Transport decarbonisation requires spatial assessment of where EV adoption is feasible, not merely whether nominal range is sufficient, but whether demographic readiness, infrastructure provision, and trip characteristics are jointly satisfied. Low-cost, short-range (frugal) EVs represent an affordability-oriented segment that may address equity gaps left by premium EVs, yet their spatial viability has not been systematically assessed at trip level. Existing studies evaluate demographic adoption determinants, trip-purpose suitability, or charging infrastructure in isolation, few integrate these dimensions at trip level or distinguish conversions achievable immediately from those that depend on targeted infrastructure investment. This study presents a transferable framework that integrates demographic adoption propensity, purpose-specific suitability, network-routed range evaluation, and spatially resolved charging infrastructure assessment into a sequential feasibility pipeline. Each trip is classified into one of three tiers: immediately feasible, constrained unlockable, or currently infeasible. Applied to 8.13 million daily car trips across Scotland, 35.5% show structural conversion potential; within this conversion-potential subset, 30.8% are immediately feasible and 69.2% are constrained unlockable. Conversion rates vary roughly twofold across the 14 trip purposes, with commuting and shopping exhibiting the highest potential. The proposed analytical methodology is transferable to any region/country with census-level demographic data and origin-destination records; trip-purpose feasibility rankings are expected to generalise across comparable contexts.

Battery swapping is an efficient recharging method for electric vehicles (EVs). In taxi fleets, its shorter recharging time reduces drivers’ concerns about remaining battery energy and shortens downtime during services. In this study, we analyse real-world operational data from 872 electric taxis to assess the effectiveness of adopting battery swapping technology (relative to plug-in charging) on reducing range anxiety and improving operational efficiency. Our findings show that battery swapping technology significantly mitigates range anxiety among electric taxi drivers. This effect is largely driven by its pronounced impact under low- and moderate temperature conditions, with non-significant effects observed in high-temperature environments. Additionally, its impact on operational efficiency exhibits a similar pattern—battery swapping significantly improves efficiency in low- and moderate temperature settings. These findings offer new empirical evidence on the operational implications of battery swapping, particularly under varying temperature conditions.

Vehicle-to-grid (V2G) technology enables electric vehicles (EVs) to discharge stored energy back into the grid, improving grid stability and renewable energy integration. Despite optimistic market forecasts projecting growth to 62 billion by 2033, V2G adoption remains predominantly at the pilot stage. This commentary reviews the current status of V2G technology, examining technical maturity, economic feasibility, stakeholder perspectives, and regulatory environments across regions. Demonstration projects in Denmark, the UK, Japan, and China have confirmed technical viability and economic promise, yet widespread commercial deployment faces significant challenges. Major barriers include immature business models, battery degradation concerns from frequent charge cycles, lack of standardized communication protocols, and insufficient consumer participation due to limited incentives and low public awareness. The study summarizes successful global pilots, identifies critical obstacles to large-scale implementation, and highlights strategic recommendations. Broad adoption requires coordinated efforts in standardizing technologies, developing clear incentives, supportive policies, and enhancing consumer engagement through targeted demonstrations and education.

Electrifying road transport is essential for net-zero transitions, yet large-scale residential EV charging can intensify peaks in residential low-voltage distribution networks and increase technical losses. Prior studies show that bidirectional charging (V2G) can reduce peak demand and that distributed solar generation can offset local electricity use, but these resources are often analysed separately and rarely within an integrated framework that jointly quantifies peak impacts and converter-side and network-side efficiency penalties. This study develops a peak-minimisation optimisation model that coordinates EV charging and discharging while coupling V2G with distributed solar generation, including rooftop PV and vehicle-integrated PV. The model incorporates plug-in availability, daily mobility energy requirements, state-of-charge bounds, charging/discharging power limits, UK smart-meter household demand profiles, travel-behaviour-informed EV energy needs, home-availability patterns, and the IEEE European low-voltage test feeder. Four scenarios are compared: unidirectional charging, V2G, V2G with a grid-renewable setting, and V2G with distributed solar. Results show that V2G alone reduces peak demand by 5.72% at 50% EV penetration, whereas coupling V2G with distributed solar achieves a larger reduction of 14.62% and lowers feeder I2R losses, despite higher conversion losses from increased energy shifting. A Monte Carlo-based uncertainty analysis further confirms that these findings remain valid under combined weather variability and stochastic EV user behaviour. Under uncertainty, the PV-aware V2G scenario achieves the lowest mean peak demand, 21.73 kW, and the largest average peak reduction, 11.58%. These findings support PV-aware coordinated charging as a practical option for peak management, feeder-loss reduction and resilient residential low-voltage energy management.

