Guido Cantelmo
Guido Cantelmo
Email verificata su tum.de
Titolo
Citata da
Citata da
Anno
An adaptive bi-level gradient procedure for the estimation of dynamic traffic demand
G Cantelmo, E Cipriani, A Gemma, M Nigro
IEEE Transactions on Intelligent Transportation Systems 15 (3), 1348-1361, 2014
522014
Two-step approach for correction of seed matrix in dynamic demand estimation
G Cantelmo, F Viti, CMJ Tampère, E Cipriani, M Nigro
Transportation Research Record 2466 (1), 125-133, 2014
232014
Incorporating activity duration and scheduling utility into equilibrium-based Dynamic Traffic Assignment
G Cantelmo, F Viti
Transportation Research Part B: Methodological 126, 365-390, 2019
142019
A Markov chain dynamic model for trip generation and distribution based on CDR
SA Di Donna, G Cantelmo, F Viti
2015 International Conference on Models and Technologies for Intelligent …, 2015
132015
A utility-based dynamic demand estimation model that explicitly accounts for activity scheduling and duration
G Cantelmo, F Viti, E Cipriani, M Nigro
Transportation Research Part A: Policy and Practice 114, 303-320, 2018
112018
A Utility-based Dynamic Demand Estimation Model that Explicitly Accounts for Activity Scheduling and Duration.
G Cantelmo, F Viti, E Cipriani, M Nigro
Transportation research procedia 23, 440-459, 2017
112017
Improving the reliability of a two-steps dynamic demand estimation approach by sequentially adjusting generations and distributions
G Cantelmo, F Viti, E Cipriani, M Nigro
92015
Incorporating trip chaining within online demand estimation
G Cantelmo, M Qurashi, AA Prakash, C Antoniou, F Viti
Transportation Research Procedia 38, 462-481, 2019
82019
A two-steps dynamic demand estimation approach sequentially adjusting generations and distributions
G Cantelmo, F Viti, E Cipriani, N Marialisa
2015 IEEE 18th International Conference on Intelligent Transportation …, 2015
82015
The impact of route choice modeling on dynamic OD estimation
E Cipriani, A Del Giudice, N Marialisa, F Viti, G Cantelmo
2015 IEEE 18th International Conference on Intelligent Transportation …, 2015
72015
Assessing the consistency between observed and modelled route choices through GPS data
SN Hadjidimitriou, M Dell'Amico, G Cantelmo, F Viti
2015 International Conference on Models and Technologies for Intelligent …, 2015
72015
Crowdsensed data learning-driven prediction of local businesses attractiveness in smart cities
A Capponi, P Vitello, C Fiandrino, G Cantelmo, D Kliazovich, U Sorger, ...
2019 IEEE Symposium on Computers and Communications (ISCC), 1-6, 2019
52019
Effectiveness of the two-step dynamic demand estimation model on large networks
G Cantelmo, F Viti, T Derrmann
2017 5th IEEE International Conference on Models and Technologies for …, 2017
52017
Generating macroscopic, purpose-dependent trips through Monte Carlo sampling techniques
A Scheffer, G Cantelmo, F Viti
Transportation Research Procedia 27, 585-592, 2017
52017
Effects of incorporating activity duration and scheduling utility on the equilibrium-based dynamic traffic assignment
G Cantelmo, F Viti
52016
A low dimensional model for bike sharing demand forecasting
C Guido, K Rafał, A Constantinos
2019 6th International Conference on Models and Technologies for Intelligent …, 2019
42019
Using passive data collection methods to learn complex mobility patterns: an exploratory analysis
B Toader, G Cantelmo, M Popescu, F Viti
2018 21st International Conference on Intelligent Transportation Systems …, 2018
42018
Explaining demand patterns during COVID-19 using opportunistic data: a case study of the city of Munich
V Mahajan, G Cantelmo, C Antoniou
European Transport Research Review 13 (1), 1-14, 2021
32021
The impact of human mobility on edge data center deployment in urban environments
P Vitello, A Capponi, C Fiandrino, G Cantelmo, D Kliazovich
2019 IEEE Global Communications Conference (GLOBECOM), 1-6, 2019
32019
Leveraging GIS Data and Topological Information to Infer Trip Chaining Behaviour at Macroscopic Level
F Carrese, G Cantelmo, G Fusco, F Viti
2019 6th International Conference on Models and Technologies for Intelligent …, 2019
32019
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20