Grouping travelers on the basis of their different car and transit levels of use

Market segmentation studies in travel behavior research are ordinarily based on socioeconomic characteristics and personality traits. This study explores the usefulness of a different approach, where the actual overall mobility levels across different ground transportation modes, along with desired changes in the use of cars and transit, are used as clustering variables. Using a given mode can in fact influence the personal representation of that mode, which in turn has been proven to be a key element in transport behaviours. We form such multimodality-based clusters from two field studies, one involving employees of the French transportation research institute INRETS and the other a representative sample of residents of the US San Francisco Bay Area. We find that strong users of a given mode would like to bring more balance to their “modal consumptions” by decreasing the use of this mode more than the average, and increasing the use of the alternative mode. However, concerning ground transport travel budgets, the desire to travel more (or less) overall seems less strongly related to the composition of the modal balance. The US dataset shows also a greater latent demand for travel than the French one. Socioeconomic characteristics of the clusters could not explain the patterns that were found, confirming the importance of taking into account multimodality issues in travel behavior research. Some policy implications from these findings are finally reported.

Studying travel-related individual assessments and desires by combining hierarchically structured ordinal variables

Ordinal measures are frequently encountered in travel behavior research. This paper presents a new method for combining them when a hierarchical structure of the data can be presumed. This method is applied to study the subjective assessment of the amount of travel by different transportation modes among a group of French clerical workers, along with the desire to increase or decrease the use of such modes. Some advantages of this approach over traditional data reduction technique such as factor analysis when applied to ordinal data are then illustrated. In this study, combining evidence from several variables sheds light on the observed moderately negative relationship between the personal assessment of the amount of travel and the desire to increase or decrease it, thus integrating previous partial (univariate) results. We find a latent demand for travel, thus contributing to clarify the behavioral mechanisms behind the induced traffic phenomenon. Categorizing the above relationship by transportation mode shows a desire for a less environmental-friendly mix of modes (i.e., a greater desire to use heavy motorized modes and a lower desire to use two-wheeled modes), whenever the respondents do not feel to travel extensively. This result, combined with previous theoretical investigations concerning the determinants of the desire to alter trips consumption levels, shows the importance of making people aware of how much they travel.

Policy implementation of multi-modal (shared) mobility: review of a supply-demand value proposition canvas

ABSTRACT

Urban mobility options have increased in recent years, assisted by the widespread availability of smart device software apps, geo-positioning technology, and convenient electronic financial transactions. Multi-modal shared mobility consists of public transit systems and shared mobilities that support first/last mile travel, denoting the capability of Mobility as a Service (MaaS), and to stimulate additional non-private car travel demand. This paper reviews the supply and demand sides of implementation of multi-modal shared mobility. It found that an abundance of shared modes of car, bike, and e-scooter that are linked to public transport, can improve transport accessibility to meet specific public preferences, reduce social inequality, and minimise dilemmas from the demand side. This study introduces government policy innovations and other supporting system to improve the implementation of multi-modal shared mobility. Government policies play a key role in supporting shared mobility and technology development. However, governments do not have much information about new products such as shared mobility, which creates difficulties in subsidising multi-modal shared mobility services and potentially leads to policy failures around shared mobility schemes. This study suggests that policy entrepreneurship in collaboration with other partners, policy innovation, and the notions of merit goods and second-best policymaking can enable policy initiatives towards multi-modal shared mobility and provide supporting arguments if policies encounter failures. Implementing multi-modal shared mobility requires a collaborative partnership for a paradigm shift: service providers and government must jointly set a merit-based business model, with the support of organisations to achieve improved infrastructure provision, and smart technology applications. The findings will assist the community, business providers and government policymakers to promote multi-modal shared mobility as a pathway towards more efficient, environmentally sustainable, and socially responsive mobility solutions.     

Sustainable mobility: Policy design and implementation in three Norwegian cities

The paper explores barriers for designing and implementing policies for the transition to more environmentally sustainable urban mobility, and strategies for overcoming these barriers in three Norwegian cities. Empirical data has been collected by interviewing key informants and analysing relevant documents. The findings show that there is broad political support for placing sustainable mobility high on the political agenda. Challenges appear when policy measures are designed and implemented. Cultural, legal, political, organisational, financial and knowledge-related barriers are identified. Many similar barriers are identified in the three case cities, but differences also appear. The results indicate that the size of the cities as well as the type of policy packages implemented are important factors, impacting both the type and strength of barriers. Several strategies for overcoming barriers have been implemented in the three case cities with success. The implementation of policy packages with a combination of “push” and “pull” measures is perhaps the most important strategy. In addition, using communication strategically and allowing for trials and step-by-step introduction of policy measures are success factors. Better methods for stakeholder involvement and planning tools for bicyclists and pedestrians may increase the acceptance of policy measures and speed up the transition toward sustainable mobility.     

