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Traffic congestion results from the spatio-temporal imbalance of demand and supply. With the advances in connected technologies, incentive mechanisms for collaborative routing have the potential to provide behavior-consistent solutions to traffic congestion. However, such mechanisms raise privacy concerns due to their information-sharing and execution-validation procedures. This study leverages secure Multi-party Computation (MPC) and blockchain technologies to propose a privacy-preserving incentive mechanism for collaborative routing in a vehicle-to-everything (V2X) context, which consists of a collaborative routing scheme and a route validation scheme. In the collaborative routing scheme, sensitive information is shared through an off-chain MPC protocol for route updating and incentive computation. The incentives are then temporarily frozen in a series of cascading multi-signature wallets in case vehicles behave dishonestly or roadside units (RSUs) are hacked. The route validation scheme requires vehicles to create position proofs at checkpoints along their selected routes with the assistance of witness vehicles using an off-chain threshold signature protocol. RSUs will validate the position proofs, store them on the blockchain, and unfreeze the associated incentives. The privacy and security analysis illustrates the scheme’s efficacy. Numerical studies reveal that the proposed incentive mechanism with tuned parameters is both efficient and implementable. 

Smart cities seek to leverage data from advanced information, communication, and sensor technologies (ICSTs) for achieving their transportation-related sustainability goals. However, the multi-source, multi-timescale nature of these disparate data sets introduces many challenges to community decision-makers, hindering the use of these technologies in an efficient, effective, and holistic manner. Here, using statistical and machine learning methods, we present a visualization platform developed for the City of Peachtree Corners, GA, comprising nine integrated data sets. This platform can capture dynamic interactions between data from different sources and has the potential to support decision-makers in developing different solution options for contemporary transportation-related problems in a smart city environment.