By Luis Ulloa
Tell me the whole truth and nothing but – trip planning apps must be able to calculate all travel options to build trust and more widespread adoption.
If you live in a city, you’re probably spoiled for choice on how to get around – public transport, bicycles, car sharing, ride sharing or taxi. Yet, despite these multiple modes of transportation, most people still jump in their cars, because it’s simple. The effort it takes to figure out the best way to travel with different operators and modes of travel is so big that most people stay with what’s familiar, which is often their cars.
A reliable, easy-to-use trip planning app can help people switch from their cars to more economical (and ecological) options.
Why Trip Planning Apps Are Difficult
For a travel app to become the first step in any trip it must be able to do three things – provide the full array of travel options available, incorporate reliable real time information on the fly, and learn individual travel preferences to make planning fast and smooth.
To illustrate the difficulty behind multimodal, multi-provider trip planning, consider how GPS navigation apps work. The GPS uses graphs to determine your best route. This works perfectly well if you stay in a single vehicle, but the graph gets too big to handle if you start switching modes – such as parking your car and hopping on a bus. Now the GPS must generate a graph with all the possible bus stops and all the possible options of travel from each of those points. Moreover, most travel options, and in particular scheduled services such as public transport, require not only an origin and destination but also a time when the service is available.
Altogether these options create a complex set of combinations that very quickly explode the size of a graph representation of the network.
You have three ways to address this complexity:
- Ignore some of the options. It’s a common approach.
- Allow travelers to switch between modes at predetermined “hubs,” as opposed to any bus stop – to use my earlier example. The graph is smaller, but you still have the problem of excluding a number of options.
- Ignore the time dimension.
None of these solutions are ideal and, in some cases, they may even generate itineraries that don’t make sense. Once again, the traveler is grabbing the car key.
Solve for Simple
Our approach is based on a completely different way of modelling the network: It avoids the unwieldy graphs and it uses an algorithm to find the best options that match the traveler’s preferences, even as it proposes other options.
As a consequence, the trip planning engine of the Xerox Mobility Companion Platform makes it easy to personalize results for each user, and add new services or city information as they become available.
Our research and development will continue to advance the features and performance of the trip planner. We believe this solution will help travelers cut back on driving and give our cities a better future.