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Public charging requirements for battery electric long-haul trucks in Europe: a trip chain approach
<p>Contact details:</p> <p>wasim.shoman at chalmers.se </p> <p>waahh7 at gmail com</p> <p><strong>Abstract of the research:</strong></p> <p>Heavy-duty vehicles (HDV) account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO<sub>2</sub> emissions from road transport. Battery electric trucks (BETs) could be deployed on a large scale to reduce greenhouse gas emissions. However, they require sufficient charging infrastructure to support long-haul operations. Therefore, assessing the required charging locations, energy, and power requirements is critical. We use a trip-chain-based model to derive charging requirements for BETs in long-haul operation (travel times over 4.5 hours or over 360 km distance traveled) for Europe in 2030. We convert an origin-destination (OD) matrix into trip chains combined with European truck driving regulations to derive break and rest stops. We show that an average charging area (defined as a 25´25 km<sup>2</sup> square with each square that could include multiple charging stations and parking lots of multiple charging points) needs to have four to five times more overnight than megawatt charging points. We estimate that about 40,000 overnight charging points (50-100 kW, combined charging system, CCS) and about 9,000 megawatt charging system (MCS, 0.7 – 1.2 MW) points are required for 15% of trucks as BETs in long-haul operation. On average, 8 and 2 CCS and MCS chargers are required per charging area, and each MCS and CCS serve, on average, 11 and 2 BETs daily, respectively. Public charging entails about 110 GWh daily electricity demand in each charging area. The model can be applied to any region with similar data. Future work can consider improving the queuing model, assumptions regarding regional differences of BET penetration, and heterogeneity of truck sizes and utilization.</p> <p><strong>The methodology:</strong></p> <p>We develop a method to place charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by HDV is identified using the ETISplus dataset. We develop a travel pattern for the HDV to convert flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving HDV. Break and rest locations for all moving HDVs are aggregated to suggest energy requirements if assuming these HDVs are BETs. The aggregated energy to charge stopped BETs is used to identify the number and type of chargers within each suggested charging station.</p> <p><strong>Datasets details</strong></p> <p>The presented datasets contain spatial information for generating charger stations with specifications according to charging needs. The datasets contain information about: Transport network model and edges, Transported flows, routes and flow center information data, region centers, and Planned transport infrastructure. </p> <p>The first dataset titled 'ChargerLocations' contains information about the locations of suggested charging stations, the number and type of chargers, and the number of visited electrified trucks in 2030. It is a shapefile with the following details for its fields:</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> <td>Data Type</td> <td>Unit</td> </tr> <tr> <td>DTN30/MainDTN</td> <td> number of electrified trucks in 2030</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChE30</td> <td> charged energy in Mega watt-hour from all charging (fast and slow)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>ChERM</td> <td> charged energy in Megawatt hour with slow charging only (rest)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_R</td> <td> number of electrified trucks using slow chargers (rest)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChEBM</td> <td> charged energy in Megawatt hour with fast charging only (break)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_B</td> <td> number of electrified trucks using fast chargers (break)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NSCh2pD</td> <td> number of slow chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NFCh30m</td> <td> number of fast chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>TotCha</td> <td> Total number of chargers</td> <td>integer </td> <td>number</td> </tr> </tbody> </table> <p> </p> <p>The second dataset titled (RestandBreaksPoints.shp) with information about the rest and break point locations. The dataset includes detailes about stop type, number of stopped trucks, and required charged energy. The dataset is a shapefile with "shp" format. </p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data Type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>ID_origin_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_origin_region</p> </td> <td> <p>National name of NUTS-3 region of origin</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ID_destination_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_destination_<br> region</p> </td> <td> <p>National name of NUTS-3 destination region</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Rest</p> </td> <td> <p>A value of ”1” indicates a rest stop</p> </td> <td> <p>Boolean</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Break</p> </td> <td> <p>A value of ”1” indicates a break stop</p> </td> <td> <p>Boolean</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ChaDisKM</p> </td> <td> <p>Charged range within a trip for stopped the truck</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>ChaEnekWh</p> </td> <td> <p>Charged energy within a trip for stopped the truck</p> </td> <td> <p>Float</p> </td> <td> <p>KWh</p> </td> </tr> <tr> <td> <p>MainDTN</p> </td> <td> <p>Number of stopped trucks for the main electrification scenario (15%)</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>ChE30M</p> </td> <td> <p>Charged energy for all stopped trucks</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>ChERM</p> </td> <td> <p>Charged energy for the trucks stopping for rest</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>MDTN_R</p> </td> <td> <p>Number of trucks stopping for rest</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>ChEBM</p> </td> <td> <p>Charged energy for the trucks stopping for break</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>MDTN_B</p> </td> <td> <p>Number of trucks stopping for break</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>X, Y coordinates</p> </td> <td> <p>geometry</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p>The following dataset titled 'flowFile' with information about the transported flow between regions and the transported routes. The dataset is in "CSV" format. Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X):</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>ID_origin_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_origin_region</p> </td> <td> <p>National name of NUTS-3 region of origin</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ID_destination_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_destination_<br> region</p> </td> <td> <p>National name of NUTS-3 destination region</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Edge_path_E_road</p> </td> <td> <p>List of the <em>network edge IDs</em> of the shortest path between the O-D pair, determined with Dijkstra's algorithm</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Distance_from_origin_<br> region_to_E_road</p> </td> <td> <p>Distance from the geometric centre of the origin region to the closest network node</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Distance_within_E_<br> road</p> </td> <td> <p>Distance of the shortest edge path between the O-D pair</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Distance_from_E_<br> road_to_destination_<br> region</p> </td> <td> <p>Distance from the geometric centre of the destination region to the closest network node</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Total_distance</p> </td> <td> <p>Sum of <em>Distance_from_origin_region_to_E_road, Distance_within_E_road</em> and <em>Distance_from_E_road_to_destination_region</em></p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2010</p> </td> <td> <p>Number of trucks that drive between the O-D pair in 2010</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2019</p> </td> <td> <p>Number of trucks that drive between the O-D pair after they had been scaled to 2019</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2030</p> </td> <td> <p>Number of trucks that drive between the O-D pair according to the forecast for 2030</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2010</p> </td> <td> <p>Number of tons that are transported between the O-D pair in 2010 according to ETISplus</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2019</p> </td> <td> <p>Number of tons that are transported between the O-D pair after they had been scaled to 2019</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2030</p> </td> <td> <p>Number of tons that are transported between the O-D pair according to the forecast for 2030</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> </tbody> </table> <p>Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Network_Node_ID</p> </td> <td> <p>Unique network node ID</p> </td> <td> <p>Integer (6 digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Network_Node_X</p> </td> <td> <p>Longitude of the location of network node</p> </td> <td> <p>Float</p> </td> <td> <p>Degrees</p> </td> </tr> <tr> <td> <p>Network_Node_Y</p> </td> <td> <p>Latitude of the location of network node</p> </td> <td> <p>Float</p> </td> <td> <p>Degrees</p> </td> </tr> <tr> <td> <p>ETISplus_Zone_ID</p> </td> <td> <p>ID of the NUTS-3 region in which the network node is located</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Country</p> </td> <td> <p>Unique country code of the country in which the network node is located (country codes are defined by ETISplus)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>Description of variables used in the network edges list (Updated_04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Network_Edge_ID</p> </td> <td> <p>Unique edge ID</p> </td> <td> <p>Integer<br> (7 digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Manually_Added</p> </td> <td> <p>Determines whether an edge had been manually added to the network (1) or not (0)</p> </td> <td> <p>Binary-integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Distance</p> </td> <td> <p>Length of the network edge</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Network_Node_A_ID</p> </td> <td> <p>Unique ID of the network node that defines one end point of the network edge</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Network_Node_B_ID</p> </td> <td> <p>Unique ID of the network node that defines one end point of the network edge</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_2019</p> </td> <td> <p>Number of trucks that drive on the edge in 2019 (both highway directions combined)</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_2030</p> </td> <td> <p>Number of trucks that drive on the edge in 2030 (both highway directions combined)</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
European Truck Parking Locations
<p><strong>### KAMO Update (v04)</strong></p> <p>This updated dataset comprises <em>N=13,323</em> real-world truck parking locations across Europe (EU-27, EFTA, and the UK), filtered for location alongside the TEN-T network. Locations origintate from from the previously published (<em>N=19,713</em>) and unpublished (<em>N=32,251</em>) locations and additional sources to refine and enhance the dataset. Documenation and methods are provided in the attached documentation. KAMO and Fraunhofer ISI does not assume any liability for completeness, correctness and accuracy of the information. </p> <p>This dataset aims to support in identifying attractive, real-world charging infrastructure locations in Europe, facilitating the planning of national and European charging networks to boost e-truck diffusion and promote sustainable road freight transport. It benefits scientists, industry players, grid operators, and public authorities by providing precise local information as well as insights for infrastructure planning, energy demand modeling, and deployment along key transport corridors (TEN-T network) as prescribed per the EU's Alternative Fuels Infrastructure Regulation (AFIR).</p> <p>We have incorporated feedback from stakeholders compared to the previously published version. The update shall:</p> <blockquote> <p>Add missing locations and increase TEN-T coverage</p> <p>Supplement planning information</p> <p>Allow conclusions on the attractiveness of locations</p> </blockquote> <p>We recommend using this location data as input (or candidate locations) for coverage or optimization algorithms to identify a highly condensed set of optimal / most attractive locations. More information is available upon request.</p> <p>More information is available upon request. </p> <p><strong>### Older versions (v01-v03)</strong></p> <p>This geospatial dataset comprises N=19,713 real-world truck parking locations across Europe (EU-27, EFTA, and the UK). Data origintated from various sources including OpenStreetMap and commercial truck routing / geocoding software to identify publicly accessible and truck-certified parking locations. Using geospatial clustering helped to condense the dataset and reduce redundancies. Refining and enhancing the dataset involved supplementary datasets and several filters to obtain the final subset. Accordingly, GPS coordinates may not match exact locations but should be considered as reference point for detailed local analyses of ambient conditions and truck accessibility. Coverage and completeness varies among countries. Fraunhofer ISI does not assume any liability for completeness, correctness and accuracy of the information. </p> <p>This dataset plays a pivotal role in identifying viable real-world locations for future alternative infrastructure sites for heavy-duty trucks, thereby acting as a crucial resource in promoting low-carbon road freight transport facilitated by electrified truck fleets. Infrastructure sites may comprise charging infrastructure for battery-electric trucks and hydrogen refuelling stations (HRS) for fuel-cell electric or hydrogen combustion trucks. Consequently, it can serve as a valuable asset for research in traffic science, future energy systems, and alternative truck powertrains. Its value extends to assisting industry stakeholders such as Charge Point Operators (CPOs), truck manufacturers, and grid network operators but also public authorities in aligning their efforts towards the deployment of alternative infrastructure.</p>
Absolute environmental sustainability assessment of renewable dimethyl ether fuelled heavy-duty trucks
