The rapid adoption of electric vehicles has highlighted the urgent need for efficient and reliable recharging infrastructure. Battery swapping technology offers advantages over traditional plug-in charging, including faster turnaround times, grid-friendliness, and improved space efficiency, and has garnered increasing attention. A centralized-charging battery swapping network comprising swapping stations (BSS) for battery replacement and centralized-charging stations (CCS) for dedicated battery charging can optimize grid load distribution and reduce infrastructure costs. Nonetheless, existing planning approaches focus on facility siting and construction costs, largely overlooking spatiotemporal resource constraints caused by battery distribution and its impact on network operation costs. This oversight results in suboptimal resource allocation, increased operational costs, and conflicts between operator investments and user service quality. To address these challenges, this study proposes a joint planning method for centralized-charging battery swapping networks. This approach integrates dynamic battery logistics with infrastructure siting, aiming to minimize the total cost, which comprises infrastructure, operational expenses, and user time losses.

This study develops a mixed-integer optimization model to formalize the co-location and capacity planning of BSSs and CCSs. The objective function minimizes the annual comprehensive cost, which includes construction and equipment costs for CCSs and BSSs, battery procurement and logistics costs, and user-related costs such as taxi operational losses due to travel, queuing, and swapping times. Constraints include proximity-based demand allocation using Voronoi partitioning, maximum queue length limits to ensure service quality, and a CCS-BSS linkage that ensures each BSS is served by its nearest CCS. A nested simulation framework couples planning with dynamic operations to capture the operational intricacies. Battery logistics are modeled as a multi-vehicle routing problem with hard time windows, which is solved using insertion heuristics after virtual-node transformations to accommodate dynamic delivery requests. Delivery costs include distance-based fuel and lease expenses. CCS charging follows the "shortest time charge first" scheduling, with charger counts derived from daily demand and charging rates. Battery inventories are updated through the operational simulation of BSS and CCS, which calculates minimum procurement thresholds based on non-negative stock levels. The model utilizes an elite-preserving genetic algorithm to optimize siting decisions, iteratively refining planning based on simulation feedback, including delivery costs and battery inventory requirements.

The framework was applied to a case study in Tianjin Binhai New Area, where the optimal configuration consisted of 7 BSSs and 2 CCSs. The cost breakdown revealed that battery procurement was the dominant expense, followed by infrastructure and user time losses, while logistics costs were minimal. Model validation against static logistics baselines demonstrated a 13.60% reduction in battery procurement and a 5.98% decrease in total cost, resulting from the integration of planning and dynamic logistics. Sensitivity analysis revealed that the battery configuration at swap stations, including battery reserve quantity and delivery request thresholds, had a significant impact on the operation of the entire battery swapping network. Additionally, the maximum queue length constraint balanced service level and station construction cost; a smaller queue length required more stations and higher costs, while the total battery purchase quantity varied with queue length to maintain service levels.

This paper integrates the operation and planning of battery swapping and CCS into a unified model that dynamically links site selection with battery delivery costs and user time loss. This approach addresses the shortcomings of traditional planning, which often overlooks delivery costs and the quantity of batteries purchased. Experimental results show a reduction in battery purchases and compressed cost compared to plans that ignore dynamic battery delivery, demonstrating its effectiveness in resource optimization and cost control. Additionally, this study's detailed co-simulation model, which captures battery charging, swapping, and delivery processes, enables multidimensional coordination of charging scheduling, battery reserves, and delivery route planning. Moreover, sensitivity analysis confirms that considering dynamic delivery guides reasonable site selection and resource allocation, while further controlling the scale of battery purchases and reducing comprehensive costs.

Optimising travel structure, improving travel efficiency, and reducing transport carbon emissions are essential paths to green and low-carbon transport development. Research into fine-grained carbon management has received much attention in recent years. However, the implementation is complex, and setting a price on carbon estimation tends to elicit negative feelings from travellers.

In the concept of mobility as a service (MaaS), the service can provide an end-to-end travel service by the combination of multi-transport modes, including roads and public transport, as well as many new forms of transportation. Thus, the service provider can realise flexible price adjustments for multi-transport modes and sections in a single trip. Consequently, this paper proposes a low-carbon-oriented pricing strategy for the service provider. From the different perspectives of the MaaS servicer, travellers and the environment, we propose a multi-objective optimisation model. The object includes maximising service providers' revenue and minimising network travel time and transportation network carbon emissions. The model is a two-layer planning model. The upper layer of the model is the process of finding decision variables to calculate the objective function. The lower layer is the joint traffic mode and route choice process, as well as traffic equilibrium allocation in a multi-modal transportation network. In this model, the joint choice of mode and route of travellers depends on the upper-layer decision variables. Then, to solve the above optimisation problem, the reference point based non-dominated sorting genetic algorithm (NSGA-Ⅲ) and the method of successive algorithm (MSA) are introduced.

The case study was conducted on an example network with 1 origin-destination pair, 16 sections in 3 traffic modes (travel by car, bus, and metro), and 6 nodes. Three representative strategies of Pareto solutions were selected, including optimise service provider benefits (OP-S), optimise network travel time (OP-T), and optimise transportation carbon emissions (OP-C). Furthermore, the original (OR) state was also presented as the background. The result showed that the travel price significantly increased in OP-S, which was unfriendly to travellers. In contrast, OP-T and OP-C were respectively metro-friendly and public transport-friendly strategies. Compared with the OR state, service benefits and carbon emissions were optimised, which means that the service provider could achieve emission reductions in multi-modal transport networks while ensuring their own profitability through rationalised regulation of service pricing. The traffic volume analysis also proved that the service provider could optimise the network travel mode structure, thereby reducing road congestion and increasing the share of public transport. By comparing the results of the optimisation strategies under different demands, we found that with the travel demand increased, the service provider benefits continued to grow (especially in OP-S). Although traffic carbon emissions increased, the optimisations could always reduce the traffic carbon emissions of the system.

This paper validates the feasibility of travel service pricing strategies in multi-modal network traffic optimisation and low-carbon transport development. Service providers should not only seek to maximise their own revenue but also take into account the cost of travel and its impact on the transport environment and take responsibility for the coordination and reduction of transport system emissions. This paper identifies the profitability and responsibilities of travel service providers in the green and low-carbon development of transport and provides a basis for service pricing strategies.