Technology Record - Issue 22: Autumn 2021

116 www. t e c h n o l o g y r e c o r d . c om V I EWPO I NT Taking the first step J OHN S T ENL AK E : M I C ROSOF T By taking advantage of real-time data, transport operators can start on the journey towards achieving truly intelligent transportation systems I n the first article of this series, we introduced the concept of the Intelligent Transportation System, or ‘ITS’ – coordinated systems for moving people and freight which run on data and match supply to demand in the most effi- cient way possible. This article will start to drill into how this can be accomplished. Let’s focus on a specific aspect of the ITS – the coordination of passenger transport within an urban environment. In today’s world there are usually multiple modes of transport available for any given journey, which might include inter- change with longer-distance transportation at some form of terminal. Chaining different forms of transport together yields ‘multi-modal’ jour- neys, and there are even applications available, such as Citymapper or Moovit, which provide some level of support for identifying options and coordinating such journeys. So where’s the problem? The issue is that the underlying forms of transport that constitute these journeys are typically not coordinated with each other in any meaningful way. This causes friction at the transition between differ- ent modes. The more steps in such a journey, the less reliable it becomes, and the efficiency of the journey usually trends downwards. To make this problem worse, these applica- tions can do relatively little to help passengers dynamically manage their journeys as the plan starts to fail. Passengers might even be locked into ticketing structures which are inflexible and incentivise them to carry on with a flawed plan rather than switching to a better one. The most significant underlying success factor to address these issues is the availability of real- time data that describes the behaviour of the different components of the transportation sys- tem and integrates this with real-time demand for services. Note that for this we do not need personally identifiable information, informa- tion of true commercial value, or proprietary information that might represent one company’s competitive edge over another. If this data is readily available, individual oper- ators of transportation services can immediately start to use this information to improve their own operations, even though there is no optimi- sation being done at a ‘central’ level. I’ll provide a few examples of this to highlight the opportunity. An urban rail station will rep- resent a start point for many intra-urban jour- neys, and the end point for others. Historically, a taxi booked to collect someone from a train would have to wait until the train gets in and the passenger shows up. The vehicle and driver are sitting idle, taking up space and possibly gener- ating emissions. Now let’s contrast this to a future mobility service that takes advantage of real-time data. They can associate requests for service with the arrival of a particular train, and then allocate vehicles dynamically to provide that service. Assuming sufficient scale, the operator can work at optimising the positioning of vehicles in the area while having them do other jobs nearby until they are needed. If the train is late, the workload is pushed back in the schedule to “Urban transport providers can immediately improve their performance given access to real-time data”