<p>Dataset associated with the publication "Absolute environmental sustainability assessment of renewable dimethyl ether fuelled heavy-duty trucks" by Margarita A. Charalambous, Victor Tulus, Morten W. Ryberg, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1039/D2SE01409B">https://doi.org/10.1039/D2SE01409B</a>. The dataset includes all the LCA inventories and numeric data required to plot all the figures embedded in the main manuscript.</p> <p>The dataset includes 4 Excel files. The content of each dataset is here elucidated:</p> <ul> <li><strong>LCA_data.xlsx:</strong> Inventory datasets used for life cycle assessment. Includes the inventory for the production of methanol from CO<sub>2</sub> and H<sub>2</sub> sources investigated in this work, carbon dioxide from direct air capture (DAC), and point source coal power plant and natural gas power plant, as well as, the production of hydrogen from biomass with CCS and polymer electrolyte water electrolysis powered with Wind power and BECCS. DME production from each methanol activity, and lastly, truck transport activities used in the study are also included. </li> <li><strong>LCA_relative_impact.xlsx</strong>: numerical values associated with the total share of safe operating space for all the assessed control variables of the seven planetary boundaries quantified in the study, for all the considered scenarios. These values represent the data used to create Figure 2 of the main manuscript.</li> <li><strong>LCA_breakdown.xlsx</strong>: numerical values associated with the breakdown of the environmental impacts for the studied scenarios, for all the control variables of the planetary boundaries. Each sheet includes data for one control variable. These values represent the data used to create Figure 3 in the main manuscript and Figures S4 and S5.</li> <li><strong>LCA_costs.xlsx: </strong>numerical values associated with the cost breakdown for each considered scenario. These values represent the data used to create Figure 4 of the main manuscript. </li> </ul>
Truck Axle Detection
<p>This dataset was created to train a neural network to recognize truck axles, applied to videos recorded in a highway in the State of São Paulo, Brazil. This is still a work in progress and will be updated regularly. More info can be found in our <a href="https://www.researchgate.net/lab/Andre-Luiz-Cunha-Lab">Researchgate Lab Page</a> or on our OrcID Profiles.</p> <p>The dataset includes 1737 cropped images of truck axles, divided in two folders:</p> <ul> <li>Axle_Dark - Images of shadowed or dark truck axles, with few features and details visible. <ul> <li>1034 images</li> <li>Format: JPEG</li> <li>Resolution: Various, 96dpi</li> </ul> </li> <li>Axle_Clear - Images of clear truck axles, with visible details and features. <ul> <li>703 images</li> <li>Format: JPEG</li> <li>Resolution: Various, 96dpi</li> </ul> </li> <li>Naming pattern: <video_name>_<color|gray>-<Region_of_Interest_ID>-<truck_ID>_<axle_number>.jpg</li> </ul> <p>If this dataset helps in any way your research, please feel free to contact the authors. We really enjoy knowing about other researcher's projects and how everybody is making use of the images on this dataset. We are also open for collaborations and to answer any questions.</p>
Talking Trucks
<p>Talking Trucks dataset (v2021.12)</p> <p> </p> <p>This dataset is used for research into Self-organizing logistics and published as part of the paper "Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics". It is managed by the TNO Sustainable Transport and Logistics department. Please contact Christian van Ommeren (christian.vanommeren@tno.nl) and Ruben Fransen (ruben.fransen@tno.nl) for details.</p> <p> </p> <p>The dataset contains 4 types of objects (files): agents, orders, route stops, and locations; and currently holds data for three experiments, represented by a UUIDv4 identifier.</p> <p> </p> <p><strong>Agents</strong></p> <p>Represents an agent that is able to transport load and is to be assigned to specific orders.</p> <p> </p> <p>This file contains the agent's unique UUIDv4 identifier; agent type; Euronorm emission norm; agent cost for driving a km in EUR; agent cost per hour in EUR; working hours start and end times; and agent starting coordinates.</p> <p> </p> <p><strong>Orders</strong></p> <p>Represents the unit that should be transported by the agents.</p> <p> </p> <p>This file contains the order's unique UUIDv4 identifier; container type; TEU; trade type (import/export); and weight.</p> <p> </p> <p><strong>Route stops</strong></p> <p>Represents the stop which is associated with an order. Stops are tied to a real-world location.</p> <p> </p> <p>This file contains the route stop's unique UUIDv4 identifier; stop index; location UUIDv4 identifier; type (origin/destination); order UUIDv4 identifier; and time window times.</p> <p> </p> <p><strong>Locations</strong></p> <p>Represents a real-world location at which logistics operations take place (for a specific duration).</p> <p> </p> <p>This file contains the location's unique UUIDv4 identifier; country designator; coordinates; Euronorm emission norm; stop duration; and service type (decoupling, live handling, craning, ...).</p> <p> </p> <p><strong>License</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a></p> <p> </p>
Experimentation Results of "Truck-multidrone same-day delivery strategies: On-road resupply vs depot return" (Supplementary Material)
Open the record for dataset details and reuse information.
Instance data for "Scheduling Trucks on Factory Premises"
<p>Instance data for the paper</p> <ul> <li>Wirth, M., & Emde, S. (2018). Scheduling trucks on factory premises. <em>Computers & Industrial Engineering</em>, <em>126</em>, 175-186.</li> </ul> <p>Every instance is in a separate text file, of which there are 54 in total (18 small, medium, and large instances, respectively). The data format is as follows.</p> <p><num> is the running index of the instance.</p> <p><NumTrucks> is the total number of trucks (jobs).</p> <p><NumDoors> is the total number of dock doors (machines) on the factory premises.</p> <p><MaxDoors> denotes the maximum number of doors to be visited per truck.</p> <p><FactoryLength> denotes the size of the factory premises in meters (used to calculate the distances).</p> <p><Distribution> is the type of probability distribution used for generating the processing and transfer times.</p> <p><Processing> is a binary matrix signalling if a truck requires processing at a door: if and only if the entry in line i and column j is 1, truck j requires processing at door i.</p> <p><ProcessingTime> is the processing time matrix: the entry in line i and column j is the processing time of truck j at door i.</p> <p><DoorDistance> is the distance matrix between doors. The entrance / exit dummy door is the first line / column.</p> <p><ReleaseDate> is the release date vector for the trucks.</p> <p><DueDate> are the due dates of the trucks.</p> <p><Cost> are the weights in the objective for each truck.</p>
Fig. 5 in Efficiency and selectivity of a trap and truck fish passage system in Brazil
Fig. 5. Relative abundance of migratory species per size class for the lower Mucuri River, in the Santa Clara tailrace (2002/ 2003) and in the Santa Clara Dam fish lift (2003/2004).
Fig. 4 in Efficiency and selectivity of a trap and truck fish passage system in Brazil
Fig. 4. Relative abundance of target species in the lower Mucuri River, in the Santa Clara tailrace (2002/2003) and in the Santa Clara Dam fish lift (2003/2004).
