Balancing of Truck Parking Demand by a Centralized Incentives/Pricing System

Vital, Filipe de Almeida Araujo; Ioannou, Petros · 2022 · ROSA P / METRANS Transportation Center (Calif.)

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Summary

This research addresses the critical issue of truck parking shortages in long-haul trucking, driven by Hours-of-Service (HOS) regulations that mandate regular driver rest stops. When parking infrastructure cannot meet peak demand, drivers face illegal parking, safety risks, increased fuel consumption, and operational costs. The study moves beyond single-vehicle planning, which fails to account for systemic interactions, by investigating how to coordinate the parking decisions of a large driver population. The primary goal is to develop a centralized incentives and pricing system that balances demand across time and space, preventing overcrowding at specific locations while ensuring drivers can find legal parking. The authors propose a Centralized Parking Coordinator (CPARC) system that utilizes a modified Truck Driver Scheduling Problem (TDSP) mixed-integer programming model. This formulation divides time into slots and assigns dynamic prices to parking locations based on time and location. Each driver’s schedule is calculated individually to minimize their own operational costs, using a common price matrix provided by the coordinator. The model accounts for heterogeneity among drivers, including origin-destination pairs, departure and delivery constraints, initial HOS conditions, and hourly operational costs. The study treats the interaction between selfish driver behaviors and centralized pricing as a non-cooperative game, aiming to find a price equilibrium that avoids overcapacity. Simulations were conducted using sample populations to observe how drivers react to price changes and to test the system’s ability to redistribute demand. Results indicate that the scheduling model is highly sensitive to even small changes in parking prices, confirming that pricing is an effective tool for influencing driver behavior. However, the system exhibits significant instability; under the tested price update rules, demand oscillates significantly before reaching a valid solution. Initial iterations often resulted in increased peak demand rather than the intended redistribution. The study attributes these unexpected shifts and oscillations to the rigid constraints imposed by HOS regulations and client delivery requirements, which limit the alternative schedules available to drivers. Consequently, it may be impossible to divert demand from specific high-demand time slots and locations sufficiently, as drivers have limited flexibility to adjust their rest times without violating regulatory or contractual obligations. The significance of this work lies in its demonstration that while centralized pricing can influence truck parking demand, it is constrained by regulatory and operational rigidities. The findings suggest that price adjustments alone may not fully solve the parking shortage due to the limited elasticity of driver schedules. Nevertheless, the proposed model remains valuable for identifying persistent demand hotspots and evaluating infrastructure investment needs. The study highlights the necessity for further research to understand system properties and develop methods to dampen oscillations, ensuring that future demand management systems can effectively balance supply and demand without causing unintended spikes in congestion.

Key finding

Parking prices effectively influence truck driver scheduling decisions, but centralized pricing mechanisms can cause significant demand oscillations and unexpected shifts due to regulatory and client constraints.

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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• temporal