To comprehensively improve vehicle ride comfort and handling stability, as well as to enhance the robustness and disturbance rejection capability of the suspension system under uncertain operating conditions, this paper first establishes a full vehicle suspension dynamic model. On this basis, an improved SH-ADD controlled suspension is proposed as a reference model through the analysis of power flow characteristics within the suspension system, and a sliding mode control (SMC) strategy is subsequently designed based on this reference model. Simulation results indicate that, compared with conventional control strategies, the proposed algorithm offers a clear advantage in reducing the root mean square (RMS) values of sprung mass acceleration, thereby effectively suppressing body vibration and improving ride comfort. In addition, both roll and pitch angular accelerations are notably reduced, contributing to enhanced body attitude stability. Moreover, when subjected to parameter uncertainties such as variations in vehicle speed and sprung mass, the proposed controller maintains excellent robustness and stable control performance, demonstrating its overall effectiveness in improving suspension system performance under complex operating conditions.
This paper analyzes pollutant emissions generated by urban road traffic in relation to the structure of the vehicle fleet. The study uses COPERT in order to quantify the contribution of different vehicle categories based on fuel type and emission standard under representative urban driving conditions. Fleet composition was derived from vehicle registration statistics, disaggregated by fuel type and emission standard, while annual mileage was allocated per vehicle category using an age-dependent activity algorithm reflecting the relation between vehicle age and average traveled distance. The methodology combines vehicle fleet distribution data with category-specific emission factors to estimate total emissions for a baseline scenario. Based on this, alternative scenarios are developed, including accelerated electrification of the fleet and fleet renewal through the replacement of older vehicles with those complying with more stringent emission standards. The main scope of the study is the comparative assessment of these strategies, all derived from the same baseline, rather than the provision of a precise inventory of current emission levels, in order to evaluate their relative effectiveness. The results highlight the significant influence of fleet composition on urban emission levels and show that emission reductions depend strongly on both the rate of electrification and the removal of high-emitting, older vehicles. Electromobility adoption is the proper solution for tailpipe CO2 mitigation, while a significant reduction in pollution can be achieved by fleet renewal.
Public transport in rapidly growing sub-Saharan African cities faces long travel times, long waits, and overcrowding. This study examines how commuters in Kigali, Rwanda, respond to a hypothetical bus service equipped with a Real-Time Passenger Information System (RTPIS), termed iBus, using a stated preference survey and a multinomial logit (MNL) model with four alternatives: conventional bus, private car, motorcycle taxi (Moto), and iBus. An online sample of 306 respondents provided 1671 valid choices. In-vehicle travel time (β = −0.0152, p < 0.001), travel cost (p = 0.029), and iBus crowding significantly affected choices, whereas waiting time did not (p = 0.210). The value of in-vehicle time was approximately 5281 RWF/hour (95% CI 1842–27,224). On the Kimironko–Nyabugogo corridor, replacing the conventional bus with an iBus that also offers shorter waiting and in-vehicle times and less crowding raised the simulated bus share from 8.6% to 29.5%; the information-related component alone raised it to 15.9%. A nested logit specification did not reject the MNL model. Because the sample is young and highly educated, the results apply mainly to digitally connected commuters. RTPIS appears most effective when combined with measures that increase bus speed and manage crowding.
The visibility of road markings and traffic signs affects drivers’ ability to react in time to the information provided by the road infrastructure. This systematic review, carried out according to the PRISMA 2020 guidelines, looked at how the visibility of markings and signs affects a driver’s reaction time and then how this impacts vehicle dynamics, accident risk, and the reliability of ADAS (advanced driver assistance systems—systems that help drivers). The search was completed by following citations, also known as snowballing. Articles from journals with peer review or full conference papers were eligible, provided they included a few explicitly identified normative sources that addressed the visibility of markings and signs, driver perception, or the performance of ADAS. A total of 114 studies (field measurements, simulator experiments, laboratory studies, accident data analysis, and reviews) were grouped into four thematic areas. Poor visibility, especially at night and in bad weather conditions, has greatly reduced the amount of visual information available to both drivers and the ADAS sensors. This decline spread, to varying degrees of certainty, across the vehicle’s dynamics and accidents. The main limitations were the varied definitions of reaction time and the small number of validated cross-study investigations. In conclusion, ADAS should be tested in realistic visibility conditions, including faded markings, rain, and glare.
Choosing between battery-electric and hydrogen fuel-cell trucks to decarbonize Chinese road freight hinges on two upstream quantities rarely assessed jointly: delivered hydrogen cost and regional grid carbon intensity. This study couples pathway-resolved delivered hydrogen costs with charging-region grid carbon intensity in one vehicle-level cost–carbon evaluation. A literature-anchored parametric simulation links six hydrogen pathways, seven regional grid settings, two truck classes, and three powertrains. It evaluates total cost of ownership (TCO), fuel-chain well-to-wheel carbon intensity (CI, vehicle manufacturing excluded), Pareto fronts, and the critical carbon price p*, with one-at-a-time sensitivity and a 10,000-draw Monte Carlo analysis. Renewable electricity is purchased at 0.25 CNY/kWh (zero-purchase supply retained as a labelled scenario), placing delivered hydrogen at 33.0–54.3 CNY/kg. For 49 t long-haul duty, TCO is 5.31 CNY/km (diesel), 4.70 (battery-electric; 11.5% lower per kilometre yet about 4% higher per tonne-kilometre, governed by the payload capacity ratio), and 7.92 (renewable-hydrogen fuel-cell; 49.1% higher). Across the same option space, WTW carbon intensity ranges from 198 to 3578 gCO2e/km. Eight finite critical carbon prices span 681.7–2936.4 CNY/tCO2, up to roughly 28 times the 2024 market band of 60–105 CNY/tCO2; parity probabilities remain near zero at current prices. Policy support should follow fuel carbon intensity and certified renewable-hydrogen siting, not vehicle purchases alone.