Fig. 6 in Efficiency and selectivity of a trap and truck fish passage system in Brazil
Fig. 6. Number of injured or dead fish after being passed by the fish lift according to species groups.
Fig. 2 in Efficiency and selectivity of a trap and truck fish passage system in Brazil
Fig. 2. Abundance (%) of captured species in the Santa Clara tailrace (2002/2003) and passed by the fish lift (2003/2004).
MarTREC Data Set for Report: Developing and Applying an Analysis Methodology to Identify Flow Generation Influences between Vessel and Truck Shipments
<p>Truck activity is logically connected to vessel activity at a port. In turn, vessel activity is also influenced by truck shipments. Although one might expect a direct and straightforward relation between these two types of shipments, that is rarely the case. For instance, many maritime containers carry consolidated cargos that have multiple and different final destinations. Also, different truck capacities, customs clearance and regulations play a critical role in determining the actual relation between these two types of shipments. This project aims at shedding light on the nuances of maritime and roadway flow relations by quantitatively analyzing the linkages between these two types of shipments.</p> <p>The study performed a statistical analysis to determine the probability distributions of vessel and truck activity, and then explore the correlation of each activity with the other. The analysis yielded coefficients that function as explanatory values for specific truck flows.</p> <p>The ultimate purpose of this study is to provide a clearer and quantitative understanding of the relationship between maritime and truck shipments, and by doing so, to provide tools to develop a system for managing trucks that maximizes efficiency for industry, while minimizing industry’s negative impacts on a region.</p> <p>For this purpose, the study selected the Port Freeport as a case study.</p>
Dinky Toys Foden Dump Truck #959 Vintage Toy
Vintage Raw scan of a vintage Dinky Toys Red on Red Foden Dump Truck Already a much better improvement of the previous model as well as a much less blurry texture. I increased the polys to around 25k to allow better detail of the hydraulics the shovel. The only issue I have here is some colour bleed on the top and the edges of the tires though I'm not quite shure how to address that besides some touch ups possibly via a bandaid tool in substance painter. Support my project through a coffee at https://ko-fi.com/jordanf Source: Objaverse 1.0 / Sketchfab
Abandoned 1970s Postal Truck (Free Raw Scan)
At one point in time, this was used for delivering mail. I suppose it was retired, but just sat around because it's not that useful as anything but a mail carrier. Processed from 310 photos taken with my Canon EOS Rebel XSI Source: Objaverse 1.0 / Sketchfab
GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer (Geo-TIDE)
<h1>Summary</h1> <p>Geojson files used to visualize geospatial layers relevant to identifying and assessing trucking fleet decarbonization opportunities with the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE) tool.</p> <h1>Relevant Links</h1> <p>Link to the <a href="https://climatedata.mit.edu/faf5/transportation/">online version of the tool</a> (requires creation of a free user account).</p> <p><a href="https://github.com/mcsc-impact-climate/FAF5-Analysis">Link to GitHub repo</a> with source code to produce this dataset and deploy the Geo-TIDE tool locally.</p> <h1>Funding </h1> <p>This dataset was produced with support from the MIT Climate & Sustainability Consortium.</p> <h1>Original Data Sources</h1> <p>These geojson files draw from and synthesize a number of different datasets and tools. The original data sources and tools are described below:</p> <table> <tbody> <tr> <td><strong>Filename(s)</strong></td> <td><strong>Description of Original Data Source(s)</strong></td> <td><strong>Link(s) to Download Original Data<br></strong></td> <td><strong>License and Attribution for Original Data Source(s)</strong></td> </tr> <tr> <td> <p>faf5_freight_flows/*.geojson</p> <p>trucking_energy_demand.geojson</p> <p>highway_assignment_links_*.geojson</p> <p>infrastructure_pooling_thought_experiment/*.geojson</p> </td> <td> <p>Regional and highway-level freight flow data obtained from the <a href="https://faf.ornl.gov/faf5/">Freight Analysis Framework Version 5</a>. Shapefiles for FAF5 region boundaries and highway links are obtained from the <a href="https://geodata.bts.gov/search?collection=Dataset">National Transportation Atlas Database</a>. Emissions attributes are evaluated by incorporating data from the <a href="https://rosap.ntl.bts.gov/view/dot/42632/dot_42632_DS2.zip">2002 Vehicle Inventory and Use Survey</a> and the <a href="https://greet.anl.gov/">GREET lifecycle emissions tool</a> maintained by Argonne National Lab.</p> </td> <td> <p><a href="https://geodata.bts.gov/datasets/usdot::freight-analysis-framework-faf5-regions">Shapefile for FAF5 Regions</a></p> <p><a href="https://geodata.bts.gov/datasets/usdot::freight-analysis-framework-faf5-network-links">Shapefile for FAF5 Highway Network Links</a></p> <p><a href="https://faf.ornl.gov/faf5/data/download_files/FAF5.5.1_2018-2022.zip">FAF5 2022 Origin-Destination Freight Flow database</a></p> <p><a href="https://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf_highway_assignment_results/FAF5_2022_HighwayAssignmentResults_04_07_2022.zip">FAF5 2022 Highway Assignment Results</a></p> <p> </p> </td> <td> <p><strong>Attribution for Shapefiles:</strong> United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Available at: https://geodata.bts.gov/search?collection=Dataset. </p> <p><strong>License for Shapefiles:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> <p><strong>Attribution for Origin-Destination Freight Flow database:</strong> <a href="https://www.ornl.gov/ntrc/" target="_blank" rel="noopener">National Transportation Research Center</a> in the <a href="https://www.ornl.gov/" target="_blank" rel="noopener">Oak Ridge National Laboratory</a> with funding from the <a href="https://www.bts.gov/" target="_blank" rel="noopener">Bureau of Transportation Statistics</a> and the <a href="https://www.fhwa.dot.gov/" target="_blank" rel="noopener">Federal Highway Administration</a>. Freight Analysis Framework Version 5: Origin-Destination Data. Available from: https://faf.ornl.gov/faf5/Default.aspx. Obtained on Aug 5, 2024. In the public domain. </p> <p><strong>Attribution for the 2022 Vehicle Inventory and Use Survey Data:</strong> United States Department of Transportation Bureau of Transportation Statistics. Vehicle Inventory and Use Survey (VIUS) 2002 [supporting datasets]. 