Location Intelligence – A key Enabler for Future Mobility

Location Intelligence (LI) is the collection and analysis of geospatial data acquired from different sources and converted into strategic insights that can solve a variety of problems or business challenges for mobility service providers. Location intelligence can provide actionable insights by involving the people and technology to visualize spatial data, identifying trends, patterns, and relationships between vehicles and infrastructure. It builds on geographical information system (GIS) tools to provide data-driven insights. Location Intelligence is helping to shape businesses of the future by adding critical context to their decision-making process.

The future of mobility will be defined on five themes – multi-modal, shared, autonomous, connected, and electrified. The use of location intelligence is vital in making all five areas function at optimum efficiency. With precise location data and technology, the automotive industry will begin to think of a vehicle as a machine to an IoT device.

Integration of GPS technology in iPhone 3 and the proliferation of smartphones as IoT devices were the defining moments for ‘Location Intelligence.’ Increasing usage of GPS technology in various products such as mobile phones, cars, watches, and other handheld devices has helped capture more and more location data. Today, millions of connected users on apps generate tons of location data per second, aided by innumerable sensor devices. This real-time availability of location data has opened up numerous opportunities for the mobility industry. Companies like Lyft and Uber in the US, Grab in Southeast Asia, or Ola in India have built huge mobility businesses enabled by a tiny GPS chip embedded in a smartphone.

In the future, we can surely expect an increase in autonomous vehicles, shared mobility, connected cars, and increased use of public transportation. Location intelligence will be a critical enabling technology that will make these future forms of mobility possible.

As shown in Exhibit 1 below, Geographic Information Systems (GIS) development can be broadly classified into five phases.

In the first phase, the first case of location intelligence was recorded in 1854, when the English physician and a leader in medical hygiene John Snow, determined the source of cholera outbreak in London with the help of hand-drawn geographical illustrations through paper maps. The second phase, the most important and significant period saw the adoption of digital technologies by leading organizations, which brought the LI to the businesses and to the common people. The third phase was all about development and bringing the GIS into the commercial marketplace with real-time updates on the location coordinates or real-time traffic updates.

The current (fourth) phase is about connected devices. We see billions of devices connected using IoT, which will lead to the next phase of machine learning, which will be an advancement focusing on improving the usability of GIS technology by making facilities more user-centric.

The mobility industry is one of the early users of location intelligence. Integrating GIS into mobile devices and then integrating GPS in vehicles has facilitated location intelligence in the automotive industry.

Location Intelligence or Geospatial technologies have been used extensively for improving urban mobility recently. Some of the areas where location intelligence has found its applications in the past include –

Improving integrated transportation management using location-based services
Improving transport system efficiency
Using advanced geospatial technologies in sustainable urban mobility planning, monitoring, and evaluation
Using geospatial technologies for intelligent urban mobility infrastructure development

The ever-increasing focus on upcoming areas such as shared mobility and autonomous mobility will make Location Intelligence even more pervasive than today since the fundamental requirement of these new areas is to be location-aware.

The critical role that LI will play in shared and autonomous mobility, and in improving public transportation

Urbanization is an irreversible trend experienced all over the globe. More and more people are moving to cities, bringing additional vehicles on the road, putting further strain on urban mobility. Traffic congestions are becoming new-normal across the globe. On the other hand, increasing penetration of smart mobile devices enables the new business model to share goods and services, leading to the sharing economy. Location intelligence is helping solve the problem of urban mobility with the adoption of the shared mobility model.

Shared mobility is an innovative transit strategy that gives people short-term access to various modes of transportation on-demand. High-quality digital maps, which are very accurate and frequently updated, form the foundation of shared mobility. Also, the ever-increasing penetration of GPS-enabled smartphones and cheaper data packs have made it very simple to use the shared mobility services. Today, global companies like Uber, Didi, Lyft, Grab, Ola and Jump, have built a large empire using the shared mobility model.

Location Intelligence is also used by car-pooling service providers such as Waze carpool, Scoop, BlaBla, and hundreds of similar companies in different countries. These companies provide the service using the mobile app, which allows friends, family, colleagues, and neighbors to identify travel routes they have in common and share the ride.

The second frontier vis-à-vis the autonomous vehicles don’t look distant dream now. Location intelligence will be at the heart of these future autonomous and connected vehicles. Autonomous vehicles would require a real-time mapping system that is based on a precise location. The map used in these vehicles would update real-time information and include the latest information about the surroundings. These maps gather and analyze data in real-time from a variety of connected devices and provide crowdsourced updates. For example, it can notify a roadblock ahead or about road conditions in other parts of the town, and the maps would update themselves in real-time.

The third frontier will be on how location intelligence will change the face of public transportation systems. Location is a crucial aspect of defining the route while planning the journey of public transportation modes. The Geographical Information System (GIS) and the Global Navigation Satellite System (GNSS) are two vital technologies used to plan and manage the complex transportation system. These technologies define public transport maps and routes, helping transport operators and local authorities inefficiently operating public transport systems in their areas/cities.

The advanced real-time updated maps can provide multi-modal options for driving a car, scooter, or bike from entire interior parts of a city, or from far away location in the town to a parking lot of nearest transit station, then riding public transport into the city center and completing the journey on transit, on foot, via car-sharing or on a scooter or bike to the last mile destination. Commuters can find the nearest transit station, get real-time updates on the dynamic schedule of the bus/train, or locate the nearest pool point.

What future possibilities will emerge with advanced location intelligence technologies?

With the ever-increasing penetration of smartphones and connected devices, a massive amount of location data is getting generated. Location intelligence uses this data and combined with business intelligence to provide never before insights that can change the course of industries as well as societies.

The future of mobility belongs to shared and connected vehicles and rapidly moving towards autonomous vehicles. What enables this future is location intelligence.

As autonomous vehicles become a reality, these newer vehicles should be aware of things surrounded and detected by sensors, cameras, and radars loaded on them, map the environment around the corner, and know in advance what is happening 100 meters ahead of them. This is exactly where the fifth phase of the geographic information systems i.e., Predictive Location Intelligence using machine learning will come into the picture, focusing on improving the usability of GIS technology by making facilities more user-centric. It will be critical to instill confidence in the user’s mind about the safety of that vehicle. Location intelligence with real-time updated maps would help these vehicles. It will help autonomous vehicles maneuver smoothly by proactively preempting the challenging road/traffic conditions.

Another trend of the shift in car ownership where various studies predict a sharp fall in private car ownership leads to the massive transition resulting in a rise in car-sharing, ride-hailing, and other mobility services. Location intelligence has made shared mobility a multi-billion industry. The future mobility services will have location intelligence at their core and help drive the new-age mobility.

References

Location intelligence is helping shape the future of mobility
The future of mobility via location intelligence

Location Intelligence – A key Enabler for Future Mobility

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Location Intelligence – A key Enabler for Future Mobility

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