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

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.