Sobolt’s Five Key Insights from the MOVE Conference

Sobolt's Five Key Insights from the MOVE Conference

 

Last week, a few of our colleagues attended the MOVE Conference in London. Sobolt managed to get in contact with over 20 potential partners in the smart mobility field, ranging from big data players to SME investors. The enthusiasm for Sobolt’s solutions, especially surrounding machine learning components and accessibility indicators, was exciting and incredibly promising for near-future opportunities. After two full days of networking, learning, listening and discussing, we settled on five key insights from the trip.

 

  1. MaaS is Everywhere

    “Mobility as a Service,” or MAAS, is sweeping the transportation field. Several forms of data collection and diverse actor groups are necessary to support this transition, making it an intriguing yet complex issue. The trick is to select relevant variables from the sea of information as to only focus on truly useful data. Sobolt thinks that AI will ensure efficient data analytics that deliver relevant insights to the MaaS field.

  2. Active mobility plays a vital role in building liveable communities

    Sobolt sat in on some great talks at MOVE related to cycling, walking and other forms of mobile transport. One of the most interesting questions centered around how to manage high-tech solutions while using lower-tech forms of mobility (walk versus EV, bike versus electric scooter). We believe that analyzing remote data, secondary to data gathered from the mobility infrastructure itself, is the way to ensure that active mobility does not get lost in the sea of “smart mobility” advancements. Combining insights from both on the ground and looking down upon it creates a full vision of sustainable mobility.

  3. The key: find relevant data and then actually bring it to the table
    Almost everything related to the trending term “smart” revolves around data. Besides finding and narrowing down relevant data, it is important for small and large companies alike to bring it forward in an appealing way. Dynamic visualizations, secure business models and sustainably-sourced high quality data all contribute to keeping information relevant and up-to-date. Sobolt specializes in understanding what data means for governments, citizens and businesses, bringing insights to the table in a manageable, understandable and relevant way.

  4. Big Tech is entering the world of Smart Mobility… and FAST
    The MOVE conference boasted some hard hitters, although some of them may not immediately seem like big players in the mobility field. Big Tech companies like Amazon, IBM and Microsoft are testing the waters of smart mobility, both as an internal benefit and an external business venture. Despite their global name, even these big companies need support when it comes to data analysis in this new field. All conference attendees, including Sobolt, believe that startups and SMEs are the way for Big Tech companies to forge new paths into smart mobility.

  5. The last (but not least) mile.
    The “last mile” is like the holy grail for smart mobility - everybody knows that it is important and it exists, but no one really knows where or how to use it. This concept applies to package delivery, worker commutes, amenity accessibility and public transit planning, to name a few. Several factors impact the final stretch of any journey, meaning that complex, integrated data solutions are necessary for optimal adaptation. Sobolt believes that the combination of remote sensing, AI algorithms and additional relevant databases delivers an effective overview of "last mile" accessibility and provides actionable plans for its improvement.

 

Were you at the MOVE conference in London? What were your insights? We would love to discuss Sobolt’s experience in machine learning within the realm of smart mobility. Check out our “Bikeability” Index on the “Solutions” page for more information or email v.luyendijk@sobolt.com (Sobolt’s Smart Mobility Expert) with specific questions about potential partnerships or use cases.

 

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