2024. https://doi.org/10.21949/1506070 </p> <p><strong>Attribution for the GREET tool (original publication):</strong> Argonne National Laboratory Energy Systems Division Center for Transportation Research. GREET Life-cycle Model. 2014. Available from <a href="https://greet.anl.gov/files/greet-model&ved=2ahUKEwiAuryGsd6HAxVMFlkFHaafHNUQFnoECBUQAQ&usg=AOvVaw29kokx-ZurrfBFsjji9UM2">this link</a>.</p> <p><strong>Attribution for the GREET tool (2022 updates):</strong> Wang, Michael, et al. Summary of Expansions and Updates in GREET® 2022. United States. https://doi.org/10.2172/1891644</p> </td> </tr> <tr> <td>grid_emission_intensity/*.geojson</td> <td> <p>Emission intensity data is obtained from the <a href="https://www.epa.gov/egrid/download-data">eGRID database</a> maintained by the United States Environmental Protection Agency.</p> <p>eGRID subregion boundaries are obtained as a shapefile from the <a href="https://www.epa.gov/egrid/egrid-mapping-files">eGRID Mapping Files</a> database.</p> </td> <td> <p><a href="https://www.epa.gov/system/files/documents/2024-01/egrid2022_data.xlsx">eGRID database</a></p> <p><a href="https://www.epa.gov/system/files/other-files/2024-05/egrid2022_subregions_shapefile.zip">Shapefile with eGRID subregion boundaries</a></p> </td> <td> <p><strong>Attribution for eGRID data: </strong>United States Environmental Protection Agency: eGRID with 2022 data. Available from https://www.epa.gov/egrid/download-data. In the public domain.</p> <p><strong>Attribution for shapefile:</strong> United States Environmental Protection Agency: eGRID Mapping Files. Available from https://www.epa.gov/egrid/egrid-mapping-files. In the public domain.</p> </td> </tr> <tr> <td> <p>US_elec.geojson</p> <p>US_hy.geojson</p> <p>US_lng.geojson</p> <p>US_cng.geojson</p> <p>US_lpg.geojson</p> </td> <td>Locations of direct current fast chargers and refueling stations for alternative fuels along U.S. highways. Obtained directly from the <a href="https://afdc.energy.gov/corridors">Station Data for Alternative Fuel Corridors</a> in the Alternative Fuels Data Center maintained by the United States Department of Energy Office of Energy Efficiency and Renewable Energy. </td> <td> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=ELEC&ev_charging_level=dc_fast&ev_connector_type=J1772COMBO&beta_min_j1772combo_150plus_port_count=4&response_format=beta_dot_corridors">US_elec.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=HY&hy_is_retail=true">US_hy.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=LNG">US_lng.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=CNG&cng_fill_type=Q&cng_psi=3600">US_cng.geojson</a></p> <p><a href="https://developer.nrel.gov/api/alt-fuel-stations/v1.geojson?access=public&status=E&country=US&download=true&utf8_bom=true&api_key=srJv3MBMvbZaDHrSssCEnhPK4IijuOLTsvs6l35L&fuel_type=LPG&lpg_include_secondary=false">US_lpg.geojson</a></p> </td> <td> <p><strong>Attribution:</strong> U.S. Department of Energy, Energy Efficiency and Renewable Energy. Alternative Fueling Station Corridors. 2024. Available from: <a href="https://afdc.energy.gov/corridors" target="_new" rel="noreferrer">https://afdc.energy.gov/corridors</a>. In the public domain. </p> <p> </p> <p>These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.</p> </td> </tr> <tr> <td>daily_grid_emission_profiles/*.geojson</td> <td> <p>Hourly emission intensity data obtained from <a href="https://www.electricitymaps.com/data-portal/united-states-of-america">ElectricityMaps</a>.</p> </td> <td> <p>Original data can be downloaded as csv files from the <a href="https://www.electricitymaps.com/data-portal/united-states-of-america">ElectricityMaps United States of America database</a></p> <p><a href="https://raw.githubusercontent.com/electricitymaps/electricitymaps-contrib/master/web/geo/world.geojson">Shapefile with region boundaries used by ElectricityMaps</a></p> </td> <td> <p><strong>License:</strong> <a href="https://opendatacommons.org/licenses/odbl/" target="_blank" rel="noopener">Open Database License (ODbL)</a>. Details here: https://www.electricitymaps.com/data-portal</p> <p><strong>Attribution for csv files:</strong> Electricity Maps (2024). United States of America 2022-23 Hourly Carbon Intensity Data (Version January 17, 2024). Electricity Maps Data Portal. https://www.electricitymaps.com/data-portal.</p> <p><strong>Attribution for shapefile with region boundaries:</strong> ElectricityMaps contributors (2024). electricitymaps-contrib (Version v1.155.0) [Computer software]. https://github.com/electricitymaps/electricitymaps-contrib.</p> </td> </tr> <tr> <td> <p>gen_cap_2022_state_merged.geojson </p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>Grid electricity generation and net summer power capacity data is obtained from the <a href="https://www.eia.gov/electricity/data/state/">state-level electricity database</a> maintained by the United States Energy Information Administration. </p> <p> </p> <p>U.S. state boundaries obtained from <a href="https://www.sciencebase.gov/catalog/item/52c78623e4b060b9ebca5be5">this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog</a>.