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

2025-05-02

AI Summary

This study investigates spatiotemporal demand patterns across two heterogeneous urban transportation systems—passenger mobility (New York City taxis) and on-demand delivery (Dhaka’s Pathao)—in high-density cities. To address the lack of cross-regional, cross-modal comparative frameworks, we propose an integrated analytical approach combining exploratory data analysis, geospatial clustering (K-means and DBSCAN), and interpretable time-series modeling (SARIMAX). Our method dynamically identifies demand peaks (e.g., morning/evening commutes, nighttime food delivery), geographic hotspots, and recurring cyclical patterns. For the first time, it quantifies demand disparities between urban cores and underserved peripheral areas, achieving a mean absolute error of less than 8.2% in demand forecasting. The framework delivers a transferable methodology and empirical evidence to support dynamic fleet dispatching and resource optimization across diverse metropolitan contexts.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Web Mining and Content Analysis: Bridging structured and unstructured data

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.

Problem

• Identify urban transportation demand trends in NYC and Dhaka
• Analyze geospatial hotspots and peak times for taxis and deliveries
• Forecast demand patterns to optimize fleet and resource allocation

Innovation

• Exploratory Data Analysis (EDA) for dataset characteristics
• SARIMAX model for time series demand forecasting
• Clustering techniques to identify demand hotspots