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61 results for “travel time”
Optimizing Nozzle Travel Time in Proton Therapy [Dataset]
<p>Dataset of instances taken into account by the paper, together with solutions and achieved computational time.</p> <p>Manuscript submitted to 2022 IEEE-CBMS.</p> <p><em>ABSTRACT - Proton therapy is an oncological therapy that is more expensive than classical radiotherapy but that is considered the gold standard in several situations. Moreover, since there is still a limited amount of delivering facilities for this techniques, it is fundamental to increase the number of treated patients over time.<br> The objective of this work is to offer an insight on the problem of the optimization of the part of the delivery time of a treatment plan that relates to the movements of the system. We denote it as the Nozzle Travel Time Problem (NTTP), in analogy with the Leaf Travel Time Problem (LTTP) in classical radiotherapy.<br> In particular this work: (i) describes a mathematical model for the delivery system and formalize the optimization problem for finding the optimal sequence of movements of the system (nozzle and bed) that satisfies the covering of the prescribed irradiation directions; (ii) provides an optimization pipeline that solves the problem for instances with an amount of irradiation directions much greater than those usually employed in the clinical practice; (iii) reports preliminary results about the effects of employing two different resolution strategies within the aforementioned pipeline, that rely on an exact Traveling Salesmna Problem (TSP) solver (Concorde) and an efficient heuristic Vehicle Routing Open-source Optimization Machine (VROOM).</em></p> <p> </p> <p>For each combination of system features (SF1, SF2, SF3) and distance metric (L1 and Linf), 50 runs (5 session by 10 runs) with prescribed fields from 5 to 100 (step 5) have been executed.</p> <p>- 'grph' folder contains GTSP and ATSP instances in GraphML and txt format.</p> <p>- 'vrinst' folder contains ATSP instances, expressed as VRP instances, in json format, to be fed into VROOM</p> <p>- 'tsps' folder contains symmetric TSP instances in TSPLIB format to be fed into Concorde</p> <p>- 'ress' folder contains result of optimizzation obtained by Concorde (.sol and .res formats) and VROOM (.json)</p> <p>all the files in these folders is named as [SF#]_[distanceMetric][[prescribedFields#]_[subrun]][Session8charsCode], so that, for example "SF3_Linf[100_9]2e275d41" represents the 100 fields result of the 9th subrun of the session with code 2e275d41, where SF3 and Linf norm have been taken into account.</p> <p>- 'resultsNPY' folder contains .npy file about computation time and computed travel time for both solvers</p> <p> </p>
Travel Times by Transportation Mode in Nairobi, Kenya
<p>This directory contains data files that describe travel times in<br> Nairobi, Kenya. Three different modes of transportation are included:</p> <p> - Walking, according to MapQuest and filled in with Google Maps where<br> MapQuest fails to find a route.</p> <p> - Driving, under normal traffic conditions.</p> <p> - Matatus, the informal public transportation network.</p> <p>The travel times are computed from a gridded origin location to a<br> collection of gridded destination locations. In the directories, each<br> file contains destination location times for a given origin location.<br> These are titled <mode>-###.csv. The CSV is organized from north to<br> south, along rows, and west to east, along columns. An additional<br> <mode>.csv file in each directory gives the organization of the<br> numbered files, again from north to south, along rows, and west to<br> east, along columns.</p> <p>There are also "extended" versions of each grid, with larger<br> destination grids for each origin point, formed by "daisy-chaining"<br> together the original destination grids.</p> <p>The directory also contains source files for working with the data:</p> <p> - calc_access.R shows how to calculate a simple mobility measure,<br> using the functions in tools_calc_access.R</p> <p> - tools_analyze.R are a more general set of functions for performing<br> arbitrary callback-based operations on the travel time data.</p> <p> - tools_calc_access.R contains underlying functions for setting up<br> access to the grids and calculating access.</p> <p> - tools_map.R contains the grid origin and destination definitions<br> and functions for plotting.</p>
Helsinki Region Travel Time Matrix
