Hours Of Service Regulations Research Articles

A Region-wide Deployment of an Automated Real-time Truck Parking Availability Detection and Information Dissemination System

Commercial heavy vehicle-borne freight will continue to be the dominant cross-region shipping mode for materials and goods for the foreseeable future. Federal hours of service (HOS) regulations mandate scheduled minimum times for of-duty and in-transit rest periods to minimize operator fatigue. Without timely, accurate information disseminated to drivers and carriers on where to park, complying to HOS regulations can often become a very challenging task for drivers. This paper describes a region-wide deployment of a unique non-intrusive, multi-camera, truck parking space detection and availability information dissemination architecture. The architecture leverages multi-view Structure and Motion methods to reconstruct a Three-dimensional representation of the environment for estimating unoccupied spatial extents to deduce truck parking availability. Unlike 2D camera sensor-based methodologies, the advantage is its immediate adaptability to a variety of parking facilities and scenarios without the need for any subsequent re-training. The approach also mitigates errors arising from occlusions, and many other environmental conditions that confound many 2D camera-based approaches. The region-wide deployment, operated by a state transportation agency, has been online since early 2019, and has thus far substantiated the technical and day-to-day operational viability of the approach. The performance throughout this period indicates an overall accuracy at or above 90%. This paper provides a general overview of the deployed system architecture and performance characteristics across varied parking facility designs, parking behaviors, and other environmental conditions.

Make or break: Coordinated assignment of parking space for breaks and rest periods in long-haul trucking

To avoid traffic accidents due to driver fatigue in long-haul trucking, there are laws and provisions all over the world that regulate breaks and rest periods of truck drivers. When aiming to follow the existing hours of service regulations, however, parking space for trucks in service areas is often scarce, especially along crowded mainlines. In this context, the paper on hand investigates whether a central platform coordinating the usage of limited parking capacity on the supply side and truck drivers’ individual drive and rest schedules on the demand side can be advantageous. Specifically, we formulate a holistic optimization problem, which jointly determines the drive and rest schedules for a given set of trucks with fixed routes and coordinates their assignment to a limited number of available parking spaces over time. To solve this problem, we introduce a heuristic solution procedure, which is benchmarked with the status quo, where each driver seeks parking space individually, and a booking platform that allows to reserve parking space ahead of arrival. Our computational results reveal that a booking platform that produces much less organizational overhead is almost as effective as a central matching platform based on perfect knowledge of all truck schedules.

Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their means

Sleepiness is a common human factor among truck drivers resulting from sleep loss or time of day and causing impairment in vigilance, attention, and driving performance. While driver sleepiness may be associated with increased risk on the road, sleepy drivers may drive more cautiously as a result of risk-compensating behaviour. This endogeneity has been overlooked in the previous driver behaviour studies and may provide new insight into the effects of sleepiness on driving performance. In addition, the Karolinska Sleepiness Scale (KSS) has been widely used to quantify sleepiness. However, the KSS is a subjective self-reported measure and is reliant on honest reporting and understanding of the scale. An alternative way of quantifying sleepiness is using drivers’ heart rate and correlating it with their sleepiness. While recent advances in data collection technologies have made it possible to collect heart rate data in real-time and in an unobtrusive way, their application in measuring sleepiness particularly among truck drivers has been unexplored.This study aims to address these gaps and contribute to analytic methods in road safety research by collecting truck drivers’ heart rate data in real-time, measuring sleepiness from those data, and using it in an instrumental variable modelling framework to investigate its effect on driving performance. To this end, a driving simulator experiment was conducted in Belgium and heart rate data were collected for 35 truck drivers via sensors installed on the steering wheel of the simulator. Additional demographic data were collected using a questionnaire before the experiment. An instrumental variable model consisting of a discrete binary logit and a continuous generalized linear model with grouped random parameters and heterogeneity in their means was then developed to study the effects of driver sleepiness on headway. Results indicate that age, years of holding driver licence, road type, type of truck transport, and weekly distance travelled are significantly associated with sleepiness among the participants of this study. Sleepy driving is associated with reduced headway for 30.5% of the drivers and increased headway for the other 69.5%, and night-time shift is associated with such varied effects. These findings indicate that there may be group- or context-specific risk patterns which cannot be explicitly addressed by hours of service regulations and therefore, transport operators, driver trainers and fleet managers should identify and handle such context-specific high risk patterns in order to ensure safe operations.

