On Road Safety Management of Hazardous Materials using Exploratory & Network analysis of GeoData using Python tools

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It was almost 15 years since I worked on multi-objective Air Quality Monitoring site selection using Geographic Information System. At that time use of ArcGIS / Python was in nascent stage in ESRI product. We were using python as a scripting languages with use around some minor level of automation. Now it has matured a lot with advanced machine learning and Deep Learning capabilities which can be used to build sophisticated deep learning analysis for network and land use usages. In this blog I am trying to give an overview of how best such technique can be used to manage the Hazardous Material Transport for an organization.

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Historically Dangerous Goods regulations are used to identify the nature of the enclosure instructions and level of precautions needed to be taken and which mode of transport is allowed and while in that mode what documentation needed to be carried on so on. Enterprise systems area used to analyze such criteria and Dangerous Goods checks and Document generation is a part of the tools. Those systems are not designed to analyze in detail the optimum path one should follow to avoid hazards from happening, be it avoiding sensitive zones, high population areas, roads with huge traffic which can pose substantial risk and so on. Even on a day today basis road selected is based on the cost which plays a important role over other hazard criteria.

Exploratory analysis of Incidents which occurred during the transport of hazardous material consisting of attributes like mode of transportation, material of construction, packing material, amount spilled, Incident severity and other attributes can be give a very good insights of the past incidents and help in avoiding the future incidents. A detailed analysis when captured can help in identifying the trend, which carrier when used has given more hazmat incidents, major routes which are causing hazardous incidents, statistics around city, region, injury information etc.

With the availability of tools like Geo-Pandas, ARCGIS PRO integration is Python and other network tools it is very much possible to do a detailed real time analysis of the paths a Hazardous material cargo to follow if it needs to reduce the loss of property, people and environment. Using Python geo analysis or ArgGIS Pro which has built in Python Jupyter notebook, detailed analysis can be carried out with layered information of sensitive zones like hospitals, schools and other sensitive zones which pose risk if inhaled, along with weather information. Modeling of pollution dispersion is very effective with Python program be it modeling dispersion equations or integrating the output from the dispersion models available from commercial vendors.

Using the output from the analysis, a detailed assessment of routes to be followed, paths to be avoided, time of the day during which transport is allowed, accident frequency rates, major evacuation centers, transportation agencies whom you need to review and other details can be easily analyzed. These detailed analysis of transportation management of hazardous material along with enterprise systems will help organizations a lot.

There are very good research publications are available on the topic of hazardous material transport, transport incidents evaluation which shows the historical hazmat incidents, which can further be analyzed in python to get an overview of analysis using GIS / Python to best manage the hazardous Material transport within an organization.



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