Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
80
datasets available to search
ShareScore release 0.9.0
Dataset results
80 results for “Truck”
Antique Toy Truck
3D model made using Artec Spider scanner. Processing completed in Artec Studio Professional 16, InstantMeshes, Blender, and Substance Designer. Source: Objaverse 1.0 / Sketchfab
Associated raw data to the PhD thesis: Design and evaluation of a camera-based indoor positioning system for forklift trucks
<p>This is a test data set for marker-based augmented reality algorithms used to locate ground conveyors in an industrial environment. It was recorded in the testing area of the chair fml at TUM to develop and evaluate algorithms for locating forklift trucks in my PhD thesis "Entwicklung und Evaluierung einer kamerabasierten Lokalisierungsmethode für Flurförderzeuge" (see https://mediatum.ub.tum.de/?id=1395267 available in German only).</p>
Problem instances for integrated truck and workforce scheduling problem
<p>We randomly generated in total 500 problem instances for the integrated truck and workforce scheduling (ITWS) problem which is proposed in the article "Tadumadze, G., Boysen, N., Emde, S., and Weidinger, F. (2019): Integrated truck and workforce scheduling to accelerate the unloading of trucks. <em>European Journal of Operational Research</em>, 278(1), 343-362, <a href="https://doi.org/10.1016/j.ejor.2019.04.024">https://doi.org/10.1016/j.ejor.2019.04.024</a>".</p> <p>The problem instances are generated for two representative truck scheduling settings:</p> <ul> <li>distribution center (ITWS-DC)</li> <li>cross-docking terminal (ITWS-CD).</li> </ul> <p>The instance size is defined by the number of dock doors for inbound operations |D|, the number of inbound trucks |J| (and the number of outbound trucks |O| for the ITWS-CD setting) and they are dubbed as follows:</p> <ul> <li>XS: |D| = 5; |J| = 20; (|O| = 5)</li> <li>S: |D|= 15; |J| = 50; (|O| = 15)</li> <li>M: |D|= 25; |J| = 100; (|O| = 25)</li> <li>L: |D|= 50; |J| = 200; (|O| = 50)</li> </ul> <p>For each setting the instances are divided in sets "computational study" and "managerial insights".</p> <p>For each setting, the dataset "computational study" is divided into two parts (“part 1” and “part 2”) consisting of 80 and 60 instances which vary in problem size (XS, S, M and L) and the expected relative width of the trucks' time windows (Ω_{max}).</p> <p>The dataset "managerial insights" consists of problem instances that vary in acceleration effect of trucks' processing times by additional logistics workers (κ) and scenarios of the workforce size |W|. The notations for the workforce scenario have following meanings:</p> <ul> <li>plentiful workforce: |W|=2 ⋅ |D|</li> <li>scarce workforce |W|=|D|.</li> </ul> <p>For each setting (i.e., ITWS-DC and ITWS-CD) subset “part 1” of "managerial insights" contains 100 instances (50 with scarce workforce scenario and 50 with plentifull workforce scenario) and “part 2” - 10 instances.</p> <p>The instances are labeled according to the following naming convention: "[problem]_[setting]_[size]_[Ω_{max}]_[κ]_[ID]" where ID is a running index of instance of the same parameter constellation. As instances in “part 1” of the dataset “managerial insights” vary in workforce scenarios the instance names are extended by [scenario] from ahead so that following naming convention is applied: "[scenario]_[problem]_[setting]_[size]_[ Ω_{max}]_[κ]_[ID]".</p> <p>For detailed instance generation scheme see Section 4.1 of the above mentioned work.</p> <p>The file for the problem ITWS-DC are structured as follows:</p> <p>|J| = <number of inbound trucks>;</p> <p>T = <length of planning horizon>;</p> <p>K_j = <vector with |J| elements: the j-th element corresponds to number of unloading modes for truck j>;</p> <p>|W| = <total number of available workers for inbound operations>;</p> <p>|D| = <number of inbound doors>;</p> <p>rho_{jk} = <|J|*K_{max} matrix (with K_max=max_{j∈J}{K_j}): each entry in k-th column and j-th row corresponds to the processing time of truck j if it is unloaded by k logistics workers (the elements in k-th column and j-th row where k<K_j is set to the default value “0”)>;</p> <p>a_j = <vector with |J| elements: the j-th element corresponds to the arrival time of truck j>;</p> <p>d_j = <vector with |J| elements: the j-th element corresponds to the arrival time of truck j>;</p> <p>alpha_j = <vector with |J| elements: the j-th element corresponds to the duration of truck j’s initial docking (i.e., step (1))>;</p> <p>beta_j = <vector with |J| elements: the j-th element corresponds to the duration of truck j’s initial docking plus preparation (i.e., step (1) + step (2))>;</p> <p>gamma_j = <vector with |J| elements: the j-th element corresponds to the duration of truck j’s trailer secluding plus undocking (i.e., step (4) + step (5))>;</p> <p>delta_j = <vector with |J| elements: the j-th element corresponds to the duration of truck j’s final undocking (i.e., step (5))>;</p> <p> </p> <p>The file for the problem ITWS-CD are extended by following additional parameters:</p> <p>|O| = <number of inbound trucks>;</p> <p>FI_{jo} = <|J|*|O| matrix: each entry in o-th column and j-th row corresponds to the weight, of a shipment delivered by inbound truck j dedicated to outbound truck o>;</p> <p>dep_o = vector with |O| elements: the o-th element corresponds to the departure time for outbound truck o>;</p> <p>fi_{go} = <|D|*|O| matrix: each entry in o-th column and g-th row corresponds to the transfer time for moving goods from dock g to outbound truck o>;</p>
