Optimizing Combined Truck Routing and Parking based on Parking Availability Prediction

Ioannou, Petros; Vital, Filipe de Almeida Araujo · 2018 · ROSA P / METRANS Transportation Center (Calif.)

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Summary

This study addresses the critical shortage of truck parking in the United States, which hinders drivers’ ability to comply with Hours-of-Service (HOS) regulations. With 36 states experiencing rest area deficits, drivers often resort to illegal parking or drowsy driving, creating safety hazards and increasing fuel consumption through idling. The authors argue that existing infrastructure can be better utilized if routing and scheduling algorithms incorporate real-time parking availability. The research aims to optimize combined truck routing and driver scheduling by integrating parking availability predictions as time-window constraints, specifically targeting long-haul trips that span multiple weeks and are subject to complex weekly HOS limits. The researchers developed two primary optimization models. First, they formulated a Mixed Integer Programming (MIP) model for the Truck Driver Scheduling Problem (TDSP) on a fixed route. This model incorporates USA HOS regulations, including daily driving limits, elapsed time constraints, and rolling weekly on-duty limits (60/70 hours over 7/8 days). Crucially, it treats parking availability at rest areas as conditional time-windows; if a driver stops at a location, their arrival must fall within predicted available windows. To address scalability issues associated with the complexity of weekly constraints, the authors analyzed conditions under which a simplified model guarantees optimality. Second, they proposed a formulation for the Vehicle Shortest Path and Truck Driver Scheduling Problem (VSPTDSP), modeling the combined routing and scheduling task as a shortest path problem with resource constraints on a road network. Experimental results demonstrated that incorporating parking availability significantly impacts schedule feasibility and duration. The study compared schedules generated with and without parking availability windows, showing that ignoring availability leads to infeasible plans in scenarios with high demand. The MIP model successfully handled long-haul trips, correctly managing the reset of weekly counters through appropriate rest periods. The analysis of the simplified model revealed that it could guarantee optimal solutions for trips where the total driving time remained below specific thresholds relative to the weekly limit, thereby reducing computational complexity for many practical cases. The VSPTDSP approach allowed for the simultaneous determination of the optimal path and schedule, ensuring that rest stops were selected not only for proximity but also for predicted availability. The significance of this work lies in its practical application to freight logistics and transportation safety. By integrating parking availability into planning algorithms, the models help mitigate the negative impacts of parking shortages, such as illegal parking, increased emissions from idling, and safety risks associated with driver fatigue. The study provides a rigorous mathematical framework for handling long-haul scheduling under complex regulatory constraints, offering a tool for logistics companies to improve operational efficiency and compliance. The findings suggest that better information dissemination and algorithmic planning can redistribute parking demand in time and space, alleviating pressure on existing infrastructure without requiring significant capital investment in new rest areas.

Key finding

Integrating parking availability time-windows into mixed integer programming models for truck driver scheduling ensures feasible schedules under USA Hours-of-Service regulations while minimizing trip duration and avoiding illegal or unsafe parking.

Methodology

modeling

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• hours of service