</p> </td> <td> <p><a href="https://www.eia.gov/electricity/data/state/annual_generation_state.xls">Annual electricity generation by state</a></p> <p><a href="https://www.eia.gov/electricity/data/state/existcapacity_annual.xlsx">Net summer capacity by state</a></p> <p><a href="https://www.sciencebase.gov/catalog/file/get/52c78623e4b060b9ebca5be5?facet=tl_2012_us_state">Shapefile with U.S. state boundaries</a></p> </td> <td> <p><strong>Attribution for electricity generation and capacity data: </strong>U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data/state/. In the public domain. </p> </td> </tr> <tr> <td>electricity_rates_by_state_merged.geojson</td> <td> <p>Commercial electricity prices are obtained from the <a href="https://www.eia.gov/electricity/data.php">Electricity database</a> maintained by the United States Energy Information Administration.</p> </td> <td> <p><a href="https://www.eia.gov/electricity/data/state/sales_annual_a.xlsx">Electricity rate by state</a></p> <p> </p> </td> <td><strong>Attribution:</strong> U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data.php. In the public domain. </td> </tr> <tr> <td> <p>demand_charges_merged.geojson</p> <p>demand_charges_by_state.geojson</p> </td> <td> <p>Maximum historical demand charges for each state and zip code are derived from a dataset compiled by the National Renewable Energy Laboratory in this <a href="https://data.nrel.gov/submissions/74">this Data Catalog.</a></p> </td> <td><a href="https://data.nrel.gov/system/files/74/Demand%20charge%20rate%20data.xlsm">Historical demand charge dataset</a></td> <td> <p>The original dataset is compiled by the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and the Alliance for Sustainable Energy, LLC ('Alliance').</p> <p><strong>Attribution:</strong> McLaren, Joyce, Pieter Gagnon, Daniel Zimny-Schmitt, Michael DeMinco, and Eric Wilson. 2017. 'Maximum demand charge rates for commercial and industrial electricity tariffs in the United States.' NREL Data Catalog. Golden, CO: National Renewable Energy Laboratory. Last updated: July 24, 2024. DOI: 10.7799/1392982.</p> </td> </tr> <tr> <td> <p>eastcoast.geojson</p> <p>midwest.geojson</p> <p>la_i710.geojson</p> <p>h2la.geojson</p> <p>bayarea.geojson</p> <p>saltlake.geojson</p> <p>northeast.geojson</p> </td> <td> <p>Highway corridors and regions targeted for heavy duty vehicle infrastructure projects are derived from a <a href="https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle">public announcement</a> on February 15, 2023 by the United States Department of Energy.</p> <p>The shapefile with Bay area boundaries is obtained from <a href="https://geodata.lib.berkeley.edu/catalog/ark28722-s7hs4j">this Berkeley Library dataset</a>.</p> <p>The shapefile with Utah county boundaries is obtained from <a href="https://gis.utah.gov/products/sgid/boundaries/county/">this dataset</a> from the Utah Geospatial Resource Center. </p> </td> <td> <p><a href="https://spatial.lib.berkeley.edu/public/ark28722-s7hs4j/data.zip">Shapefile for Bay Area country boundaries</a></p> <p><a href="https://opendata.arcgis.com/datasets/90431cac2f9f49f4bcf1505419583753_0.zip">Shapefile for counties in Utah</a></p> <p> </p> </td> <td> <p><strong>Attribution for public announcement:</strong> United States Department of Energy. Biden-Harris Administration Announces Funding for Zero-Emission Medium- and Heavy-Duty Vehicle Corridors, Expansion of EV Charging in Underserved Communities (2023). Available from https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle.</p> <p><strong>Attribution for Bay area boundaries:</strong> San Francisco (Calif.). Department Of Telecommunications and Information Services. Bay Area Counties. 2006. In the public domain. </p> <p><strong>Attribution for Utah boundaries:</strong> Utah Geospatial Resource Center & Lieutenant Governor's Office. Utah County Boundaries (2023). Available from https://gis.utah.gov/products/sgid/boundaries/county/. </p> <p><strong>License for Utah boundaries:</strong> <a href="https://gis.utah.gov/documentation/policy/license/#license">Creative Commons 4.0 International License</a>. </p> </td> </tr> <tr> <td>incentives_and_regulations/*.geojson</td> <td> <p>State-level incentives and regulations targeting heavy duty vehicles are collected from the <a href="https://afdc.energy.gov/laws/state">State Laws and Incentives database</a> maintained by the United States Department of Energy's Alternative Fuels Data Center. </p> </td> <td>Data was collected manually from the <a href="https://afdc.energy.gov/laws/state">State Laws and Incentives database</a>.</td> <td> <p><strong>Attribution:</strong> U.S. Department of Energy, Energy Efficiency and Renewable Energy, Alternative Fuels Data Center. State Laws and Incentives. Accessed on Aug 5, 2024 from: https://afdc.energy.gov/laws/state. In the public domain. </p> <p> </p> <p>These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.</p> </td> </tr> <tr> <td> <p>costs_and_emissions/*.geojson</p> <p>diesel_price_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>Lifecycle costs and emissions of electric and diesel trucking are evaluated by adapting the model developed by <a href="https://chemrxiv.org/engage/chemrxiv/article-details/656e4691cf8b3c3cd7c96810">Moreno Sader et al.</a>, and calibrated to the <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a> for the Tesla Semi collected from the 2023 PepsiCo Semi pilot by the North American Council for Freight Efficiency.</p> <p>In addition to the data sources outlined in <a href="https://chemrxiv.org/engage/chemrxiv/article-details/656e4691cf8b3c3cd7c96810">Moreno Sader et al.</a> et al. and the <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a>, this dataset incorporates:</p> <ul> <li>Emission intensity data from the <a href="https://www.epa.gov/egrid/download-data">eGRID database</a>, described elsewhere in this metadata. </li> <li>Commercial electricity price data from the US EIA <a href="https://www.eia.gov/electricity/data.php">Electricity database</a>, described elsewhere in this metadata. </li> <li><a href="https://data.nrel.gov/submissions/74">Maximum historical demand charges</a> from the National Renewable Energy Laboratory, described elsewhere in this metadata. </li> <li>Max motor power estimate of 942,900W and frontal area of 10.7 m^s for the Tesla Semi from <a href="https://www.motormatchup.com/catalog/Tesla/Semi-Truck/2022/Empty">motormatchup.com.