Location Intelligence Increases in Importance as Lyft Partners with HERE and Argo AI

California-based ridesharing and food delivery platform company Lyft announced in July that it will switch its map provider from Google to HERE and will partner with self-driving platform company Argo AI to deliver autonomous ridesharing services.

Argo AI, founded in 2016, is funded by and partnered with both Ford Motors and Volkswagen. The Lyft partnership includes 1,000 Ford passenger vehicles utilizing Argo AI’s self-driving capabilities, available for hire on the Lyft app in Miami and Austin in Q4 2021. Lyft in return will provide Argo AI with driving data from its network of drivers, including telemetry information such as driving behavior and traffic data.

According to Lyft, HERE will provide a better search engine for addresses and locations as well as more accurate predicted arrival times. This is crucial for ridesharing companies, as faulty addresses and a lack of optimized routing contribute to operational challenges that transport and logistics companies face, which drives up costs.

Location intelligence has become increasingly important for ridesharing and food delivery companies, for both financial and technological reasons. Financially, there is a need to increase the operational efficiency of vehicles transporting passengers and goods. Location intelligence helps reduce costs by ensuring each vehicle is maximizing its time and thus profitability. This will require capabilities such as route optimization, real-time road data, and granular map information, which includes accurate drop-off and pick up points. Challenges include countries with poor address quality in rural areas. In Singapore and Yangon, users might have to walk between 260 to 300 feet to reach their ride. DiDi in China registers over 20 million rides per day, with the time taken for users reach their ride amounting to over 760,000 wasted hours per day by waiting to pick up passengers, rather than driving to the next ride.

The second challenge is technology. The use of AI algorithms to optimize routes, based on real-world data, combined with self-driving vehicles can eliminate the costs of hiring a driver. Raxel Telematics has been developing predictive models for fleets, based on parameters such as lists of destinations for each road, driver profiles, and types of vehicles, which could predict a driver’s time of arrival in certain use cases. Most ride sharing and food delivery businesses depend on GPS technology, which is experiencing major upgrades to its level of precision. The Australian government funded US$12 million to develop precise point positioning (PPP) through SBAS, a GNSS augmentation. According to ABI Research’s Commercial Telematics Market Data (MD-COMT-123), the global Advanced Driver Assistance Systems (ADAS) market is forecast to grow at a CAGR of 33% between 2020 to 2026, to a US$3 billion market by 2026.

Trends in precision technology, self-driving capabilities, and AI for route optimization and prediction, coupled with financial pressure to reduce operation costs, will drive location intelligence spending for ride hailing and online food delivery services in the near-term. However, transport and logistics (T&L) companies can also derive value from implementing location intelligence into their supply chain and operations.

Location intelligence will usher in new opportunities for multimodal delivery options for last mile, enable group deliveries or contactless deliveries, improve training and the safety of drivers. These go well beyond simply reducing costs. This will require T&L companies to leverage location intelligence through cloud technology, which can enable real-time map updates and leverage APIs. Geospatial data sharing platforms are beginning to surface, proving their value in the market, and increasing importance in the location intelligence ecosystem. Some examples include the HERE platform and Intel Geospatial.

Cloud-based data sharing geospatial platforms can create a common platform for T&L companies to conduct data analysis and utilize AI for a variety of purposes. An ADAS system will require driving and traffic data in order to improve its self-driving capabilities. The Lyft and Argo AI partnership is an early indicator, which ABI Research believes will become more common, as ADAS systems will rely on this data to be successful. These partnerships should offer a common geospatial data platform to store and analyze data. Moving location intelligence to the cloud allows real-time and analytical capabilities including improved driver training and safety. Swiggy, an India-based food delivery platform, is implementing online onboarding and training for their drivers, which can provide training and feedback for different occurrences in real-time. This has been extremely helpful during the COVID-19 pandemic, where managing people face to face is challenging.