From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data

TL;DR

This paper introduces a novel method that uses Twitter data to predict next-day morning traffic congestion, especially effective during early morning hours when traditional methods struggle, by analyzing people's social media activity and sentiment.

Contribution

The study presents a new predictive framework leveraging Twitter profiles to accurately forecast morning traffic congestion, outperforming existing models without social media data.

Findings

Twitter activity correlates with congestion levels in the morning.

Higher tweet sentiment in the evening predicts lower next-day demand.

Early tweeting patterns can forecast congestion up to midnight before the morning.

Abstract

The effectiveness of traditional traffic prediction methods is often extremely limited when forecasting traffic dynamics in early morning. The reason is that traffic can break down drastically during the early morning commute, and the time and duration of this break-down vary substantially from day to day. Early morning traffic forecast is crucial to inform morning-commute traffic management, but they are generally challenging to predict in advance, particularly by midnight. In this paper, we propose to mine Twitter messages as a probing method to understand the impacts of people's work and rest patterns in the evening/midnight of the previous day to the next-day morning traffic. The model is tested on freeway networks in Pittsburgh as experiments. The resulting relationship is surprisingly simple and powerful. We find that, in general, the earlier people rest as indicated from Tweets, the more congested roads will be in the next morning. The occurrence of big events in the evening before, represented by higher or lower tweet sentiment than normal, often implies lower travel demand in the next morning than normal days. Besides, people's tweeting activities in the night before and early morning are statistically associated with congestion in morning peak hours. We make use of such relationships to build a predictive framework which forecasts morning commute congestion using people's tweeting profiles extracted by 5 am or as late as the midnight prior to the morning. The Pittsburgh study supports that our framework can precisely predict morning congestion, particularly for some road segments upstream of roadway bottlenecks with large day-to-day congestion variation. Our approach considerably outperforms those existing methods without Twitter message features, and it can learn meaningful representation of demand from tweeting profiles that offer managerial insights.