Modeling trailer-traffic flow on a Kenyan highway

Abstract

Traffic flow modelling is studied in order to ease congestion on roads. However, congestion is also caused by irregular occurrences, such as traffic accidents, poor roads, vehicle disablement, spilled loads and hazardous materials. This study explored the area of poor roads which was considered to be as a result poor planning for road network repairs. Traffic flow was categorized to be either in low, intermediate, or high volume. Modelling for every flow required a different distribution namely the Exponential, Pearson type III and Normal distributions for low, intermediate and high traffic flow volume respectively. However, we modelled the trailers traffic flow using a model that covered all the 3 states, in this case,the Pearson type III distribution. Further,we investigated the shape of the probability distribution function assumed by the trailers. After data collection,extraction and analysis, Pearson type III distribution model was calibrated to fit. Lastly, Kolmogorov-Smirnov and Chi-square tests of goodness of fit was run on the observed data. Although, the data was also fitted into Normal, Erlang, Exponential, Beta and Gamma Distributions, Pearson type III distribution provided the best fit.

Profitable inefficiency: the politics of port infrastructure in Mombasa, Kenya

“…Although development at the Kenyan port has attracted a significant amount of attention and investment in recent years, time and again its politics have been sidelined. Gekara & Chhetri (2013: 559) outline a range of ‘performance enhancing initiatives’ – notably the dredging of new berths and the construction of a second container terminal, but note that their success is limited by a constricted economic environment, poor hinterland connectivity and logistics bottlenecks along the transport corridor extending westwards (see also Kotut & Mugambi 2014; Wanjiru & Otumba 2014; Ruto & Datche 2015). Along similar lines, Nyema (2014: 39) writes of container terminal operations at the port: This study revealed that factors such as inadequate quay/gantry crane equipment, reducing berth times and delays of container ships, dwell time, container cargo and truck turnaround time, custom clearance, limited storage capacity, poor multi-modal connections to hinterland and infrastructure directly influence container terminal efficiency.Political relationships, vested or competing interests, or even simply people, are nowhere to be found.…”

Modelling of distribution of the "Matatu" traffic flow using Poisson distribution in a highway in Kenya

“…On the other hand, normal distribution did not give any acceptable fit for any of the data sets in their study. Ndichu and Otumba [13]carried out a study on modelling trailer-traffic flow on a Kenyan Highway by exploring the area of poor roads. They categorized traffic flow to be low, intermediate and high volume.…”

“…It is based on conservation of traffic flow [Jin, 2000]. Pearson type III distribution for trailer ndichu [13] whose main motivation of the study was to determine the continuous impact of load on the road surface, with an aim of approximating the total weight exerted at particular time period did not consider the impact of matatus on roads but they are the major operators and even exert more weight on our roads.Furthermore,there are few trailers on highway. A few or no study has been carried using poisson modelling which requires free road of no obstacles like the highways.…”