The subject of this paper is the extent of pavement distress impact on traffic capacity of uninterrupted road link sections. A capacity estimation method based on extrapolation from a fundamental diagram representing the relationship between traffic flow and density was used. This method assumes that density is a result of speed and flow on sections of the road link at capacity, and hence is not directly affected by road surface distress. This implies that the capacity shifts are entirely the result of speed changes. In a ‘without and with’ distress study at selected sites in Nigeria, the impact of road surface distress was estimated for one direction of the road link section. Capacities of the road section were estimated for three sections (‘without distress’, ‘transition’ and ‘with distress’) of the road link and it was found that capacities on ‘without distress’ and ‘with distress’ sections differed significantly. The paper concludes that a significant decrease in capacity of about 30% was found, and is attributable to road pavement distress.
The subject of this paper is the extent of pavement distress impact on traffic capacity of uninterrupted road link sections. A capacity estimation method based on extrapolation from a fundamental diagram representing the relationship between traffic flow and density was used. This method assumes that density is a result of speed and flow on sections of the road link at capacity, and hence is not directly affected by road surface distress. This implies that the capacity shifts are entirely the result of speed changes. In a 'without and with' distress study at selected sites in Nigeria, the impact of road surface distress was estimated for one direction of the road link section. Capacities of the road section were estimated for three sections ('without distress', 'transition' and 'with distress') of the road link and it was found that capacities on 'without distress' and 'with distress' sections differed significantly. The paper concludes that a significant decrease in capacity of about 30% was found, and is attributable to road pavement distress
Many of the speed, flow and density relationships postulated in different literatures are based on empirical evidences collected under favourable conditions. Those that veered into comparative analysis under contrasting conditions often use forced curves to describe the relationship between speed and flow mainly because the graph is not a function. However, the paper is an attempt to postulate that dynamic speed-flow, speed-density and flow-density functions have similar behavioural pattern. In the study, speed and flow relationship under adverse road surface condition depicted with potholes and edge subsidence among others was investigated. The study was carried out in Nigeria where adverse road surface condition on principal roads is prevalent under daylight, dry weather and off-peak conditions. It is based on the hypothesis that adverse road surface condition has significant impact on otherwise uninterrupted traffic stream. The paper compared empirical survey data from 11 locations on roadway segments with control and adverse sections. Optimum speeds for control and adverse road sections were estimated and compared. The study found 50% reduction in optimum speed and concluded that significant speed reduction will occur under adverse road surface condition. Keywords: adverse-conditions, dynamic, speed-flow, speed-reduction.BACkGROUND 1 Despite documented impacts of adverse conditions on highway traffic stream, their correlations remain tenuous. Limitations of dynamic passenger car equivalency as well as insufficient research activities are factors that may be held accountable, it can be argued. Adverse conditions such as traffic congestion, inclement weather and pavement distress among others have significant impacts on vehicle speed and traffic flow. With respect to motorist, they limit visibility, vehicle control, surfacing grip, driving comfort and also increase vehicle operating costs. In addition, they are source of extra maintenance and safety costs to road providers.Road surfacing is made up of wearing and base courses. It enables good ride quality to be combined with the appropriate resistance to skidding, potholes, edge subsidence and vertical deflection (speed cushions, humps and speed tables). Therefore road surfaces must be free from physical defects such as potholes, loose aggregates and broken edges, rutting and cracking, failing which the quality of road service and indeed the quantity of traffic provided would be reduced significantly. Poor road surfaces are not only recipes for congestion and road accidents, they are characterised by slower speeds, longer travel times, increased queuing and severe discomfort. However, the paper is not about how pavement distresses are acquired, rather it focuses attention on vehicle volume and speed traversing such roadways.Three primary measures namely flow, speed and density characterise the operational state of any given traffic stream. For the purpose of measuring quantity, the parameters, density and flow are important, whereas speed and flow are ...
This paper examined the effect of rainfall on traffic stream behaviour during peak and non-peak periods on a basic highway section. Data on this section which is located on the J5 was collected for four months during which 99 rainfall events occurred. The traffic consisted of 75.80% cars, 10.23% motorcycles, 3.51% trucks and 10.46% of other vehicles. Traffic was observed for both rain and no-rain conditions and the data was analysed to see the effect of the rain. The results showed decreases in the speed as the rain intensity increased. Similarly, the traffic flow rates decreased as the rain intensity increased. This trend was observed for both peak and non-peak periods and for both directions. It is concluded that the effect of rain during peak period could have more serious consequences on the traffic flow than during non-peak periods because of the higher flow rates and the constrained nature of the flow. Consequently, capacity degradations up to 30% during peak periods would require resources to be employed to manage the traffic.
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