npj Sustainable Mobility and Transport最新文献

Network effects in ridesourcing suggest fragmented (multi-platform) markets produce worse rider-driver matches than monopolistic markets. This research addresses two key gaps: (i) understanding how fragmentation costs such as longer detours for travellers, decreased driver income, and increased vehicle kilometres travelled (VKT) vary with market features and user attributes, and (ii) exploring conditions under which fragmented markets sustain. To this end, we propose an agent-based model accounting for day-to-day processes and within-day dynamics in markets with service providers offering private or shared rides. Amsterdam-based experiments reveal that two platforms can co-exist when ride-pooling is offered by just one of them, driven by differences in target demographics, or by both, due to negative network effects from detouring under limited demand. Fragmentation of the ride-pooling market yields 6.4% additional VKT. When both platforms offer (only) private rides, co-existence is feasible when a fraction of travellers and/or potential drivers engages in multi-homing.

Battery electric buses (BEBs) face significant operational constraints that limit their flexibility, especially in rotating vehicles across multiple routes. This study focuses on addressing this limitation by introducing a strategic modelling approach that incorporates BEB rotation as a decision variable into an integrated planning optimization model. The proposed model jointly determines the optimal bus-to-route assignments, charging infrastructure siting and sizing, battery capacities, and charging schedules while accounting for electricity real-time pricing (RTP) rates, greenhouse gas (GHG) emissions charges, and battery degradation. Results of a real-world transit network demonstrate that enabling BEB rotation in the planning phase reduces total system costs by 37.88%, with a 12.18% reduction in capital costs and a 59.42% reduction in operational costs. Sensitivity analyses are conducted to validate the proposed model, highlighting the influence of varying key parameters, including energy consumption, infrastructure costs, charging power, electricity RTP rates, and GHG emissions charges on the optimized outcomes.

Expanding electric vehicle (EV) charging infrastructure is essential for transitioning to an electrified mobility system. With rising EV adoption rates, firms face increasing regulatory pressure to build up workplace charging facilities for their employees. However, the impact of EV charging loads on businesses' specific electricity consumption profiles remains largely unknown. Our study addresses this challenge by presenting a mathematical optimisation model, available via an open-source web application, that empowers business executives to manage energy consumption effectively, enabling them to assess peak loads, charging costs and carbon emissions specific to their power profiles and employee needs. Using real-world data from a global car manufacturer in South East England, UK, we demonstrate that smart charging strategies can reduce peak loads by 28% and decrease charging costs and emissions by 9% compared to convenience charging. Our methodology is widely applicable across industries and geographies, offering data-driven insights for planning EV workplace charging infrastructure.

Since the beginning of motorization, intercity freight transportation modes have carried an ever-increasing load and experienced a nearly continuous decline in average unit costs and energy intensity. Using a unique dataset, we demonstrate the tight, inverse relationships between the average load carried with average transport unit costs and energy intensity, which are invariant across mode, space, and time, for the countries and time periods considered. Our subsequent statistical analysis concludes that-over the last 30-35 years-economies of scale have contributed to 65-85% of the decline in energy intensity, depending on the transport mode, with only the remaining share being due to technological progress. Significant further reductions in average unit costs and energy intensity due to increasing economies of scale seem possible for particularly surface transport modes. Instead of technological advances, their realization could require legal and infrastructure adjustments.

Electric vehicles (EVs) are often hailed as climate saviours by some and dismissed as greenwashing by others. This polarization reduces a complex issue to slogans, when what is needed is an evidence-based perspective. EVs matter, but only if researchers and policymakers place them within broader strategies that also address transport planning, equity, and energy systems.

The European long-distance passenger transport market is rapidly changing. There is a striking discrepancy between the relevance of long-distance travel for emission reduction goals and the lack of knowledge to support its design, planning and policy making. A conceptual representation of this market is provided, and four key scientific challenges are identified and discussed along with a brief review of the state-of-the-art, related knowledge gaps and a corresponding research agenda.

By adopting a more environmentally friendly driving style and promoting sustainable transportation options, automated buses (ABs) could play a role in achieving long-term sustainability goals. This study explores the factors influencing the willingness to use ABs, based on survey data from 451 bus users in Scotland. Using latent class ordered probit analysis, distinct user groups were identified based on their intentions to use unstaffed ABs, along with the key factors influencing their class membership. Concerns about driving capabilities and the implementation of technology emerged as the strongest negative predictors for urban residents without health issues. For rural residents and those with health issues, use intentions were primarily influenced by expectations of the benefits associated with ABs. These insights are valuable for bus manufacturers, service operators, and transport authorities, informing the development of policies and strategies for the future deployment of SAE level 5 ABs.

Uncrewed Aerial Vehicles (UAVs/drones) for deliveries have become a key consideration for logistics planners, particularly in the healthcare industry. Despite reported theoretical cost and speed benefits, few systems have realised long-term financially sustainable operations. This paper explores UAVs in a multi-mode logistics system (vans, UAVs, bicycles) using medical sample delivery case studies in the United Kingdom, and a computational model that accounts for routing, landing restrictions, costs, and payload constraint practicalities. Results identified potential transit time reductions of up to 90% using UAVs, though costs increased significantly (+133%). Achieving these time savings required UAV access to all sites and residents tolerating up to 40 flight movements per hour. Other time savings were possible with partial or no UAV uptake; however, all expedited solutions increased costs, raising the question of the value of such time savings, and whether any benefits would materialise, given onward supply chain limitations and changeable weather.

Learning curves accurately predict the continuing progress of clean energy and mobility technologies but are not systematically used as a basis for evidence-based policy. We present a systems-level learning model for electric trucks to illustrate how this can be done. Focussing on Europe, we use an approach based on learning curves for eTruck drivetrain and battery pack design; battery developments in cost, durability and composition; energy efficiency and CO₂ emissions; weights of all components; electricity and diesel costs; charging costs in different scenarios; and the use of an eTruck fleet with different ranges. Our model shows several tipping points that can lead to fast eTruck adoption. Policies could leverage these tipping points by rewarding longer range, faster charging, vehicle-to-grid capabilities, and an open and interoperable network of eTruck fast-chargers to drive a rapid and cost-effective transition to eTrucks.