Freight Loading Research Articles

Utilization of Granular Wastes in Transportation Infrastructure

Abstract Attributed to environmental preservation in urban infrastructure development, the recycling of waste materials produced in the coal and steel industry as well as the reusing of waste tires is a high priority in Australia. In this article, the practical applications of (i) coal wash (CW) and steel furnace slag mixtures, (ii) CW and fly ash mixtures, and (iii) rubber elements derived from recycled tires are discussed. In this regard, some examples of real-life applications are elucidated in relation to coastal reclamation as well as road and rail construction (e.g., Port Kembla, Kangaroo Valley highway, and Chullora Rail Precinct). The article outlines various aspects of site investigation, construction techniques, and the installation of instrumentation to evaluate the field performance of these waste materials in contrast to traditional (natural) quarried materials. The results from these case studies demonstrate that properly engineered granular waste mixtures can exhibit promising characteristics even to exceed the current technical standards, implying reduced intensity of maintenance. The research outcomes strongly support sustainable solutions to be embraced in the future development of transportation infrastructure, capable of withstanding increased freight loading and enhanced longevity.

Rescheduling Out-of-Gauge Trains with Speed Restrictions and Temporal Blockades on the Opposite-Direction Track

Out-of-gauge trains are trains with loading freight that exceeds the loading limitation border. Considering collision avoidance, the out-of-gauge trains have speed restriction of their own, and the trains running on the parallel track. Therefore, it is necessary to execute a train rescheduling procedure to rearrange the train paths of the out-of-gauge trains and the affected trains based on the fundamental timetable. For rescheduling the timetable, considering the blockades and the speed restrictions caused by the out-of-gauge trains, this paper proposed a time-space-state network representation for describing the out-of-gauge train rescheduling problem. A novel concept, speed allowance, is introduced to describe the train speed restriction due to the out-of-gauge trains. An integer programming model based on the time-space network is proposed to minimize the total train delay when running the out-of-gauge trains. The model can be solved by the rolling-time horizon approach for reducing computational time. A numerical example is conducted based on the conventional railway in China, demonstrating the solution performance of the model and the practical use of the methodology. Gurobi solver cannot obtain an optimal solution within 1 h when the planning-time horizon is greater than 120 min. With the rolling-time horizon approach, the rescheduled timetable can be obtained within 124 s for the 300 min planning-time horizon using 180 min rolling-time window.

Loading and Unloading Points Identification Based on Freight Trajectory Big Data and Clustering Method

Based on the GPS trajectory data of a freight enterprise in Dalian, China, this paper studies the identification of loading and unloading points by a clustering algorithm. Firstly, by analysing the characteristics of freight loading and unloading behaviour, combined with the spatial and temporal distribution characteristics of truck GPS trajectory data, three characteristic variables of the number of trucks passing through a certain place, the average speed of trucks and the average stay time of trucks in the place are extracted. Then, the clustering algorithm and visual analysis are used to obtain the target cluster, and the POI language of the geographic information is obtained according to the points in the target cluster. The meaning information is crawled to accurately identify the result of the freight loading point. Finally, two classical clustering algorithms, K-means and GMM, are evaluated and compared. The results show that the identification method designed in this paper finally identifies 2,320 freight loading and unloading points from 11,406,000 trajectory data, which can realise the accurate extraction of freight loading and unloading points.

A gauge modification-based railway out-of-gauge freight transportation route decision-making methodology

The loading outline of railway out-of-gauge freight (ROF) is beyond the railway gauge due to its larger size. The choice of a safe and economical transportation route according to the characteristics of the freights in the railway network becomes an important issue for the railway out-of-gauge freight transportation. A proposed methodology is presented in this paper with the aim to solve the problems in the railway out-of-gauge freight transportation route decision-making (ROF-TRD) process in which safety distances between the railway gauges and freight loading outlines, the curve radius and the arrival-departure traffic flow balance are taken into consideration while the railway capacity losses and transportation costs being as objective functions to construct the route decision-making model considering gauge modification. The proposed route search algorithm can be used in the model to further refine the design of an effective safety distance bilateral detection. The case study verification shows that the safest and most economical railway out-of-gauge freight transportation route can be obtained by applying the proposed model, and the results demonstrate that taking railway capacity loss and modified costs of possible ROF routes into consideration, the optimal route can save around 10–22 % of total costs.

