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Behavioral Intelligence
The Power of Data Insights in Shared Mobility for Operators and Cities

This article was written by Augustin Friedel

The ability to collect, combine and analyze data in shared mobility is getting more and more important for operators and cities across the globe. Operators mainly use the data analytics results to optimize and manage the operational setup and also to improve the targeted marketing towards different customer groups. Cities on the other hand are also showing a higher interest in business intelligence solutions around mobility data. Local authorities could use the location intelligence results to optimize the mobility and transportation system across the whole city or in certain neighborhoods. Let’s have a more detailed look on how real-life data is shaping shared mobility from the perspectives of shared service operators and cities, and how Neura’s behavior intelligence platform transforms that data into insights.

Benefits of Data for Shared Mobility Players 

Shared mobility companies started early on to use the power of data and behavior intelligence for optimizing daily operations and customer engagement. The space got more sophisticated in recent years and specialized software and consulting companies are supporting the ever-growing space of shared mobility platforms with this often-complex topic.

Analyze Usage Pattern and User Behavior

Operators of shared mobility services could use location intelligence data to optimize the day-to-day operations and utilization of their fleets. It is crucial to analyze where users want to start or end the trips, as well as the routes users are taking at which time of the day to get to their destinations. Forecasting the demand for any type of shared mobility like ride hailing, car sharing or shared micro mobility by combining historical data with external data sources like weather forecasts or special events is crucial for the success of the business. 

New software technology and a growing set of data from smartphone sensors, apps or sensors deployed within cities is allowing a real time business intelligence solution on city block or even street level in more and more markets. The unique multilayer solution with mobility patterns and persona data combined by Neura supports this strategy. With data fusion of complex insights and sources, stakeholders could get a better understanding of the customers and needs. The offerings could be tailored to the customer needs and pave the way to a demand based business expansion.

Rebalancing of the fleet by operator fleet teams or by users (e.g., incentivized by discounts or vouchers) are used to ensure that enough vehicles are at the locations where the user demand is. This is increasing the user experience and also the utilization of the assets deployed in the city, resulting in increased revenues.

It’s also interesting to know where users are going and to predict what they could do next, or which mode of transport they are switching to. The ability for behavior prediction and contextualization could be used to increase the user experience and also to tap into additional revenue streams. In a simple scenario, the route data and trip end location could be used for targeted marketing on the vehicles or in the apps that are used by riders or passengers to book the service. This could result in additional revenue for the shared service operators through targeted advertisements. First activities in this space could be seen in the verticals of ride sharing or hailing and shared micro mobility.

Optimize Internal Processes with Data

An efficient behavior intelligence solution could also help operators of e-scooter sharing or car sharing solutions to bring internal costs down. Operating these kinds of services is always linked to manage and maintain a fleet of vehicles distributed within the operations area. Combining the data about usage patterns with vehicle data could be crucial for an efficient planning of the daily fleet management. Neura has a very interesting Demand Discovery module which pinpoints exactly that, the optimum placement to maximize ridership based on dynamic demand throughout the day. Optimizing the setup of charging events, maintenance events or rebalancing tasks could bring down the total costs of ownership and increase the revenue potential for the operators of shared mobility services.

The higher the granularity and the more enhanced the data set is, the higher could be the positive impacts on either reducing costs or increasing revenue for the operators of the different shared mobility services. The next level is to include further data sources and historical routines to get an even more precise profile of users for predicting next steps.

How Cities Could Use Data from Shared Mobility 

Also, cities could benefit from the data collected by the different shared mobility services. An increasing share of cities is required with local regulations that shared mobility players are sharing the data with the authorities. Adding this data to the existing data pool of mobility and transportation related data could lead to a powerful setup to improve the overall mobility system within a city or certain areas of the city.

Optimize Infrastructure and Traffic Flow

Based on the collected data points from shared mobility providers and other sources, cities could get a very detailed picture of the traffic flow and patterns during the day. With this view, cities and authorities could adjust the traffic flow accordingly to the expected situation short term and optimize their infrastructure planning long term. 

In terms of traffic flow, red light phases or speed limits could be adjusted dynamically during commuting hours to reduce congestion or streets could be closed for certain vehicle types temporarily. Large entertainment or sport events often also have a huge impact on the mobility patterns within certain neighborhoods. A detailed data set and powerful analytics set could help to plan ahead and monitor the situation in real time.

Cities often don’t have a real-time picture of commuting and mobility patterns in their neighborhoods throughout the day and also miss information about the purposes of the trips made. Understanding this data could help to optimize the mobility options that should be offered, e.g. by launching shuttle services for commuters to pool citizens traveling in the same direction during morning or evening commutes. This would reduce the vehicles on the streets and also reduce the emissions on a way to more sustainable cities. The market expansion and rider segmentation modules provided by Neura support this mission as well as  getting a detailed understanding of the purposes of the trips made. Neura managed to identify, down to a building level the key contributors to traffic congestion, making strategic solutions to reduce traffic more targeted with greater impact.

Smart EV Charging Station Planning

Cities and shared mobility operators in different verticals are also expanding into the EV charging space by installing and operating charging stations. The demand for EVs is picking up so the setup of the charging networks in urban areas need to follow. It’s crucial to plan the locations based on behavior intelligence results to have them placed where the demand is. Using Neura’s Behavioral Intelligence platform could be a real boost for the increase of usage in a smart way, read more about it here.

For long-term planning, data from shared mobility services in combination with additional data sources about mobility and transportation behaviors could help to optimize the local infrastructure. Cities could use the data analytics results for future street planning, reallocation of car parking spaces to other use cases or to optimize the public transit offerings based on the seen demand.

Data Challenges on City Level

As of today, cities have to handle multiple challenges if it comes to location intelligence and data usage. A clear framework for data sharing is needed that is also accepted by the shared mobility operators and services. 

As mentioned at the beginning, a combination of multiple data sources is required to create a powerful city solution. The challenge is that the landscape of mobility and transportation within a city is very fragmented and the data available is extremely diverse. The format, frequency and granularity of data differs between, e.g. shared mobility data sets, data about local transit usage patterns and other transportation and traffic flow related data sets. A software solution and team of experts to combine different sources of data points and formats is recommended, to make the best decisions based on the variety of data collected. Authorities and cities could either build the tools and skill sets inhouse or start to work with technology companies and specialized consultancies, like Neura. More accurate data is replacing surveys and once a year data gathering and analytics exercises, leading to a better understanding and real impact for stakeholders in the mobility ecosystem.

This post was written by Augustin Friedel  

Augustin is a well-known shared mobility expert with passion for micro mobility, mobility on demand service and public private partnerships. After launching Uber in Germany, Augustin worked for companies like Blacklane, Rocket Internet and Deutsche Bahn subsidiary ioki. He is currently employed by Volkswagen AGbut the views expressed in this article are his personal ones and not representing any Volkswagen activity or thinking.


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