Car traffic is a growing challenge for many cities. Data and technology can help to provide customer-centric and sustainable mobility services to solve this issue.
As cities grow, air pollution and congestion pose a challenge to many cities, not to mention the impact on our climate. Most often the cause of traffic problems is not the cities’ own traffic. Instead it is induced by traffic from their outskirts, mainly by commuting flows. To solve this issue, we must fix its root cause, i.e. reduce commuting car traffic from suburban, residential areas without compromising their mobility.
Reducing transport without reducing mobility – isn’t this a contradiction? No, not necessarily. Mobility can be seen as the accessibility or portability of people, goods or services whereas transportation is the pure, physical movement of people or goods. For example, if you need a medical diagnosis, you can go to a doctor far away or next door – or you might get a video consultation supported by a medical expert system. The results may be the same – but the transport needs are very different. Mobility serves customer needs. In case needs can be served with less or without transportation, mobility is not affected. On the contrary: if you don’t need to move things or people to get a job done, you save time and money. So it is key to understand why transport happens and what the underlying customer needs are. Data can help us with that.
Insights that show the fluctuation of city visitors from outskirt areas as opposed to internal traffic congestion is highly relevant when understanding urban transport needs:
Equally important is where people are going in order to better understand their needs and areas of interest for better insights when providing customer centric mobility services:
Ideally we would know the so called Customer Journey and the Use Case which led to the respective journey of each individual rider. The Customer Journey is more than the pure physical journey. It also includes all digital interactions, decisions related to the trip, information or payment given and received as well as emotional ups and downs along the entire customer experience.
At least as important is the situational context of the journey, i.e. its use case. It describes why the journey was necessary. Common use cases are the daily commute to and from work or picking up the kids from school as well as the bi-weekly trip to the gym or for grocery shopping. For these regular trips people develop habits and don’t want the hassle of planning a new route or transportation method again and again. Changing these deeply engraved travel patterns will require significant impulses to overcome inertia and reconsider travel options.
Many people show a specific set of use cases and related travel patterns, which are very specific to their context and situations. For example a young, male student has very different needs and use cases for travel than a mother with two kids, who also takes care of her elderly father. These archetypes of people can be described in so-called personas, which give these archetypes a name and face so that planners can better relate to their mobility needs. Ideally data can tell us which set of travel patterns belongs to a meaningful large passenger group and so automatically create “real life” personas.
Neura’s insights show: personality traits per area
This is where the importance of understanding the people behind the riders is important. Being able to differentiate between the various rider personas helps inform product decisions, marketing campaigns, and BI solutions. Neura’s mobility behavioral intelligence platform focuses on this aspect of rider mobility helping companies understand the person behind the anonymous rider. Understanding this context behind a user allows for monetizable, impactful insights, enabling product advancements, market expansion, understanding of demand discovery, and consumer segmentation. Personas can be defined into different use cases for mobility and transportation companies to provide their offerings.
In addition to the regular use cases there is another set of use cases which have very different requirements: the unique, long distance travel such as business trips or traveling to a vacation resort. For these rather rare and singular events with higher importance, travelers are more willing to consciously plan the journey and compare travel options.
For example, seeing where people are most frequently going by sector is helpful for understanding the main reason people are driving their cars:
Back to our initial starting point: how to get people to leave their cars at home instead of driving it to the city. Knowing the persona’s use case can help identify sustainable alternatives, which better serve their needs than their own car. This would also require insights from car drivers, who don’t use public transport or new mobility services yet. Following the avoid-shift-improve structure, alternatives could for example be:
Is the trip really needed or can it be avoided? How could this look like? If you would work from home for just one day per week, you would reduce your commute already by 20% and save time and money at once.
Can the trip be shifted towards a more sustainable mode of transport? Why would you do the parent taxi thing and drive your kids to school by car? It’s not too far away. So you could accompany your kids by bike until they have gained traffic competence and feel comfortable to cycle on their own. They stay fit and healthy.
In case the car drive can neither be avoided nor shifted, it still might be improved. Can you use a shared, electric car instead of your own SUV? Offer a carpool to neighbors? Or drive a shorter, instead of a faster but longer route? There are often more options than you might have thought of.
Knowing these travel patterns and their situational context and purpose will enable transport authorities and mobility service providers to offer customer-centric and better alternatives to their own car, exactly when they need it. Mobility as a Service at its best. Data matters.