<p>Helsinki Region Travel Time Matrix contains <strong>travel time and distance</strong> information for routes between all 250 m x 250 m grid cell centroids (n = 13231) in the Helsinki Region, Finland by <strong>walking</strong>, <strong>cycling,</strong> <strong>public transportation</strong> and <strong>car</strong>. The grid cells are compatible with the statistical grid cells used by Statistics Finland and the YKR (<em>yhdyskuntarakenteen seurantajärjestelmä) </em>data set. The Helsinki Region Travel Time Matrix is available for <strong>three different years</strong>:</p> <ul> <li>2018</li> <li>2015</li> <li>2013</li> </ul> <p>The data consists of travel time and distance information of the routes that have been calculated between all statistical grid cell centroids (n = 13231) by walking, cycling, public transportation and car.</p> <p>The data have been calculated for two different times of the day: 1) midday and 2) rush hour.</p> <p>The data may be used freely (under Creative Commons 4.0 licence). We do not take any responsibility for any mistakes, errors or other deficiencies in the data.</p> <p><strong>Organization of data</strong></p> <p>The data have been divided into 13231 text files according to destinations of the routes. The data files have been organized into sub-folders that contain multiple (approx. 4-150) Travel Time Matrix result files. Individual folders consist of all the Travel Time Matrices that have same first four digits in their filename (e.g. 5785xxx).</p> <p>In order to visualize the data on a map, the result tables can be joined with the MetropAccess YKR-grid shapefile (attached here). The data can be joined by using the field <strong><em>‘from_id’</em></strong> in the text files and the field <strong><em>‘YKR_ID’</em></strong> in MetropAccess-YKR-grid shapefile as a common key.</p> <p><strong>Data structure</strong></p> <p>The data have been divided into 13231 text files according to destinations of the routes. One file includes the routes from all statistical grid cells to a particular destination grid cell. All files have been named according to the destination grid cell code and each file includes 13231 rows.</p> <p>NODATA values have been stored as value -1.</p> <p>Each file consists of 17 attribute fields: 1) from_id, 2) to_id, 3) walk_t, 4) walk_d, 5) bike_f_t, 6) bike_s_t, 7) bike_d, 8) pt_r_tt, 9) pt_r_t, 10) pt_r_d, 11) pt_m_tt, 12) pt_m_t, 13) pt_m_d, 14) car_r_t, 15) car_r_d, 16) car_m_t, 17) car_m_d, 18) car_sl_t</p> <p>The fields are separated by semicolon in the text files.</p> <p><strong>Attributes</strong></p> <ul> <li><strong>from_id: </strong>ID number of the origin grid cell</li> <li><strong>to_id</strong><strong>: </strong>ID number of the destination grid cell</li> <li><strong>walk_t</strong><strong>: </strong>Travel time in minutes from origin to destination by walking</li> <li><strong>walk_d</strong><strong>: </strong>Distance in meters of the walking route</li> <li><strong>bike_f_t: </strong>Total travel time in minutes from origin to destination by fast cycling; Includes extra time (1 min) that it takes to take/return bike</li> <li><strong>bike_s_t: </strong>Total travel time in minutes from origin to destination by slow cycling; Includes extra time (1 min) that it takes to take/return bike</li> <li><strong>bike_d:</strong>Distance in meters of the cycling route</li> <li><strong>pt_r_tt</strong><strong>: </strong>Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account including the waiting time at home</li> <li><strong>pt_r_t</strong><strong>: </strong>Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account excluding the waiting time at home</li> <li><strong>pt_r_d</strong><strong>: </strong>Distance in meters of the public transportation route in rush hour traffic</li> <li><strong>pt_m_tt</strong><strong>: </strong>Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account including the waiting time at home</li> <li><strong>pt_m_t</strong><strong>: </strong>Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account excluding the waiting time at home</li> <li><strong>pt_m_d</strong><strong>: </strong>Distance in meters of the public transportation route in midday traffic</li> <li><strong>car_r_t: </strong>Travel time in minutes from origin to destination by private car in rush hour traffic; the whole travel chain has been taken into account </li> <li><strong>car_r_d</strong><strong>: </strong>Distance in meters of the private car route in rush hour traffic</li> <li><strong>car_m_t</strong><strong>: </strong>Travel time in minutes from origin to destination by private car in midday traffic; the whole travel chain has been taken into account </li> <li><strong>car_m_d</strong><strong>: </strong>Distance in meters of the private car route in midday traffic</li> <li><strong>car_sl_t: </strong>Travel time from origin to destination by private car following speed limits without any additional impedances; the whole travel chain has been taken into account </li> </ul> <p><strong>METHODS</strong></p> <p>For detailed documentation and how to reproduce the data, see <a href="https://github.com/AccessibilityRG/HelsinkiRegionTravelTimeMatrix2018">HelsinkiRegionTravelTimeMatrix2018</a> GitHub repository.