Intelligent transportation systems (ITS) have gained significant attention from various communities, driven by rapid advancements in informational technology. Within the realm of ITS, navigational recommendation systems (RS) play a pivotal role, as users often face diverse path (route) options in such complex urban environments. However, RS is not immune to vulnerabilities, especially when confronted with potential information-based attacks. This study aims to explore the impacts of these cyber threats on RS, explicitly focusing on local targeted information attacks in which the attacker favors certain groups or businesses. We study human behaviors and propose the coordinated incentive-compatible RS that guides users toward a mixed Nash equilibrium, under which each user has no incentive to deviate from the recommendation. Then, we delve into the vulnerabilities within the recommendation process, focusing on scenarios involving misinformed demands. In such cases, the attacker can fabricate fake users to mislead the RS's recommendations. Using the Stackelberg game approach, the analytical results and the numerical case study reveal that RS is susceptible to informational attacks. This study highlights the need to consider informational attacks for a more resilient and effective navigational recommendation.

翻译：智能交通系统因信息技术的快速发展而受到各界的广泛关注。在智能交通领域，导航推荐系统发挥着关键作用，因为用户在复杂的城市环境中常面临多样的路径选择。然而，导航推荐系统并非无懈可击，尤其是在面对潜在的信息攻击时。本研究旨在探索此类网络威胁对推荐系统的影响，重点聚焦于攻击方偏袒特定群体或商户的局部定向信息攻击。我们研究人类行为并提出了协调性激励相容推荐系统，该系统可引导用户达到混合纳什均衡——在此均衡状态下，任何用户均无动机偏离推荐结果。随后，我们深入剖析推荐过程中的脆弱性，重点关注错误信息需求场景。在此类案例中，攻击者可通过伪造虚假用户误导推荐系统的决策。基于斯塔克尔伯格博弈方法的分析结果与数值案例研究表明，推荐系统易受信息攻击影响。本研究强调了为构建更具韧性与高效的导航推荐系统而需重视信息攻击的必要性。