风险感知视角下弹性停车激励机制泊位定价方法

以弹性停车激励机制为背景,应用条件风险价值函数刻画个体共享泊位的风险感知特征,提出基于风险感知收益最优的泊位定价方法,探究共享停车供需关系对最优租赁价格的影响规律,并以美国加州大学伯克利分校为例,灵敏度分析各关键参数对共享停车政策效能的正负导向效用.研究表明:基于风险感知收益最优的泊位定价方法能根据外部停车需求约束差异...     

浅谈高速公路不停车收费系统

介绍了高速公路电子不停车收费系统（ETC）的构成及实现原理,分析了该系统的优缺点并举例。     

中职汽修专业“双元双导 校店融合”协同育人模式实践研究

在汽车产业快速发展,以及汽车消费市场不断扩大带动下,社会对汽修专业人才的需求也随之大幅度提升。这也对中职汽修专业的教育培养工作提出了更高要求,对此,各中职院校应紧跟时代发展步伐,加强对"双元双导校店融合"协同育人模式的应用研究,以此来为中职院校拓展出更理想的发展前景,培养出更多优秀人才。     

简易陀螺仪惯性制导系统设计

在设计陀螺仪的过程中,需要注意陀螺仪应用于飞机控制飞行惯性制导系统时需要搭建传感器、单片机、晶体放大驱动三大模块。随后需要综合积分算法,从而保证陀螺仪可以自行调整飞机航线,而这项技术一般应用于自动驾驶系统中,飞行员在飞机飞行的过程中使用自动驾驶技术就能够极大的降低工作量,从而提升飞行质量。本文主要针对于简易陀螺仪在制作的过程中惯性制导系统的设计进行分析,并且对于未来的简易陀螺仪惯性制导系统设计方案进行了阐述。     

城市停车诱导系统设计研究

分析了该系统的目的、基本功能,提出了系统框架设计和功能模块设计内容 从管理层次和技术面层次,提出了系统总体设计框架,最后结合我国大城市停车现状和研究的交通拥挤状况,提出了停车诱导系统的发展方向。     

Understanding user acceptance factors of electric vehicle smart charging

Smart charging has been the focus of considerable research efforts but so far there is little notion of users’ acceptance of the concept. This work considers potentially influential factors for the acceptance of smart charging from the literature and tests their viability employing a structural equation model, following the partial least squares approach. For a sample of 237 early electric vehicle adopters from Germany our results show that contributing to grid stability and the integration of renewable energy sources are key motivational factors for acceptance of smart charging. In addition, the individual need for flexibility should not be impaired through charging control. Further well known influential factors like economic incentives do not seem to have a significant impact in the sample group under scrutiny. These and further findings should be taken into account by aggregators when designing attractive business models that incentivize the participation of early adopters and ease market rollout.     

Equilibrium scheduling of vehicle-to-grid technology using activity based modelling

Vehicle-to-grid (V2G) is a technology which can reduce the cost for power distribution network operators by storing electricity in the batteries of electric-drive vehicles and retrieving it when energy demands increase during the course of a day. Participants of V2G are reimbursed for offering their vehicles which can lead to changes in trip schedules when V2G payments are high and travelers are sensitive to the payments. However, prior studies have ignored the effects of V2G on travelers’ schedules. This research gap is addressed with a bi-level V2G market equilibrium model where the lower level model determines the equilibrium activity patterns as a result of upper level pricing and linear approximated AC flow distribution decisions. An algorithm is proposed for the model and illustrated on a simple telecommuting example where travelers can work from home and offer their vehicle charge capacity to the power provider. The model is then applied to the same case study from Lam and Yin (2001) to first replicate the lower level equilibrium problem as a special case when no V2G is present, and then to show the potential effects of the V2G policy to decrease locational marginal prices for a distribution network operator. The proposed algorithm for the V2G policy resulted in a substantial 20% increase in social welfare over the benchmark equilibrium without V2G.     

Bus arrival time calculation model based on smart card data

Bus arrival time is usually estimated using the boarding time of the first passenger at each station. However, boarding time data are not recorded in certain double-ticket smart card systems. As many passengers usually swipe the card much before their alighting, the first or the average alighting time cannot represent the actual bus arrival time, either. This lack of data creates difficulties in correcting bus arrival times. This paper focused on developing a model to calculate bus arrival time that combined the alighting swiping time from smart card data with the actual bus arrival time by the manual survey data. The model was built on the basis of the frequency distribution and the regression analysis. The swiping time distribution, the occupancy and the seating capacity were considered as the key factors in creating a method to calculate bus arrival times. With 1011 groups of smart card data and 360 corresponding records from a manual survey of bus arrival times, the research data were divided into two parts stochastically, a training set and a test set. The training set was used for the parameter determination, and the test set was used to verify the model’s precision. Furthermore, the regularity of the time differences between the bus arrival times and the card swiping times was analyzed using the “trend line” of the last swiping time distribution. Results from the test set achieved mean and standard error rate deviations of 0.6% and 3.8%, respectively. The proposed model established in this study can improve bus arrival time calculations and potentially support state prediction and service level evaluations for bus operations.     

Turning meter transactions data into occupancy and payment behavioral information for on-street parking

Over 95% of on-street paid parking stalls are managed by parking meters or kiosks. By analyzing meter transactions data, this paper provides a methodology to estimate on-street time-varying parking occupancy and understand payment behavior in an effective and inexpensive way. We propose a probabilistic payment model to simulate individual payment and parking behavior for each parker. Aggregating the payment/parking of all transactions leads to time-varying occupancy estimation. Two data sets are used to evaluate the methodology, parking spaces near Carnegie Mellon University (CMU) campus, and near the Civic Center in San Francisco. The proposed model generally provides reliable estimations of occupancies at a low error rate and substantially outperforms other naive models in the literature. From the results of the experiments we find that people generally tend to slightly underpay in CMU area, whereas for Civic Center area, payment behavior varies by time of day and day of week. For Fridays, people generally tend to overpay and stay longer in the mornings, compared to underpaying and parking for shorter durations in the late afternoons. Parkers’ payment behavior, in general, is more variable and noisier around Civic Center than around CMU. Moreover, we explore the effective granularity, defined as the highest spatial resolution for this model to perform reliably. For CMU areas, the effective granularity is around 10–20 spaces for each block of streets, while it is 150–200 spaces for the Civic Center area due to more random parking behavior.     

Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks

Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts.