I’d like to learn more about CSS Corp

  • THOUGHT LEADERSHIP BLOGS

    Cogent Views on Technological Developments Driving Business Outcomes

  • FEATURED BLOGS

    Narratives on Technology Issues that Count

  • GUEST BLOGS

    Views and Cues from Global Leaders

    Disclaimer: Views and products mentioned by guests are not necessarily endorsed by CSS Corp

Accessing Location Intelligence to Optimize Road Navigation in the Logistics Sector

Truckers or truck drivers are the unsung heroes of a nation's economy. The hours and miles they put in to ensure goods are delivered on time drives business and spur revenue. It's a tough job. They maneuver difficult terrains, traffic conditions, seasons, & environments, all for our benefit. A good driver is essential to offer quality logistics. An inefficient driver can result in longer delivery times and increased costs. Enabling driver efficiency in the fleet logistics sector is very critical, as earnings depend on getting the trucks from the start point to the destination in a cost-effective manner. This process is challenging and requires the intervention of Global Navigation Satellite System (GNSS) driven analytics to help drivers save considerable amount of time, money, and resources for their company.

shutterstock_1409325194

GNSS to the rescue

GNSS is used along with satellite navigation systems, to provide precise, continuous location positioning and timing of a truck under any weather conditions. The GNSS ecosystem consists of satellite constellations, ground control stations, and receivers. The receivers pick satellite radio signals and control stations tracks & updates satellite positions while transmitting truck positions from the earth back to the satellites.

Large sets of geospatial, GPS, and location data are linked to physical locations of the truck on the road. These include origin, destination, the vehicle being driven, goods to be delivered, road surfaces, demographics, traffic, weather, etc. The datasets are collected and analyzed by leveraging ML-driven algorithms to create a smart map, which helps to visualize the insights & recognize patterns embedded in the data. These are actionable insights and termed as location intelligence.

Data sets can be categorized into feature data to represent geographic location, raster data for continuous data representation (elevations, turns, temperature), and vector data to signify roads, buildings, stoppage zones, etc. The description of every location is attached to every data set and are termed as attributes.

All data sets are stacked as layers, and each layer can be analyzed using complex AI engines to answer questions from the driver. This is spatial data exploration at work, which combines geographic viewpoints with the statistical data in the attributes. The layers are meshed and geo-referenced by adding geographical information, and the image is displayed in the driver's device to show the real-world location. Smart maps can empower drivers through the visualization of several data attribute patterns into one map. Bivariate mapping is used to illustrate the relationship between two spatially distributed variables, it helps to differentiate patterns through color and size, which enables the exploration of data and in the display of driver-friendly information on the map.

Location intelligence is hence a powerful productivity enabler for logistics firms to plan, monitor, and manage needs at every driver touch-point.

Using location intelligence

Here are a few ways of leveraging location intelligence to optimize driver efficiencies:

  1. Manage traffic congestion: Optimize travel time by continuously monitoring the roadway by collecting traffic data from road detectors, analyzing entry and exit rates to calculate the optimal green time for traffic lights in congested areas, to help the driver move faster with lesser stoppage times. Drivers also can identify crowded areas, factors causing congestion, identify new routes, and find ways to maneuver chosen routes quickly.
  2. Fuel management: Fuel costs account for most of the fleet operating expenses. Fuel costs do add up, and drivers must use lesser fuel during trips. Real-time & past data on fuel consumption can help to calculate and reduce cost per mile for specific routes.
  3. Service predictability: By combining vehicle, asset, and driver location data, arrival times can be predicted to a good level of accuracy at every transit point. Drivers can be rerouted in case of bad weather or accident scenes, reduce loading/unloading times in warehouses, and inform customers of rescheduled timelines.
  4. Enable driver safety: Track driver habits & behaviors on wheels, alert drivers on speed limits, schedule driver workloads, vehicle locations, and engine health in real-time. Driving habits can be captured to assess a driver's risk profile, which can be used to devise customized vehicle insurance policies with relevant premiums.
  5. Queuing at toll gates: Reduce vehicle congestion in toll gates by electronic toll collection to verify whether the vehicle should be charged or not by tracking its position and trajectory. Computation of tolls is also free from errors and ensures vehicles are not charged incorrectly.

The logistics sector has the challenge of reducing rising fleet and distribution costs while meeting customer expectations for priority deliveries. Location intelligence is imperative for this sector to provide valuable insights in overcoming constraints and inefficiencies, by delivering goods on time, minimize delays or damages, while improving customer satisfaction.

The CSS Corp Editorial Team

Subscribe Here!

Posts by Categories

See all

Reach Us

Thank you for reading CSS Corp Blog. The best way to reach us would be to fill the form below and we will get back to you.

reach-us