California regulators on Thursday gave a robotic taxi service the green light to begin charging passengers for driverless rides in San Francisco, a first in a state where dozens of companies have been trying to train vehicles to steer themselves on increasingly congested roads. The California Public Utilities Commission unanimously granted Cruise, a company controlled by automaker General Motors, approval to launch its driverless ride-hailing service. The regulators issued the permit despite safety concerns arising from Cruise's inability to pick up and drop off passengers at the curb in its autonomous taxis, requiring the vehicles to double park in traffic lanes. The ride-hailing service initially will consist of just 30 electric vehicles confined to transporting passengers in less congested parts of San Francisco from 10pm to 6am. Those restrictions are designed to minimize chances of the robotic taxis causing property damage, injuries or death if something goes awry.

'Waymo' the Google's autonomous car subsidiary, announced that they would order 62,000 Pacifica minivans from Fiat Chrysler, and it's one of the latest sign that Alphabet's self-driving car company is operating on a different level from the rest of the industry. "We are excited to the idea of deepening our collaboration with FCA, which will support the launch of our self-driving service and explore future products," said John Krafcik, the boss of Waymo. "The Pacifica satisfies Waymo's need for a vehicle that can be used to move a good number of people at once. And Fiat Chrysler Automobiles (FCA) gets to look good standing next to one of the world's biggest tech giants with some of the best self-driving technology around. Waymo currently has around 600 minivans in its fleet, and some of them have been used to shuttle people around in Phoenix, Arizona and others are being tested in states like California, Washington, Michigan and Georgia.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Last fall, Zipcar founder Robin Chase released a 10-point manifesto with this philosophy: Let's not make the same mistakes with driverless cars that we made the first time we introduced the automobile into cities. It's an urbanist's platform in support of a future where we devote less city space to cars, not more. To that end, the 10th principle is that autonomous vehicles in dense urban areas should be operated only in shared fleets--as taxis, not as private cars. In addition to its role in American folklore, personal vehicle ownership has exerted enormous influence on the shape and feel of our cities, requiring expressway construction, surrounding restaurants and shops with moats of parking spaces, and injecting traffic and parking concerns into the heart of urban politics.

Two former allies of New Jersey Governor Chris Christie plan to appeal their conviction for intentionally causing traffic gridlock in Fort Lee during morning rush hour for a week in September 2013. Over the past five years, mobile tech has allowed companies like Uber, Lyft, and Juno to disrupt traditional travel with a new ride-hail industry worth billions. According to recent figures from the Massechusetts Institute of Technology (MIT), a mere 3000 ride-pools could even handle the business of New York City's entire taxi fleet with hardly any delay--provided, of course, that riders are willing to share. A study by MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) revealed this week that ride-sharing platforms similar to UberPOOL and Lyft Line could handle the passenger traffic of NYC's 14,000 taxis with just a few thousand vehicles. In addition, the team says, such programs could help reduce congestion on city streets (if not its' sidewalks) by an impressive 300%.

This research is motivated by large scale problems in urban transportation and labor mobility where there is congestion for resources and uncertainty in movement. In such domains, even though the individual agents do not have an identity of their own and do not explicitly interact with other agents, they effect other agents. While there has been much research in handling such implicit effects, it has primarily assumed de- terministic movements of agents. We address the issue of decision support for individual agents that are identical and have involuntary movements in dynamic environments. For instance, in a taxi fleet serving a city, when a taxi is hired by a customer, its movements are uncontrolled and depend on (a) the customers requirement; and (b) the location of other taxis in the fleet. Towards addressing decision support in such problems, we make two key contributions: (a) A framework to represent the decision problem for selfish individuals in a dynamic population, where there is transitional uncertainty (involuntary movements); and (b) Two techniques (Fictitious Play for Symmetric Agent Populations, FP-SAP and Soft- max based Flow Update, SMFU) that converge to equilibrium solutions. We show that our techniques (apart from providing equilibrium strategies) outperform “driver” strategies with re- spect to overall availability of taxis and the revenue obtained by the taxi drivers. We demonstrate this on a real world data set with 8,000 taxis and 83 zones (representing the entire area of Singapore).