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Predictive Analytics for Road Managers

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Road asset managers are in charge of many tasks, including overseeing driver safety, road maintenance, and cost and budget management. Road management may be made simpler, though, with the aid of tools and technology like telematics and vehicle tracking software that keep an eye on driver behaviour as well as the condition and cost of your vehicles. It is more difficult to monitor and gauge data trends when there is so much data being captured. Predictive analytics come into play in this situation. Road asset managers can use recorded data to identify road trends and improve future planning with the aid of predictive analytics. In the transportation sector, predictive analytics is primarily utilised to optimise assets and increase longevity through creative, success-guaranteed overhaul and refurbishment decisions. This system offers data on transportation asset degradations that, if not caught in time, may negatively result in unanticipated expenses, downtime, losses, or even injuries. With the help of this technology, proactive and intelligent maintenance routines can be accelerated during off-peak hours while potential risks, delays, failures, and subpar performances are quantified. By doing this, you may prevent your assets from failing at inconvenient times and your transportation services from being stopped.

​​Importance of Predictive Analytics in Transportation

The expansion of predictive analytics in the transportation sector over the past several years can be attributed to a variety of factors. The difficulties that transportation companies confront today simply cannot be resolved through traditional methods.
  • Long routes that frequently cover many nations and even continents are in greater demand as a result of rising globalisation. Companies are expected to manage these extensive processes and offer quick and affordable alternatives.
  • Planning becomes more difficult due to the increase in traffic because the shorter route isn’t always the fastest one. Companies must utilise sophisticated algorithms, time-stamp controllers, and other devices to evaluate GPS data and patterns over long periods to determine the optimum path.
  • Spending more money to keep resources on standby. In the transportation industry, demand is not constant. A business may at any time need to use numerous staff and cars all at once. It costs money to keep them on call at these crucial moments.
The need for accurate predictions becomes even more clear if we additionally take into account the rising expenses of fuel, labour, and infrastructure. Predictive analytics offers a long-term solution to the majority of the issues faced by this sector. Here are the primary advantages of implementing predictive analytics in the transportation industry.
Vehicle Maintenance

Data on the condition of your vehicle is recorded by vehicle software. Predictive analytics software can calculate wear and tear and anticipate when and whether a vehicle requires repair depending on how frequently drivers use it. Road asset managers can be alerted when a vehicle needs maintenance by monitoring this data and then applying predictive analytics. By preventing accidents and breakdowns, you can keep your vehicles in good condition and keep your drivers safe. Additionally, tracking and forecasting vehicle maintenance might help your vehicle save money. Maintaining proper tyre pressure, for instance, can help vehicles save money and energy, and regular vehicle maintenance can cut your fuel costs by about 4% overall. Planning for vehicle downtime will be made easier by estimating when maintenance is necessary.

Optimising Drivers’ Routes

The routes that will help to cut fuel costs and miles while enhancing efficiency on the road can be determined with the aid of predictive analytics technology. You can guarantee that your drivers will have the most effective journeys by combining predictive analytics, GPS, and traffic data. To save money and guarantee your drivers have safe travel, predictive analytics software will be able to determine the quickest route, taking into account road closures and busy times. By applying advanced analytics to disclose insights on the usage of vehicles and equipment, driver behaviour, and vehicle productivity, road managers can pinpoint areas where their operations may increase savings, efficiency, and safety.

Driving Behaviour

With the aid of predictive analytics, road asset managers can easily spot and rectify any irresponsible driving on the part of any driver, which will result in fewer accidents, lower expenses, and greater engagement. Predictive analytics gives road asset managers the chance to collaborate with drivers on accident avoidance while also taking some of the guesswork out of budgeting for repair costs. Driving safety can be improved by managers and employees working together. Driver retention rates improve as a result of how this influences driving behaviour. When a good prescriptive model is used in conjunction with telemetry data and at-risk drivers receive training, preventing accidents is more feasible than ever from a vehicle risk management standpoint.

Fuel & CO2 Monitoring

One very important and frequently overlooked benefit for this carbon-intensive industry is the ability to use analytics for carbon footprint reduction, which has both financial and reputational ramifications. Process automation and data analytics work together to create considerable savings that save costs, streamline business operations, and enhance coordination between shippers, carriers, and brokers. Approximately 95% of all freight emissions in 2019 were attributed to heavy-duty transportation (aviation, heavy road transport, and shipping), according to BCG. Transportation logistics operations can lessen their carbon footprint and the environmental impact of moving freight along the supply chain by increasing fuel economy and operational efficiency by utilising artificial intelligence and machine learning to drive data analytics. Road asset managers may utilise predictive analytics to monitor, track, and control fuel use in real-time, which improves fuel efficiency, lowers fuel theft and reduces fuel expenditures.

Using Predictive Analytics in Logistics

Predictive analytics has become a vital instrument for achievement in domains where high levels of time and resource efficiency are necessary. Efficiency is crucial in the modern logistics industry. Supply chain interruptions must be continuously managed by logistics businesses. In addition to anticipating consumer purchase trends, maximising vehicle efficiency, promptly adapting to changing shipping patterns, planning the best possible delivery routes, and delivering goods on time and error-free, they must as well maximise vehicle productivity. Predictive analytics is employed to streamline logistical responses to these routine operational problems as well as in other areas including vehicle maintenance, business budgeting, fuel expenses, and the adoption of essential vehicle safety measures.

Propel into New-Age Technology

Predictive analytics software can assist in identifying recurring problems with your vehicle based on historical data so you can look for a fix. It is a tool that can be used for data mining, machine learning, statistics, and modelling. It collects real information to analyse and produce statistics for every topic and general trend. Implementing predictive analytics inside your vehicle has numerous advantages. It will support budgeting, planning for the future, and estimating the vehicle’s life span. If you are already tracking the data, you may use it to make vehicles safer and more efficient as well as use it to forecast patterns in the future. Make road asset management easier by integrating this cutting-edge technology today.

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