The Smart Public Transport Lab develops new solutions and methods for public transport planning, operations and management. In the Smart PT Lab we are passionate about performing high level scientific research with a practical relevance and impactful outcomes. Welcome on-board!

 

The Smart Public Transport Lab develops new solutions and methods for public transport planning, operations and management. In the Smart PT Lab we are passionate about performing high level scientific research with a practical relevance and impactful outcomes. Welcome on-board!

SPTL Seminar (15th of July) by Mike Ma: "The Cost of Mobility on Demand"

Mike Ma

Date: Monday 15 July 2019
Time: 12:00 - 13:00
Location: CEG 2.62

Smart Public Transport Lab Seminar (Monday 15 July 2019): by Zhenliang (Mike) Ma, PhD - Lecturer/Assistant Professor at Institute of Transport Studies, Department of Civil Engineering, Monash University, Australia

Zhenliang (Mike) Ma is a Lecturer (Assistant Professor) at Monash University, Australia. Mike’s research is at the intersection of optimization, machine learning, and computer simulation. His research focuses on the inference, prediction and optimization of network control, through the integration of novel data sources, mostly from automated data collection systems, into mathematical learning models. The application areas cover public transport and shared mobility. Before joining Monash as a faculty staff, Mike was a Postdoc Research Associate at MIT Transit Lab, leading the MIT-Mass Transit Railway (MTR), Hong Kong project. Mike’s contributions on urban data analytic and applications in transportation are published in prestigious transportation journals and patented by the National Intellectual Property Administration. The established models on network state monitoring and prediction are industrialized in MTR, Hong Kong. Technology drives innovation, but smart and sustainability go beyond technology. Currently, Mike focuses on:

  • Revitalizing public transport through big data analytics – planning, monitoring, information, control and demand management;
  • Designing platform strategy and operation business models to engineering the sharing economy (in mobility) to attain predictable and socially desirable outcomes, and value creation for public and private sectors.

He held a seminar on: The Cost of Mobility on Demand

Mobility-On-Demand (MoD) services (Uber, Lyft, DiDi) are transforming urban mobility ecosystem by providing more flexibility and improved level of service to users. However, they also raise a lot of concerns for their impact on congestion, vehicle miles traveled (VMT), and competition with transit. Considering the popularity of the MoD services, promoting and increasing the ride-pooling is an important means to address these concerns. While a growing literature addresses the question of how to operate the MoD service more efficiently, much less is known about the cost of on-demand, i.e. VMT added to the system when ride-pooling is not explored to its full potential. The paper aims to empirically evaluate the cost of on-demand mobility. We present a general operating model for an advanced request version of a Demand Responsive Ride-Pooling (DRRP) system with service constraints. Advanced requests mean that all the requests are received before the time of vehicle dispatching. An efficient algorithm for request matching and vehicle routing is proposed. A large-scale dataset from the operations of a major ride-hailing company is used to systematically assess the performance, in terms of VMT, of the advanced requests system relative to current practices. The impact of various design aspects of the advanced requests system (e.g. advanced requests horizon, vehicle capacity, etc.) on its performance are investigated. The sensitivity of the results to user preferences in terms of level of service (time to be served and excess trip time) and willingness to share are explored. The results suggest that even with a short advanced request horizon, significant benefits with respect to VMT reduction can be realized, with very little deterioration of the level of service customers experience. The proposed model and results can also help evaluate the usefulness of alternative business models for ride sharing services towards more sustainable mobility concepts.

The slides used for the seminar can be found here.

 

Events

19 Sep



Transport Thursday: 3x Mobility as a Service

12:30


CEG 0.96, Delft, Netherlands

9 Oct - 11 Oct



European Transport Conference 2019

Dublin Castle, Dublin, Ireland

21 Oct



MaaS@AMS - Sharing the Future of Urban Mobility

12:00


AMS Institute, Amsterdam, Netherlands

30 Oct - 1 Nov



2nd Annual Meeting of the Cycling Research Board

Delft, Netherlands

Smart Public Transport Lab

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