How the Location of Things and Autonomous Vehicles Will Transform Transportation Logistics
Tomorrow’s urban retail centers and central business districts (CBD) will look much different than today because the once ubiquitous traffic signal will no longer be needed. While that’s a bold statement, the intelligent city infrastructure of the future promises a world of unprecedented connectivity. Therefore, if autonomous vehicles, or whatever we use to transport people and manufactured items, are broadcasting their location, speed, direction and intended destination to other transporters there will be no need for traffic signals. The familiar red-yellow-green lights will be as anachronistic as the corner phone booth.
Planning for a world without traffic signals presents transportation logistics and fleet managers as well as city planners with a new set of priorities and possibilities for managing delivery vehicles and public transportation. The digital street and sensor infrastructure required to support new modes of transportation and the changing nature of a CBD encompasses both fixed and mobile assets. In addition, the increase in e-commerce and omnichannel delivery options will put increasing pressure on the urban infrastructure.
A peek into the future shows that where there were once traffic signals there are now motion, RFID and other passive sensors that detect size, speed and direction of vehicles. The City of Palo Alto, California, for example, has done an exceptional job of thinking about how improving data analysis from the array of deployed sensors within the city’s surroundings is transforming urban traffic management. The important construct to remember is that it’s all about leveraging geospatial analytics to plan for the inevitable growth of the urban environment.
But what can we expect when connected, autonomous vehicles and truck fleets enter the road network and affect traffic? Volvo, Tesla and Toyota are now driving the autonomous marketplace after Uber and Lyft stumbled in their attempt at standing up fleets. According to an article in The Verge, “Volvo is partnering with self-driving startup Aurora on a new lineup of fully autonomous semi-trucks, the companies announced. The trucks will be deployed in North America on highly frequented hub-to-hub routes.” Despite the failures seen by the major ridesharing companies, Tesla sees an opening. “Tesla is looking at a more-than-decent chunk of a market valued at $1.2 trillion with its upcoming Tesla Network, a new Uber-like ride-hailing service with autonomous electric cars, according to Ark Invest,” according to a report by electrek.
Individuals and transportation logistics companies alike will find that the result of mixing autonomous fleets with existing, non-autonomous commuter vehicles may still be subject to control by an AI-powered algorithm. In a connected world, drivers may have to sacrifice some autonomy in much the same way as commercial air travel is managed today. Air travel routes and airport gate assignments are pre-assigned and, in a few years, motorists should expect a similar experience.
One reward that the autonomous vehicle passenger can expect is that commuting or ridesharing in the era of connected cars is likely to drastically cut commute time. The vehicle will know exactly the best route and navigate accordingly. Those that own autonomous vehicles will know where parking would be available before reaching their destination. Think of the time saved by not having to fight traffic congestion as well as looking for available parking.
Autonomous delivery of packages and passengers would alter the manner in which depots, address point locations and transit hubs are optimized. The landscape of CBDs for retail and citizen services would change to accommodate new pedestrian thoroughfares. With traffic reduced, new greenspaces could arise to accommodate a radical change in urban design and living.
More Data, Faster Data
How would the volume of sensor data be handled in such a scenario? Narrowband IoT is one possible mechanism that could handle the sensor load using a part of the LTE spectrum that would not burden newer 5G microcells. Geoprocessing of raw sensor data would be needed to visualize traffic patterns and vehicle volume, as well as sending feedback to vehicles directly to alter route directions, if necessary. Expect to see cloud data warehouse platforms, such as Snowflake and Databricks, that have some geospatial capabilities, and which can be supplemented with more advanced geospatial functions such as geocoding. routing, or route snapping to analyze large scale traffic data. While a full autonomous fleet may still seem far ahead, the need for more fleet and IoT Big Data analytics is already increasing at a rapid pace.
Autonomous vehicles comprise one part of the equation. What happens if the road could “talk” to your car? Would it tell you that repairs are desperately needed and that it would be better to take an alternate route, thus saving money on your daily commute? Or, if that road is the fastest way, would it estimate repairs over multiple time horizons and give you a heads up on oil and tire changes? And for the environmentally conscious driver, the “talking road” would help you to substantially reduce your carbon footprint by estimating fuel consumption caused by roughness, topography, and road curvature scores?
Moreover, what if your car could talk to the road? Would it say that the road is experiencing shifting substrate with insufficient subsurface support? Would it tell you that conditions are changing from merely wet to snow and ice? Would it provide a report on the degrading asphalt that would indicate that a pothole is sure to open shortly, and that this information should be sent to transportation officials? With the increase in the use of sensors and microprocessors in cars comes an increasing amount of connectively to things in proximity to the vehicle and the ability to capture data about its interaction with its environment. ZDNet reports how autonomous vehicles will rely on edge computing and 5G broadband to facilitate sensor data transmission and processing because it’s more cost effective.
What about the risk factors impacting driving due to weather? For example, how many drivers become at risk from standing water on roads every time it rains, if only a little? How many accidents could be avoided by cars not hydroplaning and causing rear-end collisions? Insurance companies that underwrite these risks will find a welcomed reduction to their book of business. These new dynamics can improve the living conditions for suburban neighborhoods, highways as well as city centers.
In a World Without…
In a world without traffic lights, there are also fewer cars; fewer cars mean less demand for massive parking garages; and fewer parking garages could open land to more productive uses of urban infrastructure. With the introduction of autonomous EVs there will be less pollution, fewer vehicle accidents and new transit behaviors may reduce the exposure to vehicular movement and pedestrian accidents. We have already seen the applications of sensor technology as, for example, the insurance industry’s reliance on telematics and usage-based insurance to better price policies. We have also seen the use of high-definition mapping technology in advanced fleet management solutions that have reduced travel time and improved last-mile delivery. We may not be there yet but thinking about the death of traffic lights means our collective angst while waiting at intersections may turn from “red” to “green.