No abstract

Accident prevention is relatively a complex issue considering the effectiveness of the injury prevention technologies as well as more detailed assessment of the complex interactions between the road condition, vehicle and human factor. For many years, highway agencies and vehicle manufacturers showed great efforts to reduce the injuries resulting from the vehicle crashes. Many researchers used a broad range of methods to evaluate the impact of several factors on traffic accidents and injuries. Recent developments lead up to capable for determining the effects of these factors. According to World Health Organization (WHO), cyclists and pedestrians comprise respectively 1.6% and 16.3% in traffic crash fatalities in 2013. Also in Turkey crash fatalities for pedestrian and cyclists are respectively 20.6% and 3% according to Turkish Statistical Instıtute data in 2013. The relationship between cycling and pedestrian rates and injury rates over time is also unknown. This paper aims to predict the crash severity with the traffic injury data of the Konya City in Turkey by implementing the Artificial Neural Networks (ANN), Regression Trees (RT) and Multiple Linear Regression modelling (MLRM) method.

In parallel to the economic developments, the importance of road transportation was significantly increased in

Various factors (illegal parking, the existence of a bus stop, road surface deformations etc.) generally cause blockage on traffic flow at signalized intersections. These factors have a negative effect on capacity of signalized intersections. This study examines the blockage effect of a bus stop and a road surface deformation on capacity of a signalized intersection by implementing a Cell Transmission Model (CTM). Study has conducted extensive simulation experiments that captures traffic parametres and evaluates complex traffic flow interactions on signalized intersections using cell transmission concept. In analyses, boundary conditions were defined by means of input and output cells with infinite sizes and proper capacity values. It is assumed that vehicles have instantaneous acceleration and deceleration properties such as vehicles' movement or stop instantaneously from their current situation when encountered a bottleneck situation. The results of simulation experiments indicate that if blockage on traffic flow is far from the signalized intersections, negative effects on the capacity were decreased. Also results pointed that the dominant factor was considered as the distance between the surface deformation area/bus stop and signalized intersection for the capacity change.

Abstract

Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.