</a></li> <li>Drag coefficient estimate of 0.36 for the Tesla Semi from <a href="https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi">notateslaapp.com.</a></li> <li>Estimates best-in-class truck rolling resistance of 0.0044 from a <a href="https://www.lrrb.org/pdf/201539.pdf">Rolling Resistance Validation report</a> prepared by the Minnesota Department of Transportation Office of Transportation System Management.</li> <li><a href="https://www.eia.gov/petroleum/gasdiesel/">Historical diesel prices</a> by state from the United States Energy Information Administration.</li> <li>Estimate of best in class diesel powertrain engine efficiency of 44% from a <a href="https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf">Fuel Efficiency Technology report</a> by the International Council on Clean Transportation.</li> </ul> </td> <td> <p> </p> <p><a href="https://runonless.com/wp-content/uploads/ROL23-Web-data.zip">NACFE Run on Less dataset</a></p> <p><a href="https://www.eia.gov/petroleum/gasdiesel/xls/psw18vwall.xls">Historical diesel prices</a></p> <p> </p> </td> <td> <p><strong>Attribution for original truck model:</strong> Moreno Sader K, Biswas S, Jones R, Mennig M, Rezaei R, Green WH. Battery Electric Long-Haul Trucking in the United States: A Comprehensive Costing and Emissions Analysis. ChemRxiv. 2023; doi:10.26434/chemrxiv-2023-48zsc (link to <a href="https://colab.research.google.com/drive/124rFu_4vHx4cP6SODtdzCxnUmLY50wbW?usp=sharing">colab notebook</a> included as supplementary material).</p> <p><strong>Attribution for GitHub repository with adapted code for the truck model:</strong> Eamer, D., Moreno-Sader, K., & Biswas, S. (2024). Green_Trucking_Analysis (Version 0.1.0) [Computer software]. https://doi.org/10.5281/zenodo.13205854</p> <p><strong>Attribution for GitHub repository with analysis of the NACFE Run on Less dataset (provides inputs to Eamer, D., Moreno-Sader, K., & Biswas, S. (2024) cited above):</strong> Eamer, D. (2024). PepsiCo_NACFE_Analysis (Version 0.1.0) [Computer software]. https://doi.org/10.5281/zenodo.13173390</p> <p><strong>Attribution for <a href="https://runonless.com/run-on-less-electric-depot-reports/">Run on Less dataset</a>: </strong>North American Countil for Freight Efficiency (2023). Run on Less – Electric DEPOT data. Available from: https://runonless.com/run-on-less-electric-depot-reports/ </p> <p><strong>Attribution for data from MotorMatchup:</strong> 2022 Tesla Semi Truck Empty Specs. Available from: https://www.motormatchup.com/catalog/Tesla/Semi-Truck/2022/Empty. Copyright 2024 by MotorMatchup</p> <p><strong>Attribution for data from Not a Tesla App:</strong> Not a Tesla App. Everything We Know About the Tesla Semi. 2024. Available from: <a href="https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi" target="_new" rel="noreferrer">https://www.notateslaapp.com/tesla-reference/963/everything-we-know-about-the-tesla-semi</a></p> <p><strong>Attribution for historical diesel prices:</strong> U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/petroleum/gasdiesel/. In the public domain.</p> <p><strong>Attribution for best in class diesel powertrain efficiency:</strong> Delgado O, Rodríguez F, Muncrief R. Fuel Efficiency Technology in European Heavy-Duty Vehicles: Baseline and Potential for the 2020–2030 Time Frame. 2017. Available from: <a href="https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf" target="_new" rel="noreferrer">https://theicct.org/sites/default/files/publications/EU-HDV-Tech-Potential_ICCT-white-paper_14072017_vF.pdf</a>.</p> </td> </tr> <tr> <td> <p>electrolyzer_operational.geojson</p> <p>electrolyzer_installed.geojson</p> <p>electrolyzer_planned_under_construction.geojson</p> <p> </p> </td> <td> <p>Data on locations and capacities of planned, under-construction, installed, operational electrolyzers was obtained from <a href="https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master">this DOE Hydrogen Program Record</a>.</p> </td> <td>Data was extracted manually from <a href="https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master">this DOE Hydrogen Program Record</a>.</td> <td><strong>Attribution:</strong> Arjona, Vanessa. DOE Hydrogen Program Record: Electrolyzer Installations in the United States. 2023. Available from https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/23003-electrolyzer-installations-united-states.pdf?Status=Master. </td> </tr> <tr> <td> <p>grid_emission_intensity/*.geojson</p> <p>gen_cap_2022_state_merged.geojson </p> <p>trucking_energy_demand.geojson</p> <p>electricity_rates_by_state_merged.geojson</p> <p>demand_charges_merged.geojson</p> <p>demand_charges_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> <p>costs_and_emissions/*.geojson</p> <p>diesel_price_by_state.geojson</p> <p>trucking_energy_demand.geojson</p> </td> <td> <p>U.S. state boundaries obtained from <a href="https://www.sciencebase.gov/catalog/item/52c78623e4b060b9ebca5be5">this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog</a>.</p> </td> <td> </td> <td><strong>Attribution: </strong>U.S. Department of Commerce, U.S. Census Bureau, Geography Division. State boundaries (generalized for mapping). 2011. In the public domain.