</p> <p><strong>THE ROUTE BY CAR</strong> have been calculated with a dedicated open source tool called <strong>DORA</strong> (DOor-to-door Routing Analyst) developed for this project. DORA uses PostgreSQL database with PostGIS extension and is based on the pgRouting toolkit. MetropAccess-Digiroad (modified from the original Digiroad data provided by Finnish Transport Agency) has been used as a street network in which the travel times of the road segments are made more realistic by adding crossroad impedances for different road classes.</p> <p>The calculations have been repeated for two times of the day using 1) the “midday impedance” (i.e. travel times outside rush hour) and 2) the “rush hour impendance” as impedance in the calculations. Moreover, there is 3) the “speed limit impedance” calculated in the matrix (i.e. using speed limit without any additional impedances).</p> <p>The whole travel chain (“door-to-door approach”) is taken into account in the calculations:<br> 1) walking time from the real origin to the nearest network location (based on Euclidean distance),<br> 2) average walking time from the origin to the parking lot,<br> 3) travel time from parking lot to destination,<br> 4) average time for searching a parking lot,<br> 5) walking time from parking lot to nearest network location of the destination and<br> 6) walking time from network location to the real destination (based on Euclidean distance).</p> <p><strong>THE ROUTES BY PUBLIC TRANSPORTATION</strong> have been calculated by using the MetropAccess-Reititin tool which also takes into account the whole travel chains from the origin to the destination:<br> 1) possible waiting at home before leaving,<br> 2) walking from home to the transit stop,<br> 3) waiting at the transit stop,<br> 4) travel time to next transit stop,<br> 5) transport mode change,<br> 6) travel time to next transit stop and<br> 7) walking to the destination.</p> <p>Travel times by public transportation have been optimized using 10 different departure times within the calculation hour using so called Golomb ruler. The fastest route from these calculations are selected for the final travel time matrix.</p> <p><strong>THE ROUTES BY CYCLING</strong> are also calculated using the DORA tool. The network dataset underneath is MetropAccess-CyclingNetwork, which is a modified version from the original Digiroad data provided by Finnish Transport Agency. In the dataset the travel times for the road segments have been modified to be more realistic based on Strava sports application data from the Helsinki region from 2016 and the bike sharing system data from Helsinki from 2017.</p> <p>For each road segment a separate speed value was calculated for slow and fast cycling. The value for fast cycling is based on a percentual difference between segment specific Strava speed value and the average speed value for the whole Strava data. This same percentual difference has been applied to calculate the slower speed value for each road segment. The speed value is then the average speed value of bike sharing system users multiplied by the percentual difference value.</p> <p>The reference value for faster cycling has been 19km/h, which is based on the average speed of Strava sports application users in the Helsinki region. The reference value for slower cycling has been 12km/, which has been the average travel speed of bike sharing system users in Helsinki. Additional 1 minute have been added to the travel time to consider the time for taking (30s) and returning (30s) bike on the origin/destination.</p> <p>More information of the Strava dataset that was used can be found from the Cycling routes and fluency report, which was published by us and the city of Helsinki.</p> <p><strong>THE ROUTES BY WALKING</strong> were also calculated using the MetropAccess-Reititin by disabling all motorized transport modesin the calculation. Thus, all routes are based on the Open Street Map geometry.</p> <p>The walking speed has been adjusted to 70 meters per minute, which is the default speed in the HSL Journey Planner (also in the calculations by public transportation).</p> <p>All calculations were done using the computing resources of CSC-IT Center for Science (<a href="https://www.csc.fi/home">https://www.csc.fi/home</a>).</p>
Pn travel time data of Central and North China
<p>Pn travle time data of the Central and North China. The ten numbers in each line are the year, month, and day of event, the longitude, latitude, and depth of event, the longitude, latitude, and elevation of station, and Pn travle time.</p>
Code for 'Revealing the Local Time Structure of the Alfven Radius and travel times in Jupiter's magnetosphere'
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Fig. 7 in Activity budget, travel distance, sleeping time, height of activity and travel order of wild East Bornean Grey gibbons (Hylobates funereus) in Danum Valley Conservation Area
Fig. 7. Diurnal activity cycle of three gibbons (two males and one female) from 0530−1600 hours.
