London taxis are famous around the world for being shown on countless film and television occasions. Their activity is strictly controlled both as regards the mechanical integrity of the vehicle and the physical and mental integrity of the driver.
An official report noted: "Little is known about the strict regulation of the industry outside of the insiders." The Public Carriage Office, which regulated taxi business and issued licenses, was transferred from the Metropolitan Police to Transport for London in 2000.
In 2017 the production of the LEVC TX started, produced by the newly formed LEVC company founded by Geely after having acquired the production branch from Manganese Bronze, heir to the FX series introduced by Austin and the TX series of LTI.
The new TX is an extended range hybrid black cab with an electric motor and a Volvo petrol-powered thermal one that acts as a generator to recharge the batteries. A few years earlier the new refillable hybrid Metrocab was also presented, designed by Ecotive in collaboration with Frazer Nash and destined to go into production in the future.
London taxi drivers: A review of neurocognitive studies and an exploration of how they build their cognitive map of London, published on the London taxi drivers: A review of neurocognitive studies and an exploration of how they build their cognitive map of London, told : "Licensed London taxi drivers have been found to show changes in the gray matter density of their hippocampus over the course of training and decades of navigation in London (UK).
This has been linked to their learning and using of the" Knowledge of London, "the names and layout of over 26,000 streets and thousands of points of interest in London. Here we review past behavioral and neuroimaging studies of London taxi drivers, covering the structural differences in hippocampal gray matter density and brain dynamics associated with navigating London.
We examine the process by which they learn the layout of London, detailing the key learning steps: systematic study of maps, travel on selected overlapping routes, the mental visualization of places and the optimal use of subgoals.
Our analysis provides the first map of the street network covered by the routes used to learn the network, allowing insight into where there are gaps in this network. The methods described could be widely applied to aid spatial learning in the general population and may provide insights for artificial intelligence systems to efficiently learn new environments."