Consumer intentions for alternative fuelled and autonomous vehicles: A segmentation analysis across six countries

Rapid advances in the development of autonomous and alternative-fuel vehicles (AFVs) are likely to transform the future of mobility and could bring benefits such as improved road safety and lower emissions. Achieving these potential benefits requires widespread consumer support for these disruptive technologies. To date, research to explore consumer perceptions of transport innovations has tended to consider them in isolation (e.g., driverless cars, electric vehicles). The current paper examines the predictors of consumer interest in and willing to pay for both AFVs and autonomous vehicles through a choice experiment conducted in six diverse markets: Germany, India, Japan, Sweden, UK and US. Using Latent Class Discrete Choice Models, we observe significant heterogeneity both within and across the country samples. For example, while Japanese consumers are generally willing to pay for autonomous vehicles, in most European countries, consumers need to be compensated for automation. Within countries, though, we found some segments – typically, those with a university degree, and self-identifying as having a pro-environmental identity and as being innovators– are more in favour of automation. Significantly, we also found that support for autonomous vehicles is associated with support for AFVs, perhaps, due to common demographic or socio-psychological predictors of both types of innovative technology. These findings are valuable for policymakers and the automotive industry in identifying potential early adopters, as well as consumer segments or cultures less convinced to adopt these innovative transport technologies.     

城市交通运行状况对机动车碳排放的影响研究

为定量分析不同城市交通运行状况对机动车碳排放的影响，利用高德平台提供的拥堵延时指数(Congestion Delay Index, CDI)数据，在分析我国交通拥堵城市时空分布特征以及CDI特征的基础上，通过构建基于速度的CO2排放因子，利用VISSIM模拟不同交通运行状况时的交通量， 实现不同交通运行状况下机动车碳排放的估算。结果显示：交通拥堵城市分布具有空间依赖性和聚集性，在长三角经济区和珠三角经济区形成两个高聚集中心；CDI具有明显的周期性(7 d)波动规律，且受天气和人类活动等的影响较大，疫情打破了此规律；城市交通运行状况(CDI)对机动车CO2排放有较大的影响，当交通处于轻度拥堵时(CDI为1.582)，交通高峰期我国城市机动车年排放的CO2总量约为0.77亿 t，是畅通状态下的4.51倍；当交通保持基本畅通时(CDI为1.35)，交通高峰期我国城市机动车CO2年排放总量可减少0.29亿 t；当交通达到中度拥堵时(CDI为1.909)，交 通高峰期我国城市机动车CO2的年排放增加0.22亿 t；当处于交通严重拥堵时(CDI达2.394)，交通高峰期我国城市机动车CO2的年排放总量可达1.33亿 t。改善城市交通运行状况，可大幅度降低机动车的CO2排放量。     

Hydrogen refueling station networks for heavy-duty vehicles in future power systems

A potential solution to reduce greenhouse gas (GHG) emissions in the transport sector is to use alternatively fueled vehicles (AFV). Heavy-duty vehicles (HDV) emit a large share of GHG emissions in the transport sector and are therefore the subject of growing attention from global regulators. Fuel cell and green hydrogen technologies are a promising option to decarbonize HDVs, as their fast refueling and long vehicle ranges are consistent with current logistic operational requirements. Moreover, the application of green hydrogen in transport could enable more effective integration of renewable energies (RE) across different energy sectors. This paper explores the interplay between HDV Hydrogen Refueling Stations (HRS) that produce hydrogen locally and the power system by combining an infrastructure location planning model and an electricity system optimization model that takes grid expansion options into account. Two scenarios – one sizing refueling stations to support the power system and one sizing them independently of it – are assessed regarding their impacts on the total annual electricity system costs, regional RE integration and the levelized cost of hydrogen (LCOH). The impacts are calculated based on locational marginal pricing for 2050. Depending on the integration scenario, we find average LCOH of between 4.83 euro/kg and 5.36 euro/kg, for which nodal electricity prices are the main determining factor as well as a strong difference in LCOH between north and south Germany. Adding HDV-HRS incurs power transmission expansion as well as higher power supply costs as the total power demand increases. From a system perspective, investing in HDV-HRS in symbiosis with the power system rather than independently promises cost savings of around seven billion euros per annum. We therefore conclude that the co-optimization of multiple energy sectors is important for investment planning and has the potential to exploit synergies.     

Will autonomous vehicles change auto commuters’ value of travel time?