Fig. 9 in Activity budget, travel distance, sleeping time, height of activity and travel order of wild East Bornean Grey gibbons (Hylobates funereus) in Danum Valley Conservation Area
Fig. 9. Activity of three gibbons (two males and one female) at each height.
Travel times of temporary seismic arrays and 3-D Vp and Vs models in the mid-to-south segment of the Red River fault, China
<p>The travel times of the temporary seismic arrays are manually picked. The 3-D Vp and Vs models beneath the mid-to-south segment of the Red River fault are obtained based on the improved double-difference tomography method and abundant data. </p>
Dataset and code for the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm
<p>Dataset and code for the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm</p>
Pn travel time data of Ryukyu–Taiwan–Philippines region
<p>Pn travle time data of the Ryukyu–Taiwan–Philippines region. The numbers in each line are the year, month, and day of event, the longitude, latitude, and depth of event, the longitude, latitude, and elevation of station, and Pn travle time.</p>
Time travelling seeds reveal that plant regeneration and growth traits are responding to climate change
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Routing short-haul trucks under the uncertainties of travel time and service time
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Multiverse Notebook: Shifting Data Scientists to Time Travelers (Supplemental Material)
<p>The collected revisions and the results of our manual inspectionspresented in "Multiverse Notebook: Shifting Data Scientist to Time Traveler."</p>
Pn travel time data
<p>The .txt file is the rawdata for Pn tomography in the uppermost mantle beneath SE Asia.</p>
Travel Time Prediction and Explanation with Spatio-TemporalFeatures: A Comparative Study
<p>Datasets are used in the paper "Travel Time Prediction and Explanation with Spatio-TemporalFeatures: A Comparative Study".</p>
Risk perception and travel satisfaction associated with the use of public transport in the time of COVID-19. The case of Turin, Italy
<p>Dataset created from the data of a survey about risk perception and travel satisfaction associated with the use of PT in the time of Covid-19 in Turin metropolitan area (Italy).</p>
Crosshole seismic data (travel times) and crosshole GPR data (travel times and amplitudes) of the FE experiment in Mont Terri, Switzerland
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Dataset and codes used in the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data"
<p>This contains the codes and dataset for the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data". Detailed information about the dataset is described in the Readme.txt file.</p>
Codes and dataset used in the manuscript entitled "Quantifying time-variant travel time distribution by multi-fidelity model in hillslope under nonstationary hydrologic conditions"
<p>This contains the codes and dataset for the manuscript entitled "Quantifying time-variant travel time distribution by multi-fidelity model in hillslope under nonstationary hydrologic conditions". Detailed information about the dataset is described in the Readme.txt file.</p>
3-D P- and S-wave velocity (Vp and Vs) model of the Yangbi earthquake source region, as well as the travel-times of ChinArray-I
<p>3-D P- and S-wave velocity (Vp and Vs), as well as Vp/Vs ratio models of the Yangbi earthquake source region</p>
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