Joint modeling of arrivals and parking durations for freight loading zones: Potential applications to improving urban logistics

This paper analyzes truck parking patterns in urban freight loading zones by jointly modeling the vehicle arrival rates and the parking durations. Three models were explored: 1) Count data (Negative Binomial) for vehicle arrivals, 2) Survival (Weibull) model for parking duration and 3) A joint model for arrivals and duration. The count data model estimates the parking demand i.e., the rate of truck arrival, while the survival model estimates the probability that a truck is parked for one more minute. The joint model is compared with separate models for predictability and performance. The dataset used in this research is obtained using a mobile phone parking application, at eight loading zones in the city Vic, Spain over an 18-month period from July 2018 to December 2019, comprised of vehicle parking durations, date, time of arrival and departure, professional activity, and vehicle type (weight). The parking activity data are complemented with built in environment variables of the loading zones, such as the number of establishments in a certain radius, the average walking distance to establishments, the presence of pedestrian pavement, the number of traffic lanes, among others. The joint model outperforms the models estimating the arrival rates and durations separately in goodness of fit and predictability. The model results showed that truck arrival rates vary significantly across days of the week, months, and arrival times. The parking durations are highly dependent on professional activity, vehicle type, and size. Tuesdays and Wednesdays have higher arrival rates compared to other days of a week (except Sundays). Among activities, the transport and parcels require longer parking durations. Among the vehicle types, trucks with gross weight larger than 3.5 tons park longer. This paper concludes by explaining the potential of these modeling approaches in improving urban freight operations, evaluation of various policy implications, limitations, and future research.

Application of Different Data Analytics for Evaluation of Heavy Vehicle Vulnerability in Cold-Region Rural Highways

Understanding the impacts of different driving conditions on truck speed is critical to the development and maintenance of resilient highway freight transportation systems. This study attempts to evaluate the combined impact of road weather, travel lane, vehicle type, and truck payload conditions on drivers’ speed choice by modelling speed distributions as normal distributions. Two data analytics, a regression-based approach (RBA) and a central limit theorem (CLT)-based approach (CBA), are adapted to model context-specific speed distributions. The regression-based approach models population-level speed distributions by considering samples of individual speed data, whereas the CBA uses sampling distributions produced according to the CLT. A holistic approach is proposed to identify overall vehicle-specific collision risks imposed by different road-weather conditions, based on the speed distribution parameters estimated. Implications of the study results pertaining to the trucking industry are threefold. First, adapting different data analytics leads to different study results; yet, the CBA is recommended to model speed distributions. Second, truck speeds are significantly affected by the presence of adverse road-weather conditions, yet marginally varied under different loading conditions. Third, overall, tractor–trailer combinations (TTCs) entail high collision risks, particularly when transporting a freight load under adverse road-weather conditions. The study results would be useful to policymakers, particularly for effective speed management in extremely cold regions. Trucking companies may use the study results to identify the least risk-posing road-weather conditions to deploy safe freight transport operations. The resulting speed distribution models are also useful as input to calibrate traffic micro-simulation models.

Internet of Things enabled real time cold chain monitoring in a container port

Abstract Purpose Seaports are regarded as significant actors in global logistics and supply chains since a large part of the cargoes carried over the globe are being processed there. When the cold chain broken down during transport and storage in the ports, the humidity, nutrition, temperature and time conditions to be required for the growth of the bacteria occur, and rapid reproduction occurs and the properties of the products are rapidly deteriorating. It is imperative that especially medicines, some chemical substances and foodstuffs need to be transported without breaking the cold chain in the logistics. The monitoring and control of the temperature and humidity level is important in the time period between the loading of these containers in special areas in ports, the loading of freight in open areas, or the loading of freight on roads and railway carriages. For this reason, precise monitoring and control of the system is vital in the port logistics management. Method In this study, an IoT-enabled system is designed for Container Ports by developing software, interface and equipment that will enable remote monitoring of temperature, humidity and other necessary key status parameters. Findings The developed IoT-based system provides audible and visual warning, e-mail and SMS communication, similar to a monitoring screen such as a heart graph monitor, when the instant values of the refrigerated container are transmitted to the database, when the defined upper and lower values are approached. All these data and major change information are archived in the database and retrospective situation analysis and data analysis can be performed. Conclusion Using technologies such as Wireless Sensor Network (WSN) and RFID, an IoT-enabled Cold Chain Logistics system has been proposed that provides real-time monitoring of products in containers at ports, providing DS services to logistics providers and customers. In this context, it was explained how the ambient parameter values were collected in real time using WSN and IEEE 802.15.4, how the collected data was sent to the server via the GSM gateway. In the port scenario, activation devices such as IEEE 802.15.4 and RFID were modeled using the OPNET simulator. The developed model was carried out in accordance with the principles of EPCglobal Gen 2. With the proposed approach, smart solutions provide a smarter flow of information. The results show that IoT- enabled cold chain systems have a great potential for managing, monitoring, receiving and determining abnormal events related to temperature-sensitive products in real time.

