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2 results for “long-distance trips”
TRANSIT long-distance multimodal trips model results - a Spanish case study
<p>The files downloaded present the results per scenario considered obtained with the agent-based model developed for the assessment of the Intermodal Timetable Synchronisation solution proposed in the scope of the TRANSIT project (<a href="https://www.transit-h2020.eu/">https://www.transit-h2020.eu/</a>).</p> <p>An agent-based modelling framework called <a href="https://github.com/StefanoPenazzi/jtap/tree/main">J-TAP</a> has been developed and put at work to implement a Spanish long-distance multimodal trips model. The enhanced version of J-TAP used in this work can be found on a <a href="https://github.com/NommonSolutionsAndTechnologies/jtap">github repository</a>.</p> <p>The case study is focused on modelling the long-distance travel patterns of the residents in the Valencia (Spain) area. The destinations considered include the whole of Spain. The period under study is a full year from March 2019 to February 2020 (both inclusive).The files are structured in three different scenarios:</p> <ol> <li><strong>CS01 - Baseline</strong>. The current state of the network is considered and the actual long-distance travel patterns are obtained.</li> <li><strong>CS02 - HSR connection with Madrid-Barajas airport</strong>. The long-distance travel patterns are modelled with hard measures, the high-speed rail is connected to Madrid-Barajas airport.</li> <li><strong>CS03 - HSR connection with Madrid-Barajas airport and timetable synchronisation</strong>. The effects of the timetable synchronisation are modelled.</li> </ol> <p>Each scenario includes the following files:</p> <ul> <li>ctapModelParameters. A folder containing all the information extracted from the <a href="https://neo4j.com/product/graph-data-science/?utm_program=emea-prospecting&utm_source=google&utm_medium=cpc&utm_campaign=emea-search-offers&utm_adgroup=dynamic&utm_content=dynamic&utm_placement=&utm_network=g&gclid=Cj0KCQiAwJWdBhCYARIsAJc4idAo4CEi9lU8TXwmBym8MNHpEIZHPBs3x_4phxbu76y1XKbYlFoZCjIaAiGhEALw_wcB">neo4j</a> graph database created to model the multimodal network and the agents. J-TAP contains packages that simplify network creation in the graph database. This information is stored in .json files (e.g., "Os2DsTravelCostParameter.json" contains the generalised cost for each OD pair and transport mode, "AttractivenessParameter.json" contains the attractiveness by destination, activity, time of the year and agent, etc.). The solver included in the J-TAP framework uses this information to calculate the agents plans.</li> <li>population.json. The result of the J-TAP optimisation. It includes the fitness value for each agent plan evaluated during the J-TAP execution. The best plan for each agent is selected as the plan performed by the agent. An agent plan includes: <ul> <li>activities - Sequence of activities.</li> <li>locations - Sequence of locations</li> <li>ts - Initial time of the activity</li> <li>te - Final time of the activity</li> </ul> </li> <li>LinkTimeFlow.csv. It is obtained after processing the previous file. It contains the number of agents using each link in the network (i.e., road, rail, air and cross links) in each time interval. The first column represents the link id and the rest of columns indicates the number of agents in each interval.</li> </ul> <p>The J-TAP simulation framework is explained in detail in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D5.1_Modelling_Framework_v02.00.00.pdf">D5.1. TRANSIT Modelling and Simulation Framework</a> and the complete description of the case studies and scenarios tested is included in TRANSIT's deliverable <a href="http://www.nommon-files.es/transit/TRANSIT-D6.1_Assessment_of_Intermodal_Concepts_00.02.00.pdf">D6.1. Impact Assessment of New Intermodal Concepts and Passenger Information Services: Conclusions and Recommendations</a>.</p> <p>Thank you for downloading the dataset! It would be very helpful if you share your view on the data show with us. </p>
Data and code from: Three decades of wildlife-vehicle collisions in a protected area: main roads and long-distance commuting trips to migratory prey increase spotted hyena roadkills in the Serengeti
<p>This is the first release. Potential updates will be available on GitHub: <a href="https://github.com/MarwanNaciri/Three_decades_of_spotted_hyena_roadkill_in_a_protected_area">https://github.com/MarwanNaciri/Three_decades_of_spotted_hyena_roadkill_in_a_protected_area</a></p>
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