New York City taxi rides form the core of the traffic in the city of New York. The many rides taken every day by New Yorkers in the busy city can give us a great idea of traffic times, road blockages, and so on. Predicting the duration of a taxi trip is very important since a user would always like to know precisely how much time it would require of him to travel from one place to another. Given the rising popularity of app-based taxi usage through common vendors like Ola and Uber, competitive pricing has to be offered to ensure users choose them. Prediction of duration and price of trips can help users to plan their trips properly, thus keeping potential margins for traffic congestions. It can also help drivers to determine the correct route which in-turn will take lesser time as accordingly. Moreover, the transparency about pricing and trip duration will help to attract users at times when popular taxi app-based vendor services apply surge fares. Thus in this research study, we used real-time data which customers would provide at the start of a ride, or while booking a ride to predict the duration and fare. This data includes pickup and drop-off point coordinates, the distance of the trip, start time, number of passengers, and a rate code belonging to the different classes of cabs available such that the rate applied is based on a regular or airport basis. Hereafter, we applied XGBoost and Multi-Layer Perceptron models to find out which one of them provides better accuracy and relationships between real-time variables. At last, a comparison of the two mentioned algorithms facilitates us to decide that XGBoost is more fitter and efficient than Multi-Layer Perceptron for taxi trip duration-based predictions.

纽约市出租车是纽约市交通的核心。纽约人每天在繁忙的城市乘坐的许多游乐设施可以让我们对交通时间、道路堵塞等情况有一个很好的了解。预测出租车旅行的持续时间非常重要，因为用户总是想准确地知道他从一个地方旅行到另一个地方需要多长时间。鉴于 Ola 和 Uber 等常见供应商基于应用程序的出租车使用日益普及，必须提供有竞争力的价格以确保用户选择它们。对行程的持续时间和价格的预测可以帮助用户正确规划他们的行程，从而为交通拥堵保留潜在的利润。它还可以帮助司机确定正确的路线，相应地，该路线将花费更少的时间。而且，当流行的基于出租车应用程序的供应商服务应用激增票价时，定价和旅行持续时间的透明度将有助于吸引用户。因此，在这项研究中，我们使用客户在乘车开始或预订乘车时提供的实时数据来预测持续时间和票价。该数据包括上车点和下车点坐标、行程距离、开始时间、乘客数量以及属于不同可用出租车等级的费率代码，因此应用的费率基于常规或机场基础。此后，我们应用 XGBoost 和多层感知器模型来找出其中哪一个提供更好的准确性和实时变量之间的关系。最后，