How Apps Support Carpooling and Ride Sharing with GIS Data
Electric vehicles (EVs) have been increasingly gaining popularity due to their numerous benefits over traditional gasoline-powered cars. One such advantage is the reduced carbon footprint, making them a more sustainable transportation option.
From 2012 to 2021, a total of 17 million electric vehicles (including both all-electric and plug-in hybrid models) were purchased globally.
Predictions indicate that by 2030, there will be a total of 145 million electric vehicles on the road, encompassing electric cars, buses, vans, and heavy trucks. In 2022, electric car sales saw a surge of 40%, and EVs achieved another historic year, with more than 10% of new vehicles sold being electric.
However, to make the most of the potential of EVs, it is crucial to encourage shared mobility, such as carpooling and electric vehicle sharing. This article will explore how GIS (Geographic Information Systems) can be used to visualise and analyse the potential for EV sharing and carpooling.
What is GIS Technology?
GIS is a mapping technology that allows us to visualise, analyse and understand data in a geographic context. It enables us to create maps and perform spatial analysis to uncover patterns, relationships, and trends that are not immediately apparent in tabular data.
Understanding the potential of electric vehicle sharing and carpooling
Sharing vehicles can have several benefits, be it through carpooling or ride-sharing. It can help reduce the number of cars on the road, leading to lower traffic congestion and emissions.
Additionally, shared mobility can make EVs accessible to those who may not have the means to own one themselves. By visualising and analysing the potential for EV sharing and carpooling, cities can make informed decisions on how to encourage and support these initiatives.
How GIS can help visualise EV carpooling data?
GIS technology can be used to create interactive maps that display the distribution of EVs, charging stations, and potential carpooling locations. This allows for a better understanding of the current state of shared mobility in a given area and helps identify areas with potential for improvement.
For example, we create a map with the locations of all EV charging stations in New Delhi with GIS. Using this data to identify areas with a high concentration of EVs and charging stations, it is easier to establish carpooling routes in those areas.
How GIS can analyze EV carpooling data?
In addition to visualising the potential for EV sharing and carpooling, GIS also enables analysing this data. GIS can be used to identify areas with a high demand for EVs but a low number of charging stations. This information helps prioritise the installation of electric vehicle charging stations in locations with the greatest need.
GIS is used to analyse the potential for carpooling. By using data on commuting patterns and traffic flow to identify the most efficient carpooling routes.
For example, GIS helps analyse the concentration of EV stations in New Delhi, and match it with the no of EV users in that area. We can calculate the efficiency of ride-sharing with GIS data in this particular area. Thus GIS data gives us the locations of the most popular commuter routes.
GIS data helps analyse the impact of EV sharing and carpooling initiatives on the environment and local communities. We can assess the impact on greenhouse gas emissions with continuous EV usage. This helps identify areas that can benefit from carpooling and reduced emissions.
In Conclusion
In conclusion, GIS is a powerful tool for visualising and analysing the potential for electric vehicle carpooling. Cities can make informed decisions on how to encourage and support ride-sharing initiatives, with GIS data to back them up.
Decision-makers can make informed decisions about deploying EV charging stations, car-sharing hubs, and carpooling services. As EVs become more prevalent, GIS will play an increasingly important role in shaping the future of sustainable transportation.