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

Bidyarthi Paul, Fariha Tasnim Chowdhury, Dipta Biswas, Meherin Sultana

TL;DR

The paper addresses understanding geospatial and temporal demand in urban transportation by comparing NYC taxi trips with Pathao food deliveries. It adopts an integrated pipeline of Exploratory Data Analysis, geospatial analysis, SARIMAX time-series forecasting, and clustering to identify hotspots, forecast demand, and guide fleet allocation. Key findings include Manhattan as a high-demand hub, Queens with longer trips, weekend spikes in food delivery, and SARIMAX-based forecasts capturing weekly seasonality with measurable forecast error. The work provides practical insights for fleet optimization and urban mobility planning, though it is constrained by data limitations such as the Pathao dataset's lack of geospatial coords and a single-month NYC subset, suggesting the need for broader datasets in future work.

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.

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

TL;DR

The paper addresses understanding geospatial and temporal demand in urban transportation by comparing NYC taxi trips with Pathao food deliveries. It adopts an integrated pipeline of Exploratory Data Analysis, geospatial analysis, SARIMAX time-series forecasting, and clustering to identify hotspots, forecast demand, and guide fleet allocation. Key findings include Manhattan as a high-demand hub, Queens with longer trips, weekend spikes in food delivery, and SARIMAX-based forecasts capturing weekly seasonality with measurable forecast error. The work provides practical insights for fleet optimization and urban mobility planning, though it is constrained by data limitations such as the Pathao dataset's lack of geospatial coords and a single-month NYC subset, suggesting the need for broader datasets in future work.

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.
Paper Structure (22 sections, 7 figures)

This paper contains 22 sections, 7 figures.

Table of Contents

  1. Introduction
  2. Related Works
  3. Dataset
  4. Data Collection
  5. NYC Taxi Trip Dataset
  6. Pathao Food Trip Dataset
  7. Data Pre-processing
  8. Train-Test Splits
  9. Methodology
  10. Exploratory Data Analysis (EDA)
  11. NYC Taxi Trip Dataset
  12. Pathao Food Trip Dataset
  13. Geospatial Analysis
  14. Time Series Analysis
  15. Clustering
  16. ...and 7 more sections

Figures (7)

  • Figure 1: Geospatial area in New York City
  • Figure 3: EDA on Pathao Food Trip Dataset
  • Figure 4: EDA on NYC Taxi Trip Dataset
  • Figure 5: Average Trip Duration and Distance by Borough
  • Figure 6: Geographical Clustering of NYC Taxi Trips
  • ...and 2 more figures