This report documents the second year of a project using economics experimental techniques to investigate novel approaches for mitigating congestion caused by non-recurring traffic incidents. The first year demonstrated the feasibility of this approach and carried out a number of experiments using University of California, Irvine (UCI) undergraduates as experimental subjects. The experimental platform is described in Section 3 of this report. Most of the experiments conducted during the first year examined different variable message sign (VMS) wording, and later experiments examined standard road pricing schemes. It was discovered that providing any incident-related information via VMS improves system performance relative to the no-information baseline, but also found that more complicated dynamic messaging with feedback did not always improve system performance relative to standard VMS messaging. The first year results were documented in the final report for the first year project. (Brownstone et al. 2016) The second year of this project had three goals: 1) show the results from UCI undergraduates are representative of behavior in the larger driving population, 2) investigate theoretically superior auction-based road pricing schemes, and 3) make the driving simulator more realistic. Due to unforeseen problems with achieving the first goal, the third goal of making the simulator more realistic was abandoned. Follow-up interviews with experimental subjects did not indicate that they had troubles relating the existing simulator to real-world driving conditions. Given the difficulty of getting a representative sample of drivers to come to the UCI experimental laboratory, it was decided to implement the real-time experiments using the Amazon Mechanical Turk (MTurk) platform. This approach made it affordable to run experiments using a much larger and representative pool of experimental subjects. Unfortunately, the limitations of the MTurk platform, coupled with the challenges of remotely administered sessions, made converting and running experiments much more difficult than anticipated. Nevertheless, enough experiments were completed to show that the first year results were not substantially altered by using a more diverse and representative experimental subject pool. The work with the MTurk platform is described in Section 4 of this report. Dynamic road pricing is another possible tool for managing road congestion. However, optimal pricing requires that system operators know the distribution of the Value of Time (VOT) for road users. It is difficult to measure the VOT distribution using standard transportation survey techniques, and there is evidence that VOT varies across trip purposes and time. The second goal of this project was to investigate the possibility of using preference elicitation methods to elicit the VOT for each road user. This procedure was implemented in the experimental platform and carried out a series of experiments using UCI experimental subjects. Although the method used gives incentives for subjects to truthfully reveal their VOT, the results show that due to the cognitive complexity of the process many subjects reported erroneous VOT values. Nevertheless, the efficiency loss due to these errors was small, demonstrating this is a promising new method of managing congestion. This work is described in Section 5 of this report.

The effective management of traffic incidents and other irregular disruptions on roadways is key to minimizing travel delay and improving the quality of life for urban residents and businesses. We are currently using economic experiments involving human subjects and a networked, realistic driving simulation to study driver behavior in response to information displayed by variable message systems and to dynamic road pricing schemes. Based on our existing results, we propose four new extensions to our study: the addition of more realistic driving mechanics to test driver responses to our treatments under increased cognitive load, the recruitment of subjects outside the UCI student body to confirm the validity of our results with different demographic groups, the implementation of treatments to study the impact of private information messaging systems (e.g. Waze, Google Maps, etc.), and the implementation of treatments to study a novel value-of-time based auction system for toll lane pricing and allocation. Improvements to the driving realism and the representativeness of our experimental subject pool will strengthen the robustness and validity of our study’s results, while the investigation of private information messaging and value-of-time auction scenarios will shed light on their potential for improving transportation management.

We demonstrate the possibility of conducting synchronous, repeated, multi-game economic decision-making experiments with hundreds of subjects in-person or remotely with live streaming using entirely mobile platforms. Our experiment provides important proof-of-concept that such experiments are not only possible, but yield recognizable results as well as new insights, blurring the line between laboratory and field experiments. Specifically, our findings from 8 different experimental economics games and tasks replicate existing results from traditional laboratory experiments despite the fact that subjects play those games/task in a specific order and regardless of whether the experiment was conducted in person or remotely. We further leverage our large subject population to study the effect of large (N = 100) versus small (N = 10) group sizes on behavior in three of the scalable games that we study. While our results are largely consistent with existing findings for small groups, increases in group size are shown to matter for the robustness of those findings.