Location data is an integral part of the transportation and logistics industry. Consistent monitoring of mobility patterns helps organisations plan and manage the usage of services in logistics, supply-chain, transportation, fleet management, and more.
Mobile location data provides actionable intelligence into the scope expansion and optimisation of those services - both in public and private sectors. It can help businesses and governments improve efficacy of their operations and save costs by identifying challenges and providing insights on how to overcome them.
Analyze movement patterns and unlock valuable insights on how people move around the physical world
Federal governments and city administrations can use location data to improve public transit systems, build smarter urban systems, and manage traffic, while logistics businesses and transportation service providers can use mobility intelligence to improve their services and boost profits. Location data has proven extremely valuable for transit management during the Covid-19 pandemic as agencies all over the globe used it to enable social distancing and other safety measures while still allowing people to commute. Researchers can use mobile location data to highlight social inequalities and build solutions for welfare, such as accessible transportation, affordability and more.
Assess demands based on population saturation to improve availability and consumption of public transport.
Predict passenger movement based on historical data and density frequency and inform pricing models to boost profits.
Monitor vehicular & crowd movement to avoid congestion, control traffic, manage peak hours & enable public safety.
Study the usage of public transit facilities and help local administrations provide better services to citizens.
Assess availability of transportation modes in underprivileged areas and build solutions to bridge gaps in public transit.
Analyse efficiency of logistics at a granular scale and reduce the cost of lost goods with better supply chain planning.