On March 14th, 2023, Renming Liu successfully defended his Ph.D. thesis entitled “Design, Optimization and Simulation of Tradable Mobility Credit”.
Examiners were Professors Thomas Kjær Rasmussen (DTU), Michel Bierlaire (EPFL), Erik Verhoef (Vrije Universiteit Amsterdam), and Yiik Diew Wong (NTU).
The defense session was chaired by Prof. Ninette Pilegaard.
You can find below the abstract of the thesis. The thesis includes five interesting papers! Two published and three under review. For more information, feel free to contact Renming at firstname.lastname@example.org. Renming will continue his journey at DTU as a Postdoctoral Researcher! We wish him all the best in his future endeavors.
Road traffic congestion is a critical problem affecting urban mobility worldwide and its severity continues to increase, causing significant costs at the individual, environmental, economic, and societal levels. While a significant agenda has been put forward on the transport supply side, mostly driven by vehicle technology (automation and electrification), demand shifts are often considered a hard-to-reach but effective means to reduce the social and environmental costs associated with transport. Demand management has thus become an increasingly important focus of the policy agenda in many metropolitan areas.
Congestion pricing as a demand management instrument has been widely investigated in both theory and practice motivated by its potential gains in social welfare. Nevertheless, congestion pricing often receives political and social resistance as it is perceived as a tax and, in some contexts, inequitable. An alternative market-based solution called a tradable credit scheme (TCS), already deployed in other economic sectors, has been receiving attention in recent years within the mobility sector. In a typical TCS system, a regulator predetermines a total quota of credits available for the area and period of interest and distributes these credits to all potential travelers. The credits can be bought and sold in a free market at a price determined by credit demand and supply. When performing a trip, a traveler may then be charged for credits. Consequently, a tradable credit scheme has mainly three potential advantages over congestion pricing (without revenue redistribution): (i) TCS is revenue neutral as there is no monetary transfer to or from the regulator; (ii) TCS can be more equitable than congestion pricing since the inconvenience caused by the limited use of vehicles is compensated by selling extra credits; (iii) TCS has been shown to yield efficiency gains under uncertainty when congestion is significant. The first two features of TCS could help address the long-standing issue of public opposition to congestion pricing.
This thesis is divided into three parts: i) Part I presents two studies on the formulation and application of machine-learning techniques to optimize the design of tariff (credit charging) schemes, ii) Part II includes two studies proposing different trading mechanisms (peer-to-regulator and peer-to-peer) for trip- and area-based credit charging in urban networks, and iii) Part III proposes a detailed and flexible simulation framework for assessing the impact of different closer-top-practice TCS designs by extending a state-of-the-art agent-based mobility simulation platform (SimMobility).
In summary, this PhD study contributes to the body of literature in mobility demand management, namely in tradable credit schemes, covering the topics of machine-learning based optimization, market design for TCS, and the design and assessment using more realistic and complex modelling frameworks. This thesis provides promising simulation-based optimization approaches for the optimal design of tariffs, enabling the efficient design of demand management instruments, such as TCS and road pricing. The findings of this thesis also bring insights into the properties of the area-based TCS as well as key modeling and implementation frameworks for the design of future TCS including trading behaviors, credit allocation, expiration, trading patterns, price adjustment, and complex behavioral changes. Ultimately, this thesis sheds light into a new policy alternative to manage mobility and, more specifically, congestion, contributing to the ultimate priority of a more sustainable transportation sector in the years to come.