How Predictive Analytics in the Transportation Industry Can Help Increase Revenue

  • Date Published
  • Categories Blog
  • Reading Time 7-Minute Read

Predictive transport analytics is a leading trend for the transportation business. What are the challenges, benefits and opportunities?

Customer expectations in the transportation market are growing at an incredible pace. Today, individuals and communities are more careful and intentional when it comes to their commuting experiences and are less tolerant of inconveniences. To keep up with these expectations, transportation companies actively leverage data technology.

There are many ways to apply data to increase transportation companies’ revenue, but predictive analytics might be the biggest and most effective method. Armed with predictive tools, providers can forecast routes, costs, time expenses, demand, and prevent accidents. It’s a road to higher revenue, safer commuting, and a better experience, overall.

Importance of Predictive Analytics in Transportation

There are many reasons why predictive analytics in the transportation industry has seen so much growth in the last few years. Transportation providers face new challenges that just can’t be solved through traditional methods.

  • Increased globalization leads to increased demand for long routes that often span across countries and even continents. Companies are expected to control these far-range processes and provide fast and cheap options.
  • The increased number of vehicles on the road makes planning harder, as the shorter route isn’t necessarily the fastest one. To predict the best route, companies have to use smart algorithms that analyze GPS data and trends over a long period of time, time-stamp controllers, etc.
  • Keeping resources on standby increases expenses. The demand in the transportation field isn’t uniform. At any point, a company may suddenly need to involve many employees and vehicles at once. Keeping them on standby, awaiting these critical moments, is expensive.

If we also factor in the increased costs of fuel, human resources, and infrastructure, the need for efficient predictions becomes even more apparent.

Predictive analytics offers a long-term solution to most of the challenges faced by this industry and here are the main benefits of adopting predictive analytics in the transportation and logistics sector.

1. Helps to Determine Alternative Options and Mitigate Risks

Just having a ‘plan B’ is no longer enough for transportation companies. With overcrowded roads and increased customer expectations, companies need to navigate in many scenarios. How do you optimize a route if there’s a traffic jam? What’s the best way of dealing with an accident? These and similar questions require multiple solutions and answers.

Predictive analytics provides enough data for companies to build realistic scenarios and make educated decisions in unforeseen situations. For instance, a system can analyze GPS data on roads nearby to create 10-20 alternative routes in real-time.

2. Identifies and Predicts the Likely Occurrence of Routine Unplanned Accidents

Predictive analytics doesn’t have to be reactive, it’s also a great proactive tool. Predictive analytics software analyzes routes, vehicle status, and the risk of accidents in the area.

It can create accident scenarios before the vehicle departs and provide updates in real-time. This is an excellent way to keep track of road safety, vehicle status, and update customers right away if there is an issue.

If, however, a vehicle gets in an accident, the system can source data from similar accidents and predict costs, delays, and other possible risks for the company.

3. Helps to Determine the Impact of Unplanned Events

Understanding the cost of critical situations is essential for making educated management decisions. A smart predictive tool can, for instance, let the team know that risks of executing a particular route are too high and helps determine if the order is unreasonable, especially in cases where the risk of damage is high.

Predictive analytics tools analyze terabytes of GPS data, weather forecasts, customer satisfaction information, and revenue dynamics. All this is done to help management make educated decisions that reduce risks and increase profit.

4. Gains Real-Time Insights and Improves Customer Experience

Data analytics is useful for managing internal operations, but it’s not the only purpose. Predictive tools can analyze user reviews and satisfaction and provide comprehensive reports. The transportation team receives this data, analyzes it, and determines which directions can be improved.

Use Cases of Predictive Analytics In the Transportation Industry

The situations where transportation analytics tools are predominantly used can be broken down into two categories — workforce utilization and route optimization — with corresponding challenges for each.

These questions can be quickly addressed with predictive analytics. During order fulfillment and operations, the predictive system can track the performance of your team, vehicles, expenses, and revenue. Here are some of the best practical ways in which predictive analytics and transportation, as an industry, can be combined to work towards a single goal.

Demand Forecasting

By analyzing the number of fulfilled routes in a day, week, month, quarter, and year, a system can provide managers with short and long-term predictions of supply growth and fluctuation. This is useful to predict seasonal changes, profit changes, and potential increases for human resources.

Transportation Management Systems (TMS)

By integrating predictive analytics in transport management systems, companies can use all the data recorded over time. In real-time, employees will receive notifications and reports on new routes, possible risks, etc.

Predictive Maintenance

Predictive analytics keeps track of a vehicle’s state and route safety. Based on the vehicle’s history and globally available GPS data, the system can make a data-based evaluation and predict accidents accurately. As a result, companies spend less on repairs, and avoid reputational damage and customer dissatisfaction.

Read more about the benefits of predictive maintenance here.

Last-Mile Delivery

Last-mile delivery is a booming transportation trend all over the world. It’s also a challenging one — customers expect to receive deliveries momentarily. You need to take into account the slightest changes in routes and update plans in real-time. Inputting this information manually for dozens of vehicles is impossible, so this is where predictive analytics comes in.

Route Modeling

By analyzing available GPS data and company performance, a predictive system can create 5-10 optimal routes. Reports are updated in real-time, so that a company can avoid unexpected risks.

Operational and Distribution Network

Establishing a transportation network requires data-based decisions. Company management needs to decide on allocating logistic facilities (transit points, production plants, distribution), points for demand (spots where vehicles interact with customers), and determining best flows. As a powerful method for approaching complex problems, predictive analytics provides real-time insights for each end of the network.

Predictive Analytics for Smart Public Transportation

Most transport analytics systems for public transportation should focus on the time of arrival, since delays and commute speeds are crucial factors of customer satisfaction. The system that uses predictive analytics can analyze data records and GPS data to build a consistent prediction horizon.

Experiments conducted by the Nashville University have shown that introduction of a basic arrival prediction system can reduce time prediction errors by 25% for arrivals within an hour. In a 15-minute timeframe, the accuracy is increased by almost 50%.

Here, practical execution can be done via three stages:

  1. Client Application
    The most common option is a mobile app with personalized dashboards.
  2. Deployment
    Data can be deployed on a local network or Cloud. A data feed layer features historical insights based on past activities. The prediction data layer is where insights on future arrivals are stored.
  3. Workflow
    To provide a conclusion, the system runs the prediction model based on accumulated data. It compares the scheduled time of arrival with the actual data and determines the delay. This data is communicated to the user via an application and notifications. All our professionals have more than 5 years of legal experiences. We like what we do.

Other benefits of predictive analytics in public transportation:

  • Transparency and Control:
    Communities, transportation services, and authorities have a clear picture on transportation arrivals.
  • Alternative Scenarios:
    Predictive analytics comes up with different routes for getting to the same destination.
  • Predicting Passenger Flux:
    Predictive systems allow forecasting the number of passengers, and letting transportation companies know when to deploy more vehicles.
  • Parking Availability:
    During peak hours, predictive systems find convenient parking spots in different parts of the city.

Final Thoughts

Implementing big data analytics in transportation flows brings noticeable results almost immediately after deployment. It’s an investment that provides both short-term and long-term results. Our clients reported noticeable increases in profit and customer satisfaction within the first few months of deployment.

If you’d like to integrate predictive analytics into your transportation business, contact our development team via chat or through the form at the bottom of this page.