This paper examines the potential impact of autonomous vehicles on commuters’ value of travel time (VOTT). In particular, we focus on the effect on auto commuters in small and medium-sized metropolitan areas, concerning the spatial variability across urban areas, suburbs, and rural areas. We design a stated choice experiment to elicit potential changes in 1,881 auto commuters’ valuation of travel time in autonomous vehicles and apply a mixed logit model to quantify the changes in the value of travel time if taking autonomous vehicles. The results of this study suggest that the effect of autonomous vehicles on the VOTT is spatially differentiated. We find that riding in a private autonomous vehicle reduces the commuting VOTT of suburban, urban, and rural drivers by 32%, 24%, and 18%, respectively, compared to 14%, 13%, and 8% for riding in a shared autonomous vehicle. Finally, we discuss the implications of these lower values of time on transportation and land use planning.     

Determining the optimal electric vehicle DC-charging infrastructure for Western Australia

Take-up rates of electric vehicles (EV) are increasing and are predicted to accelerate rapidly. Public EV charging networks will be required to support future EV fleets. If unplanned, public charging networks are highly likely to be suboptimal. Planners need to understand and plan for future EV charging infrastructure requirements, particularly public DC fast charging networks, as both the upfront investment costs and the consequences of misallocation are high. However, the task of determining the optimal locations and allocations (types and numbers) of public EV charging infrastructure is complicated as it requires knowledge of many variables. These include EV driver behaviors, driving patterns, predicting evolutionary changes in EV and EV charging technologies, future EV take-up rates, and what investment may or may not occur in the absence of government funding support.     

Optimization of submerged floating tunnel cross section based on parametric Bézier curves and hybrid backpropagation - genetic algorithm

The cross-section geometry of a submerged floating tunnel (SFT) has a large effect on hydrodynamic characteristics, structural behavior and service level, making the tunnel cross section the primary factor in optimizing efficiency. Minimizing the mean drag and the dynamic variability in the lift of the SFT cross section under bi-directional (i.e., tidal) flow has a dramatic impact on the reduction of structural displacements and mooring loads. Based on a parametric Bézier curve dynamically comprising the leading-edge radius, tunnel height and width to define the SFT geometry, a sensitivity analysis of the Bézier curve parameters for a fixed aspect ratio with prototype dimensions under uniform flow conditions was conducted by applying Computational Fluid Dynamics (CFD), and the pressure distribution around the SFT cross-section surface was analyzed. A theoretical method comprising the Kármán vortex street parameters was employed to verify the CFD simulation results. In order to determine the SFT cross section with optimal hydrodynamic properties, the mean drag and Root Mean Square (RMS) lift coefficients were selected as optimization objectives, and four Bézier curve parameters were the input variables, in a neural network and genetic algorithm optimization process (a hybrid BP-GA structure), which is less likely to become trapped in local minima. The results show the optimal tunnel cross section has a mean drag and a RMS lift coefficient reduced by 0.9% and 6.3%, respectively, compared to the original CFD dataset.     

城市工程运输车源头治理短板及对策研究

源头治理是城市工程车管理链条中重要一环,同时也是最薄弱的一环,存在管理盲区。本文从“人、车、企、道路、工地”五大源头总结梳理工程车源头治理中存在的六个方面问题,结合业务特点和各地经验做法,针对性提出六大对策建议,具有可实施性,可有效强化工程车源头治理。     

大型货运车辆生态驾驶及节油潜力评估

改善驾驶行为、推广生态驾驶是未来降低机动车辆燃油消耗和污染排放的重要潜在方式.分析由车辆远程在线监控技术(OBD)获取的199辆大型货车在观测的4 d内的位置、速度、油耗等微观运行状态的逐秒数据，提出判定急加速、过急加速、急减速、过急减速和超长怠速等5种不良驾驶行为的统计方法，并建立面板数据固定效应回归模型分析5种不良驾驶行为对大型货车油耗的影响，进而定量评估改善不良驾驶行为的节油潜力.研究结果表明，超长怠速和过急减速行为会显著增加大型货车的油耗，减少超长怠速时间和过急减速行为的节油潜力分别可达2.6%和3.8%.     

Impacts of automated vehicles on travel behaviour and land use: an international review of modelling studies

ABSTRACT

Automated vehicles (AVs) could completely change mobility in the coming years and decades. As AVs are still under development and gathering empirical data for further analysis is not yet possible, existing studies mainly applied models and simulations to assess their impact. This paper provides a comprehensive review of modelling studies investigating the impacts of AVs on travel behaviour and land use. It shows that AVs are mostly found to increase vehicle miles travelled and reduce public transport and slow modes share. This particularly applies to private AVs, which are also leading to a more dispersed urban growth pattern. Shared automated vehicle fleets, conversely, could have positive impacts, including reducing the overall number of vehicles and parking spaces. Moreover, if it is assumed that automation would make the public transport system more efficient, AVs could lead to a favouring of urbanisation processes. However, results are very sensitive to model assumptions which are still very uncertain (e.g. the perception of time in AVs) and more research to gain further insight should have priority in future research as well as the development of the models and their further adaptation to AVs.