Flexibility. Multi-modal transport optimization can be a tool that smart cities use to accomplish specific transportation goals. If pollution is a major problem, then a city can effectively optimize its transport system to promote bus use over private car use, and subway use over bus use. Or if a city suffers from rush-hour bus congestion, it can optimize its transport system to increase subway use during that time.
Achieve asset optimization. The goal is to ensure a city can extract maximum value from its transportation infrastructure and instrumentation investments. This includes calculating precisely which transportation assets should be replaced or repaired and when, to achieve maximum return on investment.
Pursue predictive analytics. The importance of using analytics to predict when elements of a transportation infrastructure are close to failure can’t be overstated. Consider the value of predictive maintenance, for example, in relation to the integrity of critical infrastructure such as bridges and highways. Not only can predictive maintenance save money, it can also save lives.
Enable dynamic, demand-based pricing. Smart cities have systems in place to use dynamic, demand-based pricing as a tool to influence customer behavior. As cities better understand people’s transportation behavior through instrumentation and analytics, they can influence that behavior by changing prices throughout the day to accomplish their transportation goals.
For example, a city with crippling morning smog can analyze vehicle use at that time and tailor parking prices for vehicles based on distance traveled. Or a city with high road congestion can toll the road with variable pricing and/or alter its bus and subway pricing in targeted areas to reduce traffic. Cities have different transportation circumstances and priorities, and different political operating environments, so the use of dynamic pricing to influence behavior is likely to differ from city to city.