Smart charging management for shared autonomous electric vehicle fleets: A Puget Sound case study

Increasingly, experts are forecasting the future of transportation to be shared, autonomous and electric. As shared autonomous electric vehicle (SAEV) fleets roll out to the market, the electricity consumed by the fleet will have significant impacts on energy demand and, in turn, drive variation in energy cost and reliability, especially if the charging is unmanaged. This research proposes a smart charging (SC) framework to identify benefits of active SAEV charging management that strategically shifts electricity demand away from high-priced peak hours or towards renewable generation periods. Time of use (TOU), real time pricing (RTP), and solar generation electricity scenarios are tested using an agent-based simulation to study (1) the impact of battery capacity and charging infrastructure type on SAEV fleet performance and operational costs under SC management; (2) the cost reduction potential of SC considering energy price fluctuation, uncertainty, and seasonal variation; (3) the charging infrastructure requirements; and (4) the system efficiency of powering SAEVs with solar generation. A case study from the Puget Sound region demonstrates the proposed SC algorithm using trip patterns from the regional travel demand model and local energy prices. Results suggest that in the absence of electricity price signals, SAEV charging demand is likely to peak the evening, when regional electricity use patterns already indicate high demand. Under SC management, EVs with larger battery sizes are more responsive to low-electricity cost charging opportunities, and have greater potential to reduce total energy related costs (electricity plus charging infrastructure) for a SAEV fleet, especially under RTP structure.     

The impacts of connected vehicle technology on network-wide traffic operation and fuel consumption under various incident scenarios

ABSTRACT

Incidents are a major source of traffic congestion and can lead to long and unpredictable delays, deteriorating traffic operations and adverse environmental impacts. The emergence of connected vehicles and communication technologies has enabled travelers to use real-time traffic information. The ability to exchange traffic information among vehicles has tremendous potential impacts on network performance especially in the case of non-recurrent congestion. To this end, this paper utilizes a microscopic simulation model of traffic in El Paso, Texas to investigate the impacts of incidents on traffic operation and fuel consumption at different market penetration rates (MPR) of connected vehicles. Several scenarios are implemented and tested to determine the impacts of incidents on network performance in an urban area. The scenarios are defined by changing the duration of incidents and the number of lanes closed. This study also shows how communication technology affects network performance in response to congestion. The results of the study demonstrate the potential effectiveness of connected vehicle technology in improving network performance. For an incident with a duration of 900?s and MPR of 80%, total fuel consumption and total travel time decreased by approximately 20%; 26% was observed in network-wide travel time and fuel consumption at 100% MPR.     

高铁动车组司机连续作业时长的研究

本文在分析动车组司机感知、判断、操作特性的基础上,采用实验心理学测试方法采集司机的各项指标,根据试验测试数据,通过贝叶斯统计学的方法确定合理的高铁动车组司机乘务作业时长,建立乘务作业时长与各项测试指标之间的关系模型。通过算例验证,动车组司机安全可靠度测试等级为良好,并提出相应的改进措施     

Willingness-to-pay for alternative fuel vehicle characteristics: A stated choice study for Germany

In the light of European energy efficiency and clean air regulations, as well as an ambitious electric mobility goal of the German government, we examine consumer preferences for alternative fuel vehicles (AFVs) based on a Germany-wide discrete choice experiment among 711 potential car buyers. We estimate consumers’ willingness-to-pay and compensating variation (CV) for improvements in vehicle attributes, also taking taste differences in the population into account by applying a latent class model with 6 distinct consumer segments. Our results indicate that about 1/3 of the consumers are oriented towards at least one AFV option, with almost half of them being AFV-affine, showing a high probability of choosing AFVs despite their current shortcomings. Our results suggest that German car buyers’ willingness-to-pay for improvements of the various vehicle attributes varies considerably across consumer groups and that the vehicle features have to meet some minimum requirements for considering AFVs. The CV values show that decision-makers in the administration and industry should focus on the most promising consumer group of ‘AFV aficionados’ and their needs. It also shows that some vehicle attribute improvements could increase the demand for AFVs cost-effectively, and that consumers would accept surcharges for some vehicle attributes at a level which could enable their private provision and economic operation (e.g. fast-charging infrastructure). Improvement of other attributes will need governmental subsidies to compensate for insufficient consumer valuation (e.g. battery capacity).     

时间维度下上海市共享单车骑行模式研究

基于上海市摩拜单车一个星期的骑行数据，从时间角度深入挖掘骑行数据背后隐藏的行为模式和规律. 统计结果显示，工作日和周末的骑行行为存在显著差异，工作日呈现两个明显骑行高峰时段，周末的骑行分布则比较均匀. 在区分工作日和周末的基础上，根据骑行行为对每日的骑行者进行分类，每组骑行者均表现出独特的骑行开始时间和骑行时长. 基于周中的一天与骑行开始时间，以及周中的一天与骑行时长这两个组合分别计算赫希曼指数 (HHI)，证实共享单车骑行高度的时间规律性；周中的一天与骑行开始时间组合的HHI值具有更大的标准差，显示了共享单车的灵活性. 对共享单车骑行模式的探究，能够在一定程度上促进城市交通系统的持续改善，吸引更多出行者使用绿色交通方式.     

动态设计在边坡治理中的应用

山体因工程施工开挖而失去原有的平衡，需重新寻找平衡点，同时因地质构造、水文等诸多因素影响，山体在重新平衡过程中容易出现山体滑塌、坡面开裂、下沉等失稳现象。因此，在施工中必须根据现场山体边坡发生滑塌、开裂等具体情况进行动态设计，制定有针对性、切实可行的治理方案。文章结合工程实例提出了相应的边坡整治措施，为类似的边坡治理积累了一定的经验。