对比纽约与达卡的出租车和外卖数据，发现交通需求的时空规律。
Geospatial and Temporal Trends in Urban Transportation: A Study of NYC Taxis and Pathao Food Deliveries
- 用探索性分析和聚类识别高/低需求区域
- 通过SARIMAX模型捕捉周内与季节性需求波动
- 适合城市规划者和物流平台优化调度
城市交通对现代都市生活至关重要，影响人员与物资的流动效率。本研究基于纽约市出租车行程数据（NYC Taxi Trip dataset）和孟加拉达卡市外卖配送数据（Pathao Food Trip dataset），分析交通需求、高峰时段与重要地理热点。首先进行探索性数据分析（EDA）以理解数据基本特征；随后开展地理空间分析，绘制高/低需求区域分布图；采用SARIMAX模型进行时间序列分析，捕捉季节性与周周期变化；最后运用聚类技术识别显著的高/低需求区。研究结果为乘客运输与外卖配送服务的车队管理与资源调配提供关键洞察，有助于提升服务效率、满足客户需求，并改善多样化城市环境中的交通系统。
原文摘要 · Abstract (English)
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.
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