The dedicated lanes management policy is a possible solution to the issues arising from the coexistence of human-driving vehicles (HDVs) and connected and autonomous vehicles (CAVs) in traffic. Although numerous studies have been conducted on the network deployment problem of CAV-dedicated lanes, the safety implications of CAV and its dedicated lanes are ignored. This study proposes a mathematical approach to optimize the deployment of CAV-dedicated lanes incorporating efficiency and safety concerns. An integrated framework is developed based on headway distributions to systematically evaluate the efficiency and safety performance of the road network. The platoon intensity index is utilized to model the platooning effect of CAVs on traffic safety and efficiency. A safety performance estimation method is proposed to account for the potential collision risk of mixed traffic flow and heterogeneity in car-following behavior. A bi-level programming model is adopted to solve the optimal deployment problem. The upper-level model is formulated as a bi-objective model to minimize the total travel time and the safety risk. The lower-level model describes the multi-class user equilibrium state of the CAV-HDV mixed traffic flow on the network. A genetic algorithm is utilized to solve the bi-level programming model and obtain the Pareto-optimal solution set. Two numerical studies are conducted to validate the proposed model and algorithm. The results revealed that the optimal deployment plan can significantly improve the road network’s safety and efficiency performance, whereas higher platoon intensities have a negative impact on traffic safety and efficiency for certain headway settings. Moreover, the results highlighted a trade-off between efficiency and safety in the optimal deployment problem, which may help decision-makers choose the optimal deployment plan based on the road network design needs.

With the rapid development of e-commerce and the dramatic upsurge in direct-to-consumer deliveries, the last-mile problem has become increasingly apparent. With the distinct advantages of bringing economies of scale and providing 24/7 contactless self-service, smart parcel lockers play a critical role in solving the last-mile problem. However, due to a lack of planning, myopia expansion, and an ambiguous profit model, smart parcel locker suppliers in China have been suffering huge economic losses, restricting their further development. In the study, on the basis of an in-depth analysis of the cost elements and major revenue sources of smart parcel lockers, we propose a bilevel programming model to optimize the location of community smart parcel lockers with the upper-level model maximizing the profit of a third-party smart parcel locker supplier and the lower-level model maximizing user satisfaction. Then, a solution algorithm based on the genetic algorithm is proposed. Finally, some numerical experiments are carried out based on a medium-scale residential community in Jiading District, Shanghai. The sensitivity analyses conducted in this study reveal how the user satisfaction evaluation and the investment budget influence the expected profit. The modelling framework and numerical results can provide third-party smart parcel locker suppliers with significant theoretical support and practical guidance on planning the investment budget and optimizing the smart parcel locker locations to maximize their profit.