Increasing the understanding of truck parking behavior in congested urban areas: Behavioral data acquisition and analysis with cumulative prospect theory.

摘要

Congested urban areas are faced with the difficulty of balancing the needs of their residents, businesses, and goods movements, all of which are competing for coveted real estate and curbside. It is increasingly more difficult to park a delivery truck in these areas due to size and location restrictions, which causes drivers to take illegal actions, such as double parking. These parking decisions are influenced by the uncertainty of the levels of congestion and the strictness of parking ticket enforcement.;To investigate this problem, a survey was conducted on truck drivers who make routine deliveries in congested urban areas. In addition to the respondents' demographics, their behavior when facing uncertainty was collected through a series of chained lottery-style questions, which leveraged the tradeoff method in conjunction with cumulative prospect theory (CPT). Behavioral parameters for the CPT functions were estimated through a three-stage limited information maximum likelihood model, which was first validated using 2,300 sets of synthetic data.;Diminishing sensitivity was observed for outcomes in the gain domain, but not for losses. Probabilities in the gain domain were not transformed, while those in the loss domain were overweighted, indicating pessimism. Slight loss aversion was detected with a coefficient of 1.168, which is much lower than the original CPT estimate of 2.25. Drivers working for less than truckload operations exhibited a higher degree of pessimism in the loss domain. Those delivering food or beverages displayed a traditional inverse-S curve in the gain domain, contrary to the linear results for all respondents, and overweighted these probabilities, indicating optimism. These results suggest that, within the bounds of this study, it may be more impactful to focus on enforcement efforts rather than increasing the amount of the parking fine. Future data collection efforts would benefit from finer details pertaining to industry sectors and delivery locations.