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201.
The "Stated Adaptation" survey is an interactive technique which allows us to obtain a clearer picture of the attitudes and behaviours of individuals when confronted with hypothetical situations, in particular inexperienced travel conditions. This method makes use of a simulation game whose purpose is to explore on small samples individuals' choice processes when selecting between the different transport alternatives which are available to them. This paper describes how gaming-simulation is designed, with reference to the issues tackled by two surveys which have recently been carried out in France (reactions to urban road pricing and perception of electric vehicles). It describes the benefits of this experimental approach which allows stated behaviours to be checked to a considerable degree. The limits and potential developments of this survey technique are also discussed. 相似文献
202.
203.
铁路旅客乘车行为的层次分析 总被引:3,自引:1,他引:2
研究铁路旅客乘车行为对旅客列车开行方案的进一步优化、客运服务质量的提高和客运新产品的开发都具有重要意义。在分析铁路旅客乘车行为的基础上,采用层次分析法建立了铁路旅客乘车选择的层次结构模型,并给出了算法和步骤,用于计算旅客对不同种类列车的选择行为。最后给出了算例,所得结果较为合理。 相似文献
204.
黄天民 《西南交通大学学报》1992,5(2):96-101
有限偏序集上满足一定条件的组合计数问题,一直使人们很感兴趣。{1,2,…,2几}这2件个自然数
中选取件个,使其中任两个不具有整除关系,本文讨论了哪些数可以选取,哪些数不能选取,并得
到了所允许选取的最小数及所有不同选取种数的两个简捷的计数公式。 相似文献
205.
城市轨道交通接驳方式的选择 总被引:2,自引:0,他引:2
以北京市第三次居民出行调查数据为基础,在考虑影响接驳方式选择的个人、家庭社会经济属性的基础上,构建城市轨道交通接驳方式选择模型.利用模型估计得到参数,通过计算事件发生比,分析变量对接驳方式选择的影响程度.用一个案例,阐述接驳方式选择模型的运用,结果表明:接驳方式选择模型适用于研究个体的出行决策,预测正确率达到70.5%.轨道交通接驳方式研究在实践中的应用,对提高城市轨道交通吸引能力和扩大其影响范围有重要作用. 相似文献
206.
城市机动车辆停放选择模型 总被引:8,自引:2,他引:6
为了增强出行前停车信息查询功能和提高停车诱导效果,提出一种服务于驾驶员出行前对目的地首选以及备选停车场进行车辆停放选择的优化模型,建立了以驾驶员使用最便利,可达性最强,出行停放成本最低为目标的车辆停放选择模型,利用启发式算法计算了模型的备选解集。经实例验证,该模型可以得到多个满足约束条件的合理性停车方案,从而为驾驶员在出行前选择停车场提供了决策依据。 相似文献
207.
Lin HeWei Chen Guenter Conzelmann 《Transportation Research Part D: Transport and Environment》2012,17(3):208-214
We analyze the vehicle usage and consumer profile attributes extracted from both National Household Travel Survey and Vehicle Quality Survey data to understand the impact of vehicle usage upon consumers’ choices of hybrid electric vehicles in the US. In addition, the key characteristics of hybrid vehicle drivers are identified to determine the market segmentations of hybrid electric vehicles and the critical attributes to include in the choice model. After a compatibility test of two datasets, a pooled choice model combining both data sources illustrates the significant influences of vehicle usage upon consumers’ choices of hybrid electric vehicles. Even though the data-bases have in the past been used independently to study travel behavior and vehicle quality ratings, here we use them together. 相似文献
208.
In the US, the rise in motorized vehicle travel has contributed to serious societal, environmental, economic, and public health
problems. These problems have increased the interest in encouraging non-motorized modes of travel (walking and bicycling).
The current study contributes toward this objective by identifying and evaluating the importance of attributes influencing
bicyclists’ route choice preferences. Specifically, the paper examines a comprehensive set of attributes that influence bicycle
route choice, including: (1) bicyclists’ characteristics, (2) on-street parking, (3) bicycle facility type and amenities,
(4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics.
The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study
emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists’ demographics in bicycle
route choice decisions. The empirical results indicate that travel time (for commuters) and motorized traffic volume are the
most important attributes in bicycle route choice. Other route attributes with a high impact include number of stop signs,
red light, and cross-streets, speed limits, on-street parking characteristics, and whether there exists a continuous bicycle
facility on the route.
