Meteorology fields are important as they have a direct impact on air pollution concentrations. During periods of high precipitation or high speed winds, emissions from a city are swept away and do not have an impact on concentrations. On the other hand, during the winter months when temperatures and inversion heights are low, there is a greater impact of emissions on pollution concentrations. Low temperatures also affect behaviour through the need for space and water heating — which in turn has increases emissions.
In Phase 1, base year for all the calculations was In Phase 2, all the calculations are updated for year We customized the SIM-air family of tools to fit the base information collated from disparate sources.
Apart from the official reports, resource material ranges from GIS databases of land use, land cover, roads and rail lines, water bodies, built up area represented in the adjacent figure , commercial activities such as hotels, hospitals, kiosks, restaurants, malls, cinema complexes, traffic intersections, worship points, industrial hubs, and telecom towers , to population density and meteorology at the finest spatial resolution possible 1-km.
A detailed description of these resources is published as a journal article in , which also includes a summary of baselines and pollution analysis for 20 Indian cities. This emissions inventory is based on available local activity and fuel consumption estimates for the selected urban airshed represented in the grid above. This information is collated from multiple agencies ranging from the central pollution control board, state pollution control board, census bureau, national sample survey office, ministry of road transport and highways, annual survey of industries, central electrical authority, ministry of heavy industries, and municipal waste management, and publications from academic and non-governmental institutions.
For the road transport emissions inventory, besides the total number of vehicles and their usage information, we also utilized vehicle speed information to spatially and temporally allocate the estimated emissions to the respective grids.
This is a product of google maps services. For the city of Guwahati, we extracted the speed information for representative routes across the city for multiple days. This data is summarized below for a quick look. Network analysis in GIS rests firmly on the theoretical foundation of the mathematical sub disciplines of graph theory and topology. The most common and familiar implementations of network models are those used to represent the networks with which much of the population interacts every day: transportation and communications networks [ 7 ].
Routing is the act of selecting a course of travel, and it is arguably the most fundamental logistical operation in network analysis. Although network analysis in GIS has been largely limited to the simplest routing functions, the recent past has seen the development of object oriented data structures, the introduction of dynamic networks [ 8 ], the ability to generate multi-modal networks, and the use of simulation methods to generate solutions to network problems.
There are, of course, many important network design problems that are very difficult to solve optimally due to their combinatorial complexity [ 9 ]. To allocate and provide urban facilities in an area with complex road network, determination of the shortest route as well as travel demand trips generated and attracted from the facility to the concerned area is a must. Generating the shortest path between two locations in a road network is a problem that can be solved by various map services and commercial navigation products [ 10 ].
There are several extremely efficient algorithms for determining the optimal route, the most widely cited of which was developed by Edsgar Dijkstra in the year The algorithm [ 11 ] is represented in brief as below:. Sort the vertices in V-S according to the current best estimate of their distance from the source.
To run the analysis over the digitized road network, a network geo dataset was created which resulted in a layer consisting of the junctions and edges connected topologically to each other. Ward analysis was done using Intersection tool to get the statistics of all the categorized roads created as layers. The origin and destination points were selected to solve the network for determination of the shortest route and to serve the purpose of our study Fig.
An attribute table, tabular arrangement of meaningful data, of the digitized road network was generated to get the latitudes and longitudes in degrees , road ID and name and the distances in meters between the junctions or origin to destination Fig. Guwahati city has been divided into 31 main municipal wards which are further subdivided forming a total of 90 wards. From the ward-road network analysis, it has been seen that-. Ward 22C has the largest area 9. The 3 major roads pass through the 44 wards of Guwahati city out of which the ward 1B covers the maximum number of major roads 2 with a total length of 7.
All the minor roads pass through 85 wards of the city out of which the ward 10A holds the maximum number of minor roads 9 with a total length of 4. The lanes and by-lanes cover up all the wards among which ward 6C has highest number of lanes traversing throughout with a total length of Such deficiency of road networks is mostly seen in the outer areas cum rural urban of the city including the rural villages.
Limited roads and poor network is one of the reasons for traffic congestion. Traffic congestion leads to delay of vehicles, increased vehicle operating cost, increase in air and sound pollution, emission of poisonous gases like Carbon Monoxide CO , road accidents, warming up of surrounding urban area, human frustration leading to road rage etc.
From the field survey, it has been observed that for feeder trips, both transit and Para transit use some other routes other than the shortest route as per GIS. The reasons are numerous like transport movement policy, poor pavement conditions, unawareness of non-commuters etc. A network analysis layer stores the inputs, properties, results of a network analysis and is always performed on network dataset.
This paper is presented taking into account the route analysis layer and the analysis is based on real time network problems independent of the hierarchy major roads, minor roads and lanes. GIS networks consist of interconnected lines known as edges and intersections known as junctions that represent routes upon which people, goods, etc.
Network analysis helps in modeling as well as planning and management of moderate to heavy traffic routes. One common type of network analysis is finding the shortest path between two points. Junctions or nodes and edges have certain attributes affixed to them which help in modeling.
Edges and junctions are topologically connected to each other-edges must connect to other edges at junctions, and the flow from edges in the network is transferred to other edges through junctions.
Determination of the shortest route between the origin and destination using the GIS Network Analyst will not only help the tourists or business entrepreneurs to access the tourist places or the trade centers with ease but it will also reduce cost and avoid traffic congestion resulting in less emission of pollutants.
Dijkstra proposed a graph search algorithm that can be used to solve the single-source shortest path problem for any graph that has a non-negative edge path cost [ 12 ]. Network attributes are properties of the network elements that control movement over the network and helps in finding the shortest route based on type of attribute such as distance, vehicle restrictions, turn restrictions etc. This paper presents the shortest route based on the assumptions that traffic congestions are not considered and the calculations are based on road distances Figs.
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