mobility-modeling

There are 25 repositories under mobility-modeling topic.

  • lcodeca/LuSTScenario

    Luxembourg SUMO Traffic (LuST) Scenario

    Language:TeX95161243

  • lcodeca/MoSTScenario

    Monaco SUMO Traffic (MoST) Scenario

    Language:Python849928

  • sash-ko/simobility

    simobility - light-weight mobility simulation framework. Best for quick prototyping

    Language:Python38479

  • lcodeca/SUMOActivityGen

    An Activity-based Multi-modal Mobility Scenario Generator for SUMO. This project is available in the Eclipse SUMO contributed tools section (https://github.com/eclipse/sumo/tree/master/tools/contributed) under the name SAGA (SUMO Activity GenerAtion).

    Language:Python3653023

  • zihenglin/LSTM-Mobility-Model

    LSTM Mobility Model implementation using Tensorflow

    Language:Python21506

  • EnvironmentalSystemsLab/Urbano

    Urbano: Plugin for Rhino/Grasshopper

    17772

  • lcodeca/PyPML

    Python Parking Monitoring Library for SUMO

    Language:Python17404

  • Leot6/AMoD

    Autonomous Mobility-on-Demand Simulation

    Language:Python13133

  • ruihuili/DRL_UAV_CellularNet

    Simulator developed for the mobility management of UAV base stations project featuring A3C

    Language:Jupyter Notebook132010

  • Leot6/AMoD2

    A high-capacity on-demand ride-sharing simulator, with three representative vehicle dispatch algorithms implemented.

    Language:C++11402

  • WanzhengZhu/SHMM

    A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling (AAAI 2018)

    Language:Java9303

  • wenjian0202/mod-abm-2.0

    A modern agent-based modeling platform for mobility-on-demand simulations.

    Language:C++8117

  • codewithpatelo/aopjs

    More than a billion of the rural merchants in the developing world commonly depend on hiring on-demand transportation services to commute people or goods to markets. The process of selecting the optimal fare involves handling decision-making characterised with multiple alternatives and competing criteria. Decision support systems are commonly used to solve these types of problems. However, most widely used systems are based on object-based approaches which lack high-level abstractions needed to effectively model and scale human-machine communication. This paper reviews previous literature on the field and introduces an improved preliminary agent-based decision-support approach to overcome those challenges. As a proof of concept, we developed a two-agent simulation that, given a request from one of the agents, the other one takes a dataset of a stratified sample of 104 Ethiopian commuter criteria preferences taken from the Dukem region and an exemplary dataset of fare alternatives. The assistant agent computes those datasets using widely used HPA and TOPSIS algorithms to weight, score, rank those alternatives. Once we run the simulation, in a matter of milliseconds the assistant agent effectively returns an optimal prescription to the other agent, storing all interactions in a self-contained memory resulting in an architecture that allows developers to program further customisation as interactions scale.

    Language:JavaScript6303
  • safegraph-simulation

    jpes707/safegraph-simulation

    Codebase for SIGSPATIAL ARIC 2020: Data-Driven Mobility Models for COVID-19 Simulation

    Language:Python5404

  • hygeng/humanflow

    Official implementation for UIC-22 "HMES: A Scalable Human Mobility and Epidemic Simulation System with Fast Intervention Modeling"

    Language:C++4100

  • 7cb15/Mobility-in-US-Cities

    Exploring mobility patterns in US cities

    Language:Jupyter Notebook3600

  • BabakAp/encounter-traffic

    Code for "Learning the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study" MSWiM 2018 (ACM: https://dl.acm.org/citation.cfm?id=3242137, Tech Report: https://arxiv.org/abs/1808.03842)

    Language:Jupyter Notebook3200

  • antonindanalet/trip-generation-in-microcensus

    Orderd logit model based on the Mobility and Transport Microcensus 2015 explaining the number of trips (from home to work for now) in Switzerland.

    Language:Python2211

  • Nibamot/NS3-tests

    Test on various scenarios using NS3

    Language:C++2200

  • chrisvoncsefalvay/sadie

    Sadie: Stochastic Agents in DIscrete time and Euclidean space

    Language:Python1341

  • fblamanna/SUMO-to-TrajDataFrame

    Convert SUMO Floating Car Data (FCD) to a TrajDataFrame for scikit-mobility

    Language:Jupyter Notebook0101

  • mgpopinjay/pev-simulation

    Fleet Supply-Demand Simulator for Autonomous Bike Sharing Services using Boston as Test Bed (2016-2019)

    Language:JavaScript08130

  • raviq/Agent_mobility

    Distributed agents for mobility generation

    Language:C0200

  • earthat/RandomWayPointModel

    MATLAB Code for Random Way point Model in WSN

    10

  • Magica-Chen/social-predictability

    Language:Jupyter Notebook30