Ride-sharing services operated by transportation network companies (TNCs) have the potential to expand capacity and accommodate increasing urban mobility demands, presenting an alternative to traditional ride-hailing services. This study introduces a high-capacity ride-sharing (HCRS) system that leverages user-specific travel choices and incentive-based pricing schemes. This innovative system enhances the dynamic matching problem of HCRS by incorporating a nested choice model and dynamic fare adjustment strategies to boost profitability while encouraging shared travel behaviours. Additionally, a rolling horizon solution approach is employed, including a shared choice set generation algorithm for creating shared alternatives and an Adaptive Large Neighborhood Search (ALNS)-based method for optimal matching. By leveraging a real dataset from Beijing's ride-hailing services, this research underscores that the HCRS service can significantly improve system efficiency and service quality, achieving more than 10.44% reduction in operating costs, and reducing average fares (¥3.31) and emissions (3.49 kg) across various users, compared to traditional ride-hailing services. The findings also demonstrate that users' decision-making is profoundly affected by changes in incentives, highlighting the importance of incentive settings in enhancing user engagement and system performance.

Rutting is one of the most common distresses in early damage to asphalt pavements. It can raise the risk of ride safety issues, accelerate pavement deterioration, and increase maintenance costs. To investigate the factors that affect the rutting resistance of asphalt mixtures, internal factors (such as aggregate gradation, asphalt content, and layer thickness), external factors (including temperature and traffic loading), and human factors (such as compaction degree) were tested using wheel tracking tests. The test results showed that the rutting resistance of asphalt mixtures can be improved by designing a tightly interlocked aggregate skeleton using the Bailey method's primary control sieve, using an optimal asphalt content, achieving sufficient compaction, maintaining a layer thickness of 2.5–3 times its nominal maximum aggregate size, using an asphalt softening point higher than the pavement temperature, and avoiding overloaded vehicles. In highly rutted areas, it is recommended to use a stone mastic asphalt with a stable aggregate skeleton matrix and styrene-butadiene-styrene modified asphalt with a softening point higher than the highest pavement temperature.

This paper presents a novel framework for customised modular bus systems that leverages travel demand prediction and modular autonomous vehicles to optimise services proactively. The proposed framework addresses two prediction scenarios with different forward-looking operations: optimistic operation and pessimistic operation. A mixed integer programming model in a space-time-state network is developed for the optimistic operation to determine module routes, schedules, formations and passenger-to-module assignments. For the pessimistic case, a two-stage optimisation procedure is introduced. The first stage involves two formulations (i.e., deterministic and robust) to generate cost-saving plans, and the second stage adapts plans with control strategies periodically. A Lagrangian heuristic approach is proposed to solve formulations efficiently. The performance of the proposed framework is evaluated using smartcard data from Beijing and two state-of-the-art machine learning algorithms. Results indicate that the proposed framework outperforms the real-time approach in operating costs and highlights the role of module capacity and time dependency.