Siting of Safe and Secure Truck Parking Areas in Greece and definition of their security level with the use of Multi-Actor Multi-Criteria Analysis (MAMCA)

The purpose of this paper is the siting of safe and secure truck parking areas in Greece and the definition of their security level. The increase of road freight transport, and the need for the strengthening of its safety, led to the need for developing a plan for the deployment of safe and secure Heavy Goods Vehicles (HGVs) parking areas. A demand and supply survey led to the development of a plan to address 29.83% of the total demand. Nine (9) stakeholder groups, represented by twelve (12) participants, participated in a Multi-Actor Multi-Criteria Analysis (MAMCA) of the criteria evaluation for choosing the most suitable security level out of the following levels: Bronze, Silver, Gold, and Platinum, for Aigio’s Rest Area. The Platinum level was commonly chosen as the most appropriate.

Evaluation of the causes of flexible pavement failure; case study of Enugu/Port-Harcourt expressway

This study examined the causes of flexible pavement failure, taking the Enugu/Port-Harcourt expressway as a case study; to understand possible peculiarities. Chainages 101+400 and 125+925 were the most critical, having potholes of 500mm in depth; hence, soil samples were taken from these spots. The following tests were conducted: Particle size distribution, Atterberg limits, Permeability, Compaction, and California Bearing Ratio (CBR). The results obtained showed evidence of presence of clay in the subgrade; hence, concluding that the failure was mainly caused by poor soil material. From visual condition survey, it was noted that there were no drainages even at the critical paths of the alignment, and there was a significant proportion of Heavy Goods Vehicles (HGVs) which may not have been adequately considered during design.

Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data

This paper addresses crashes involving heavy goods vehicles (HGV) in Europe focusing on long-haul trucks weighing 16 tons or more (16t+). The identification of the most critical scenarios and their characteristics is based on a three-level analysis: general crash statistics from CARE addressing all HGVs, results about 16t+ trucks from national crash databases and a detailed study of in-depth crash data from GIDAS, including a crash causation analysis. Most European HGV crashes occur in clear weather, during daylight, on dry roads, outside city limits, and on non-highway roads. Three main scenarios for 16t+ trucks are characterized in-depth: (1) rear-end crashes in which the truck is the striking partner, (2) conflicts during right turn maneuvers of the truck and a cyclist riding alongside and (3) pedestrians crossing the road in front of the truck. Among truck-related crash causes, information admission failures (e.g. distraction) were the main causing factors in 72% of cases in scenario (1) while information access problems (e.g. blind spots) were present for 72% of cases in scenario (2) and 75% of cases in scenario (3). The results provide both a global overview and sufficient depth of analysis in the most relevant cases and thereby aid safety system development.

Evaluating the impacts of autonomous cars on the capacity of freeways in Brazil using the HCM-6 PCE methodology

This paper analyses the factors that affect the impact of autonomous vehicles (AVs) on the capacity of a freeway in Brazil using an adaptation of the HCM-6 procedure for truck PCE estimation. A version of Vissim, recalibrated to represent traffic streams and AVs on Brazilian freeways, was used to simulate more than 25,000 scenarios representing combinations of traffic (e.g., AV fleets, AV platoons, percentage of AVs and of heavy goods vehicles) and road (grades and number of lanes) characteristics. AV impacts on capacity were evaluated by means of the capacity adjustment factor (CAF) and a model to estimate CAF from control variables was fitted and validated. The results indicate increases of up to 30% in capacity with 60% of platooning-capable AVs. Statistical analyses show that the fraction of AVs in the stream and the proportion of platooning-capable AVs are the factors with the greatest impact on this increase in capacity.

