城市公交车与社会车辆混合流速度模型及交通运行状态分析

为了精确解析城市公交车和社会车辆混合运行的状态,在基本路段车速模型适用性分析的基础上,引入公交车流量、社会车辆流量、公交车比例等参数,建立了改进的混合机动车运行速度模型,分别选取单向二车道和单向三车道路段进行交通试验调查,采用Metrocount 5600气压管式车辆分型系统进行数据采集并用于改进模型的参数标定,并分别建立了2种车型的速度差模型,提出了路段混合车流3种不同交通运行状态的评价方法。研究结果表明：同等车流量情况下,不同公交车比例对社会车辆速度的影响表现为3个显著的变化区间;随着路段饱和度的增加,社会车辆和公交车之间的速度差呈现出从几乎不变、快速缩小到接近于零3个较为明显的运行状态;考虑车流组成中公交车比例的变化可以细化路段车流畅通状态、拥堵形成状态以及拥堵状态的判别。     

城市道路拥挤收费福利影响研究

城市道路拥挤收费虽然使总的社会福利增加,但对不同时间价值出行者的福利影响却不尽相同。以福利经济学为理论基础,以出行的货币成本和时间成本为影响因素,量化分析收费后不同出行者的福利得失。研究发现,在确定拥挤方案后如果能得到交通量的变化量,就可以得到不同选择方案出行者的福利得失。时间价值较低的出行者只有选择公共交通时才能从拥挤收费中受益,而时间价值较高的出行者,选择付费出行使他们获益最大。     

基于多参数状态时间序列的交通状态预测方法

利用多个参数描述交通状态时,交通流数据表现为多维空间数据。提出了将属于每个状态的多维空间数据转换为一维时间序列的方法,对于此状态时间序列采用BP神经网络进行了下1个时段的交通状态预测。实验结果表明,多参数状态时间序列比单个参数时间序列能更准确地描述交通流状态变化过程,且算法简单,具有较强的预测实时性。     

基于RFID电子车牌数据的公交行程时间预测方法

为了给公交优先信号配时系统提供足够的"思考"时间和准确的控制依据,基于重庆市RFID电子车牌数据提出了一种采用自适应渐消卡尔曼滤波和小波神经网络组合模型动态预测公交行程时间的方法。综合分析公交行程时间的动态和静态影响因素,选取的模型输入参量为标准车流量、路段车辆平均行程时间、平均车速离散性和前班次公交行程时间。利用RFID电子车牌系统采集重庆市鹅公岩大桥路段车辆行驶数据,选取3 000组实际运行数据完成公交行程时间预测模型的训练,另筛选50组数据验证模型的有效性和准确性。研究结果表明：组合模型可动态自适应预测公交行程时间,预测值平均相对误差为3.23%,绝对误差集中在8 s左右,明显优于2种单一模型和基于传统GPS数据的公交行程时间预测模型,可认为选择RFID电子车牌数据作为组合模型的输入,能够明显改善模型预测精度;组合模型预测值的残差分布更为集中、鲁棒性较好,泛化能力强。选择平均绝对误差值、均方根误差值和平均绝对百分比误差作为模型评价指标,结果进一步表明,组合模型的综合预测效果明显优于单一的自适应渐消卡尔曼滤波和小波神经网络。研究方案可为先进公交信息化系统提供良好的技术支撑。     

Discussing the “positive utilities” of autonomous vehicles: will travellers really use their time productively?

ABSTRACT

Autonomous vehicles (AVs) are expected to reshape travel behaviour and demand in part by enabling productive uses of travel time—a primary component of the “positive utility of travel” concept—thus reducing subjective values of travel time savings (VOT). Many studies from industry and academia have assumed significant increases in travel time use and reductions in VOT for AVs. In this position paper, I argue that AVs’ VOT impacts may be more modest than anticipated and derive from a different source. Vehicle designs and operations may limit activity engagement during travel, with AV users feeling more like car passengers than train riders. Furthermore, shared AVs may attenuate travel time use benefits, and productivity gains could be limited to long-distance trips. Although AV riders will likely have greater activity participation during travel, many in-vehicle activities today may be more about coping with commuting burdens than productively using travel time. Instead, VOT reductions may be more likely to arise from a different “positive utility”—subjective well-being improvements through reduced stresses of driving or the ability to relax and mentally transition. Given high uncertainty, further empirical research on the experiential, time use, and VOT impacts of AVs is needed.     

