In this way, Machine Learning techniques can help authorities detect and better predict which bridges are most likely to fail.
For instance, researchers have trained classifiers like SVMs and Random Forests to identify high-risk bridges based on features such as the seismic potential of the earth and the structural characteristics of the bridge itself. Such work allows authorities to close and fix bridges, roads, and traffic infrastructure while they are cheaper to fix and before they cut off major transportation routes, cause injury, or even fatalities.
4. Predicting Vehicle Maintenance Needs
Researchers are also exploring methods for predicting vehicle maintenance needs based on real-time data collected by sensors in a vehicle. One way of predicting a vehicle’s maintenance needs is to build a database of deviations (from normal vehicle functions) that are known to cause unplanned repairs in the long term.
For instance, Prytz monitored engine sensors for a bus fleet and identified aberrant engine sensor data using histograms of the entire fleet’s sensor data. Specifically, he assigned “anomaly scores” to each bus’s sensor data based on how much the bus diverged from the general fleet histogram for that sensor (see here for more on histogram-based anomaly detection). One sensor that proved to be an especially useful proxy for distinguishing buses was a measure of each bus’s coolant gauge percentage. Prytz found that within weeks, buses with anomalous coolant gauge percentages often needed repair for runaway cooling fans.
Training a classifier to recognize deviations in damaging features like coolant gauge percentage could be a major boon for public transportation services, where early detection of vehicle problems has the potential to save public money. In addition, such a classifier could ultimately identify engine problems for individual drivers, so they can fix their vehicles for cheaper preemptive servicing before they need a tow.
Additionally, sensors within vehicles could continue to collect more data and augment existing databases of vehicle deviations–allowing for improved maintenance prediction as time goes by and more vehicles use the classifier.
Finally, with more data, there is the promise that engine and vehicle design may be optimized by manufacturers to improve both reliability and potentially fuel efficiency by monitoring typical engine and vehicle conditions for example.
5. Public Transportation Optimization
One of the most difficult factors to account for in Public Transportation is the time of arrival for bus services. Buses and trains may be late for any number of reasons, from traffic congestion to bad weather, to vehicle failures. Late buses can cause riders to opt for other forms of transit, losing revenue for the transit authority and encouraging car usage.
Machine learning techniques can be used here to accurately predict the time of bus arrivals based on real-time bus position data and factors like traffic congestion, expected operational delays, as well as the time it takes to load passengers at different stops.
Researchers have shown that a combination of clustering analysis and Kalman filtering leads to more accurate predicted times of arrival than location-based or heuristic measures. More accurate predictions of this kind may save transit authorities money and give commuters fewer headaches when they are taking public buses.
In a recent paper, NTU scholars analyzed data from mobile phones (with approximate cell-tower locations) to accurately predict passenger wait times with >95% accuracy depending on.
On the logistics side of public transportation, a common problem is the “bus bunching” phenomenon. When buses are scheduled to come every ten minutes, for instance, buses and trains can bunch together if any of the buses experience delays.
You can see the phenomenon for yourself here in Lewis Lehe’s excellent Bus Bunching Simulation:
Bunching results in higher wait times for customers and unbalanced passenger loads in the buses–an inefficient result that could be avoided if buses came every ten minutes as planned.
Using real-time bus location data and simple linear regression models to predict delays, though, authorities can predict when a bus driver should leave a bus stop to allow a full ten minutes between buses and prevent bus bunching. By evenly spacing themselves out in this way, buses may become less crowded overall and decrease passenger wait-times.
These are just five of many transportation domains that are being revolutionized by machine learning techniques. It remains to be seen how long it will take for data-driven optimization strategies to be implemented by government authorities, or whether self-driving cars will instantly become a mass phenomenon. However, in the long run, machine learning techniques show great promise for making our commute safer, faster, and cheaper.