Dan Patterson: What role does artificial intelligence, advanced analytics, and machine-learning play in helping cities and transportation companies make transit more efficient?
Tom Kirschbaum: Those companies are very much driven by planning, they love to plan for the next ten years. And for those who live in the digital era, we're used to responsive, real-time responsive services. These two worlds need to be brought together. Plan for shorter cycles, for example, a planned on-demand service for the next weekend, which is simulated and operated. It implements machine-learning so the planning and simulation becomes more intelligent. You meet these scenarios, which happen on the street, more precisely, each day. In a conservative and risk-averse industry, the more you nail what's going to happen, the easier it is to drive change. It's important to provide technology to provide the tools they need to transform from what can only be described as their old-fashioned ways, and to move towards a much more sophisticated, innovative scenario.
Dan Patterson: What about the Internet of things, qnd other end points, data aggregation devices? How do these new technologies help make transportation more effective?
Tom Kirschbaum: Look at the Internet of transportation, the fleet of vehicles on the street, whether the subway or flexible vehicles, and look at the demand, people going from A to B, a literal Internet of things. This is one way to apply the Internet of things with transportation, with mobility.
The ideal set-up of an urban mass-transit system is exactly an Internet of things, a network where everything is automated, where algorithms decide on allocating resources. It's interesting to apply technology to urban transportation, and to logistics. Nothing is really happening in this regard today. If you do that, apply it, the changes will be significant in terms of how much space you gain back, how efficient it will be, and Also, how cheap it can be in terms of costs saved.
Tom Kirschbaum: Those companies are very much driven by planning, they love to plan for the next ten years. And for those who live in the digital era, we're used to responsive, real-time responsive services. These two worlds need to be brought together. Plan for shorter cycles, for example, a planned on-demand service for the next weekend, which is simulated and operated. It implements machine-learning so the planning and simulation becomes more intelligent. You meet these scenarios, which happen on the street, more precisely, each day. In a conservative and risk-averse industry, the more you nail what's going to happen, the easier it is to drive change. It's important to provide technology to provide the tools they need to transform from what can only be described as their old-fashioned ways, and to move towards a much more sophisticated, innovative scenario.
Dan Patterson: What about the Internet of things, qnd other end points, data aggregation devices? How do these new technologies help make transportation more effective?
Tom Kirschbaum: Look at the Internet of transportation, the fleet of vehicles on the street, whether the subway or flexible vehicles, and look at the demand, people going from A to B, a literal Internet of things. This is one way to apply the Internet of things with transportation, with mobility.
The ideal set-up of an urban mass-transit system is exactly an Internet of things, a network where everything is automated, where algorithms decide on allocating resources. It's interesting to apply technology to urban transportation, and to logistics. Nothing is really happening in this regard today. If you do that, apply it, the changes will be significant in terms of how much space you gain back, how efficient it will be, and Also, how cheap it can be in terms of costs saved.
