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

The Effects of Picking Up Primary School Pupils on Surrounding Street’s Traffic: A Case Study in Hanoi

Dinh Hiep, Vu V. Huy, Teppei Kato, Aya Kojima, Hisashi Kubota

Introduction:

One of the significant characteristics of schools in Vietnam is that almost all parents send their children to school and/or pick up their children from school using private vehicles (motorcycles). The parents usually stop and park their vehicle on streets outside the school gates, which can lead to serious congestion and increases the likelihood of traffic accidents.

Methods:

The objective of this study is to find out factors affecting the picking up of pupils at primary school by evaluating the typical primary schools in Hanoi city. A binary logistic regression model was used to determine factors that influence the decision of picking up pupils and the waiting duration of parents. The behavior of motorcyclists during the process of picking up pupils at the primary school gate has been identified and analyzed in detail by the Kinovea software.

Results and Discussion:

The study showed that, on the way back home, almost all parents use motorbikes (89.15%) to pick up their children. During their waiting time (8.48 minutes in average), they made a lot of illegal parking actions on the street there by, causing a lot of “cognitive” errors and “crash” points surrounding in front of the primary school entrance gate. Risky picking-up behaviors were significantly observed, i.e. picking-up on opposite side of the school, making a U-turn, backing-up dangerously, parking on the middle of street, and parking on the street next to sidewalk).

Conclusion:

Based on the analyzed results, several traffic management measures have been suggested to enhance traffic safety and reduce traffic congestion in front of school gates. In addition, the results of the study will provide a useful reference for policymakers and authorities.


December 31, 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