Where Truck Drivers Stop – Application of Vehicle Tracking Data for the Identification of Rest Locations and Driving Patterns

Road transport plays an essential role in freight transport throughout Europe, therefore, conditions that may hinder seamless operations in this sector require thorough consideration for evidence-based action. Critical amongst these key conditions is how, when, and where truck drivers stop, as a common set of rules strictly regulates their driving times and rest periods, which causes mandatory interruptions in the supply chains. However, approximating reliable estimations of freight traffic flows and road infrastructure usage constitutes a considerable challenge for researchers. This paper presents a robust data processing approach to designate rest area stops and to calculate the pertaining driving and rest times. Drawing on the abundance of navigation information provided by private fleet toll registration services, a comprehensive spatial-temporal truck stop database on all major rest areas along the toll road network in Hungary has been compiled. Based on the assessment and comparison of driving and rest times, driving and parking times have been analysed, including micro-scale analysis of particular rest areas. Both the methods applied and the results achieved can be of strategic interest to better understand truck driving patterns, as well as to develop targeted and cost-effective measures to streamline freight transport operations in other contexts.

Development of a Modular Tensegrity Robot Arm Capable of Continuous Bending

In this study, we present a tensegrity robot arm that can reproduce the features of complex musculoskeletal structures, and can bend like a continuum manipulator. In particular, we propose a design method for an arm-type tensegrity robot that has a long shape in one direction, and can be deformed like a continuum manipulator. This method is based on the idea of utilizing simple and flexible strict tensegrity modules, and connecting them recursively so that they remain strict tensegrity even after being connected. The tensegrity obtained by this method strongly resists compressive forces in the longitudinal direction, but is flexible in the bending direction. Therefore, the changes in stiffness owing to internal forces, such as in musculoskeletal robots, appear more in the bending direction. First, this study describes this design method, then describes a developed pneumatically driven tensegrity robot arm with 20 actuators. Next, the range of motion and stiffness under various driving patterns are presented as evaluations of the robot performance.

Comparison and Analysis of GPS Measured Electric Vehicle Charging Demand: The Case of Western Sweden and Seattle

Electrification of transportation using electric vehicles has a large potential to reduce transport related emissions but could potentially cause issues in generation and distribution of electricity. This study uses GPS measured driving patterns from conventional gasoline and diesel cars in western Sweden and Seattle, United States, to estimate and analyze expected charging coincidence assuming these driving patterns were the same for electric vehicles. The results show that the electric vehicle charging power demand in western Sweden and Seattle is 50–183% higher compared to studies that were relying on national household travel surveys in Sweden and United States. The after-coincidence charging power demand from GPS measured driving behavior converges at 1.8 kW or lower for Sweden and at 2.1 kW or lower for the United States The results show that nominal charging power has the largest impact on after-coincidence charging power demand, followed by the vehicle’s electricity consumption and lastly the charging location. We also find that the reduction in charging demand, when charging is moved in time, is largest for few vehicles and reduces as the number of vehicles increase. Our results are important when analyzing the impact from large scale introduction of electric vehicles on electricity distribution and generation.

The Effect of Driving Cycle Duration on its Representativeness

There is an increasing interest in properly representing local driving patterns. The most frequent alternative to describe driving patterns is through a representative time series of speed, denominated driving cycle (DC). However, the DC duration is an important factor in achieving DC representativeness. Long DCs involve high testing costs, while short DCs tend to increase the uncertainty of the fuel consumption and tailpipe emissions results. There is not a defined methodology to establish the DC duration. This study aims to study the effect of different durations of the DCs on their representativeness. We used data of speed, time, fuel consumption, and emissions obtained by monitoring for two months the regular operation of a fleet of 15 buses running in two flat urban regions with different traffic conditions. Using the micro-trips method, we constructed DCs with a duration of 5, 10, 15, 20, 25, 30, 45, 60, and 120 min for each region. For each duration, we repeated the process 500 times in order to establish the trend and dispersion of the DC characteristic parameters. The results indicate that to obtain driving pattern representativeness, the DCs must last at least 25 min. This duration also guarantees the DC representativeness in terms of energy consumption and tailpipe emissions.

Drivers of Avian Diversity and Abundance Across Gradients of Human Influence.

Context: Identifying factors driving patterns of species communities in heterogenous human-dominated landscapes remains elusive despite extensive research. Biodiversity is thought to decrease with habitat modification, as sensitive species are lost. Conversely, diversity has also been shown increase at moderate levels of landscape modification where greater habitat heterogeneity supports a diverse suite of species.Objectives: We explore patterns of avian and diversity and abundance in heterogenous landscapes using a novel integration of multiple dimensional gradients of human-mediated disturbance.Methods: We attempt to identify aspects of landscape heterogeneity driving patterns of avian diversity and abundance in agro-urban-rural systems. Specifically, we utilize an intuitive multi-dimensional gradient distinguishing between two axes of human-influence, variation in the built environment (hard - soft) and in agricultural development (green - brown). We use these as covariates in community N-mixture models to describe variation in species abundance and diversity.Results: Avian diversity was greatest in more heterogeneous regions of the landscape. Responses of individual species were variable, with sensitive species declining, while generalist species increased, leading to higher overall diversity in human-dominated regions. Conclusions: Species abundance and diversity is maximized in more heterogeneous parts of landscape mosaics. By characterizing distinct axes of human influence that capture spectrum of land use, we can identify differential effects confounded in traditional landscape metrics. Critically, we demonstrate that multi-dimensional landscape gradients provide a more nuanced understanding of how patterns of biodiversity emerge. Acknowledging that biodiversity is not always negatively impacted by habitat disturbance offers encouraging insight to guide conservation and management in human-dominated landscapes.