Planning Wireless with Uber
The recent announcement by Uber to release anonymized data for over 2 billion trips to improve urban planning through the Uber Movement site could also improve what Uber relies on the most, wireless networks. The greatest challenge to mobile wireless network is simply mobility. As long as customers stay relatively in-place, the better the wireless network performs. Uber’s route data represents where mobility is the most critical (and most often) across an urban area. This helps wireless operators understand cell performance and optimize network settings accordingly.
The practice of old was to acquire federal, state, county or municipality vehicle traffic count data. This data typically came from static traffic count meters placed where the entity felt it most vital to know counts. Although a valued set of importance, it didn’t fully show the complete or typical route of drivers, just the counts at a given point on the road. Over the last 5-10 years many mobile network operators (MNOs) have discounted the data all together feeling networks already cover major roads and highways. Static vehicle traffic data simply had become a use case of the past.
Uber’s Movement data provides a refresh on the approach for MNOs to plan and optimize one of the most vital requirements of Uber’s operations, the wireless network of their customers. The ride share concept is based around utilizing a smart-phone to hail, pay, and rate the service to over-simplify it. Without the wireless network working where Uber customers are, Uber will not have as many end users or drivers available to generate revenue off of. It with this data in mind that having MNOs utilize the data to optimize wireless service (coverage, quality, and capacity) is a win-win for both sides with this data sharing initiative. Analysis of network features and performance against complete route will allow MNOs to see if their network truly covers the major routes travelled by their customers end to end as well as the critical points of origin where applications such as Uber are reliant the most on network coverage. This data allows MNOs to plan improved in-building coverage and performance from source locations, ensure routes have enough coverage and capacity for users and drivers, as well as small cell and future 5G planning to cover or even avoid extreme mobility routes.
The major goal of Uber Movement is to improve urban planning. Urban planning in tomorrows “smart cities” will be more reliant than ever on wireless networks. It is with this long term vision of their businesses that Uber and MNOs can benefit from this detailed route data so each may operator more efficiently in the future for their users. It is also in the short term that MNOs may utilized this data to improve the customer experience and deliver the most reliable network for their users. Utilizing this data-set in conjunction with existing network planning and performance data and various other crowd sourced information available brings urban planning improvements at all levels.