2022-04-26 – David Levinson, Transportist

Abstract

Under the current practice of benefit-cost analysis, the direct economic benefits produced by a newly built transit facility are assessed based on how it affects travel time and various costs that are associated with transport needs and travel behavior. However, the time-saving-based benefit calculation approach has been questioned and criticized. Given the strong correlation between accessibility and land value, we propose the access-based land value benefit assessment as an alternative, and apply this assessment method to analyzing the Second Avenue Subway project in Manhattan, New York. The primary principle of the access-based method is that the economic value of a transport project’s intangible gains is largely capitalized by nearby properties’ value appreciation, which is directly caused by improved transport accessibility. We find that: (i) the actual travel time saving is lower than originally forecast; (ii) a strong positive correlation between residential property value and job accessibility by transit is observed; (iii) the appreciation in sold property value and rented property value both far exceed total project cost; and (iv) such results support the decision to approve and construct the Second Avenue Subway.

Congratulations to Ang Ji for “satisfying the requirements for the award of the degree of Doctor of Philosophy at the University of Sydney.”

Thesis Title: Traffic programming: Aligning incentives for socially efficient lane changes among non-connected vehicles.

Lead Supervisor: Professor David Levinson.

Abstract: This dissertation explores the rationality of drivers’ risky and aggressive behaviors in lane-changing scenarios and discusses some feasible ways to hold selfish drivers accountable for their decisions. Regardless of potential congestion and crashes suffering by other road users, rational drivers prefer to maximize their gains and demand others’ yielding. However, when all of them have such thoughts, conflicts (dilemmas) are embedded in their interactions, leading to unexpected consequences for the whole traffic. This question is investigated analytically by exploiting the game theory concept. A simplified 2×2 non-cooperative game is built to model strategies executed by human drivers without communications. This research learns driver behavior in two predefined sub-phases: `Stay’ and `Execution’ from empirical data. This procedure examines the factors that impact drivers’ execution of lane changes. From the results, we understand that lane-changing is motivated by the urgency to change and the dissatisfaction with current circumstances. The analytical model is then established by integrating driver incentives into payoff functions. The `greed’ and `fear’ of drivers in this process are quantified by speed advantages and possible crash costs respectively, so they trade off these factors and make decisions based on their own and opponents’ estimated payoffs. Using a numerical case study, we find that social gaps exist between user-optimal and system-optimal strategies when drivers mostly engage in selfish behaviors, significantly deteriorating the total system benefit. Pricing can be a sufficient tool to incentivize users to cooperate with others and achieve win-win outcomes. It is posited that the designed pricing schemes may promote the negotiation between drivers, reducing collision risks and improving operational traffic efficiency. Several simulation experiments are then conducted to evaluate this dissertation’s hypotheses on the performance of pricing rules. Overall, the proposed framework develops a behavioral model and improvement schemes from the perspective of microscopic vehicular interactions. The conclusions will hopefully find their applications in autonomous vehicle-human interaction algorithms and future transportation systems.

Journal articles related to the dissertation include:

Ji, Ang and Levinson, D. (2020) Estimating the Social Gap with a Game Theory Model of Lane Changing. IEEE Intelligent Transportation Systems Transactions. 22(10) 6320-6329. [doi][VIDEO]
Davis, Blake, Ji, Ang, Liu, Bichen, and Levinson, D. (2020) Moving Array Traffic Probes. Frontiers in Future Transportation. doi: 10.3389/ffutr.2020.602356. [doi][VIDEO]
Ji, Ang and Levinson, D. (2020) Injury severity prediction from two-vehicle crash mechanisms with machine learning and ensemble models. IEEE Open Journal of Intelligent Transportation Systems. [doi][VIDEO]
Ji, Ang and Levinson, D. (2020) An energy loss-based vehicular injury severity model. Accident Analysis and Prevention. 146 October 2020, 105730. [doi][VIDEO]
Ji, Ang and Levinson, D. (2020) A Review of Game Theory Models of Lane Changing. Transportmetrica A. 16(3), 1628–1647. [doi][VIDEO]

Dr. Ji now has a position at Southwest Jiaotong University in Chengdu, one of China’s leading transport programmes.

The idea of Traffic Programming was first raised in this blog a while back (in 2016).

We recently were awarded a grant from the Australian Research Council to examine this question in further depth.

Design of micro-decisions in automated transport. Australian Research Council DP220100882 Professor David Levinson; Professor Michael Bell; Dr Mohsen Ramezani; Professor Dr Kay Axhausen; Professor Dr Hai Yang.