Boosting Algorithms for Delivery Time Prediction in Transportation Logistics

Abstract

Travel time is a crucial measure in transportation. Accurate travel time prediction is also fundamental for operation and advanced information systems. A variety of solutions exist for short-term travel time predictions such as solutions that utilize real-time GPS data and optimization methods to track the path of a vehicle. However, reliable long-term predictions remain challenging. We show in this paper the applicability and usefulness of travel time i.e. delivery time prediction for postal services. We investigate several methods such as linear regression models and tree based ensembles such as random forest, bagging, and boosting, that allow to predict delivery time by conducting extensive experiments and considering many usability scenarios. Results reveal that travel time prediction can help mitigate high delays in postal services. We show that some boosting algorithms, such as light gradient boosting and catboost, have a higher performance in terms of accuracy and runtime efficiency than other baselines such as linear regression models, bagging regressor and random forest.

Estimated Time of Arrival in Autonomous Vehicles Using Gradient Boosting: Real-life case study in public transportation

“…Concurrently, predicting the arrival time remains a predominant challenge for other means of PT [8], i.e bus itineraries, or demand-responsive transport (DRT), whether taxi routes [16] [17] or private trips [18], with applications even in the field of logistics supply chains [19]. Considering DRT, the authors of [16] collected GPS taxi data from New York and developed prediction models based on both regression and classification.…”

“…In [19], the authors utilise real-time GPS data from Austria and optimisation methods for vehicle path tracking. Six variants of GB algorithms were considered, namely GB Decision Trees, Adaboost, XGBoost, LightGBM, CatBoost and Histogram GB.…”

Chair for Intelligent Transportation Systems–Sustainable Transport Logistics 4.0

“…Accurate travel time prediction is also a fundamental topic of current research. We examine methods, including linear regression models and tree-based ensembles such as random forest, bagging, and boosting, that enable delivery time predictions by conducting extensive experiments that consider a variety of scenarios [23].…”

Simulation-Based Impact of Connected Vehicles in Platooning Mode on Travel Time, Emissions and Fuel Consumption

“…Further research towards a sustainable transport strategy includes traveling time and origin-destination matrices to improve travel schedules through the analysis of the most convenient routes for a specific trip [4]. In this context, a reliable delivery time prediction contributes to eliminating accessory customer trips to a pick up location or avoiding additional travel from the side of the delivery company to return the goods to the sender [5].…”