The transportation sector is the largest contributor of U.S. greenhouse gas emissions, accounting for 28% of total emissions. President-Elect Joe Biden undoubtedly recognizes this. Cutting transit emissions is a big part of his $2 trillion climate change plan, aiming for 100% of new sales for light- and medium-duty vehicles to be electrified among other policies.
While it is certain that to get ahead of the carbon-neutral curve, the transportation sector — public transit and private charters alike — needs to focus on electrification, the industry must also figure out how to optimize the current technology it is using. Research has found that moving to hybrid technology will improve fuel efficiency by 30%. When combined with additional optimization measures, efficiency increases by an extra 14%.
This bump can happen when the transportation sector takes digitization to the next level, managing the entire electrified transit system through a network approach. This requires a fundamentally different way of looking at current transportation ecosystems. Once full penetration of sensors and existing technology is achieved, the digital paradigm can be expanded and the sector can move on from using digital tools to just predict traffic or optimize routes. Instead, the industry can take a more holistic approach to actually mitigate traffic problems in real time from a centralized control point.
Here’s a closer look at how to establish this network of vehicles, dispatchers, and maintenance workers so you can optimize routes, minimize electric and gas usage, accurately forecast traffic patterns, and ultimately even manage congestion through real-time control, such as sequencing of lights, etc.
Utilizing digital tools to optimize the route
One of the biggest benefits of digitalization is route optimization. Through it, you can minimize fuel consumption by avoiding high-idle areas or identifying a route that better manages stops. In the last few years, route analytics and optimization have taken off thanks to new software and real-time data collection techniques. It replaces the batch processing methodology, which created a significant time delay in the collection and processing of data.
Technology like AI or 5G are key to this optimization. AI crunches the data, while 5G connectivity helps ensure continuous, uninterrupted data, whether on the number of riders, road congestion, or more. Together, these tools can help organize fleet schedules to avoid peak rush hour, ensure faster delivery times by providing alternative routes in real-time, and reveal less-congested traffic areas for individual drivers to take. In fact, with AI alone the transport sector could cut greenhouse gas emissions globally by 1.7%.
We’re seeing optimization tech like this in play for rail, which is using it to improve power efficiency. HS1, the UK’s first high-speed railway, uses automation to adjust line voltages to keep power running to trains efficiently.
Identifying and leveraging existing data
While it can be easy to want to implement the latest technology, budgets don’t always exist for it. This doesn’t mean your optimization journey is over. Data oftentimes already exists under your nose, whether it’s through GPS trackers, traffic light cameras, or even the CB radios used by trucking fleets.
While it can feel like a data deluge, first identify the specific transit constraint you’re looking to overcome. For example, if the biggest constraint for your railway system is being on time, then the operator will need to review data that outlines the events of previous train runs. Operators can then reference this data when deciding whether to redirect train routes to avoid conflicts and delays.
Though digital technologies like AI and 5G likely come to mind first when building an electrified transit network, it is critical to first understand what data exists and how you can consume it before investing in another form of technology. You’ll be surprised at how much previously hidden value you’ll turn up.
Forecasting anticipated capacity and need
The last key in transportation optimization is predicting what will happen when, and how to adjust for it. For instance, how many vehicles or individuals will use a specific mode of transportation or facility at a given time, either next year or 10 years in the future. This could be the number of passengers on a subway line or of vehicles taking a specific route during rush hour. The data for these insights come from a variety of places, but AI and other analyses will be needed to make sense of the information.
Ultimately, forecasting can help organizations make educated decisions about future transportation infrastructures, such as the number of lanes a bridge should have, when to schedule road work, or construction. It can also help organizations identify how many fleet vehicles to purchase, what size, and the frequency of routes. When it comes to electrification as well, forecasting will be instrumental in giving vehicles only as much energy as needed to optimize charging time and energy use.
As the U.S. aims to cut down on emissions, electrification will be paramount, however, public transit, private charters, and railways should look to digitization to create a connected network that will further maximize efficiencies. By leveraging digital tools to optimize route transportation, as well as using data that already exists to help forecast, organizations can build a network of infrastructure and assets that operators can easily use to manage transit, keeping sustainability top of mind. While most of the public and private sector are relying on these tools already, optimizing and layering upon all this technology can help control a centralized network, boosting efficiency, and enhancing the overall transportation experience. And with greenhouse gas emission posing a dangerous risk to our country, this connected network is necessary, yet can only be achieved when the transportation sector prioritizes digitalization.
Bryan Friehauf is Head of Software for Hitachi ABB Power Grids