Geospatial and Temporal Trends in Urban Transportation: A Study of NYC Taxis and Pathao Food Deliveries

TL;DR

This study analyzes urban transportation patterns in NYC and Dhaka using datasets, applying geospatial, time series, and clustering analyses to identify demand trends and hotspots for better resource management.

Contribution

It introduces a comprehensive approach combining geospatial, temporal, and clustering analyses to understand urban transportation demand patterns across two different cities.

Findings

Identification of key demand hotspots in NYC and Dhaka

Forecasting of demand patterns using SARIMAX models

Clustering of high and low demand regions

Abstract

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.