Research has shown that the rising costs of owning and running a car and a lack of alternative transport links are cutting people off from work, healthcare and social opportunities, writes Nuno Mendes of Stratio Automotive.
A reliable transport system, on the other hand, makes it three times more likely that people can access vital services. Moreover, with cars representing 16% of the EU’s overall greenhouse gas emissions, or 72% of EU road transport emissions, it’s clear that fundamental change in our approach to mobility will be key to addressing ecological, social and economic needs. In short, making public transport systems more reliable, efficient, and ubiquitous unlocks social, healthcare, education, and employment opportunities, while improving our carbon footprint.
Despite environmental concerns and the growing price tag associated with car ownership however, the private vehicle has continued to dominate commuter habits. Buses travel on the same roads as cars, but take longer, are less reliable and come at a premium cost. Although schemes such as dedicated right of way bus lanes have attempted to ease the issue of congestion, and despite the calls for Government-led fare cuts to encourage customers back on board, these initiatives fail to tackle the central problem for consumers when considering public transport: service reliability.
Vehicle failures and the resulting downtime and service disruptions undermine passengers’ confidence in the reliability of a bus route or even the entire service network. Less incentivised to use public transport services that they deem inefficient, they will choose an alternative. Bus operators lose out on revenue, forcing fares up and leading to further service reductions, contributing to transport poverty for those without a car. Meanwhile an over-reliance on private vehicles from those lucky enough to be able to afford them creates pollution and traffic congestion for all. It’s a vicious circle of transport decline, and a lose-lose situation. Finding a maintenance strategy that keeps buses on the road and in service, getting passengers where they need to be, when they need to be there is the answer to the problem of bus usage.
Finding the right maintenance strategy
In the face of rising fuel costs, disrupted supply chains, material shortages and an imminent expensive transition to electric vehicles, many fleet operators have already been trying to extend the life cycle of vehicle components to enhance reliability, reduce downtime, bring down maintenance spend and protect revenue streams. The principal challenge for service teams lies in balancing safety and reliability with the need to keep costs low. This is a puzzle that each transport operator approaches differently. Strategies range from reactive to preventive and predictive, but not all deliver on the ideal of zero downtime, failure prevention and reduced costs.
Take reactive maintenance as an example, with repairs occurring after an issue has already happened. While this saves money on preventative servicing, unfortunately emergency repairs involving short notice, more expensive delivery of parts and potentially replacement bus rental can end up being more costly still. Moreover, recalling the bus from the road and disrupting service routes does not enhance the customer experience.
Preventive maintenance, based on estimations of when a failure might occur, can help extend a vehicle’s durability with regular checks. However, it doesn’t optimise costs and results in more time spent servicing a vehicle when it might not need it – again impacting the service provided to the customer by keeping buses off the road.
AI-powered analytics
The most advanced approach to achieving reliability is predictive maintenance. By leveraging the automated collection and analysis of vehicle data using AI, maintenance managers are provided with real-time, actionable insights into individual bus components. Maintenance and repairs can be scheduled more accurately, contributing to better fleet utilisation and cost savings. But more importantly, by preventing equipment failure, vehicle breakdowns can be pre-empted to reduce downtime and protect both revenue and customer experience.
Unlike the preventive approach, predictive maintenance can also allow for the natural variations between vehicles assigned to different routes, saving time on visual inspections. For example, a city bus brakes more frequently than a long-haul, inter-city vehicle. AI predictive maintenance means bus operators can depend on algorithms to spot patterns in the data collected to predict when a replacement brake pad will be required. By knowing when replacements will be needed, parts can be ordered in bulk, and maintenance scheduled during off-peak periods. This brings down the costs associated with unplanned or pre-emptive part replacement, and significantly reduces the downtime and associated operational impact.
Despite common misconceptions that AI is replacing people, an AI-enabled predictive maintenance solution doesn’t eliminate the need for solid, knowledgeable service teams. Instead, it enables the digitisation of repetitive, mundane tasks that are time-consuming and prone to error. The automation of jobs such as odometer readings, coolant checks, oil changes, and more, free up teams to perform higher-value tasks that contribute more to the network. In addition, the system can proactively alert engineers of possible risks, enabling improved intervention planning.
Choosing the best route
Whilst reactive maintenance may result in short-term cost savings, it will inevitably lead to significant overspending on emergency repairs. Preventive maintenance is more effective at keeping costs down and minimising unscheduled downtime, but it can be resource-intensive and has multiple limitations. To truly embrace the pursuit of zero downtime, boosting reliability and therefore customer trust in bus services, operators must turn towards proactive AI-powered predictive maintenance strategies. By using data and analytics to create an early warning system for equipment or vehicle failure, detection time can be optimised, and early intervention and effective risk mitigation strategies enabled. Predictive maintenance represents the only way to accurately minimise unplanned downtime while saving money on resources and avoiding emergency repairs.
Ensuring service reliability and getting the public back onto public transport will not only lead to revenue growth for operators, but will also be crucial for reducing transport poverty, connecting people with opportunities and services, curbing car reliance and curtailing environmental pressure. In this way, reliable public bus networks underpinned by AI-powered predictive maintenance will serve the ecological, social and economic needs of future generations.
