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Concept

Taxi

Publications associées (11)

Modeling and Optimization of Ridesplitting Operations

Caio Vitor Beojone

Ridesourcing has driven a lot of attention in recent years with the expansion of companies like Uber, Lift, and many others around the world. Companies use mobile applications connected through the internet to match drivers and their passengers real-time. ...

EPFL2023

Hierarchical Pricing Game for Balancing the Charging of Ride-Hailing Electric Fleets

Nikolaos Geroliminis, Nils Gustav Nilsson, Marko Maljkovic

Due to the ever-increasing popularity of ride-hailing services and the indisputable shift towards alternative fuel vehicles, the intersection of the ride-hailing market and smart electric mobility provides an opportunity to trade different services to achi ...

2023

A data fusion approach for ride-sourcing demand estimation: A discrete choice model with sampling and endogeneity corrections

Michel Bierlaire

Ride-sourcing services offered by companies like Uber and Didi have grown rapidly in the last decade. Understanding the demand for these services is essential for planning and managing modern transportation systems. Existing studies develop statistical mod ...

2023

Modeling lane changing and lane choice using naturalistic data from a swarm of drones

The pNEUMA dataset is the result of a unique field experiment using a swarm of ten drones flying over the central district of Athens, Greece. Using this newly available dataset, this paper focuses on two directly related topics: i) lane changing and ii) la ...

2022

On the inefficiency of ride-sourcing services towards urban congestion

Nikolaos Geroliminis, Caio Vitor Beojone

The advent of shared-economy and smartphones made on-demand transportation services possible, which created additional opportunities, but also more complexity to urban mobility. Companies that offer these services are called Transportation Network Companie ...

2020

Inferring Accurate Bus Trajectories from Noisy Estimated Arrival Time Records

Lakmal Buddika Meegahapola

Urban commuting data has long been a vital source of understanding population mobility behaviour and has been widely adopted for various applications such as transport infrastructure planning and urban anomaly detection. While individual-specific transacti ...

IEEE2019

Anytime Heuristic for Weighted Matching Through Altruism-Inspired Behavior

Boi Faltings, Aris Filos Ratsikas, Panayiotis Danassis

We present a novel anytime heuristic (ALMA), inspired by the human principle of altruism, for solving the assignment problem. ALMA is decentralized, completely uncoupled, and requires no communication between the participants. We prove an upper bound on th ...

2019

What Regulations For Ict-Based Mobility Services In Urban Transportation Systems? The Cases Of Ride-Booking Regulation In Sao-Paulo And Rio De Janeiro

Maxime Ugo Julien Audouin

Urban transportation is currently experiencing major changes, namely because of disruptive innovations driven by the information and communication technologies (ICTs). During the past few years, ride-booking has emerged in many cities around the world as o ...

WIT PRESS2018

PrivateRide: A Privacy-Enhanced Ride-Hailing Service

Jean-Pierre Hubaux, Kévin Clément Huguenin, Italo Ivan Dacosta Petrocelli, Florian Tramèr, Thi Van Anh Pham

In the past few years, we have witnessed a rise in the popularity of ride-hailing services (RHSs), an online marketplace that enables accredited drivers to use their own cars to drive ride-hailing users. Unlike other transportation services, RHSs raise sig ...

2017

Collaborative Transportation Systems

We propose a new class of applications for Intelligent Transportation Systems (ITSs), called collaborative transportation applications that aim at solving transportation problems such as congestion and parking. Specifically, we define two applications: Sma ...

IEEE2010

Automatic skill acquisition in Reinforcement Learning using connection graph stability centrality

Martin Hasler, Ali Ajdari Rad

Reinforcement Learning (RL) is an approach for training agent's behavior through trial-and-error interactions with a dynamic environment. An important problem of RL is that in large domains an enormous number of decisions are to be made. Hence, instead of ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010