A New Approach of Smart Mobility for Heavy Goods Vehicles in Casablanca

Managing mobility, both of people and goods, in cities is a thorny issue. The travel needs of urban populations are increasing and put pressure on transport infrastructure. The Moroccan cities are no exception and will struggle, in the short term, to respond to the challenges of the acceleration of the phenomenon of urbanization and the increase in demand for mobility. This will inevitably prevent them from turning into smart cities. The term smart certainly alludes to better use of technologies, but smart mobility is also defined as “a set of coordinated actions intended to improve the efficiency, effectiveness and environmental sustainability of cities” [1]. The term mobility highlights the preponderance of humans over infrastructure and vehicles. Faced with traffic congestion, the solutions currently adopted which consist of fitting out and widening the infrastructures, only encourage more trips and report the problem with more critical consequences. It is true that beyond a certain density of traffic, even Intelligent Transport Systems (ITS) are not useful. The concept of dynamic lane management or Advanced Traffic Management (ATM) opens up new perspectives. Its objective is to manage and optimize road traffic in a variable manner, in space and in time. This article is a summary of the development of a road infrastructure dedicated to Heavy Goods Vehicles (HGV), the first of its kind in Morocco. It aims to avoid the discomfort caused by trucks in the urban road network of the city of Casablanca. This research work is an opportunity to reflect on the introduction of ITS and ATM to ensure optimal use of existing infrastructure before embarking on heavy and irreversible infrastructure projects.

An Empirical Tire-Wear Model for Heavy-Goods Vehicles

ABSTRACT Tire selection has an important impact on the operational costs of heavy-goods vehicles (HGVs). HGV tires are designed on a tradeoff between wear resistance, rolling resistance, and adhesion (skid resistance). High wear resistance tires (high mileage) are replaced less often but use more fuel during operation, and vice versa for low rolling resistance tires. Presently, finding the optimal tire to minimize replacement costs and fuel consumption (greenhouse gas emissions) is challenging due to the difficulty in predicting tire wear for a given operation, since its rate varies with different vehicle configurations (e.g., load, vehicle length, number of axles, type of axle, etc.) and road types (e.g., motorways/highways, minor roads, urban roads, etc.). This article presents a novel empirical tire-wear model that can be used to predict the wear for multi-axle vehicles based on route data and a vehicle model. The first part of the article presents the analytical and experimental development of the model. The second part presents the experimental validation of the model based on 10 months of in-service data totaling 37,000 km of operation. The model predicts tire tread depth within 8% (average error of 2%).

Analysis of unit costs at the Polish international truck freight transport companies in 2019 - eastern markets

The article represents the next part of the periodical publications of the Department of Economic Research in the field of cost analysis of the Polish international freight transport companies. It summarises and makes comparative assessments of the average unit costs of these companies for the 2019 in relation to the results of the previous period. The survey involved carriers operating on the Eastern markets. The article presents the statistical characteristics of the entities surveyed, taking into account their size, determined based on the number of the fleet’s heavy goods vehicles operated. It also shows how, the average costs of one vehicle-kilometre (veh-km) of mileage for a truck above 12.0 Mg GVW, are shaped, according to the size of the companies and taking into account the destinations of transport. The generic costs structure of the companies examined, has also been analysed. Presented in the tabular and graphic form is the evolution of unit costs for both 2019 and the whole 2009-2019 period.

Comparative analysis of the unit costs of the Polish International freight transport companies operating on the EU markets, in the years 2009-2019

The article represents the next part of the periodical publications of the Department of Economic Research in the field of cost analysis of the Polish international freight transport companies. It summarises and makes comparative assessments of the average unit costs of these companies for 2019 in relation to the results of the previous period. The survey involved both, the carriers operating on the eastern markets and in the European Union countries. The article presents statistical characteristics of the companies surveyed, taking into account their size, determined, based on the number of the fleet’s heavy goods vehicles being operated. It also shows how the average costs of a single vehicle-kilometre (veh-km) of mileage, for a truck above 12.0 Mg GVW are shaped, according to the size of the companies and taking into account the direction of transport (EU countries). The generic structure of the costs of the companies examined has been analysed. The evolution of unit costs for both 2019 and the whole period 2009-2018 were presented in tabular and graphic form.

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.