most citedKnowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network

7 citations · 9 across the 3 of their papers we have counts for

collaborators

5 papers

math.OC2020

A chance-constrained dial-a-ride problem with utility-maximizing demand and multiple pricing structures

Xiaotong Dong, Joseph YJ Chow, S Travis Waller +1

The classic Dial-A-Ride Problem (DARP) aims at designing the minimum-cost routing that accommodates a set of user requests under constraints at an operations planning level, where…

cs.AI20207 cited

Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network

Can Li, Lei Bai, Wei Liu +2

Accurate demand forecasting of different public transport modes(e.g., buses and light rails) is essential for public service operation.However, the development level of various mod…

math.OC2020

Incentive-compatible mechanisms for online resource allocation in mobility-as-a-service systems

Haoning Xi, Wei Liu, David Rey +2

In the context of `Everything-as-a-Service', the transportation sector has been evolving towards user-centric business models in which customized services and mode-agnostic mobilit…

math.OC2019

Freeway network design with exclusive lanes for automated vehicles under endogenous mobility demand

Shantanu Chakraborty, David Rey, Michael W. Levin +1

Automated vehicles (AV) have the potential to provide cost-effective mobility options along with overall system-level benefits in terms of congestion and vehicular emissions. Addit…

physics.soc-ph20192 cited

Integrating Travel Demand and Network Modelling: a Myth or Future of Transport Modelling

Ali Najmi, David Rey, Taha H. Rashidi +1

In this paper, a novel transport planning model system (TPMS) is formulated which is built on the concepts of supernetworks, multi-modality, integrity and calibration. In the propo…