediction. However, conventional k-NN still shows some deficiencies under certain conditions. Methods: This article identifies the cases where conventional k-NN has shortcomings and proposes an improved k-NN method that employs a correlation coefficient as a measure of distance and applies a regression equation to compensate for the difference between current and historical TT. Results: The superiority of the suggested method over conventional k-NN was verified using DSRC probe data gathered on a signalized suburban arterial in Korea, resulting in a decrease in TT prediction error of 3.7 percent points on average. Performance during transition periods where TTs are falling immediately after rising exhibited statistically significant differences by paired t-tests at a significance level of 0.05, yielding p-values of 0.03 and 0.003 for two-day data. Conclusion: The method presented in this study can enhance the accuracy of real-time TT information and consequently improve the productivity of road users.

scholarly journals Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jiandong Zhao ◽  
Yuan Gao ◽  
Jinjin Tang ◽  
Lingxi Zhu ◽  
Jiaqi Ma
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Nearest Neighbor ◽  
Sensor Nodes ◽  
Tensor Completion ◽  
Volume Data ◽  
K Nearest Neighbor ◽  
Travel Time Prediction ◽  
Data Sparseness ◽  
Time Prediction

Remote transportation microwave sensor (RTMS) technology is being promoted for China’s highways. The distance is about 2 to 5 km between RTMSs, which leads to missing data and data sparseness problems. These two problems seriously restrict the accuracy of travel time prediction. Aiming at the data-missing problem, based on traffic multimode characteristics, a tensor completion method is proposed to recover the lost RTMS speed and volume data. Aiming at the data sparseness problem, virtual sensor nodes are set up between real RTMS nodes, and the two-dimensional linear interpolation and piecewise method are applied to estimate the average travel time between two nodes. Next, compared with the traditional K-nearest neighbor method, an optimal KNN method is proposed for travel time prediction. optimization is made in three aspects. Firstly, the three original state vectors, that is, speed, volume, and time of the day, are subdivided into seven periods. Secondly, the traffic congestion level is added as a new state vector. Thirdly, the cross-validation method is used to calibrate the K value to improve the adaptability of the KNN algorithm. Based on the data collected from Jinggangao highway, all the algorithms are validated. The results show that the proposed method can improve data quality and prediction precision of travel time.

Real time bus travel time prediction using k-NN classifier

2017 ◽  
Vol 11 (7) ◽  
pp. 362-372 ◽  
Author(s):  
B. Anil Kumar ◽  
R. Jairam ◽  
Shriniwas S. Arkatkar ◽  
Lelitha Vanajakshi
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Bus Travel Time
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