A Branch-Cut-and-Price Approach for the Single-Trip and Multi-Trip Two-Echelon Vehicle Routing Problem with Time Windows

The paper studies the two-echelon capacitated vehicle routing problem with time windows, in which delivery of freight from depots to customers is performed using intermediate facilities called satellites. We consider the variant of the problem with precedence constraints for unloading and loading freight at satellites. In this variant allows for storage and consolidation of freight at satellites. Thus, the total transportation cost may decrease in comparison with the alternative variant with exact freight synchronization at satellites. We suggest a mixed integer programming formulation for the problem with an exponential number of route variables and an exponential number of precedence constraints that link first-echelon and second-echelon routes. Routes at the second echelon connecting satellites and clients may consist of multiple trips and visit several satellites. A branch-cut-and-price algorithm is proposed to solve efficiently the problem. This is the first exact algorithm in the literature for the multi-trip variant of the problem. We also present a postprocessing procedure to check whether the solution can be transformed to avoid freight consolidation and storage without increasing its transportation cost. It is shown that all single-trip literature instances solved to optimality admit optimal solutions of the same cost for both variants of the problem either with precedence constraints or with exact synchronization constraints. Experimental results reveal that our algorithm can be used to solve these instances significantly faster than another recent approach proposed in the literature.

Algorithm to Estimate the Capacity Reserve of Existing Masonry Arch Railway Bridges

Most railway masonry arch bridges were designed according to codes that predate the 1950s; therefore, assessing their load-carrying capacity to comply with current codes is of the utmost importance. Nonetheless, acquiring the necessary information to conduct in-depth analyses is expensive and time consuming. In this article, we propose an expeditious procedure to conservatively assess the Load Rating Factor of masonry arch railway bridges based on a minimal set of information: the span, rise-to-span ratio, and design code. This method consists in applying the Static Theorem to determine the most conservative arch geometry compatible with the original design code; assuming this conservative geometrical configuration, the load rating factor, with respect to a different design load, is estimated. Using this algorithm, a parametric analysis was carried out to evaluate the Load Rating Factor of old arch bridges in respect of the modern freight load of the Trans-European Conventional Rail System, for different spans, rise-to-span ratios, and original design codes. The results are reported in easy-to-use charts, and summarized in simple, practical rules, which can help railway operators to rank their bridges based on capacity deficit.

Automatic Construction of Loading Algorithms With Interactive Genetic Programming

The design of a freight loading pattern is often conducted by skilled workers, who handle unquantifiable objectives and/or preferences. Our previous study presented an automatic construction technique for loading algorithms using genetic programming-based hyper-heuristics; however, this technique is only applicable to fully quantifiable loading problems. Thus, the approach described in this paper integrates an interactive framework with users into our previous technique to automatically construct algorithms that derive loading patterns adapted to user objectives and/or preferences. Thus, once a loading algorithm has been derived with user interactions, it can be reused to obtain the preferred loading patterns on other problems without any additional interactions. Experimental results show that the proposed algorithm can produce loading algorithms adapted for user preferences under a limit of 50 human interactions. Further, we also show that the derived loading algorithms can be applicable to different loading situations without any additional user interactions. Thus, these observations suggest the benefit of our approaches in reducing the burden placed on skilled workers for practical LPD tasks.

DEVELOPMENT OF EMISSION INTENSITY FACTORS FOR A SOUTH AFRICAN ROAD-FREIGHT LOGISTICS SERVICE PROVIDER

The Global Logistics Emissions Council (GLEC) framework developed logistics emission factors to be used uniformly in North America and Europe. It included an approximation for African countries; however, actual South African emissions were not accurately reflected. Therefore, in this study, carbon emissions factors were calculated using calculated tonne-kilometres and the energy-based methodology. The authors obtained several datasets from a logistics service provider (LSP) consisting of vehicle routes, refuelling data, and freight load data. The project developed factors for each individual trip, for similar repetitive trips, and for the entire data set. These different sets of factors were developed to allow the use of different emission calculation and reporting standards. The LSP could use these emission intensity factors to estimate carbon emissions using the activity-based approach, report emissions according to legislation, and predict how much carbon emissions would be emitted to move a customer’s shipment. Keywords carbon emissions; emission intensity factor; logistics service providers; road freight; road transport; South Africa

