Urban freight distribution policies aim to improve the efficiency of deliveries of goods in cities. Local policy makers intervene on rooted, complex and pre-existent relationships. Various are the agents, both collaborating and competing, involved in providing and buying freight distribution services. Retailers, transport providers and own-account agents are among the most important actors; they are all potentially characterized both by inter and intra agent heterogeneity in preferences. Heterogeneity in preferences, whenever present, has relevant implications for policy intervention. There is a knowledge gap related to the peculiarities of these agents’ preferences and behavior, notwithstanding some recent attempts to bridge it, that call for a thorough agentspecific analysis. This paper focuses on urban freight distribution with specific reference to the impact that variations of policy characteristics (e.g. time windows, number of loading and unloading bays, entrance fees, etc.) might cause on own-account agents’ behavior. It is important to underline that, de facto, own-account agents are among the least studied operators in this context. This lack of attention is mostly attributable to the toil needed to acquire relevant data to study their preferences and behavior. This lack of knowledge has favored the birth of a widely accepted presumption concerning their inefficiency that, in turn, has produced specifically targeted policies often hindering their activities. This paper reports the empirical results of a study conducted in the limited traffic zone in Rome’s city center in 2009 thanks to a Volvo Research Foundation grant. The analysis is based on a comprehensive and representative data set including: 1) general information on the respondent, 2) company characteristics, and 3) stated ranking exercises. The ranking data were subsequently transformed in choice data. The paper describes own-account operators’ preferences as they emerge from the stated ranking exercises and proposes a systematic comparison among them via willingness to pay measures. The compared estimates are derived under different assumptions concerning agents’ preference heterogeneity. More in detail we discuss results assuming: 1) no heterogeneity (multinomial logit), 2) covariates-explained heterogeneity (multinomial logit including interactions with relevant socio-economic variables), 3) flexible heterogeneity (investigating the systematic and stochastic components of the utility function). Heterogeneity analysis, apart from relevant theoretical implications, has important policy repercussions in as much as it impacts on the willingness to pay measures of the policies implemented. An appropriate treatment of heterogeneity is therefore functional to obtaining undistorted and reliable policy forecasts to be fed to micro simulation models used to support the decision-making process. The paper: 1) addresses methodologically relevant issues, 2) uses a new, detailed and significant data set, 3) tackles policy relevant questions, 4) provides worthwhile information for policy-makers. The estimation of willingness to pay/willingness to accept measures for hypothetical policies sets a benchmark for policy makers and researchers alike.