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Real time optimization of multi-modal shared mobility systems


Matthias Binder

Joe Warrington, Tyler Summers

Shared mobility and integrated transportation networks are becoming a cornerstone of many cities' development strategies. Understanding how these may be operated more efficiently is essential in order to overcome issues such as congestion and pollution plaguing modern cities.

This project will build on our previous successful work in the area, and develop new theoretical formulations for the operation of shared mobility systems incorporating multiple modes of transportation. These include bike sharing systems, short-term vehicle rental systems, and Autonomous Mobility on Demand (AMoD), in which driverless vehicles are "summoned" to help a passenger carry out a planned journey.

The following steps will be carried out:
  1. Review some suggested recent literature on shared mobility optimization, with particular focus on multi-modal/integrated transport operations.
  2. Extract bulk data from the New York City bike sharing scheme ( in order to build a model of short-trip transport demand in the city. We propose NYC because of its data availability and the convenience of Manhattan's grid-like layout.
  3. Develop an optimization formulation and simulation approach for a coordinated transport problem including higher-speed vehicles such as autonomous or self-driving cars. We have previously developed a simulator for bike sharing London, which may be useful as a starting point.
  4. Make simplifications to the global problem in order to be able to solve the approach at scale. In particular, we wish to pay attention to behaviours that arise from rational decisions of individual journey-making "agents", and operational decisions which must be made by a central coordinator.
  5. Use the model to gain design insights, e.g. optimal number of bikes vs cars on the streets, and the resulting cost of using staff to re-balance the system throughout the day.

Further information on our past work on shared mobility systems can be found in previous publications that have arisen from this work:

Paper 1
Paper 2
Paper 3

Weitere Informationen

John Lygeros

Art der Arbeit: 50% theoretical, 50% simulations (buiding on existing simulators or developing new tools as desired)
Voraussetzungen: Some optimization and control knowledge and an interest in modelling large-scale systems. Relevant programming experience would be a plus.
Anzahl StudentInnen: 1
Status: done
Projektstart: Summer/Sept 2017
Semester: Autumn 2017