Abstract: In recent years, much attention has been given to truck drivers’ fatigue management. Long working hours negatively influence truck drivers’ physiology, health, and safety. However, there is little empirical research in the heavy vehicle transport sector in Australia to identify the influence of working hours’ management on drivers’ fatigue and consequently, on the risk of crashes and injuries. There is no national legislation regulating the number of hours or kilometres travelled by truck drivers. Consequently, it is almost impossible to define a standard number of hours or kilometres for truck drivers in a safety management system. This paper reviews the existing studies concerning safe system interventions such as tachographs in relation to fatigue caused by long working hours. This paper also reviews the literature to identify the influence of frequency of rest breaks on the reduction of work-related road transport accidents involving trucks. A framework is presented to manage truck drivers’ fatigue, which may result in the reduction of injuries and fatalities involving heavy vehicles.
Abstract: In this paper, a learning algorithm using neuronal networks to improve the roll stability and prevent the rollover in a single unit heavy vehicle is proposed. First, LQR control to keep balanced normalized rollovers, between front and rear axles, below the unity, then a data collected from this controller is used as a training basis of a neuronal regulator. The ANN controller is thereafter applied for the nonlinear side force model, and gives satisfactory results than the LQR one.
Abstract: One of the functions of the commercial heavy vehicle
is to safely and efficiently transport goods and people. Due to its size
and carrying capacity, it is important to study the vehicle dynamic
stability during cornering. Study has shown that there are a number of
overloaded heavy vehicles or permissible Gross Vehicle Weight
(GVW) violations recorded at selected areas in Malaysia assigned by
its type and category. Thus, the objective of this study is to
investigate the correlation and effect of the GVW on heavy vehicle
stability during cornering event using simulation. Various selected
heavy vehicle types and category are simulated using IPG/Truck
Maker® with different GVW and road condition (coefficient of
friction of road surface), while the speed, driver characteristic, center
of gravity of load and road geometry are constant. Based on the
analysis, the relationship between GVW and lateral acceleration were
established. As expected, on the same value of coefficient of friction,
the maximum lateral acceleration would be increased as the GVW
increases.
Abstract: Paper presents a study about dynamic effects obtained
from the dynamic load testing of the city highway bridges in Latvia
carried out from 2005 to 2012. 9 prestressed concrete bridges and 4
composite bridges were considered. 11 of 13 bridges were designed
according to the Eurocodes but two according to the previous
structural codes used in Latvia (SNIP 2.05.03-84). The dynamic
properties of the bridges were obtained by heavy vehicle passing the
bridge roadway with different driving speeds and with or without
even pavement. The obtained values of the Dynamic amplification
factor (DAF) and the bridge natural frequency were analyzed and
compared to the values of built-in traffic load models provided in
Eurocode 1. The actual DAF values for even bridge pavement in the
most cases are smaller than the value adopted in Eurocode 1. Vehicle
speed for uneven pavements significantly influence Dynamic
amplification factor values.
Abstract: This study investigates the causes, effects and remedies of traffic congestion which has become a common sight in most highways in Nigeria; Mowe/Ibafo section of the Lagos-Ibadan expressway was used as the case-study. 300 Structured questionnaires were distributed among the road users comprising drivers (Private and Commercial), passengers, pedestrians, traffic officers, church congregations, community leaders, Mowe/Ibafo residents, and other users of the road.
300 questionnaires were given out; the average of 276 well completed returned questionnaires formed the basis of the study and was analyzed by the Relative Importance Index (R.I.I.). The result from the study showed the causes of traffic congestion as inadequate road capacity, poor road pavement, poor traffic management, poor drainage system poor driving habit, poor parking habit, poor design junctions/round-about, presence of heavy trucks, lack of pedestrian facilities, lack of road furniture, lack of parking facilities and others. Effects of road congestion from the study are waste of time, delay movement, stress, accident, inability to forecast travel of time, fuel consumption, road rage, relocation, night driving, and environmental pollution. To drastically reduce these negative effects; there must be provision for adequate parking space, construction of proper drainage, enlarging the width of the road, rehabilitate all roads needing attention, public enlightenment, traffic education, hack down all illegal buildings/shops built on the right of way (ROW), create a separate/alternative root for trucks and heavy vehicles, provision of pedestrian facilities, In-depth training of transport/traffic personnel, ban all form of road trading/hawking, and reduce the number of bus-stop where necessary. It is hoped that this study will become the foundation of further research in the area of improve road traffic management on our major highway.
Abstract: Understanding driving behavior is a complicated
researching topic. To describe accurate speed, flow and density of a
multiclass users traffic flow, an adequate model is needed. In this
study, we propose the concept of standard passenger car equivalent
(SPCE) instead of passenger car equivalent (PCE) to estimate the
influence of heavy vehicles and slow cars. Traffic cellular automata
model is employed to calibrate and validate the results. According to
the simulated results, the SPCE transformations present good
accuracy.