Vehicle electrification presents challenges and opportunities across multiple sectors, including the automotive, energy and infrastructure domains. Battery charging and swapping are the two primary technologies for refuelling electric vehicles (EVs). However, the involvement of multiple participants and various factors makes EV refuelling a complex and multi-domain issue. Since conventional conductive charging requires vehicles to remain stationary for a period of time, parking naturally provides opportunities for EV charging. Therefore, parking and EV charging are intrinsically connected in how they are organised and planned. This paper presents a comprehensive literature review on the features of EV refuelling demand and its relation to parking patterns. The review focuses on key study issues related to the interaction between EVs and the power grid, namely forecasting, planning, and scheduling. These issues are examined at three different scales: the individual, station, and regional levels. Based on the findings from the literature, an integrated framework is provided to capture the features and linkages between refuelling demand and parking patterns across the different study issues and scales. Finally, the paper proposes several open issues that could be explored in future studies from the perspective of integrating parking and refuelling analysis.

Departure time models are key tools for understanding time-varying travel demand. Nonetheless, there is limited research focusing on the analysis of trip scheduling decisions in the context of public transport users. In particular, research on how public transport users adapt departure times when the activity and travel landscape are altered as a consequence of disruptive events (e.g. pandemics, social unrest), is yet to be conducted. Smart card data, which passively records time-stamped departure locations of public transport users, offers the opportunity to investigate such shifts in detail but is yet to be utilised. The paper aims to address these two gaps by using smart card data to investigate the trip scheduling decisions of bus commuters amid disruptive events. This goal is achieved by estimating departure time choice models (DTCMs) for characteristic episodes between 2019 and 2022 for Santiago's bus system, a city affected to different degrees by two types of disruptive events within this timeframe: the COVID-19 pandemic and social unrest. The paper addresses the methodological challenges of calculating schedule delay with smart card data by estimating preferred arrival times as a random variable within a mixed multinomial logit model. The approach is assessed through the valuation of the trade-off between travel time and schedule delay (TVSD), with the results falling within the range of values previously reported in the literature. The model results highlight the existence of multi-temporal differences in the arrival time preferences of bus commuters, as well as in their TVSD amid disruptive events. It was found that bus commuters were less willing to accept an increase in their travel time to reduce their schedule delay during disruptive episodes. The heterogeneity between bus travellers was also explored: recurrent bus commuters exhibited higher TVSDs than occasional commuters. The outcome of this study supports using smart card data as a feasible source to investigate how public transport passengers allocate their trip scheduling both during normal periods and amid external disruptions.

Existing day-to-day models assume that travellers make decisions based on traffic conditions from previous days rather than those on the actual travel day. However, this approach cannot capture frequent within-day updates of travel information, significantly influencing both day-to-day and within-day dynamics. To address this, we proposed the concept of a within-day decision-making framework and developed a mathematically simple and concise Markovian-based model. Additionally, to characterise and quantify the impacts of the proposed model, we introduced three multi-faceted measures: the convergence speed towards the stationary distribution, its key characteristics, and the within-day adaptation capability, all designed to reflect the stability and resilience of the transport system. We conducted numerical experiments on the departure time choice problem in both a single-link network and the Sioux Falls network. The results clearly demonstrate the impacts of the proposed decision-making framework through the proposed measures, particularly highlighting its effects on the stability and resilience of the transport system.

