Urban transportation plays a vital role in modern city life, affecting how efficiently people and goods move around. This study analyzes transportation patterns using two datasets: the NYC Taxi Trip dataset from New York City and the Pathao Food Trip dataset from Dhaka, Bangladesh. Our goal is to identify key trends in demand, peak times, and important geographical hotspots. We start with Exploratory Data Analysis (EDA) to understand the basic characteristics of the datasets. Next, we perform geospatial analysis to map out high-demand and low-demand regions. We use the SARIMAX model for time series analysis to forecast demand patterns, capturing seasonal and weekly variations. Lastly, we apply clustering techniques to identify significant areas of high and low demand. Our findings provide valuable insights for optimizing fleet management and resource allocation in both passenger transport and food delivery services. These insights can help improve service efficiency, better meet customer needs, and enhance urban transportation systems in diverse urban environments.

In recent years, bikesharing systems have become increasingly popular as affordable and sustainable micromobility solutions. Advanced mathematical models such as machine learning are required to generate good forecasts for bikeshare demand. To this end, this study proposes a machine learning modeling framework to estimate hourly demand in a large-scale bikesharing system. Two Extreme Gradient Boosting models were developed: one using data from before the COVID-19 pandemic (March 2019 to February 2020) and the other using data from during the pandemic (March 2020 to February 2021). Furthermore, a model interpretation framework based on SHapley Additive exPlanations was implemented. Based on the relative importance of the explanatory variables considered in this study, share of female users and hour of day were the two most important explanatory variables in both models. However, the month variable had higher importance in the pandemic model than in the pre-pandemic model.

The ubiquity of AI leads to situations where humans and AI work together, creating the need for learning-to-defer algorithms that determine how to partition tasks between AI and humans. We work to improve learning-to-defer algorithms when paired with specific individuals by incorporating two fine-tuning algorithms and testing their efficacy using both synthetic and image datasets. We find that fine-tuning can pick up on simple human skill patterns, but struggles with nuance, and we suggest future work that uses robust semi-supervised to improve learning.

Uber, Lyft, Ola and many more online ride hailing services are trying hard to use their extensive data to create data products such as pricing engines, driver allotment etc. To improve the efficiency of taxi dispatching systems for such services, it is important to be able to predict how long a driver will have his taxi occupied or in other words the trip duration. This project will cover techniques to extract important features and accurately predict trip duration for taxi trips in New York using data from TLC commission New York.

As businesses around the world are embarking on building innovative solutions, we're seeing a growing trend adopting data science workloads across various industries. Recently, we've seen a greater push towards reducing the friction between data engineers and data scientists. Data scientists are now enabled to run their experiments on their local machine and port to it powerful clusters that can scale without rewriting the code. You have many options for running data science workloads, such as running it on your own managed Spark cluster. Alternatively there are cloud options such as Amazon SageMaker, Amazon EMR and Amazon Elastic Kubernetes Service (Amazon EKS) clusters.

We propose to distinguish between deterministic conditioning, that is, conditioning on a sample from the joint data distribution, and stochastic conditioning, that is, conditioning on the distribution of the observable variable. Mostly, probabilistic programs follow the Bayesian approach by choosing a prior distribution of parameters and conditioning on observations. In a basic setting, individual observations are In a basic setting, individual observations are samples from the joint data distribution. However, observations may also be independent samples from marginal data distributions of each observable variable, summary statistics, or even data distributions themselves . These cases naturally appear in real life scenarios: samples from marginal distributions arise when different observations are collected by different parties, summary statistics (mean, variance, and quantiles) are often used to represent data collected over a large population, and data distributions may represent uncertainty during inference about future states of the world, that is, in planning. Probabilistic programming languages and frameworks which support conditioning on samples from the joint data distribution are not directly capable of expressing such models. We define the notion of stochastic conditioning and describe extensions of known general inference algorithms to probabilistic programs with stochastic conditioning. In case studies we provide probabilistic programs for several problems of statistical inference which are impossible or difficult to approach otherwise, perform inference on the programs, and analyse the results.

Travel time is a crucial measure in transportation. Accurate travel time prediction is also fundamental for operation and advanced information systems. A variety of solutions exist for short-term travel time predictions such as solutions that utilize real-time GPS data and optimization methods to track the path of a vehicle. However, reliable long-term predictions remain challenging. We show in this paper the applicability and usefulness of travel time i.e. delivery time prediction for postal services. We investigate several methods such as linear regression models and tree based ensembles such as random forest, bagging, and boosting, that allow to predict delivery time by conducting extensive experiments and considering many usability scenarios. Results reveal that travel time prediction can help mitigate high delays in postal services. We show that some boosting algorithms, such as light gradient boosting and catboost, have a higher performance in terms of accuracy and runtime efficiency than other baselines such as linear regression models, bagging regressor and random forest.

In this paper, we consider the task of predicting travel times between two arbitrary points in an urban scenario. We view this problem from two temporal perspectives: long-term forecasting with a horizon of several days and short-term forecasting with a horizon of one hour. Both of these perspectives are relevant for planning tasks in the context of urban mobility and transportation services. We utilize tree-based ensemble methods that we train and evaluate on a dataset of taxi trip records from New York City. Through extensive data analysis, we identify relevant temporal and spatial features. We also engineer additional features based on weather and routing data. The latter is obtained via a routing solver operating on the road network. The computational results show that the addition of this routing data can be beneficial to the model performance. Moreover, employing different models for short and long-term prediction is useful as short-term models are better suited to mirror current traffic conditions. In fact, we show that accurate short-term predictions may be obtained with only little training data.

Uber, Lyft, Ola and many more online ride hailing services are trying hard to use their extensive data to create data products such as pricing engines, driver allotment etc. To improve the efficiency of taxi dispatching systems for such services, it is important to be able to predict how long a driver will have his taxi occupied or in other words the trip duration. This project will cover techniques to extract important features and accurately predict trip duration for taxi trips in New York using data from TLC commission New York.

Good Features are the backbone of any machine learning model. And good feature creation often needs domain knowledge, creativity, and lots of time. TLDR; this post is about useful feature engineering methods and tricks that I have learned and end up using often. Have you read about featuretools yet? If not, then you are going to be delighted. Featuretools is a framework to perform automated feature engineering.