## 1 Introduction  
Traffic congestion occurs as a result of an imbalance in demand and supply on a spatio-temporal scale. The well-known Braess' Paradox demonstrates that relying solely on supply side solutions that focus on increasing the capacity of existing infrastructure without regard for traveler behavior may have a negligible or even detrimental effect on the performance of traffic networks. Demand-side strategies employ behavioral interventions to encourage shifts in travel mode, travel routes, and departure times [1, 2]. Of these, en route re-routing is the most challenging problem due to the inherent dynamics and randomness. Over the last few decades, effective behavioral intervention strategies have been developed, such as toll and incentive mechanisms, using model-based [3, 4, 5, 6, 7] or model-free [8, 9, 10] approaches to influence travelers' en route behavior and thereby alleviate traffic congestion. However, these intervention strategies frequently overlook individual-level heterogeneity, rely on multiple user classes to reflect distinct behavioral patterns, and suffer from computational tractability issues associated with centralized computation. Emerging connected technologies enable information sharing among vehicles and infrastructure through vehicle-to-everything (V2X) communications in a connected traffic environment [11, 12, 13, 14, 15], facilitating more informed collaborations among connected vehicles (CVs) in the re-routing process [16, 17, 18]. [PERSON] et al. [19] proposed a routing method facilitatingthe navigation through passing time windows in a connected vehicle environment; this has informed the direction of our study. Additionally, [PERSON] et al. [20] developed a self-evolving routing method, which introduces a novel formulation in the spatial domain that resolves the mismatch between routing and planning found in conventional studies. This spatial domain-based planning method represents a key contribution to the field of cooperative routing. Nevertheless, individual heterogeneities are still not captured in these studies. [PERSON] et al. [21] proposed a novel incentive mechanism for collaborative routing in a connected and autonomous vehicular environment, which leverages individual heterogeneity in the route preferences to enhance the system performance while ensuring user satisfaction. A decentralized computational framework was developed to enable efficient network-level deployment. On the other hand, individual heterogeneity necessitates that each passenger discloses their personal preferences (e.g., value of time) as model inputs, which can represent sensitive information that can be exploited. Also, as collaborative routing reveals model outputs, travelers' updated routes, and incentives to other travelers, it raises privacy issues. Moreover, validation of execution (i.e., validating whether each vehicle travels on the route selected in the incentive mechanism) can cause a substantial computation burden and result in more privacy leaks if traditional centralized methods like sharing GPS traces are used. Further, such privacy risks may act as a barrier to participation in the system for societally vulnerable groups (e.g., sharing a low value of time may indicate a low income level), further impairing mobility equity. Blockchain has been gaining enormous attention since the whitepaper on Bitcoin [22]. A blockchain is a decentralized ledger with tamper resistance ensured by cryptography. The popularity of blockchain is not limited to cryptocurrencies. Due to its decentralized nature, it has demonstrated its promise in Internet of Things and vehicular ad hoc networks [23; 24; 25]. However, currently, a blockchain is mainly used to record and share public information such as traffic incidents [26] rather than sensitive personal information in transportation applications. In other studies [27], a blockchain is also used to store virtual credits (which quantify the level of trust that users can place in certain users based on their historical behavior).