Instances of the Truck-Drone-ATV Routing Problem
<p>This dataset contains the test instances of the Tuck-Drone-ATV Routing Problem as described in our Article "A Genetic Algorithm for Solving the Truck-Drone-ATV Routing Problem" written for the 6th World Congress on Global Optimization (WCGO 2019).</p>
Automated Truck Lanes in Urban Area for Through and Cross Border Traffic
<p>Autonomous trucks will soon be operational across our nation’s roadways. Yet, it is unclear how the design of our highway infrastructure should be modified to accommodate autonomous trucks, to enable them to operate in such a way to maximize the economic, capacity and safety benefits. The University of Texas at El Paso (UTEP) is proposing to develop and demonstrate, through microscopic traffic simulations, the concept of operations of autonomous truck lanes along the interstate freeways. Using the I-10 Freeway in the El Paso, TX region as the testbed, the research team will: (i) assess the existing structural, geometric and traffic designs in handling fully automated trucks, including the entrances, exits, and connectors; (ii) perform microscopic traffic simulations at critical locations to demonstrate design issues and the recommended design improvements; (iii) conduct a preliminary cost estimation on such infrastructure improvements.</p>
Evaluation of Comparative Damaging Effects of Multiple Truck Axles for Flexible Pavements
<p>Corresponding data set for Tran-SET Project No. 17PUTA01. Abstract of the final report is stated below for reference:</p> <p>"This study aims at evaluating the effect of overlapping flexible pavement strain responses from truck axles that are not part of multiple axle configurations (i.e., tandem, triple and quad). For this purpose, a newly constructed pavement was instrumented with strain gauges installed at the bottom of the asphalt concrete base layer on US-287 south of Mansfield, TX. This pavement structure is typically used for medium- to high-volume roads in the South-Central region of the United States. The strain gauges were used to measure longitudinal and transverse strains under several passes of a test vehicle. This was a class 6 truck with a steering axle load of 56.9 kN (12.8 kips) and a tandem drive axle load of 161.9 kN (36.4 kips). The speed and the lateral position of the vehicle were recorded for each test vehicle pass. Sufficient quantities of the top two layers of asphalt concrete were obtained during construction to allow dynamic modulus testing in the laboratory. The general-purpose finite element program Abaqus was used to model the instrumented pavement section and compute the longitudinal and transverse strains at the location of the strain gauges. In the analysis, the asphalt concrete layers were modeled as visco-elastic materials. The Abaqus estimated strains were found in good agreement with the measured strains. Both the field measurements and the finite element analysis showed that the strains under the passing of the steering axle were of similar magnitude as the strains under the passing of the rear tandem axle. The measured transverse strains were in general slightly larger than the corresponding longitudinal strains. This can be attributed to the accumulation of strain from the front axle and the rear axle that takes place only in the transverse direction. However, the finite element model computed higher strains in the longitudinal direction than in the transverse direction. These findings suggest the need to account for the overlap in strain responses from the steering and the following axles of trucks. Furthermore, findings suggest that both the longitudinal and the transverse strain responses need to be considered in evaluating the fatigue damage impacted from trucks."</p>
Impacts of Freight Consolidation and Truck Sharing on Freight Mobility
