Russian Arctic Ports : Problems with the development and management of tourist flows

The potential changes to the territory of the Russian Arctic open up unique possibilities for the development of tourism. More favourable transport opportunities along the Northern Sea Route (NSR) create opportunities for tourism development based on the utilisation of the extensive areas of sea shores and river basins. A major challenge for the Russian Arctic sea and river ports is their strong cargo transport orientation originated by natural resource extraction industries. A careful assessment of the prospects of current and future tourism development is presented here based on the development of regions located along the shores of the Arctic ocean (including Murmansk and Arkhangelsk oblast, Nenets Autonomous okrug (AO), Yamal-Nenets AO, Taymyr AO, Republic of Sakha, Chykotsky AO). An evaluation of the present development of tourism in maritime cities suggests that a considerable qualitative and quantitative increase of tourism activities organised by domestic tourism firms is made virtually impossible. There are several factors contributing to this. The previously established Soviet system of state support for the investments into the port facilities as well as the sea fleet were not effectively replaced by creation of new structures. The necessary investments for reconstruction could be contributed by the federal government but the priorities are not set towards the increased passenger transportation. Having in mind, increased environmental pressures in this highly sensitive area it is especially vital to establish a well-functioning monitoring and rescue system in the situation of ever increasing risks which come not only from the increased transports along the NSR, but also from the exploitation of the offshore oil and gas reserves in the Arctic seas. The capacity and knowledge established in Nordic countries (Norway, Finland) concerning cruise tourism should not be underestimated and the already functioning cooperation in Barents Region should expand towards this particular segment of the tourism industry. The current stage of economic development in Russia makes it clear that tourism development is not able to compete with the well-needed increase in the cargo transportation, which means that Russia’s fleet is going to be utilised by other industries. However, opening up this area to both local and international visitors could contribute to the economic prosperity of these remote areas and if carefully managed could sustain already existing maritime cities along the shores of the Arctic Ocean.

Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means

Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.