Real-Time Adaptation of Driving Time and Rest Periods in Automated Long-Haul Trucking: Development of a System Based on Biomathematical Modelling, Fatigue and Relaxation Monitoring

Hours of service regulations govern the working hours of commercial motor vehicle drivers, but these regulations may become more flexible as highly automated vehicles have the potential to afford periods of in-cab rest or even sleep while the vehicle is moving. A prerequisite is robust continuous monitoring of when the driver is resting (to account for reduced time on task) or sleeping (to account for the reduced physiological drive to sleep). The overall aims of this paper are to raise a discussion of whether it is possible to obtain successful rest during automated driving, and to present initial work on a hypothetical data driven algorithm aimed to estimate if it is possible to gain driving time after resting under fully automated driving. The presented algorithm consists of four central components, a heart rate-based relaxation detection algorithm, a camera-based sleep detection algorithm, a fatigue modelling component taking time awake, time of day and time on task into account, and a component that estimates gained driving time. Real-time assessment of driver fitness is complicated, especially when it comes to the recuperative value of in-cab sleep and rest, as it depends on sleep quality, time of day, homeostatic sleep pressure and on the activities that are carried out while resting. The monotony that characterizes for long-haul truck driving is clearly interrupted for a while, but the long-term consequences of extended driving times, including user acceptance of the key stakeholders, requires further research.

Long-Haul Vehicle Routing and Scheduling with Biomathematical Fatigue Constraints

Hours of service (HOS) regulations are among the conventional safety constraints that are compiled by long-haul truck drivers. These regulations have been considered in models and algorithms of vehicle routing problems to assign safe schedules to drivers. However, the HOS regulations neglect a few crucial fatigue risk factors and, at times, fail to generate fatigue-reducing schedules. In this study, a set of biomathematical fatigue constraints (BFCs) derived from biomathematical models are considered for a long-haul vehicle routing and scheduling problem. A BFC scheduling algorithm and a BFC-HOS scheduling algorithm have been developed and then embedded within a tabu search heuristic to solve the combined vehicle routing and scheduling problem. All the solution methods have been tested on modified Solomon instances and a real-life instance, and the computational results confirm the advantages of employing a sophisticated and fatigue-reducing scheduling procedure when planning long-haul transportation.

The Bi-objective Long-haul Transportation Problem on a Road Network

In this paper we study a long-haul truck scheduling problem where a path has to be determined for a vehicle traveling from a specified origin to a specified destination. We consider refueling decisions along the path while accounting for heterogeneous fuel prices in a road network. Furthermore, the path has to comply with Hours of Service (HoS) regulations. Therefore, a path is defined by the actual road trajectory traveled by the vehicle, as well as the locations where the vehicle stops due to refueling, compliance with HoS regulations, or a combination of the two. This setting is cast in a bi-objective optimization problem, considering the minimization of fuel cost and the minimization of path duration. An algorithm is proposed to solve the problem on a road network. The algorithm builds a set of non-dominated paths with respect to the two objectives. Given the enormous theoretical size of the road network, the algorithm follows an interactive path construction mechanism. Specifically, the algorithm dynamically interacts with a geographic information system to identify the relevant potential paths and stop locations. Computational tests are made on real-sized instances where the distance covered ranges from 500 to 1500 km. The algorithm is compared with solutions obtained from a policy mimicking the current practice of a logistics company. The results show that the non-dominated solutions produced by the algorithm significantly dominate the ones generated by the current practice, in terms of fuel costs, while achieving similar path durations. The average number of non-dominated paths is 2.7, which allows decision-makers to ultimately visually inspect the proposed alternatives.

Scheduling truck drivers with interdependent routes under European Union regulations

When transporting goods, truck drivers may have to perform load transfers between two vehicles at cross-dock or transfer locations. These transfers create interdependencies between the routes of different truck drivers and require careful temporal synchronization. The situation becomes even more complicated when driver schedules must comply with customer time windows, working periods and regulatory resting requirements. Although the scheduling of hours of service for truck drivers is a well-known problem, existing algorithms cannot cope with interdependent routes. We introduce the Truck Driver Scheduling Problem with Interdependent Routes to successfully accommodate this real-world characteristic. This paper proposes a mathematical model and a label propagation algorithm to solve this new problem under the regulations of the European Union. Experiments indicate that the algorithm can quickly schedule a large number of interdependent routes and outperforms a mathematical programming approach. The algorithm produces valid schedules for multiple drivers and thus becomes a foundation for future vehicle routing algorithms that address interdependent routes and hours of service regulations.

Driver Activity Recognition by Means of Temporal HTN Planning

When delivering a transport service, scheduled driver workplans have to be aligned with world wide complex hours of service (HoS) regulations which constraint the amount of working and driving time without resting. The activities of such workplans are recorded by onboard sensors in large temporal event logs. Transport companies are interested on recognizing what a driver is doing, based on the temporal observations from event logs, considering the terms defined by HoS regulations. This work presents an application of temporal HTN planning to plan and goal recognition that, starting from a real event log extracted from a tachograph, identifies different sub-sequences of a driver's daily and weekly driving activity and labels them according to the terms defined by HoS regulation.