<p>Corresponding data set for Tran-SET Project No. 17ITSOKS02. Abstract of the final report is stated below for reference:</p> <p>"The trucking industry is an important sector of the U.S. economy. However, it is quite fragmented, hindering the efficiency of cargo transportation and the ability for small carriers to identify demands to fill full truck loads. The focus of this research is to study models and algorithms in order to aid online freight marketplaces to identify efficient consolidation strategies. To accomplish this aim, a new mixed integer programming model for the pickup and delivery problem has been developed. The model is geared towards identifying effective freight consolidation opportunities. A branch-and-cut algorithm to solve the model was also developed. The model was applied to several case studies using Transplace, Inc. (a third party logistics company) route data to identify optimized, consolidated routes. Emission impacts were also estimated for both the existing and consolidated routes using monetary equivalent cost (MEC) values. A linear regression model was developed to predict freight movement between metropolitan statistical areas (MSAs). Results of the case studies were then applied to estimate the operation and environment-related costs associated with freight movement from New Orleans MSA to Oklahoma City MSA for all commodities. Finally, the results of the case studies were applied at the national level and projected for future years, to estimate the potential cost savings in freight consolidation. Results indicate that it may be possible to consolidate cargo with only 67% of the currently used number of trucks, which may reduce the operation cost by 23% and MEC by 17%."</p>
Dump truck object detection with manual annotations
<p>Doing manual annotations can sometimes be resource heavy, depending on the amount of data. This dataset was designed to created to use in conjunction with a semi-automatic annotation method based on linear interpolation. The dataset contains 799 images, where 679 lies in the trainingset, and the rest lies in the validationset. The images are taken from 6 different video streams, where a remote controlled wheel loader approaches a miniature dump truck at different angles. 4 of the videos are used in the trainingset. The labels can contain up to 5 classes which are:</p> <p>0 - front wheel <br> 1 - middle wheel<br> 2 - back wheel<br> 3 - tipping body<br> 4 - cap</p> <p>This dataset was used to train a YOLOv3 model, hence the labels will be written in the YOLO labeling format.</p>
Fig. 1 in Efficiency and selectivity of a trap and truck fish passage system in Brazil
Fig. 1. Mucuri basin, indicating the location of Santa Clara Dam.
Problem instances for outbound truck loading and scheduling problem
<p>The following dataset contains problem instances for the outbound truck scheduling and loading problem, which are proposed in the work "Giorgi Tadumadze & Simon Emde (2021): Loading and scheduling outbound trucks at a dispatch warehouse. <em>IISE Transactions</em>, DOI: <a href="https://doi.org/10.1080/24725854.2021.1983923">10.1080/24725854.2021.1983923</a>”.</p> <p>The problem instances are stored in table “instances”, where columns of tables can be interpreted as follows:</p> <ul> <li>ID: <running index>;</li> <li>name: <instance name, specifying the number of items <span class="math-tex">\(m\)</span>, number of trucks <span class="math-tex">\(n\)</span>, value of parameter <span class="math-tex">\(\alpha\)</span>, [value of parameter <span class="math-tex">\(\Delta\)</span>], and the width of trucks’ time windows>;</li> <li>O: <number of served OEMs>;</li> <li>m: <number of items>;</li> <li>n: <number of trucks>;</li> <li>Q: <total number of available workers>;</li> <li>D: <total number of available dock doors>;</li> <li>w_i: <vector with <span class="math-tex">\(m\)</span> elements: the <span class="math-tex">\(i\)</span>-th element corresponds to the size (required space) of item <span class="math-tex">\(i\)</span>>;</li> <li>d_i: <vector with <span class="math-tex">\(m\)</span> elements: the <span class="math-tex">\(i\)</span>-th element corresponds to the deadline of item <span class="math-tex">\(i\)</span>>;</li> <li>r_i: <vector with <span class="math-tex">\(m\)</span> elements: the <span class="math-tex">\(i\)</span>-th element corresponds to the relative importance (penalty cost per time unit of earliness) of item <span class="math-tex">\(i\)</span>>; </li> <li>c_j: <vector with <span class="math-tex">\(n\)</span> elements: the <span class="math-tex">\(j\)</span>-th element corresponds to the capacity of truck <span class="math-tex">\(j\)</span>>;</li> <li>a_j: <vector with <span class="math-tex">\(n\)</span> elements: the <span class="math-tex">\(j\)</span>-th element corresponds to the earliest possible departure time of truck <span class="math-tex">\(j\)</span>>;</li> <li>b_j: <vector with <span class="math-tex">\(n\)</span> elements: the <span class="math-tex">\(j\)</span>-th element corresponds to the latest possible departure time of truck <span class="math-tex">\(j\)</span>>;</li> <li>q_i: <vector with <span class="math-tex">\(m\)</span> elements: the <span class="math-tex">\(i\)</span>-th