Two-seater battery electric microcars (BEMs), combining lightweight architectures with small battery packs, offer a frugal, energy-saving electrification option for urban mobility. However, their scenario-specific energy-saving characteristics and incremental environmental impacts under contrasting electricity-generation mixes remain insufficiently quantified, constraining the evidence base for targeted deployment. This study integrates a validated GT-SUITE vehicle simulation framework with a well-to-wheel (WTW) assessment to quantify scenario-dependent energy consumption and WTW CO2 and NOX emissions, energy-related WTW PM2.5 emissions, and non-exhaust PM2.5 emissions for a representative two-seater BEM, benchmarked against a typical mid-size battery electric vehicle (BEV) and a diesel internal combustion engine vehicle (ICEV). Energy consumption under WLTC-based urban, rural, and motorway operating scenarios is analysed, and WTW assessments are conducted for five countries with markedly different electricity-generation mixes. The results show that the representative two-seater BEM achieves its largest energy-saving advantage in urban stop-and-go driving, reducing energy consumption by ∼43% relative to the typical mid-size BEV, where low-speed, high-acceleration driving amplifies the benefit of lower mass. However, this advantage largely vanishes on motorways, falling to ∼4%, as steady high-speed cruising reduces mass sensitivity and BEM-specific powertrain and aerodynamic losses become more influential. Relative to the mid-size BEV, the two-seater BEM, owing to its energy-saving characteristics, concentrates its environmental benefits in urban operation, and the magnitude of these benefits is strongly governed by grid carbon intensity. In carbon-intensive electricity systems such as those in China, the BEM's urban energy-related WTW reductions in CO2, NOx, and PM2.5 emissions were found to be approximately 32, 45, and 40 times larger than under clean-electricity conditions such as those in Norway (Based on 2025 country-average electricity-generation mixes). When benchmarked against a diesel ICEV, the BEM delivers more pronounced mitigation benefits for WTW CO2 and NOx. However, in carbon-intensive electricity systems such as China, the BEM's energy-related WTW PM2.5 emissions can exceed those of the ICEV, with the largest exceedance occurring under motorway operation.

We study the stochastic assignment game and extend it to model multimodal mobility markets with a regulator or a Mobility-as-a-Service (MaaS) platform. We start by presenting general forms of one-to-one and many-to-many stochastic assignment games. Optimality conditions are discussed. The core of stochastic assignment games is defined, with expected payoffs of sellers and buyers in stochastic assignment games as payoffs from a hypothetical "ideal matching" that represent sellers' and buyers' expectations under imperfect information. To apply stochastic assignment games to the urban mobility markets, we extend the general stochastic many-to-many assignment game into a stochastic Stackelberg game to model MaaS systems, where the platform is the leader, and users and operators are the followers. The platform sets fares to maximize revenue. Users and operator react to the fare settings to form a stochastic many-to-many assignment game considering both fixed-route services and Mobility-on-Demand (MOD). The Stackelberg game is formulated as a bilevel problem. The lower level is the stochastic many-to-many assignment game between users and operators, shown to yield a coalitional logit model. The upper-level problem is a fare adjustment problem maximizing revenue. An iterative balancing algorithm is proposed to solve the lower-level problem exactly. The bilevel problem is solved through an iterative fare adjusting heuristic, whose solution is shown to be equivalent to the bilevel problem with an additional condition when it converges. Two case studies are conducted. The model can be applied to design MaaS fares maximizing income of the platform while anticipating the selfish behavior and heterogeneity of users and operators. Public agencies can also use the model to manage multimodal transportation systems.

We study the dynamics and equilibria of a new kind of routing games, where players - drivers of future autonomous vehicles - may switch between individual (HDV) and collective (CAV) routing. In individual routing, just like today, drivers select routes minimizing expected travel costs, whereas in collective routing an operator centrally assigns vehicles to routes. The utility is then the average experienced travel time discounted with individually perceived attractiveness of automated driving. The market share maximising strategy amounts to offering utility greater than for individual routing to as many drivers as possible. Our theoretical contribution consists in developing a rigorous mathematical framework of individualized collective routing and studying algorithms which fleets of CAVs may use for their market-share optimization. We also define bi-level CAV - HDV equilibria and derive conditions which link the potential marketing behaviour of CAVs to the behavioural profile of the human population. Practically, we find that the fleet operator may often be able to equilibrate at full market share by simply mimicking the choices HDVs would make. In more realistic heterogenous human population settings, however, we discover that the market-share maximizing fleet controller should use highly variable mixed strategies as a means to attract or retain customers. The reason is that in mixed routing the powerful group player can control which vehicles are routed via congested and uncongested alternatives. The congestion pattern generated by CAVs is, however, not known to HDVs before departure and so HDVs cannot select faster routes and face huge uncertainty whichever alternative they choose. Consequently, mixed market-share maximising fleet strategies resulting in unpredictable day-to-day driving conditions may, alarmingly, become pervasive in our future cities.

