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Connected and Automated Vehicles (CAVs) have the potential to enhance traffic safety and efficiency. In contrast, aligning both vehicles’ utility with system-level interests in scenarios with conflicting road rights is challenging, hindering cooperative driving. This paper advocates a game theory model, which strategically incorporates deceptive information within incomplete information vehicle games, operating under the premise of imprecise perceptions. The equilibria derived reveal that CAVs can exploit deceptive strategies, not only gaining advantages that undermine the utility of the other vehicle in the game but also posing hazards to the overall benefits of the transportation system. Vast experiments were conducted, simulating diverse inbound traffic conditions at an intersection, validating the detrimental impact on efficiency and safety resulting from CAVs with perception uncertainties, and employing deceptive maneuvers within connected and automated transportation systems. Finally, the paper proposes feasible solutions and potential countermeasures to address the adverse consequences of deception in connected and automated transportation systems. It concludes by calling for integrating these insights into future research endeavors and pursuing to fully realize the potential and expectations of CAVs in enhancing the whole traffic performance. 

Emerging on-demand service platforms (OSPs) have recently embraced teamwork as a strategy for stimulating workers’ productivity and mediating temporal supply and demand imbalances. This research investigates the team contest scheme design problem considering work schedules. Introducing teams on OSPs creates a hierarchical single-leader multi-follower game. The leader (platform) establishes rewards and intrateam revenue-sharing rules for distributing workers’ payoffs. Each follower (team) competes with others by coordinating the schedules of its team members to maximize the total expected utility. The concurrence of interteam competition and intrateam coordination causes dual effects, which are captured by an equilibrium analysis of the followers’ game. To align the platform’s interest with workers’ heterogeneous working-time preferences, we propose a profit-maximizing contest scheme consisting of a winner’s reward and time-varying payments. A novel algorithm that combines Bayesian optimization, duality, and a penalty method solves the optimal scheme in the nonconvex equilibrium-constrained problem. Our results indicate that teamwork is a useful strategy with limitations. Under the proposed scheme, team contest always benefits workers. Intrateam coordination helps teams strategically mitigate the negative externalities caused by overcompetition among workers. For the platform, the optimal scheme can direct teams’ schedules toward more profitable market equilibria when workers have inaccurate perceptions of the market. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services. Funding: This work was supported by the National Science Foundation [Grant FW-HTF-P 2222806]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/serv.2023.0320 . 

Truck platooning enabled by connected automated vehicle (CAV) technology has been demonstrated to effectively reduce fuel consumption for trucks in a platoon. However, given the limited number of trucks in the traffic stream, it remains questionable how great an energy saving it may yield for a practical freight system if we only rely on ad-hoc platooning. Assuming the presence of a central platooning coordinator, this paper is offered to substantiate truck platooning benefits in fuel economy produced by exploiting platooning opportunities arising from the United States’ domestic truck demands on its highway freight network. An integer programming model is utilized to schedule trucks’ itineraries to facilitate the formation of platoons at platoonable locations to maximize energy savings. A simplification of the real freight network and an approximation algorithm are used to solve the model efficiently. By analyzing the numerical results obtained, this study quantifies the importance of scheduled platooning in improving trucks’ fuel economy. Furthermore, the allowable platoon size, schedule flexibility, and fuel efficiency all play a crucial role in energy savings. Specifically, by assuming that following vehicles in a platoon obtain a 10% energy reduction, an average energy reduction of 8.48% per truck can be achieved for the overall network if the maximum platoon size is seven, and the schedule flexibility is 30 min. The cost–benefit analysis provided at the end suggests that the energy-saving benefits can offset the investment cost in truck platooning technology.