This study examines ECB mode substitution using a mixed-methods approach combining travel diaries from 42 households participating in real-world ECB trials across Leeds, Oxford, and Brighton with 111 interviews. ECB trips mostly replaced car travel (60%), followed by conventional cycling (23%) and walking (17%). Regression analysis showed that car substitution was more likely among men, older adults, retirees, multi-car households and for passenger-carrying, shopping, child-escorting, and leisure trips. Qualitative findings further described a gradual process of building confidence, adapting routines, and integrating the ECB into everyday mobility, enabling progressively greater car substitution. For some, the ECB was viewed as a realistic alternative to a second car. The findings show that ECBs offer a versatile family mobility solution with substantial sustainability potential by reducing vehicle mileage and emissions, while supporting transitions towards car ownership renunciation. The study details targeted policy interventions that can maximise ECBs uptake and their contribution to sustainability.
Driving cycles are essential for vehicle energy and emission standards, but standard cycles often overlook regional variations. This study proposes a novel framework for constructing representative driving cycles for heavy-duty vehicles by optimizing a Markov chain with a hybrid Multi-Population Genetic Algorithm and Wavelet Threshold Denoising (hMPGA-WTD). The methodology involves: (1) spectral clustering of 1.27 million telematics data points to identify operational scenarios; (2) generating scenario-specific cycles using a two-dimensional Markov chain; and (3) enhancing driving cycle fidelity with hMPGA-WTD to reduce feature distortions. The method achieves a mean relative error of 4.61% across key parameters, with velocity standard deviation error below 1%. It outperforms micro-trip concatenation and conventional Markov approaches. Fuel consumption is validated via real-world and chassis dynamometer tests under identical loads, showing a 4.26% deviation, better than the 7.71% under CHTC-TT. The framework offers a robust basis for efficiency assessment and standard development.
Commuters in urban areas face prolonged exposure to air pollution. This study assessed whether providing commuters with real-time, trip-specific air pollution information can encourage a shift towards public transit modes. Sequential integrated choice and latent variable model with a correlated mixed logit component is developed using stated choice data and user perceptions for motorized transport users in Delhi. Real-time pollution information for current modes was obtained through a multimodal router. Perceptions included air quality awareness, availability and usability of air quality information, and willingness to change behavior for primary trips. The results indicate that people with a better understanding of air quality information and its usage are inclined to choose public transit. Motorized two-wheeler users are more sensitive to exposure compared to car users, showing a stronger preference for public transit. These findings highlight the importance of real-time information in mode shift. Policymakers can formulate strategies to reduce individuals’ exposure in public transit.
A field experiment is conducted to investigate operational benefits of speed-limit control in platooning traffic, where the leading vehicle travels at a constant cruise speed while an additional speed-limit control is imposed on following vehicles. First, the effects of speed limits on traffic oscillation dynamics and traffic flow performance are analyzed. Results indicate that restricting the maximum speed of vehicles can stabilize traffic flow, reduce oscillation frequency, and lower emissions and energy consumption (EEC), but may reduce traffic throughput due to an increase in platoon length. A simulation framework is then developed to extend this investigation to broader speed levels and bottleneck scenarios. The results show that speed-limit control should vary with cruising speed to balance EEC and traffic efficiency, and that the proposed solutions apply to traffic management strategies. These findings enrich our understanding of traffic dynamics under speed-limit control strategies and provide practical insights for their real-world implementation.
Cities increasingly require sustainable and land-efficient transportation infrastructure. While Integrated Rail-Road Bridge (IRRB) optimizes urban land use by combining rail and road into a single bridge, integration alone is insufficient to minimize environmental and economic burdens. Consequently, as horizontal alignment is the primary determinant of economic and environmental footprints, optimizing horizontal geometry is imperative for developing land-efficient and sustainable urban corridors. To address this, we propose a Bayesian-optimized surrogate-assisted Multi-Objective Horizontal Curve Optimization (MOHCO) framework. It comprises a data-driven phase for initial impact datasets, a Bayesian-optimized surrogate model for rapid, high-fidelity impact predictions across high-dimensional design spaces, and a Non-dominated Sorting Genetic Algorithm (NSGA-II) multi-objective optimization incorporating CO2 emissions and contribution to local temperature. Applied to a real-world case, the framework achieved high predictive accuracy (R2 > 0.98), reduced cost by 5.64%, environmental impact by 38.90%, and improved safety by 44.40% compared to manual designs, supporting sustainable urban transportation development.
This study develops an integrated decision-support framework for assessing the readiness of major Turkish cities for sustainable autonomous vehicle (AV) infrastructure. İstanbul, Ankara, İzmir, Bursa, and Antalya are evaluated using 16 sub-criteria grouped under Physical Infrastructure, Smart Mobility, Sustainability, and Adaptive Capacity. Expert judgments are consolidated through the Modified Delphi method and analyzed using Pythagorean Fuzzy Indifference Threshold-based Attribute Ratio Analysis (PF-ITARA) and Pythagorean Fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (PF-MARCOS). The results identify Economic Feasibility, Municipal Capacity, and R&D Ecosystem as the most influential criteria. İzmir ranks first, followed by İstanbul, Ankara, Bursa, and Antalya. Sensitivity analyses confirm that İzmir remains the leading city under alternative threshold structures and 32 criterion-weight perturbation scenarios. The findings show that autonomous vehicle readiness depends on balanced technological, financial, institutional, and sustainability capabilities and provide practical guidance for urban infrastructure planning and phased deployment.
Accessibility matters for subjective well-being, yet its underlying mechanisms remain insufficiently understood. Using survey data from 5591 respondents in ten Chinese cities, this study examines how perceived accessibility relates to hedonic and eudaimonic well-being through domain-specific travel satisfaction and social exclusion. After accounting for the indirect pathways, the structural equation modeling results show that perceived accessibility remains positively associated with flourishing and negatively associated with negative affect, whereas its total effect on positive affect is not significant. The mediation effect varies across travel domains and well-being outcomes. Leisure travel satisfaction emerged as the most important mediator for flourishing, while shopping travel satisfaction played a mediation role across all outcomes. Commute travel satisfaction was particularly salient for affective well-being. Social exclusion, meanwhile, constituted a strong and consistent pathway. The findings highlight that accessibility contributes to well-being not only through reaching destinations, but also through satisfying travel experiences and social inclusion.
This study proposes an integrated planning framework for robotaxi deployment in medium-sized cities by combining MCDM, GIS-based spatial analysis, and robustness assessment. Intended primarily for local governments and urban transport authorities, the framework also provides operators and investors with an analytical baseline for fleet sizing and charging-infrastructure planning. Seven main criteria and 32 sub-criteria were weighted using FWZIC and RANCOM, with strong agreement confirmed by Pearson correlation (r = 0.9620), Spearman correlation (ρ = 0.9643), and MAD (0.00728). A normalized [0–1] suitability map was used to delineate feasible service areas. The baseline scenario assumes 200 robotaxis, including 150 active and 50 standby vehicles, with rem-based allocation for staging. Using Tesla Model Y Juniper data as a proxy, total grid-side fleet energy demand was estimated at 9.96 MWh/day. Peak-period sizing indicates that at least nine DC fast-charging units are required for the daytime DC charging component.
