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1. A Chain Reaction of Infrastructural Development and Its Local Social Consequences: The Case of the Western Section of the BAM.

   https://doi.org/10.24412/1026-8804-2021-1-125-145  

   Kuklina, Vera V ; Krasnoshtanova, Natalia Ye ( January 2021 , Rossiâ i ATR)

   In this paper we propose to discuss the issues of a chain reaction caused by the development of different types of transport infrastructure. The issues of infrastructural projects impact on local communities have long been widely discussed by social researchers. However, the northern and Arctic regions remain less studied in this regard while the situation there is more complex, especially at the intersections of various types of transport infrastructure. The problems of interaction between different kinds of infrastructure are especially manifested in the regions of extractive development, such as the north of the Irkutsk Region and the Republic of Buryatia. The materials for the analysis were obtained as a result of field studies in the north of the Irkutsk Region and the Republic of Buryatia in 2016, 2017, 2019, and 2020, as well as on the basis of the analysis of satellite data and maps of the regional infrastructure development. The construction of the Baikal-Amur Mainline and later oil and gas pipelines became an impetus for the development of other types of infrastructure, not only in the oil and gas extractive activities but also in logging. However, limited access to the pipeline and oil service roads has caused conflicts betweenmore »the industrial companies and the local population. Local hunting trails are at the lowest level in such a hierarchy and are often destroyed and blocked by infrastructure development. A deeper and more detailed analysis of the social consequences of infrastructural development contributes to an understanding of the hierarchies of power that exist in Russia.« less
2. Smart Traffic Management System using Deep Learning for Smart City Applications

   https://doi.org/10.1109/CCWC.2019.8666539  

   Lingani, Guy M. ; Rawat, Danda B. ; Garuba, Moses ( January 2019 , 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC))

   Already known as densely populated areas with land use including housing, transportation, sanitation, utilities and communication, nowadays, cities tend to grow even bigger. Genuine road-user's types are emerging with further technological developments to come. As cities population size escalates, and roads getting congested, government agencies such as Department of Transportation (DOT) through the National Highway Traffic Safety Administration (NHTSA) are in pressing need to perfect their management systems with new efficient technologies. The challenge is to anticipate on never before seen problems, in their effort to save lives and implement sustainable cost-effective management systems. To make things yet more complicated and a bit daunting, self-driving car will be authorized in a close future in crowded major cities where roads are to be shared among pedestrians, cyclists, cars, and trucks. Roads sizes and traffic signaling will need to be constantly adapted accordingly. Counting and classifying turning vehicles and pedestrians at an intersection is an exhausting task and despite traffic monitoring systems use, human interaction is heavily required for counting. Our approach to resolve traffic intersection turning-vehicles counting is less invasive, requires no road dig up or costly installation. Live or recorded videos from already installed camera all over the cities canmore »be used as well as any camera including cellphones. Our system is based on Neural Network and Deep Learning of object detection along computer vision technology and several methods and algorithms. Our approach will work on still images, recorded-videos, real-time live videos and will detect, classify, track and compute moving object velocity and direction using convolution neural network. Created based upon series of algorithms modeled after the human brain, our system uses NVIDIA Video cards with GPU, CUDA, OPENCV and mathematical vectors systems to perform.« less
3. Comparing Directed and Weighted Road Maps

   https://doi.org/10.1007/978-3-319-89593-2_4  

   Bittner, Alyson ; Fasy, Brittany Terese ; Grudzien, Maia ; Hajra, Sayonita Ghosh ; Huang, Jici ; Pelatt, Kristine ; Thatcher, Courtney ; Tumurbaatar, Altansuren ; Wenk, Carola ( July 2018 , Association for Women in Mathematics series)

   With the increasing availability of GPS trajectory data, map construction algorithms have been developed that automatically construct road maps from this data. In order to assess the quality of such (constructed) road maps, the need for meaningful road map comparison algorithms becomes increasingly important. Indeed, different approaches for map comparison have been recently proposed; however, most of these approaches assume that the road maps are modeled as undirected embedded planar graphs. In this paper, we study map comparison algorithms for more realistic models of road maps: directed roads as well as weighted roads. In particular, we address two main questions: how close are the graphs to each other, and how close is the information presented by the graphs (i.e., traffic times, trajectories, and road type)? We propose new road network comparisons and give illustrative examples. Furthermore, our approaches do not only apply to road maps but can be used to compare other kinds of graphs as well.
4. Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data

   https://doi.org/10.1080/13658816.2019.1694681  

   Qiang, Yi ; Xu, Jinwen ( January 2020 , International Journal of Geographical Information Science)

   Climate change and natural hazards pose great threats to road transport systems which are ‘lifelines’ of human society. However, there is generally a lack of empirical data and approaches for assessing resilience of road networks in real hazard events. This study introduces an empirical approach to evaluate road network resilience using crowdsourced traffic data in Google Maps. Based on the conceptualization of resilience and the Hansen accessibility index, resilience of road network is measured from accumulated accessibility reduction over time during a hazard. The utility of this approach is demonstrated in a case study of the Cleveland metropolitan area (Ohio) in Winter Storm Harper. The results reveal strong spatial variations of the disturbance and recovery rate of road network performance during the hazard. The major findings of the case study are: (1) longer distance travels have higher increasing ratios of travel time during the hazard; (2) communities with low accessibility at the normal condition have lower road network resilience; (3) spatial clusters of low resilience are identified, including communities with low socio-economic capacities. The introduced approach provides ground-truth validation for existing quantitative models and supports disaster management and transportation planning to reduce hazard impacts on road network.
5. Planning for Special Needs Shelters: A Hurricane Track Uncertainty-Based Approach Integrating Coastal Inundation and Accessibility

   Yang, J. ( January 2023 , Transportation Research Board Conference)

   Storm surge and evacuation traffic under the observed track of Hurricane Michael (2018) showed clear accessibility and evacuation challenges for Panama City, Florida although the city was not hit directly. Since a possible Hurricane Michael track within National Hurricane Center (NHC)'s forecasted hurricane cone was Panama City, this paper tries to answer the following questions: What if Hurricane Michael hit Panama City directly? How would the special needs populations and their accessibility to Special Needs Shelters (SpNS) be impacted, and what could have been done to alleviate this impact? A previously validated storm surge model was used to predict storm surge inundations under this different hurricane track. Based on the impact of these coastal inundations, a GIS-based optimization methodology was developed to evaluate the accessibility and siting of special needs shelters. Results indicate that if Hurricane Michael had shifted to Panama City in 2018, most of the coastal region of Panama City would have been inundated, compelling residents to evacuate. The possible landfall of Michael in this simulation would also lead to a maximum storm surge of 5 to 6 m on the coast, which is above FEMA's 100-year flood elevation. In addition, the only evacuation route out of Panamamore »City area, when the bridges with their access roads were flooded, was US 231. This would have been life-threatening since there is only one SpNS in the north of the city accessible by this roadway. The proposed analysis studies the accessibility of this SpNS shelter and provides a reasonable approach for SpNS shelter siting or repurposing regular shelters for this purpose based on the hypothesized travel time most likely to be experienced on roadway networks based on the impact of Hurricane Michael. Emergency plans can be updated by the results of this optimization model, which can locate additional sites or shelter locations while minimizing the travel costs and integrating the impact of storm surge modeling and transportation accessibility analysis.« less