Gati Shakti Multi-Modal Cargo Terminal- An Introduction to Indian Railways' New Initiative in Freight Operations

Indian Railways play a pivotal role in transporting goods along the length and breadth of India. Obviously, Indian Railways occupies a lion's share in the supply chain network nationwide. Private parties are allowed to construct their own terminals for loading freight onto goods trains. But, as per railway policy, they are required to bear the entire expenses for construction at the take-off point even if the land on which it is constructed is owned by railways. The private parties are also required to undertake the maintenance of such terminals. It becomes a cumbersome exercise for private parties both in financial and technical terms to run a private freight terminal. Recently, the prime minister announced a policy to bring together all infrastructure projects in a single frame. The railways have introduced the Gati Shakti Multi-Modal Terminal, whereby all the expenditures are borne by the railways and private parties are free from the burden of bearing the expenditure. Since the concept was introduced recently and is gaining popularity, an attempt to measure the outcome is too early. Nevertheless, this article throws light on the major aspects of the Gati Shakti Multi-Modal Terminal policy implemented by Indian Railways.

Exploring Freight Loading Management by Deep Learning: a Case Study in Home Furnishing Industry

Exploring Freight Loading Management by Deep Learning: a Case Study in Home Furnishing Industry

Evaluation of long-term prospects of the structure of freight rail transportation

The most important business of the domestic railway industry is freight transportation, which decisively affects the financial and economic stability and efficiency of the Russian railways. Successful activities in the field of rail freight transport are determined by both the volume and the structure of these transportations. In the long term perspective, there is a risk of a reduction in the freight base of railways due to the global paradigm of decarbonization of the economy, as well as the implementation of a “circular economy”, which assumes a continuous cycle of material processing with close to 100% utilization of products that have completed their life cycle and the use of appropriate materials for the production of new products. Reducing the consumption of coal, oil and petroleum products in the course of decarbonization will lead to a corresponding reduction in the volume of their transportation by rail, and the implementation of the “closed cycle economy” — to a reduction in the transportation of non‑hydrocarbon raw materials, which also occupies a significant share in rail transportation. Taking into account the noted trends, the article provides an expert categorization of goods transported by rail by the level of long‑term prospects. All freights are divided into four categories: non‑promising, low‑promising, promising and highly promising. Based on the proposed categorization with the use of retrospective data on rock loading of freights on railway transport, a significant change in the structure of loading was revealed in terms of its long‑term prospects. For a generalized evaluation of the level of long‑term prospects of the loading structure, a new indicator is proposed — an indicator of long‑term prospects of loading. Its retrospective analysis was carried out. The results of the analysis are given a qualitative evaluation based on the developed scale of zonal values of this indicator. The necessity of solving the problem of timely replacement of freights, the traffic volumes of which will decrease in the future due to structural changes, with freights with a higher level of long‑term prospects, has been substantiated. The target structure of freight loading on the railway network is proposed and the time horizon on which it is desirable to achieve it is substantiated. The directions for the development of research on this topic have been determined.

INTERACTION DYNAMICS OF SOME TYPES OF FREIGHT CARS WITH A RAILWAY TRACK

Purpose. An integral and essential feature of the modern wagon fleet is its large scale. For the entire fleet of rolling stock, even the smallest structural changes take on enormous proportions. The aim of the work is a theoretical study of the influence of the inertial characteristics of bodies of various types of freight rolling stock, taking into account the loading mode and the movement speed, on their main dynamic and interaction indicators with the track structure. Methodology. Theoretical studies were carried out by the method of mathematical and computer modeling of the dynamic load during the movement of some types of freight cars: gondola cars model 12-532, hopper cars for transporting coal model 12-4034 and flat wagons model 13-401 on standard bogies 18-100 at speeds in the range from 50 to 90 km/h on curves of small and medium radii. Findings. The analysis of theoretical studies of the dynamic qualities and interaction indicators of freight rolling stock and the railway track is presented. In the course of theoretical studies and after modeling taking into account the oscillation processes of freight cars at different loading modes the dependences of the main dynamic indicators on the movement speed were obtained. Originality. The influence of inertial characteristics of bodies of various types of freight rolling stock and loading modes on the dynamic load of a car was first explored in order to solve the problem of predicting the rolling stock dynamics and indicators of its interaction with the track. The results of theoretical studies taking into account the movement speed along curved track sections of small and medium radius were obtained. Practical value. The presented calculation results make it possible to determine the optimal values of such parameters as dead weight, height of the mass center and car base length when solving the problems of modernizing the operated car fleet and determining the reserves for increasing their carrying capacity. They make it possible to solve the problems of finding the optimal directions for modernizing the rail vehicle; contribute to the creation of technical conditions for the manufacture of new and modernization of the operated freight cars and are aimed at increasing the level of reliability and safety of the transportation process in modern conditions on the railway transport.

