A Predictive and Optimization Approach for Enhanced Urban Mobility Using Spatiotemporal Data

TL;DR

This paper presents a data-driven system combining machine learning and real-time traffic data to predict travel times and optimize routes, significantly improving urban mobility in cities like New York.

Contribution

It introduces an integrated spatiotemporal analysis and real-time route optimization framework using Spark MLlib and GraphHopper, advancing urban transportation management.

Findings

Improved accuracy in journey time prediction

Enhanced route efficiency through real-time optimization

Potential for widespread urban mobility improvements

Abstract

In modern urban centers, effective transportation management poses a significant challenge, with traffic jams and inconsistent travel durations greatly affecting commuters and logistics operations. This study introduces a novel method for enhancing urban mobility by combining machine learning algorithms with live traffic information. We developed predictive models for journey time and congestion analysis using data from New York City's yellow taxi trips. The research employed a spatiotemporal analysis framework to identify traffic trends and implemented real-time route optimization using the GraphHopper API. This system determines the most efficient paths based on current conditions, adapting to changes in traffic flow. The methodology utilizes Spark MLlib for predictive modeling and Spark Streaming for processing data in real-time. By integrating historical data analysis with current traffic inputs, our system shows notable enhancements in both travel time forecasts and route optimization, demonstrating its potential for widespread application in major urban areas. This research contributes to ongoing efforts aimed at reducing urban congestion and improving transportation efficiency through advanced data-driven methods.