Can Vehicle Data Wise Up City Planners?

Article By : George Leopold

A leading platform vendor argues new transportation infrastructure would allow city planners to leverage growing volumes of car data.

Otonomo Technologies, anointed by our colleague Egil Juliussen as the leader among purveyors of connected car data, continues to make the case that vehicle data can be used to make cities smarter and therefore more livable.

Given pandemic-driven changes in commuting patterns, that goal remains a tall order.

In-house research released this week during the Consumer Electronics Show is billed as demonstrating that traffic management is among the key use cases for vehicle data. But progress toward smarter cities has been stymied by reliance on legacy infrastructure such as traffic cameras and road sensors widely used to capture vehicle data.

Otomoto reported that 62 percent municipal planners said they are using vehicle data for traffic management, zoning and urban planning as well as monitoring high-accident roadways. Still, only 22 percent are currently using vehicle data for real-time traffic management. Most still rely on cameras, toll-payment sensors and mobile phone data.

The Israeli company makes the case that leveraging vehicle data will streamline data processing while enabling predictive analytics, the latter being the “engine that powers smart city planning.”

Source: Otomoto Technologies

City planners face new challenges brought about by the pandemic, including a sharp drop in public-transit riders and car polling. For example, the New York Times reported during the holidays that city traffic has shifted from Manhattan to New York City’s outer boroughs, creating midtown-like traffic jams.

City planners suspect snarled traffic in Brooklyn, the Bronx and Queens reflects marked changes in commuting patterns as office dwellers continue to work from home. Meanwhile, Amazon Prime, FedEx and UPS drivers block borough thoroughfares making ever-more residential deliveries.

Which raises the question: Are vehicle data platforms like Otomoto’s agile enough to discern these patterns and, if so, how can data be quickly disseminated to traffic planners struggling to unclog, for example, the parking lot that is the Brooklyn-Queens Expressway.

Vehicle data vendors like Otomoto claim their “mobility intelligence” platforms offer a better means of coping with evolving traffic patterns. To that end, Otomoto also announced this week it is partnering with Mercedes-Benz Connectivity Services in an effort aimed at improving road safety in Europe.

Earlier in December, Otomoto announced a partnership with NXP Semiconductors intended to leverage “vehicle edge computing” to reduce data processing costs. The partnership combines NXP’s S32G vehicle network processor with Otomoto’s cloud-based vehicle data platform. The combination would enable in-vehicle data processing, the partners said.

Those capabilities could someday be used by city planners struggling to cope with evolving commuting patterns like those seen in New York City. Otomoto’s research found that 50 percent of municipalities responding to its survey said they are designing new transportation apps aimed at aiding commuters and maximizing utilization of transportation infrastructure.

Meanwhile, as more electric vehicles are sold, about one-third of respondents said they are considering EV networks that tap into growing volumes of EV data. Still, securing data remains a challenge: fully 78 percent of respondents said reliable EV data remains hard to come by.

Otomoto insists that upgraded infrastructure will help ease data access. For now, it found that only 8 percent of respondents are using connected vehicle data, but added, “We expect this number to grow significantly due to its higher quality.”

This article was originally published on EE Times.

George Leopold has written about science and technology from Washington, D.C., since 1986. Besides EE Times, Leopold’s work has appeared in The New York Times, New Scientist, and other publications. He resides in Reston, Va.


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