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Network topology is the basic for the development of traffic management and control. In a road network, bayonets with installation of surveillance facilities are key components to recognize traffic congestion from time to time. Therefore, identifying the essential bayonets in a road network becomes one of the most efficient ways to alleviate traffic congestion for traffic engineers and transport department. To do so, this paper aims to propose a novel sorting algorithm based on similarity measurements and traffic flow information to efficiently identify key bayonets in road networks. Our research results show that by analyzing the bayonet data in a fixed period of time in a medium-sized city of China, we have successfully identified the location of key bayonet points. Most of these key bayonet points are closed to residential areas and important traffic stations. The rank of these bayonet points can help the city managers better understand the topological characteristics of the road network as well as the propagation of congestion so as to make the traffic policies or control strategies for traffic congestion alleviation. [ABSTRACT FROM AUTHOR]

With the implementation of the freeway free policy during the holidays, traffic congestion in the freeway becomes a common phenomenon. In order to alleviate traffic pressure, traffic flow prediction during the holidays has become a problem of great concern. This paper proposes a hybrid prediction methodology combining discrete Fourier transform (DFT) with support vector regression (SVR). The common trend in the traffic flow data is extracted using DFT by setting an appropriate threshold, which is predicted by extreme extrapolation of the historical trend. The SVR method is applied to predict the residual series. The experimental results with measured data collected from the toll stations in Jiangsu province of China show that the proposed algorithm has higher accuracy compared with the traditional method, and it is an efficient method for traffic flow prediction during the holidays. [ABSTRACT FROM AUTHOR]

Short period traffic counts (SPTCs) are conducted routinely to estimate the annual average daily traffic (AADT) at a particular site. This paper uses Indian traffic volume data to methodically and extensively study the effect of four aspects related to the design of SPTCs. These four aspects are: (i) for how long, (ii) on which days should SPTCs be carried out, (iii) how many times, and (iv) on which months should SPTCs be carried out? The analyses indicate that the best durations for conducting SPTCs are 3 days (starting with a Thursday) and 7 days, for total traffic and truck traffic, respectively. Further, these counts should be repeated twice a year keeping a separation of two months between the counts to obtain good estimates of AADT at minimal cost. An additional outcome of this study has been the determination of seasonal factor values for roads in developing economies, like India. [ABSTRACT FROM PUBLISHER]