预售环境下考虑消费者时间偏好的定价与订货策略

在市场规模不确定的条件下,以最大化销售商利润为目标,综合考虑消费者时间偏好及策略型行为,构建无预售、仅预售和两阶段预售策略的定价和订货模型,得出相应的最优预售价格和订货量。此外,对无预售与2种预售策略展开对比研究,探讨预售策略实施的条件,通过数值算例分析系统参数对预售最优决策及利润的影响。研究结果表明:两阶段预售策略下最优预售价格低于现售价格,给现售阶段的最优订货量不超过无预售策略;两阶段预售策略总是优于无预售策略;当且仅当预售时长小于某一临界值时,仅预售策略才会优于无预售策略;两种预售策略的优劣取决于销售商能否有效地根据产品现售价格和采购成本设置合理的预售时长。     

Investigating empirical implications of hysteresis in day-to-day travel time variability

Day-to-day travel time variability plays a significant role in travel time reliability. Nowadays, travelers not only seek to minimize their travel time on average, but also value its variation. The variation in the mean and the variance of travel time (across days, for the same departure time) has not been thoroughly investigated. A temporary decrease in capacity (e.g. congestion caused by an active bottleneck) leads to a quite significant difference in the variance of travel time for congestion onset and offset periods. This phenomenon results in hysteresis loops where the departure time periods in congestion offset exhibit a higher travel time variance than the ones in congestion onset with the same mean travel time. The aim of this paper is to identify empirical implications that yield to the hysteresis phenomenon in day-to-day travel times. First, empirical hysteresis loop observations are provided from two different freeway sites. Second, we investigate the potential link with the hysteresis observed in traffic networks on macroscopic fundamental diagram (MFD). Third, we build a piecewise linear function that models the evolution of travel time within the day. This allows us to decompose the problem into its components, e.g. start time of congestion, peak travel time, etc. These components, along with their probability distribution functions, are employed in a Monte Carlo simulation model to investigate their partial effects on the existence of hysteresis. Correlation among critical variables is the most influential factor in this phenomenon, which should be further investigated regarding traffic flow and traffic equilibrium principles.     

Going fast or going green? Evidence from environmental speed limits in Norway

This paper studies the impact of speed limits on local air pollution using a series of date-specific speed limit reductions in Oslo over the 2004–2011 period. We find that lowering the speed limit from 80 to 60 km/h reduces travel speed by 5.8 km/h. However, we find no evidence of reduced air pollution as measured next to the treated roads. Our estimates suggest an annual time loss of the speed limit reductions of 66 USD per affected vehicle. Our findings imply that policy makers need to consider other actions than speed limit reductions to improve local air quality.     

Forecasting journey time distribution with consideration to abnormal traffic conditions

Travel time is an important index for managers to evaluate the performance of transportation systems and an intuitive measure for travelers to choose routes and departure times. An important part of the literature focuses on predicting instantaneous travel time under recurrent traffic conditions to disseminate traffic information. However, accurate travel time prediction is important for assessing the effects of abnormal traffic conditions and helping travelers make reliable travel decisions under such conditions. This study proposes an online travel time prediction model with emphasis on capturing the effects of anomalies. The model divides a path into short links. A Functional Principal Component Analysis (FPCA) framework is adopted to forecast link travel times based on historical data and real-time measurements. Furthermore, a probabilistic nested delay operator is used to calculate path travel time distributions. To ensure that the algorithm is fast enough for online applications, parallel computation architecture is introduced to overcome the computational burden of the FPCA. Finally, a rolling horizon structure is applied to online travel time prediction. Empirical results for Guangzhou Airport Expressway indicate that the proposed method can capture an abrupt change in traffic state and provide a promising and reliable travel time prediction at both the link and path levels. In the case where the original FPCA is modified for parallelization, accuracy and computational effort are evaluated and compared with those of the sequential algorithm. The proposed algorithm is found to require only a piece rather than a large set of traffic incident records.     

Probe vehicle data sampled by time or space: Consistent travel time allocation and estimation

A characteristic of low frequency probe vehicle data is that vehicles traverse multiple network components (e.g., links) between consecutive position samplings, creating challenges for (i) the allocation of the measured travel time to the traversed components, and (ii) the consistent estimation of component travel time distribution parameters. This paper shows that the solution to these problems depends on whether sampling is based on time (e.g., one report every minute) or space (e.g., one every 500 m). For the special case of segments with uniform space-mean speeds, explicit formulae are derived under both sampling principles for the likelihood of the measurements and the allocation of travel time. It is shown that time-based sampling is biased towards measurements where a disproportionally long time is spent on the last segment. Numerical experiments show that an incorrect likelihood formulation can lead to significantly biased parameter estimates depending on the shapes of the travel time distributions. The analysis reveals that the sampling protocol needs to be considered in travel time estimation using probe vehicle data.