Bidirectional labeling for solving vehicle routing and truck driver scheduling problems

This paper studies the vehicle routing and truck driver scheduling problem where routes and schedules must comply with hours of service regulations for truck drivers. It presents a backward labeling method for generating feasible schedules and shows how the labels generated with the backward method can be combined with labels generated by a forward labeling method. The bidirectional labeling is embedded into a branch-and-price-and-cut approach and evaluated for hours of service regulations in the United States and the European Union. Computational experiments show that the resulting bidirectional branch-and-price-and-cut approach is significantly faster than unidirectional counterparts and previous approaches.

On a multi-trip vehicle routing problem with time windows integrating European and French driver regulations

In the European Union, truck drivers must comply with Regulation (EC) No 561/2006, which provides a set of rules on the daily or weekly working time and on break or rest periods after specific working times. Truck drivers can be back to the depot much earlier than the end of their working period, and be able to perform other trips. The resulting problem is a Multi-Trip Vehicle Routing Problem with Time Windows, where truck drivers have the possibility to accomplish, during their time horizon, more than one trip, combined with a Truck Driver Scheduling Problem. It aimed to find and assign a set of routes to a fleet of vehicles allowing each customer to be visited within its given time window, and the accumulated load to be delivered (or collected) not exceeding the capacity of the vehicle. Each truck driver must comply with applicable hours of service regulations, and the objective is to minimize total transportation cost. To solve this problem, we have developed a hybrid method composed of a genetic algorithm and a local neighborhood search. Experiments have been conducted on instances from the literature and real-life cases from SYSTRANS, a specialized French software company.

Hours of service regulations for professional drivers in continental Latin America

ObjectivesTo describe the hours of service provisions in continental Latin America. DesignInformation on regulations of service hours was extracted from either the national transportation authorities or ministries of transportation (or the equivalent institution) from each country. SettingSeventeen sovereign countries in continental Latin America (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay, Venezuela). ParticipantsN/A Intervention (if any)N/A MeasurementData on (a) limit on work hours, (b) mandatory daily time off (or rest), (c) overall schedule (mandatory weekly time off), and (d) daily breaks were extracted and summarized. ResultsOf the 17 countries surveyed, 9 countries have provisions limiting the daily amount of hours of service for professional drivers. Ten have provisions for mandatory daily rest, but only 5 have explicit provisions limiting the number of continuous working days, with mandatory uninterrupted time off >35 hours. Eight countries have provisions for mandatory breaks that limit the hours of continuous driving (ranging from 3 to 5:30 hours). ConclusionRegulations that govern a population with 6 million injuries and over 100,000 deaths per year due to motor vehicle accidents leave important gaps. A minority, 6, of the countries regulated all 3 aspects; daily hours, breaks, and time off, and 3 regulate none of these. The regulations are less precise and restrictive than those in high-income countries, despite the doubled road injury mortality, and likely expose professional drivers and other road users to an increased risk of fatigue-related accidents.

Battling Blind Spots: Hours of Service Regulations and Contentious Mobilities in the BC-Based Long Haul Trucking Industry

This article explores arenas of contention in which long haul truckers’ workplace mobilities are enmeshed. I critically analyze the grounded implications of Hours of Service (HoS) regulations, a primary regulatory mechanism for addressing the dangers posed by truck driver fatigue. I argue that HoS regulations enforce a neoliberal individualization of responsibility that fails to account for industry power dynamics or truckers’ lived experiences of labour mobility. These dynamics add to concerns about the potential exploitation of migrant truck drivers, including through the Temporary Foreign Worker Program. Inasmuch as they fail to address the classed, gendered and racialized dynamics of trucking mobilities, HoS regulations are implicated in perpetuating hierarchies of power in the industry. As such, they are inadequate and – in contextually specific ways – counterproductive to promoting employment equity or overall public safety. These issues are particularly evident in the contentious politics of blame concerning heavy truck-involved collisions.

Regulating danger on the highways: hours of service regulations

ObjectivesCurrent hours of service regulations governing commercial truck drivers in place in the United States, Canada, Australia, and the European Union are summarized and compared to facilitate the assessment of the effectiveness of such provisions in preventing fatigue and drowsiness among truck drivers. MethodsCurrent hours of service provisions governing commercial truck drivers were derived from governmental sources. ResultsThe commercial truck driver hours of service provisions in the United States, Canada, and the European Union permit drivers to work 14 hours and those of Australia permit drivers to work 12 hours a day on a regular basis. The regulations do not state what a driver may do with time off. They are consistent with a driver being able to drive after 24 hours without sleep. They do not take into account circadian rhythm by linking driving or rest to time of day. ConclusionsCurrent hours of service regulations governing commercial truck drivers leave gaps—permitting drivers to work long hours on a regular basis, permitting driving after no sleep for 24 hours, and failing to take into account the importance of circadian rhythm, endangering the public safety and the truck drivers themselves.

