Research on the supply–demand dynamics of Shared Automated Vehicle (SAV) services has grown rapidly. However, the resulting models are often highly complex nonlinear systems that are difficult to solve. This study has two main objectives: (1) to investigate profit-maximising pricing and fleet management strategies for SAV services while accounting for supply–demand interactions, congestion effects, and travellers’ heterogeneous income levels; and (2) to develop and compare several solution methods for determining these strategies. We formulate a mixed-integer nonlinear programming model for the SAV fleet management problem under three pricing strategies: a base fare plus a distance-based fare, a distance-based fare only, and an income class–based fare. To solve this model, we propose three algorithms designed to address its nonlinearities: a linearisation-based method (P1), a hybrid particle swarm optimisation method (P2), and a hybrid Bayesian optimisation method (P3). We also examine the feasibility of the solutions obtained by these methods with respect to the original problem, and introduce a correction procedure when infeasibility arises. The proposed methods are evaluated through a small case study and a larger case study based on Delft, the Netherlands. The results show that all three methods can find good-quality solutions within short computational times in the small case study, while P3 achieves the best objective value in the Delft case study. From a practical perspective, the Delft case study shows that the first pricing strategy can encourage active transport for short trips, the second leads to the lowest congestion but also the lowest profit, and the third achieves the highest profit by attracting more demand and exploiting the lower price sensitivity of high-income travellers, despite causing higher congestion. Additional analyses further examine the effects of income class segmentation, income distribution, and congestion.

Vehicle-to-grid (V2G) technology enables electric vehicles (EVs) to function as both energy consumers and suppliers, potentially improving power grid stability while offering economic benefits to users. Before large-scale implementation, however, the viability of V2G must be assessed, specifically, the extent to which people are willing to adopt it. This study investigates people's willingness to switch from their current mode of transport to two V2G-enabled options: private EVs with V2G, and carsharing EVs with V2G. To achieve this goal, a stated choice experiment was designed and conducted in the Netherlands. For the data analysis, we introduce a random-parameters rank-ordered logit model to capture preference heterogeneity and unobserved correlations between the two V2G options. The results indicate strong inertia towards retaining the current (status quo) travel mode compared with the two V2G alternatives. Cost savings from V2G participation significantly increase the likelihood of choosing both V2G options, whereas the minimum guaranteed battery range has a limited effect once trip-range requirements are satisfied. Preferences also differ systematically between respondents with different status quo travel modes and across socio-demographic groups. These findings support targeted policy design to accelerate V2G adoption.

Ride-hailing companies will face the emergence and gradual expansion of AVs-only zones in urban areas where only automated vehicles (AVs) are allowed to circulate. When owning a mixed fleet (automated and conventional taxis), a ride-hailing company has to determine the optimal fleet size as a function of the gradually expanding coverage of AVs-only zones while taking into account interactions with privately-owned human-driven vehicles. To model this problem, we propose a bi-level framework in which the lower level captures the mixed routing behaviour of the vehicles and the endogenous traffic congestion, and the upper level determines fleet sizes to maximise profit. A parallel genetic algorithm is introduced to solve this bi-level framework, which is embedded with a tailored algorithm for solving the lower-level model. Numerical experiments are conducted on instances based on a small network and the network of the city of Delft, The Netherlands, to demonstrate the performance of the proposed solution method and investigate the impacts of AVs-only zones on traffic and ride-hailing operations. Results indicate that the fleet size of automated taxis increases nonlinearly with the expansion of the AVs-only zone while that of conventional taxis decreases as demand shifts from human-driven vehicles to automated taxis. The fleet size decision depends heavily on the fleet's cost structure, the location and the distribution of parking depots. Furthermore, the existence of an AVs-only zone leads to detours for human-driven vehicles in the early stages, but it will bring major benefits by reducing congestion as its size increases.

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments.

Shared automated vehicles (SAV) are expected to benefit the sustainable development of urban regions and alleviate the negative impacts brought by the increasing number of private cars. In this paper, we envision a future scenario where non-pooled SAVs replace private cars and provide public on-demand mobility services to satisfy the mobility needs of a city's residents. To help service providers make profitable fleet sizing and management decisions, we develop a mixed-integer non-linear programming model that considers the congestion effects and the mode choice of urban travellers in different income classes, between SAVs and bicycles. Our model optimises both strategic decisions (fleet size, initial fleet distribution, and service quality level) and operational decisions (trip assignment, vehicle routing, parking, and relocation). Travellers’ preference for both transport modes is described through a binary logit model and congestion effects are described by dynamically varying travel times with respect to traffic flow in a non-linear fashion. In addition, we investigate two types of accept/reject mechanisms (mandatory vs. non-mandatory acceptance) which lead to an endogenously determined acceptance rate that can affect travellers’ willingness to use SAV services. The computational challenge posed by the non-linear and non-convex nature of the model is addressed through reformulation and the use of outer-inner approximation methods combined with a breakpoint generation algorithm. We demonstrate the effectiveness of our proposed method in a case study of the city of Delft in The Netherlands, as well as a scaling analysis on three toy networks with various sizes and demand profiles. A sensitivity analysis of key parameters is carried out to assess system performance. Computational results indicate that fleet sizing decisions are influenced not only by the population's geographical distribution and land use patterns but also by the pricing strategy, unit operating costs of the SAV fleet, network congestion level, and traveller behaviour. When the price rate of using SAVs is low, the fleet sizing decisions can also be influenced by the trip accept/reject mechanism and the travellers’ sensitivity to the service quality level. In addition, a low price of SAV service will attract more users but may not necessarily bring a higher profit because of the increased traffic congestion.

The era of intelligent transportation with automated vehicles (AVs) is coming. Nonetheless, the transition to this system will be a gradual process. On the one hand, some zones in the city may be dedicated to AVs with a fully intelligent traffic management system geared toward high performance. On the other hand, automated and conventional vehicles may have to be allowed to drive in the remaining zones of the urban network in a transition stage. In this paper, we consider a situation where AVs are deployed by a taxi operating company to serve door-to-door travel requests. Facing this transition period, a strategic flow-based vehicle routing model is developed to determine the optimal fleet size of automated and conventional taxis as a function of the gradually increasing coverage of the AVs-only dedicated area. Traffic congestion is considered through flow-dependent travel times. Two taxi company service regimes are tested: the User Preference Mode (UPM) and the System Profit Mode (SPM). In the UPM, passengers can choose their preferred vehicle type according to their preference. In the SPM, the taxi company will take charge of the vehicle assignment to maximize the system profit. The developed model formulations are applied to a case study of a large toy network. The results give insight into the performance of the heterogeneous taxi system on a hybrid network. Strategies are presented on how to adjust the fleet size of automated and conventional taxis to get the best system profit while satisfying the mobility demand. The SPM can bring more profit to the operating company by reducing the detour and relocation cost of taxis, reducing the salaries for drivers through a bigger fleet size of AVs, and reducing the delay penalty, compared to the UPM.