Trends in the Global Market for Freight Aircraft

Globally, national economies differ in levels of development, openness, and participation in international trade. Market saturation is accompanied by different levels of international trade. In addition, passenger and freight loads in air transportation are changing. On the other hand, economic principles are inadequately developed; and research on methodological principles regarding transportation and logistics is sparse. Those circumstances point to the need for analysis of the position, role, and significance of airfreight as an economic category.

Research on Vibration Law of Railway Tunnel Substructure under Different Axle Loads and Health Conditions

In this paper, 25-ton and 27-ton axle heavy trucks are used to carry out moving loading and dynamic real vehicle test on the cracked section, the intact section, and the repaired section of a railway tunnel foundation to test the dynamic performance of the tunnel basement structure with the change of axle loads and health conditions. By analyzing the influence law of dynamic response and fatigue life of heavy haul train under different basement conditions (intact, damaged, and repaired), the adaptability of railway tunnel equipment to freight trucks axle load is clarified. The results show that (1) the intact section of the tunnel can meet the normal operation of 25-ton and 27-ton axle load freight trains in good condition. (2) The normal operation of 25-ton and 27-ton axle load freight trucks is seriously affected by the cracked section of the tunnel. When the cracks in the tunnel basement are gradually hollowed out by groundwater, serious traffic accidents such as vehicle shaking and derailment are likely to occur. (3) The repaired section of the tunnel can meet the normal operation of 25-ton and 27-ton axle load freight trains after adopting the integrated comprehensive treatment of “Anchor-Injection-Drainage”. The research results will have reference significance for the condition assessment and disease treatment of the basement structure of the heavy haul railway tunnel.

Freight Loading Space Provision: Evidence from the US

Abstract Limited freight loading space provision in city centers increases illegal loading behaviors such as double parking or parking in bicycle lanes or sidewalks. Such traffic violations have ca...

Load Optimization using Genetic Algorithm and Decision Support System

The global trade supply chain consists of container shipment of hundreds of millions of metric tons of deadweight. With the ever-increasing demand for a freight shipment, the need of the hour is optimized freight loading pattern and plan. This scholarly paper aims to optimize the loading pattern within a container to make space for more cargo making underutilization of space inside a container almost near to obsolete. In this paper, we have surveyed research papers that the existing system uses. The proposed way out to overcome the flaws is a model which is an amalgamation of Decision Support System (DSS) and Genetic Algorithm (GA). This hybrid algorithm is first fed the container size and the number of different sizes of cargo individually. The input data will then run GA against a certain set of predefined target parameters. Upon multiple iterations, GA will generate multiple near-optimal solutions. This pool of solutions will be processed with the help of DSS to decide the optimal loading pattern. The optimal solution is fed in the form of coordinates to the Unity engine. The Unity engine will use the given coordinates to simulate a 3D optimized loading pattern inside a container. The user can see a step-by-step simulation of loading the cargo into the container where the cargo will be color-coded. This result can be stored to refer to and transport a similar cargo.

Load factors of less-than-truckload delivery tours: An analysis with operation data

The load factor is an essential indicator of vehicle utilization efficiency. This paper is the first attempt to explore the factors impacting Less-than-Truckload (LTL) freight load factors at a disaggregated level to the best of our knowledge. The paper analyzes the operation data provided by a leading LTL logistics company in China, using the beta regression finite mixture models. The results show that the under-loaded tours and over-loaded tours behave differently in terms of load factor determination. All variables' impacts vary across the different components, confirming the need to analyze these two scenarios separately using finite mixture models. For under-loaded tours, high shipping demand and the use of small vehicles are always positively and significantly associated with the load factor. For over-loaded LTL tours, the effects of transportation distance and leased vehicles are always positive and significant. Finally, implications for operations strategies and public policies are discussed based on the estimated results. These findings shed light on an in-depth understanding of LTL freight load factors and provide essential references for LTL logistics companies, transportation planners, policymakers, and researchers.