Balancing of Truck Parking Demand by a Centralized Incentives/Pricing System
Published Web Locationhttps://doi.org/10.7922/G2NG4NZZ
Due to hours-of-service (HOS) regulations, commercial drivers are required to stop and rest regularly, thus reducing fatigue-related crashes. Nevertheless, if the parking infrastructure cannot cope with the demand generated by these required stops, new issues arise. In particular, this is the case for long-haul trucking, which is the focus of this work. Drivers often have difficulty finding appropriate parking, leading to illegal parking, safety risks, and increased pollution and costs. In this project, the researchers consider the issue of coordinating the parking decisions of a large number of long-haul trucks. More specifically, how to model the behavior of a region’s driver population and how it could be influenced. Understanding how truck parking demand is affected by the interaction of individual drivers’ selfish planning behaviors (in the sense that they minimize their own costs, not the overall system cost) and how parking prices affect optimal schedules are important steps in developing a system able to balance demand. The study presents a formulation that uses a modified TDSP (Truck Driver Scheduling Problem) mixed-integer programming model which tracks parking usage by dividing time into time-slots and charging drivers per time slot used. Results show that if truck drivers are following optimal schedules, then parking prices would be effective in changing which locations and time slots would be chosen by each driver. However, price adjustments can cause demand to shift in unexpected and not always beneficial ways, likely due to HOS regulations and client constraints limiting the possible alternative schedules. Therefore, further study is required to better understand the system’s properties and how to avoid or dampen these oscillations. Furthermore, due to HOS rules and client constraints, it might be impossible to divert demand from specific time slots and locations sufficiently. Nevertheless, this model could still aid in identifying these spots and contribute to the evaluation of infrastructure investment needs.