On the public citation graph

Controlling the Transport Demand of the FIFA World Cup 2022 in Qatar

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4615-5203-1_2openalex-extracted

Discrete choice models and their applications to short term travel decisions

inferredcites →https://doi.org/10.1007/b101877openalex-extracted

Handbook of Transportation Science

inferredcites →https://doi.org/10.1016/j.cosrev.2017.03.001openalex-extracted

Agent Based Modelling and Simulation tools: A review of the state-of-art software

inferredcites →https://doi.org/10.1016/j.ijtst.2017.05.005openalex-extracted

Assessing the impacts of deploying a shared self-driving urban mobility system: An agent-based model applied to the city of Lisbon, Portugal

inferredcites →https://doi.org/10.1016/j.jtrangeo.2007.12.004openalex-extracted

Travel-to-school mode choice modelling and patterns of school choice in urban areas

inferredcites →https://doi.org/10.1016/j.tranpol.2019.04.005openalex-extracted

A discrete choice analysis of transport mode choice causality and perceived barriers of sustainable mobility in the MENA region

inferredcites →https://doi.org/10.1016/j.tranpol.2021.01.002openalex-extracted

Taxi vs. demand responsive shared transport systems: An agent-based simulation approach

inferredcites →https://doi.org/10.1109/tits.2010.2048313openalex-extracted

A Review of the Applications of Agent Technology in Traffic and Transportation Systems

inferredcites →https://doi.org/10.1155/2022/8382754openalex-extracted

Fixed-Route vs. Demand-Responsive Transport Feeder Services: An Exploratory Study Using an Agent-Based Model

inferredcites →https://doi.org/10.1017/cbo9780511805271openalex-snapshot

Discrete Choice Methods with Simulation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.5225835openalex-snapshot

Real-Time Passenger Flow Balancing for Urban Rail Transit: A Computationally Efficient Optimization Approach for Peak Demand Management

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