</td> </tr> <tr> <td> <p>refinery.geojson</p> </td> <td> <p>Locations and production rates of hydrogen from refineries are obtained from the following two complementary datasets on the <a href="https://h2tools.org">Hydrogen Tools Portal</a>:</p> <p><br>1) <a href="https://h2tools.org/hyarc/hydrogen-data/captive-purpose-refinery-hydrogen-production-capacities-individual-us">Captive, On-Purpose, Refinery Hydrogen Production Capacities at Individual U.S. Refineries</a>, and </p> <p><br>2) <a href="https://h2tools.org/hyarc/hydrogen-data/merchant-hydrogen-plant-capacities-north-america">Merchant Hydrogen Plant Capacities in North America</a></p> </td> <td> <p><a href="https://h2tools.org/file/9338/download?token=0IWTving">Dataset for Captive, On-Purpose, Refinery Hydrogen Production Capacities at Individual U.S. Refineries</a></p> <p><a href="https://h2tools.org/file/2050/download?token=Wp-XDY-h">Dataset for Merchant Hydrogen Plant Capacities in North America</a></p> </td> <td> <p><strong>Attribution: </strong>Copyright © 2024 by H2Tools; H2 Tools is intended for public use. It was built, and is maintained, by the Pacific Northwest National Laboratory with funding from the DOE Office of Energy Efficiency and Renewable Energy's Hydrogen and Fuel Cell Technologies Office. All Rights Reserved. </p> </td> </tr> <tr> <td> <p>Truck_Stop_Parking.geojson</p> <p>infrastructure_pooling_thought_experiment/*.geojson</p> </td> <td> <p>Obtained from the DOT Bureau of Transportation Statistics's <a href="https://geodata.bts.gov/datasets/usdot::truck-stop-parking">Truck Stop Parking database</a></p> </td> <td> <p>Original dataset can be downloaded using the Shapefile download link at https://geodata.bts.gov/datasets/usdot::truck-stop-parking (link for hosted download changes regularly). </p> </td> <td> <p><strong>Attribution: </strong>United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Truck Stop Parking. Available at https://geodata.bts.gov/datasets/usdot::truck-stop-parking. </p> <p><strong>License:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> </td> </tr> <tr> <td> <p>Principal_Port.geojson</p> </td> <td> <p>Obtained from the DOT Bureau of Transportation Statistics's <a href="https://geodata.bts.gov/datasets/usdot::principal-ports-1/about">Principal Ports database</a></p> </td> <td> <p>Original dataset can be downloaded using the Shapefile download link at https://geodata.bts.gov/datasets/usdot::principal-ports-1 (link for hosted download changes regularly). </p> </td> <td> <p><strong>Attribution: </strong>United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Truck Stop Parking. Available at https://geodata.bts.gov/datasets/usdot::principal-ports-1. </p> <p><strong>License:</strong> This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.</p> <p><strong> </strong></p> </td> </tr> <tr> <td> <p>ZEF_Corridor_Strategy/*.geojson</p> </td> <td> <p>Visualizes the corridors, facilities, and hubs targeted by the National Zero-Emission Freight Corridor Strategy, a framework developed by the U.S. Joint Office of Energy and Transportation to support the coordinated deployment of medium- and heavy-duty zero-emission vehicle (ZEV) infrastructure along critical freight corridors. The strategy, outlined in the publication <a href="https://driveelectric.gov/files/zef-corridor-strategy.pdf">National Zero-Emission Freight Corridor Strategy</a>, identifies priority corridors and infrastructure investment needs to accelerate the transition to zero-emission medium- and heavy-duty vehicles.</p> </td> <td> <p>Original dataset can be downloaded from https://driveelectric.gov/files/zef-gis-files.zip</p> </td> <td> <p><strong>Attribution:</strong> Chu, K.-C. (J.), Miller, K. G., Schroeder, A., Gilde, A., & Laughlin, M. (2024, September). <em>National Zero-Emission Freight Corridor Strategy: Prioritizing investments, planning, and deployment for medium- and heavy-duty vehicle fueling infrastructure to advance zero-emission freight along our nation’s corridors</em>. Joint Office of Energy and Transportation; U.S. Department of Energy. Accessed from: https://driveelectric.gov/files/zef-corridor-strategy.pdf.</p> </td> </tr> </tbody> </table> <p> </p>
Multiple Accurate Monster Truck Chassis
Might or might not be in scale, but all are fairly accurate. Look annotations for extra information. https://www.artstation.com/blogs/jorma_rysky/eryV/frankenstein-monster-truck Source: Objaverse 1.0 / Sketchfab
Dinky Toys Bedford Garbage Truck 252 Scan
Dinky made these in the late 50's and features a handle that you can turn to raise and lower the rear as well as sliding panels on the sides. This one isn't the best scan however it provided a great challenge as a solid bit of it was rendered wrong and I experimented in substance with the clone stamp tool, this is most notable on the wheels as they were textured copletely green for some reason. I may need to experiment with a different background however as with all the these scans thus far have been modeled with a large white blob surrounded them that needs quite a bit of clean-up and refining. Support my work through a nice cold-brew coffee at https://ko-fi.com/jordanf Source: Objaverse 1.0 / Sketchfab
Fire Truck
This is a 1941 Indianapolis Fire Department 8-P 750 GPM Fire Truck. This truck was built by hand by Firefighters for Firefighters in the old repair shops in Fountain Square, Indianapolis. Firefighters loved working off these trucks, the oldest one on record was used up until 1968. Service Record: Engine 17 - 12/01/1941. Engine 12 - 12/03/1952. Engine 16 - 10/20/1955. Reserve - 01/10/1966. Sold - 06/16/1969. The truck was 3D scanned by the IUPUI University Library using a Creaform Spark, and Creaform Go Scan 50. Portions of the truck were re-created using reverse engineering methods. Scanning took about a week with a crew of three people. This item was 3D scanned by [Connections XR](https://www.connectionsxr.com/) using a [Creaform Go! Scan Spark](https://www.onlineresourcesinc.com/product/GoSCAN-Spark), and a [Creaform Go Scan 50](https://www.onlineresourcesinc.com/). To see this truck in person visit: https://www.visitindy.com/indianapolis-firefighters-museum-historical-society Source: Objaverse 1.0 / Sketchfab
Electrification shares of trucks
<p>XX_Tno',' daily truck number for XX scenario</p> <p>XX_TnoF',' daily truck number for XX scenario using fast chargers</p> <p>XX_TnoS', daily truck number for XX scenario using slow chargers</p> <p>XX_FC, Fast chargers number for XX scenario</p> <p>XX_SC, Slow chargers number for XX scenario</p>
Old Dodge Truck (~1929)
Quick 3D scan taken of a Old Model T found in Barstow, CA. LiDar scanning using the Trnio Plus (beta) app. Source: Objaverse 1.0 / Sketchfab
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.