Benefiting from unified sensors and long-term traffic engagement, for-hire vehicles (FHVs) are widely considered the mainstay for vehicular crowd sensing (VCS) tasks. However, incentivizing FHVs to participate in sensing tasks remains a fundamental challenge for FHV-enabled VCS systems: for one thing, the distribution diversity of orders and tasks limits FHVs from participating in VCS; for another, FHVs’ operating states determine whether they are free to execute sensing tasks. To address the above issues, this article proposes SHIP, a State-aware Hybrid Incentive Program for FHV-enabled VCS systems. Our proposal finely categorizes FHVs’ operating states and provides a hybrid incentive scheme that incorporates both opportunistic and participatory approaches. We also introduce coverage diversity to reflect the distribution of FHVs and sensing tasks. By combining coverage diversity with vehicle revenue, we establish a dynamic multi-objective optimization model to select appropriate FHVs for sensing to achieve a multi-win situation. Experiments based on real-world datasets show that our proposal can effectively utilize FHVs with different operating states to improve the quality of sensing tasks while increasing FHVs’ revenue.

With the development of intelligent transportation technology, more and more traffic information can be obtained to enhance arterial coordination efficiency. A time-varying arterial coordination control program is proposed in this paper. By extracting Origin-Destination (OD) information from Automatic number plate recognition (ANPR) data, an OD-based Multipath-Band method (MP-BAND) is developed to achieve the function of the program. The proposed method breaks vehicle routing at intersections, and treats one link as an analysis unit. The method can automatically select link paths for coordination, and multiple paths can be synchronized simultaneously according to the actual traffic condition. The overlap bandwidth is introduced to capture the connectivity between paths of adjacent intersections. The PM-BAND is formulated as a mixed-integer linear program, which can be solved by the standard branch-and-bound technique. Numerical tests are conducted to evaluate the performance of MP-BAND under different traffic conditions. The results have demonstrated that MP-BAND outperformed MULTIBAND and AM-BAND in various aspects. MP-BAND can improve network performance significantly almost in all scenarios. Moreover, the intersection efficiency was improved significantly, especially the efficiency of left-turning vehicles. Also, MP-BAND can well recognize major routes and improve traffic efficiency. Compared with Synchro, MP-BAND also had superiority when flow rate is not too high. Thus, MP-BAND has a much wider applicability, and can be treated as an optimization core to achieve time-varying arterial coordination.

Autonomous Vehicle Platooning (AVP) presents a promising approach to reducing energy expenses, improving traffic safety, and enhancing capacity. In multi-platoon vehicular networks, platoons can collaborate to ensure better data transfer reliability. However, the existing secure information-sharing methods between platoons face efficiency and security challenges. These challenges arise from (1) a lack of an efficient method to simultaneously ensure confidentiality, authenticity, and non-repudiation of the transmitted information; (2) difficulty in cross-domain dynamically sharing ciphertexts to multiple recipients is challenging. To overcome these challenges, we propose a Flexible Cross-domain Data Access Control (FC-DAC) system in this paper. Our FC-DAC system ensures confidentiality, authenticity, and non-repudiation of transmitted information while enabling high-efficiency ciphertext sharing between various platoons. Additionally, the FC-DAC solution allows vehicles with valid authorizations to access signcrypted information quickly and efficiently, even if they belong to different platoons. Our theoretical and experimental analysis demonstrates that the FC-DAC scheme is resistant to various common attacks and is applicable to multi-platoon vehicular networks.

A mixed traffic environment of manual driving and automatic driving will become the norm in future intelligent transportation systems. The deep reinforcement learning (DRL) method has shown significant promise in cooperative control for traffic lights and connected autonomous vehicles in a mixedtraffic environment.However, the uncertainty and noise in integrating agents' observations can lead to inadequate exploration of environmental data by DRL algorithms. Consequently, these algorithms are prone to overfitting and becoming trapped in local optimal, which limits the performance of control strategies. To more effectively harness the gathered environmental data and thereby facilitate improved decision-making by agents, a DRL-based cooperative control method with fuzzy feature fusion (F3DRL) was proposed in this paper. First, the Adaptive Fuzzy Inference Module is implemented to adaptively mitigate information uncertainty as the data from connected autonomous vehicles (CAV) is aggregated. Then, a Deep Information Extraction Module was introduced and integrated with the output of the adaptive fuzzy inference module to establish a Parallel Feature Fusion Module. The Adaptive Fuzzy Inference Module mitigates uncertainty in the extracted traffic environmental states, while the Deep Information Extraction Module facilitates the extraction of a more comprehensive environmental representation. The fusion of features derived from these two distinct modules aids DRL agents in making better action selections, which significantly enhances the effectiveness and stability of the F3DRL method. In simulations, F3DRL significantly reduced travel and delay times, fuel consumption, and CO2 emissions, outperforming both traditional and state-of-the-art methods