逐日路径演化中出行者信息偏好的实验分析

在逐日出行过程中,出行者对各类信息的偏好是一个动态变化的过程,不同出行者对同类信息的偏好也不同.本研究搭建不同信息条件下逐日路径选择实验场景,用信息偏好系数表征不同类别信息对出行者感知时间影响的相对权重,研究出行者信息偏好的演化规律与分布特征.实验结果表明,出行个体信息偏好的演化存在3种类型,出行群体对感知时间的信息偏好系数先震荡增加后逐步稳定,出行个体对感知时间的信息偏好系数在出行群体中成正态分布.与只提供完全历史信息相比,同时提供基于指数平滑法的系统预测信息会降低出行者对感知时间的偏好.     

基于偏好顺序集的非补偿式铁路旅客选择行为估计方法

精细化需求预测是铁路提高收益的重要基础.本文考虑了铁路产品属性对于旅客选择行为的影响,构建具有一定偏好顺序的产品集合来表征不同类型的旅客.在此基础上,利用客票存根及余票数据,建立预售期内旅客选择过程的极大似然函数,求解得到不同旅客类型的出现概率.首先通过仿真算例验证了模型的计算可行性,然后通过实证数据,估算了北京南至上海虹桥方向不同旅客类型在各时段的出现概率,进一步统计得到旅客在不同时段下铁路旅客buy-up行为概率及属性敏感度的变化,并提出一些可用于优化售票策略的建议.     

Consumer preferences and willingness to pay for advanced vehicle technology options and fuel types

The automotive industry is witnessing a revolution with the advent of advanced vehicular technologies, smart vehicle options, and fuel alternatives. However, there is very limited research on consumer preferences for such advanced vehicular technologies. The deployment and penetration of advanced vehicular technologies in the marketplace, and planning for possible market adoption scenarios, calls for the collection and analysis of consumer preference data related to these emerging technologies. This study aims to address this need, offering a detailed analysis of consumer preference for alternative fuel types and technology options using data collected in stated choice experiments conducted on a sample of consumers from six metropolitan cities in South Korea. The results indicate that there is considerable heterogeneity in consumer preferences for various smart technology options such as wireless internet, vehicle connectivity, and voice command features, but relatively less heterogeneity in the preference for smart vehicle applications such as real-time traveler information on parking and traffic conditions.     

高速路节日免费通行对小汽车出行者选择行为影响研究

出行成本是出行者在选择出行方式时的重要考虑因素。随着我国高速公路网和收费政策的逐步完善,自驾已成为出行者考虑选择的主要出行方式。为研究节日里选择小汽车出行的用户特征及影响因素,通过意向调查的方式对小汽车出行者的偏好进行调查。应用二项Logit模型建模分析,结果显示个人属性、出行属性以及出行环境等都在不同程度上影响出行者的节日出行选择行为,其中小汽车拥有状况、驾龄、旅行时间、出行者在以往节日里选择的出行方式、通行费、线路熟悉程度等对节日出行行为影响较为显著,该研究成果在交通预测、交通管控等方面具有指导性作用。     

Enhancing stated preference surveys in transportation research: the contribution of geographic information systems

In this paper we argue that visualization, data management and computational capabilities of geographic information systems (GIS) can assist transportation stated preference research in capturing the contextual complexity of many transportation decision environments by providing respondents with maps and other spatial and non-spatial information in graphical form that enhance respondents' understanding of decision scenarios. We explore the multiple inherent contributions of GIS to transportation stated preference data collection and propose a framework for a GIS-based stated preference survey instrument. We also present the design concepts of two survey prototypes and their GIS implementation for a sample travel mode choice problem.     

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.     

基于社交网络的民航旅客偏好建模

从社会网络的视角，提出了一种旅客个体偏好与关系偏好相结合的建模方法．首先，从旅客的历史出行记录中，构建基于共同出行关系的旅客社会网络；然后，构建旅客个体偏好模型和旅客关系偏好模型；最后，基于旅客偏好模型给旅客推荐座位．在民航领域的一个真实的数据集上进行了实验，证明本文提出的偏好模型能够有机地将旅客个体偏好与关系偏好结合起来，较好地描述旅客对航班座位的偏好．     

Findings from subjective evaluations and driver ratings of vehicle dynamics: steering and handling

This paper investigates subjective assessments (SA) of vehicle handling and steering feel tests, both numerical and verbal, to understand drivers’ use of judgement scales, rating tendencies and spread. Two different test methods are compared: a short multi-vehicle first-impression test with predefined-driving vs the standard extensive single-vehicle free-driving tests, both offering very similar results but with the former saving substantial testing time. Rating repeatability is evaluated by means of a blind test. Key SA questions are identified by numerical subjective assessment autocorrelations and by generating word clouds from the most used terms in verbal assessments, with both methods leading to similar key parameters. The results exposed in this paper enable better understanding of SA, allowing improving the overall subjective testing and evaluation process, and improving the data collection and analysis process needed before identifying correlations between SA and objective metrics.     

道路交通统计生命价值测算模型及其验证

基于参数服从SB分布的混合Logit 模型进行道路交通统计生命价值的测算研究. 首先，结合意愿选择法和正交实验法设计出行路径选择调查问卷；然后，基于死亡风险系数服从对数正态分布和SB分布的混合Logit 模型，构建统计生命价值测算模型；接着以大连市私家车出行者为调查对象获得调查数据，并利用Monte Carlo 仿真方法进行模型参数标定；最后，对模型进行比较分析，并获得统计生命价值的测算值. 研究结果表明：死亡风险参数服从限制域为(0.0, 0.5)SB分布的混合Logit 模型，精确性更高且更合理；道路交通统计生命价值测算值为 105.76万元，这一结果可以作为道路交通安全项目经济评价的参考数据.     

Evolution of latent modal captivity and mode choice patterns for commuting trips: A longitudinal analysis using repeated cross-sectional datasets

This paper presents an investigation of the temporal evolution of commuting mode choice preference structures. It contributes to two specific modelling issues: latent modal captivity and working with multiple repeated crossectional datasets. In this paper latent modal captivity refers to captive reliance on a specific mode rather than all feasible modes. Three household travel survey datasets collected in the Greater Toronto and Hamilton Area (GTHA) over a ten-year time period are used for empirical modelling. Datasets collected in different years are pooled and separate year-specific scale parameters and coefficients of key variables are estimated for different years. The empirical model clearly explains that there have been significant changes in latent modal captivity and the mode choice preference structures for commuting in the GTHA. Changes have occurred in the unexplained component of latent captivities, in transportation cost perceptions, and in the scales of commuting mode choice preferences. The empirical model also demonstrates that pooling multiple repeated cross-sectional datasets is an efficient way of capturing behavioural changes over time. Application of the proposed mode choice model for practical policy analysis and forecasting will ensure accurate forecasting and an enhanced understanding of policy impacts.