And, for real-time train information, the feature was developed with the help of Where Is My Train app, which was acquired by Google. This feature will be available for Delhi and Bengaluru initially and will soon be extended to more cities. This feature is supposed to enable the commuter to factor in real-time traffic and plan their trip accordingly. This new update where Google Maps can be used to track live traffic data and public bus schedules to calculate delays and provide accurate travel times is the first product of its kind - launching first in India. Optimise the “linear system” where each observed trajectory assigns a total duration to the sum of the many units it spans.Train models of individual units’ durations and.The model developed here is simpler because of the sequence structure that allowed the researchers to : This better prepares the model for later queries about areas where we were short on training data. Some examples are kept with the exact bus route and street, others keep only neighbourhood - or city-level locations, and others yet have no geographical context at all. To make the prediction more robust, the researchers simulated the possibility of queries that pop up about the areas which were not the part of training data.Īs different cities and neighbourhoods also run to a different beat, so the model is made to combine its representation of location with time signals. Not only the forecast of the duration but also to capture unique properties of specific streets, neighbourhoods, and cities, the model is made to learn a hierarchy of representations for areas with a timeline unit’s geography locating road or a bus stop more precisely. So, for better approximation, each unit is made to predict its duration independently, and the final output is the sum of the per-unit forecasts. Every unit is used to forecast the duration of the travel.Įven within a small neighbourhood, the model needs to translate car speed predictions into bus speeds differently on different streets. These units can be anything from visits to street blocks to bus stops. The model is split into a sequence of timeline units. These inputs are aligned with the car traffic speeds on the bus’s path during the trip.Īs can be seen in the plot below, how the car’s delay at 800m mark changes the bus timings. To develop the new model to predict delays, the machine learning developers at Google extracted training data from sequences of bus positions over time, as received from transit agencies’ real-time feeds. Mixed-mode commute suggestions that now combine auto-rickshaw and public transport.Live train status for Indian Railways trains.Bus travel times from live traffic in 10 of the largest cities in India,.Google Maps in India, especially, has released three updates: To solve this, Google Maps is launching live traffic delays for buses in places where there is no real-time information from local transit agencies. But, in densely populated countries like India, traffic congestion is a major problem both for those in transit and the algorithm that serves Google Maps. From bus timings to route maps, Google services are quite significant. In India, where the majority of the population prefers public transport for a daily commute, Google Maps has a key role to play. Google Maps has single-handedly reduced the ambiguity of the commuters over the past decade.
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