Aims and Scope
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
Articles
- July 31, 2020
Conjoint Vehicle License Plate Identification System
July 30, 2020
Underground Roundabouts: Analysis of Several Layouts for A Case Study in Urban Area
July 06, 2020
A Comparison of Traffic Flow Performance of Roundabouts and Signalized Intersections: A Case Study in Nigde
June 29, 2020
Data Processing Techniques for Real-Time Traveler Information: Use of Dedicated Short-Range Communications Probes on Suburban Arterial
June 01, 2020Socio-Economic Situation and Substance Use Disorder Influences on Safety Practices among Truckers
May 23, 2020
Measuring the Reliability of Methods and Algorithms for Route Choice Set Generation: Empirical Evidence from a Survey in the Naples Metropolitan Area
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
Other Post
- January 18, 2018
Effect of Traffic Flow, Proportion of Motorcycle, Speed, Lane Width, and the Availabilities of Median and Shoulder on Motorcycle Accidents at Urban Roads in Indonesia
January 22, 2018
A Simulation Model to Determine the Capacity of a Y-Type Waterway Intersection for a Real Seaport
January 29, 2018
Effectiveness of Experimental Left-Turn Sign Usage in Terms of Crashes and Analyzing Severity of Left Turn Crashes in Alaska
January 29, 2018
Collisions Between Pedestrians and Reversing Vehicles in Public Settings in France
February 28, 2017
Impact of Integrated Multimodal Traveler Information on Auto Commuter’s Mode Switching Propensity
March 23, 2017
From Macro to Microscopic Trip Generation: Representing Heterogeneous Travel Behavior