The coupling of microscopic traffic simulation models with emission models offers a powerful tool for assessing and optimising traffic control strategies to reduce fuel consumption and vehicle emissions. Although many studies use traffic simulation for emission analysis and designing traffic control measures, most focus on calibrating a selected traffic model to replicate observed traffic flow. This raises a critical question: are the resulting optimal emission control strategies adequately designed to account for the sensitivity of traffic models in capturing vehicle dynamics and emissions? To address this issue, we compared three car-following models—the Krauss model, the Intelligent Driver Model (IDM), and the Wiedemann model—each rooted in distinct theoretical frameworks to understand traffic dynamics. We evaluated their performance in optimising road speed limits to minimise (PMx) emissions in a school case study. A school was selected as the case because children are highly vulnerable and particularly exposed to pollutants during their school commute, and their exposure can be mitigated through optimal traffic control. Our findings reveal that, even when tuned to achieve comparable levels of traffic flow, the models displayed significant differences in their objective functions for traffic control optimisation. These discrepancies stemmed from variations in fuel consumption and particulate matter (PMx) emission patterns resulting from the traffic dynamics captured by the selected traffic model. At a macroscopic level (e.g., average speed, flow, and density), the models exhibited minimal differences. However, at a microscopic level (e.g., acceleration, deceleration rates, and deviations from the mean), pronounced differences became evident. These results highlight that while certain traffic control strategies appeared less effective, revisiting and critically examining the limitations of the models is essential to ensure robust and tailored solutions for emission reduction.

This study investigates the mathematical properties and network performance of the P-0 policy through numerical analysis. We consider a static traffic assignment problem with a P-0 policy in a simple signal-controlled network. We then calculate the equilibrium states while changing the demand level from unsaturated to near-saturated to investigate whether uniqueness holds for various traffic demand levels. After investigating the uniqueness through numerical analysis, we examine the stability of the computed equilibrium states using a graphical approach. This approach enables us to assess stability under natural evolutionary dynamics without explicitly specifying the dynamical system. Through these analyses, we demonstrate the occurrence of multiple equilibria with different total travel times at certain moderate traffic demand levels and investigate their stability. We demonstrate that such multiple stable equilibria cause a hysteresis loop, implying that different stable equilibria with different total travel times emerge when traffic demand increases and then decreases.

Reciprocal communication between road users is a vital element of road user interaction. Non-cooperative game theory is an effective framework for modelling and characterising communicative behaviour between road users, which enables the study of emergent benefits for both the issuer and recipient of communicative signals. In this paper, we introduce discretionary communication to gain an advantage over the other road user by masking one’s intent if beneficial to do so. We conduct a series of experiments with simulated interactions and compare interaction outcomes where communication is mandatory against those where communication is discretionary. Our findings further support the premise that non-cooperative game theory is an effective paradigm for modelling and producing emergent behaviours which benefit the network. Moreover, we see that including a layer of discretionary communication reaps benefits in interaction outcome to the communicator. It also provides benefits in safety to all parties involved above and beyond the benefits seen from mandatory communication.

As vehicle exhaust regulations become more stringent, non-exhaust particulate matter (PM) emissions, particularly from brake wear, which accounts for up to 55% mass of these emissions, have become major contributors to traffic-related PM. However, how low-emission driving behavior influences brake wear PM emissions in real-world conditions remains unclear. In this study, we developed a low-emission driving assistance application and, for the first time, evaluated the real-world impact of low-emission driving behavior (LEDB) on brake wear PM2.5 and PM10 emissions. LEDB training was implemented for volunteer drivers in Leeds and Helsinki, resulting in average reductions in brake wear PM2.5 emissions by 22.8% and PM10 emissions by 26.1%. Additionally, the promotion strategies for LEDB training are discussed, and the expected emission reduction effects across different implementation scenarios are analyzed. These findings demonstrate that LEDB represents a promising and cost-effective approach that could contribute to reductions in brake wear emissions and improved air quality.

Multi-region Macroscopic Fundamental Diagram (MFD) traffic equilibrium models have been developed as a more easily calibratable, maintainable, and computationally efficient alternative to traditional link-network traffic assignment models with full disaggregate network representation. There are four gaps in the research into these models that we highlight: i) the lack of stochasticity accounted for in the modelling of regional path choice, ii) the estimation of parameters of regional path choice models within the traffic equilibrium, iii) regional path choices being based on region travel times actually experienced (rather than instantaneous travel times), and iv) the paucity of real-life case studies. Motivated by these gaps, this paper presents a new dynamic multi-region MFD Stochastic User Equilibrium (SUE) model, and applies it in a real-life case study. The traffic dynamics are described by a new traffic propagation model utilising features of a space-time graph. Regional path choices can be based on region travel times actually experienced. The model produces continuous equilibrated regional path choice probability outputs, thereby facilitating the development of a rigorous statistical estimation procedure for calibrating parameters from tracked regional path choice data. This estimation procedure is operationalised in a large-scale and detailed multi-region MFD system, with 39 underlying rural and urban regions and 96 directional, superimposed motorway regions, 135 regions in total. Results provide empirical evidence to support hypotheses that regional path choice modelling should consider stochasticity, regional path overlap, multiple attributes, and experienced region travel times. Numerical experiments also demonstrate continuity, differences between the instantaneous and experienced dynamic models, relative insensitivity to the time-slice grain, and realism of the model.