A model-based decision support system for solving vehicle routing and driver scheduling problems under hours of service regulations

In principle, hours-of-service (HOS) regulations are intended to help ensure truck drivers get adequate rest and perform safe operations. The new HOS regulations, however, may lead to substantial cost increases for regional common carriers which have already been hit hard by rising fuel prices and declining shipping demands. In addition, the new HOS regulations complicate driver schedules by not only restricting the driver's consecutive driving hours, but also expanding off-duty hours. To deal with this complex challenge, we propose a model-based decision support system (DSS) that helps determine the truck driver's working hours, rest periods, and his/her assigned truck's schedules and routes under HOS regulations. As a core of this model-based DSS, we developed a mixed-integer programming model and a simulated annealing metaheuristic for solving it. This model was also integrated with a geographical information system and relational database management system to enhance interfaces between the model and its parametric data using spatial, graphical displays.

An Exact Method for Vehicle Routing and Truck Driver Scheduling Problems

In most developed countries working hours of truck drivers are constrained by hours of service regulations. When optimizing vehicle routes, trucking companies must consider these constraints to assure that drivers can comply with the regulations. This paper studies the combined vehicle routing and truck driver scheduling problem (VRTDSP), which generalizes the well-known vehicle routing problem with time windows by considering working hour constraints. A branch-and-price algorithm for solving the VRTDSP is presented. This is the first algorithm that solves the VRTDSP to proven optimality.

Hours of service regulations in the United States and the 2013 rule change

This paper studies the revised hours of service regulations for truck drivers in the United States which entered into force in July 2013. It provides a detailed model of the new regulation and presents and a new simulation-based method to assess the impact of the rule change on operational costs and road safety. Unlike previous methodologies, the proposed methodology for assessing the impact of hours of service regulations takes into account that, by optimizing routes and schedules, carriers can minimize the economic impact of stricter regulations. Simulation experiments are conducted indicating that the monetized safety benefit of reducing the daily driving time limits is on the same order of magnitude as the increase in operational costs.

A Fundamental Study on Commercial Vehicle Drivers’ Hours of Service Regulations in Japan

A Fundamental Study on Commercial Vehicle Drivers’ Hours of Service Regulations in Japan

U.S. motor carrier hours of service regulations: Their impact on carrier profitability and productivity

This paper discusses the history of hours of service regulations for U.S. motor carriers and investigates the changes to individual carrier profitability and productivity from the last major change to those regulations in 2003. The results of the analysis indicate that operating ratio worsened and sales per employee improved, and return on assets and return on equity were unchanged due to hours of service changes. The implications of these results given the recent changes to hours of service regulations in 2011 are also discussed.

Hours of Service Regulations in Road Freight Transport: An Optimization-Based International Assessment

Driver fatigue is internationally recognized as a significant factor in approximately 15%–20% of commercial road transport crashes. In their efforts to increase road safety and improve working conditions of truck drivers, governments worldwide are enforcing stricter limits on the amount of working and driving time without rest. This paper describes an effective optimization algorithm for minimizing transportation costs for a fleet of vehicles considering business hours of customers and hours of service regulations. The algorithm combines the exploration capacities of population-based metaheuristics, the quick improvement abilities of local search, with forward labeling procedures for checking compliance with complex hours of service regulations. Several speed-up techniques are proposed to achieve an overall efficient approach. The proposed approach is used to assess the impact of different hours of service regulations from a carrier-centric point of view. Extensive computational experiments for various sets of regulations in the United States, Canada, the European Union, and Australia are conducted to provide an international assessment of the impact of different rules on transportation costs and accident risks. Our experiments demonstrate that European Union rules lead to the highest safety, whereas Canadian regulations are the most competitive in terms of economic efficiency. Australian regulations appear to have unnecessarily high risk rates with respect to operating costs. The recent rule change in the United States reduces accident risk rates with a moderate increase in operating costs.

The Canadian minimum duration truck driver scheduling problem

In Canada transport companies must ensure that truck drivers can comply with Canadian Commercial Vehicle Drivers Hours of Service Regulations. Canadian regulations comprise the provisions found in US hours of service regulations as well as additional constraints on the maximum amount of driving and the minimum amount of off-duty time on each day. This paper presents a mixed integer programming formulation and an iterative dynamic programming approach for minimising the duration of truck driver schedules complying with Canadian hours of service regulations. Computational experiments show that schedule durations can be significantly reduced compared with a previously presented approach which only focuses on feasibility.