element corresponds to the number of required workers to prepare and load item <span class="math-tex">\(i\)</span>>;</li> <li>rho_i: <vector with <span class="math-tex">\(m\)</span> elements: the <span class="math-tex">\(i\)</span>-th element corresponds to the handling time of item <span class="math-tex">\(i\)</span>>;</li> <li>B_i: < <span class="math-tex">\(m \times n\)</span> matrix: each entry in <span class="math-tex">\(j\)</span>-th column and <span class="math-tex">\(i\)</span>-th row corresponds to the binary parameter which has a value 1 if set of available trucks <span class="math-tex">\(B_i\)</span> contains truck <span class="math-tex">\(j\)</span> (i.e., if truck <span class="math-tex">\(j\)</span> departs towards the OEM, who ordered item); 0 otherwise>;</li> </ul> <p>The first 270 entries (ID between 1-270) contain OTSLP instances with different instance sizes, used for the computational performance experiments (Section 5.1).</p> <p>The following 100 entries (ID between 271-370) contain 40 OTSLP instances with the varying time window width for each truck (ID between 271-310), 30 OTSLP instances with the varying level of available workers (ID between 311-340), and 30 OTSLP instances with the varying level of available dock doors (ID between 341-370), used for the managerial inside experiments (Section 5.2).</p> <p>The detailed computational results for each instance and solution approach are reported in tables, which are named with the following convention: <results_<approach>”. Specifically, we report the required computational runtime in CPU seconds, status of the found solution (“Optimal”, “Infeasible”, “Feasible” / “AbortTimeLim”), as well as the best found upper (and lower) bound in columns “runtime”, “status”, “UB” and “LB”.</p>
Data Set: Renewable hydrogen fuels versus fossil fuels for trucking, shipping and aviation: A holistic cost model
<p>Data Set: Renewable hydrogen fuels versus fossil fuels for trucking, shipping and aviation: A holistic cost model</p>
Raw strain and temperature data for a dump truck, IFV, and semi-truck
<p>Horizontal strain measurements for a dump truck (D), an IFV (K), and a semi-truck (L). The measurements were taken from DTU smart road, which consists of four sections, each with six horizontal strain gauges placed at the bottom of the AC (150 mm depth). There is a reinforcement grid in section 1, 2, and 3. The data was taken as a part of the Master thesis: Investigation of asphalt pavements loaded by heavy off-road vehicles. The thesis contains further details.</p>
BQE WIM Data Year 4 Project (Implementation and Effectiveness of Autonomous Enforcement of Overweight Trucks in an Urban Infrastructure Environment)
<p>BQE (Brooklyn-Queens Expressway) WIM Data for QB (Queens Bound) and SIB (Staten Island Bound)</p>
Data from: Optimisation design and analysis of mobile pump truck frame using response surface methodology
<p><span>In order to realize the lightweight design of mobile pump truck, this paper takes the frame of a certain type of mobile pump truck as the research object. The response surface method is used to carry out lightweight design of the longitudinal beam structure of the frame, and the finite element method is used to establish the finite element model to analyze and compare the frame before and after optimization.The results show that the height, width and thickness of the optimized longitudinal beam section are reduced by 10 mm, 11 mm, and 0.8 mm respectively, and the weight of the whole frame is reduced by 35.8 kg.</span> <span>Before and after optimization, the displacement and stress changes of the frame are small in four motion situations, which meet the lightweight requirements of optimization design.</span></p>
Back Alley Sake Truck
Auckland NZ 2018. Using the Xperia XZ premium and the 3D Creator App I was able to capture this model in just 3mins. It was a good test to see what kind of scale I can capture, this is approx. 5x5m and though depth is stretching back, there is still some extra detal on the back and front of the truck. It'd work well to limit the viewing angle of the camera on this. Source: Objaverse 1.0 / Sketchfab
Tech4Rest Trial With Team Truck Drivers
ClinicalTrials.gov study NCT03108599. IPD Sharing: NO. Countries: 1. Publications: 13.
Truck idling and parking data for AB 617 disadvantaged communities study
Open the record for dataset details and reuse information.
Truck and grid code from: Impact of truck electrification on air pollution disparities in the U.S
Open the record for dataset details and reuse information.
Routing short-haul trucks under the uncertainties of travel time and service time
Open the record for dataset details and reuse information.
Data from: Optimisation design and analysis of mobile pump truck frame using response surface methodology
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.