Frugal electric vehicles (EV) are designed for lower energy consumption with lower battery capacity, but their life-cycle emissions have been overlooked in previous studies. Herein, this paper collects 2.38 million light-duty EV operational data to analyse usage patterns based on energy consumption, daily mileage and annual utilisation. By integrating 12 recognised usage patterns with 6 electricity mixes, the study assesses the emission reduction potential and trade-off period of frugal EVs compared to counterpart internal combustion engine vehicles (ICEVs). The results show that about 70 % of frugal EV users would like to take a short daily travel with high utilisation rate and low energy consumption. From macro perspectives, CO2 and VOC emissions reductions are significant, while NOx, SOx, and PM are not achieved significant reductions. In terms of micro usage patterns, low-utilisation and low-daily travel result in less emission reduction opportunities. In general, about 79 % of frugal EVs can achieve the CO2 emission reduction compared to frugal ICEVs based on 12-year longevity simulation. 30 % and 30.3 % of frugal EVs can achieve PM2.5 and NOx emissions reductions in high clean electricity regions. In summary, this study provides insights for policymakers and manufacturers aiming to enhance the sustainability of frugal EVs.

Electric quadricycles offer significant potential for enhancing sustainable urban mobility due to their compact design and efficiency. To effectively shape this sustainable future, it is vital to understand the associated trends and challenges. Accordingly, this study analysed expert reviews of 13 heavy and light electric quadricycles by implementing an advanced topic modelling approach to identify dominant themes and examined sentiment polarity across reviews using artificial intelligence. The study revealed eight key topics: design and technology, driving experience, urban mobility and acceptance, performance, battery and efficiency, pricing options, market and production, and classification and regulations. Additionally, the assessment quantified experts' positive and negative perceptions of specific elements within these topics. The findings indicate that (1) the majority of discussions focused on design and technology, (2) experts frequently appreciated spacious interiors, innovative swappable battery solutions, and agile and playful driving characteristics, (3) negative sentiments primarily pertained to safety, comfort, purchase price, and build quality, and (4) overall, experts held optimistic views regarding the role of electric quadricycles in urban mobility. These insights support a data-driven and user-centred approach to electric quadricycle design, assisting manufacturers and policymakers in advocating for electric quadricycles as practical solutions for sustainable urban mobility of the future.

Evaluating the impact of privately-owned Mobility-on-Demand (MoD) services is important from a regulatory perspective. There is a need to model multimodal equilibria with MoD to support policymaking. While there exists a large body of literature on MoD services focusing on service design under equilibrium modeling, these studies commonly adopt assumptions of MoD operational policies. However, such policies might not be shared with regulatory agencies due to commercial privacy concerns of private operators. We model multimodal equilibrium with MoD systems in an operation-agnostic manner based on empirical observations of flow and capacity. This is done with a Flow-Capacity Interaction (FC) matrix that captures systematic effect of congestible capacities, a phenomenon in MoD systems where capacities are affected by flows. The FC matrix encapsulates the operation and demand patterns by capturing the empirical equilibrium relationship between flows and capacities. An operation-agnostic logit-based stochastic user equilibrium (SUE) formulation is proposed and proof of equivalence of the SUE formulation is derived. The proof shows that, unlike static capacities, path delays are not just the sum of the Lagrange multipliers of the links on the paths, but dependent on the whole network. We name this phenomenon as “non-separable link delays”. A solution algorithm that finds SUE with a bounded path set is proposed, with a custom Frank-Wolfe algorithm to solve the non-linear SUE formulation. Since the FC matrix cannot be directly observed, an inverse optimization problem is introduced to estimate it with observed flow and capacity data. Two numerical examples are provided with sensitivity tests. An empirical example with yellow taxi data of downtown Manhattan, NY is provided to demonstrate effectiveness of estimating the FC matrix from real data, and for determining the equilibrium that captures the underlying flow-capacity dynamics.