Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

Bo Qiu,Wei (David) Fan

doi:10.3390/su13137454

Abstract

Due to the increasing traffic volume in metropolitan areas, short-term travel time prediction (TTP) can be an important and useful tool for both travelers and traffic management. Accurate and reliable short-term travel time prediction can greatly help vehicle routing and congestion mitigation. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm using the available data. In this study, the travel time data was provided and collected from the Regional Integrated Transportation Information System (RITIS). Then, the travel times were predicted for short horizons (ranging from 15 to 60 min) on the selected freeway corridors by applying four different machine learning algorithms, which are Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory neural network (LSTM). Many spatial and temporal characteristics that may affect travel time were used when developing the models. The performance of prediction accuracy and reliability are compared. Numerical results suggest that RF can achieve a better prediction performance result than any of the other methods not only in accuracy but also with stability.

Highlights

Shortterm travel time prediction (TTP) is a key component of the Advanced Travelers Information System (ATIS) in which in-vehicle route guidance systems (RGS) and real-time TTP enable the generation of the shortest path for travelers, which connects the destinations and current locations [1]
We focus on tree-based ensemble learning, which consists of multiple base models (i.e., Decision Trees (DT) model), each of which provides an alternative solution to the problem
It has been approved that cross-validation can improve the TTP model performance

Summary

Introduction

Accurate TTP on the future state of traffic enables travelers and transportation agencies to plan their trips and mitigate congestion along with specific road segments (such as rerouting traffic or optimising the signaling time of traffic lights), leading to the overall reduction of total travel time and cost. These measures can help reduce greenhouse gas emissions, as the CO2 emission rates in congested conditions can be up to 40% higher than those seen in free-flow conditions [2]. The paper develops different machine learning prediction models and compares their performance based on a case study from the City of Charlotte, North Carolina

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Conclusion