Aims and Scope

The Open Transportation Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, letters and guest edited single topic issues in the field of design and/or analysis of transportation systems. Areas that are covered include: traffic modeling, transportation networks, optimization, queuing, control, statistical and other models of transportation systems, cost models and other works aiming at providing the most complete and reliable source of information on current developments in the field.


The Open Transportation Journal, a peer reviewed journal, is an important and reliable source of current information on developments in the field. The emphasis will be on publishing quality articles rapidly and freely available worldwide.


Recent Articles

Wheel Slip-based Road Surface Slipperiness Detection

Jinhwan Jang

Background:

Faced with the high rate of traffic accidents under slippery road conditions, agencies attempt to quickly identify slippery spots on the road and drivers want to receive information on the impending dangerous slippery spot, also known as “black ice.”

Methods:

In this study, wheel slip, defined as the difference between both speeds of vehicular transition and wheel rotation, was used to detect road slipperiness. Three types of experiment cars were repeatedly driven on snowy and dry surfaces to obtain wheel slip data. Three approaches, including regression analysis, support vector machine (SVM), and deep learning, were explored to categorize into two states-slippery or non-slippery.

Results:

Results indicated that a deep learning model resulted in the best performance with accuracy of 0.972, only where sufficient data were obtained. SVM models universally showed good performance, with average accuracy of 0.965, regardless of sample size.

Conclusion:

The proposed models can be applied to any connected devices including digital tachographs and on-board units for cooperative ITS projects that gather wheel and transition speeds of a moving vehicle to enhance road safety in winter season though collecting followed by providing dangerous slippery spots on the road.


September 08, 2020
READ MORE

Editor's Choice

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

Jinhwan Jang

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.


September 30, 2019
READ MORE

Quick Links

Indexing Agencies

READ MORE