Ipek N. Sener is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. She received her M.S. degrees in Civil Engineering and in Architecture, and her B.S. degree in Civil Engineering from the Middle East Technical University in Ankara, Turkey. Naveen Eluru is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. He received his M.S. degree in Civil Engineering from The University of Texas at Austin, and his Bachelors in Technology Degree from Indian Institute of Technology in Madras, India. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
Chandra R. Bhat (Corresponding author)Email: |
Ipek N. Sener is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. She received her M.S. degrees in Civil Engineering and in Architecture, and her B.S. degree in Civil Engineering from the Middle East Technical University in Ankara, Turkey. Naveen Eluru is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. He received his M.S. degree in Civil Engineering from The University of Texas at Austin, and his Bachelors in Technology Degree from Indian Institute of Technology in Madras, India. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
209.
Erika Spissu Abdul Rawoof Pinjari Ram M. Pendyala Chandra R. Bhat 《Transportation》2009,36(4):403-422
In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles
drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study
explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e.,
self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive
distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components.
The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation
approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling
in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.
Erika Spissu is currently a Research Fellow at the University of Cagliari (Italy). She received her Ph.D. from the University of Palermo and University of Cagliari (Italy) in Transport techniques and economics. She spent the past 2 years at The University of Texas at Austin as a Research Scholar focusing primarily in activity-based travel behavior modeling, time use analysis, and travel demand forecasting. Abdul Pinjari is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of South Florida, Tampa. His research interests include time-use and travel-behavior analysis, and activity-based approaches to travel-demand forecasting. He has his Ph.D. from The University of Texas at Austin. Ram M. Pendyala is a Professor of Transportation Systems in the Department of Civil, Environmental, and Sustainable Engineering at Arizona State University. He teaches and conducts research in travel behavior analysis, travel demand modeling and forecasting, activity-based microsimulation approaches, and time use. He specializes in integrated land use-transport models, transport policy formulation, and public transit planning and design. He is currently the Vice-Chair of the International Association for Travel Behavior Research and is the immediate past chair of the Transportation Research Board Committee on Traveler Behavior and Values. He has his PhD from the University of California at Davis. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
Chandra R. Bhat (Corresponding author)Email: |
Erika Spissu is currently a Research Fellow at the University of Cagliari (Italy). She received her Ph.D. from the University of Palermo and University of Cagliari (Italy) in Transport techniques and economics. She spent the past 2 years at The University of Texas at Austin as a Research Scholar focusing primarily in activity-based travel behavior modeling, time use analysis, and travel demand forecasting. Abdul Pinjari is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of South Florida, Tampa. His research interests include time-use and travel-behavior analysis, and activity-based approaches to travel-demand forecasting. He has his Ph.D. from The University of Texas at Austin. Ram M. Pendyala is a Professor of Transportation Systems in the Department of Civil, Environmental, and Sustainable Engineering at Arizona State University. He teaches and conducts research in travel behavior analysis, travel demand modeling and forecasting, activity-based microsimulation approaches, and time use. He specializes in integrated land use-transport models, transport policy formulation, and public transit planning and design. He is currently the Vice-Chair of the International Association for Travel Behavior Research and is the immediate past chair of the Transportation Research Board Committee on Traveler Behavior and Values. He has his PhD from the University of California at Davis. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
210.
Stephane Hess David A. Hensher 《Transportation Research Part A: Policy and Practice》2012,46(3):626-644
Stated choice surveys are used extensively in the study of choice behaviour across many different areas of research, notably in transport. One of their main characteristics in comparison with most types of revealed preference (RP) surveys is the ability to capture behaviour by the same respondent under varying choice scenarios. While this ability to capture multiple choices is generally seen as an advantage, there is a certain amount of unease about survey length. The precise definition about what constitutes a large number of choice tasks however varies across disciplines, and it is not uncommon to see surveys with up to twenty tasks per respondent in some areas. The argument against this practice has always been one of reducing respondent engagement, which could be interpreted as a result of fatigue or boredom, with frequent reference to the findings of Bradley and Daly (1994) who showed a significant drop in utility scale, i.e. an increase in error, as a respondent moved from one choice experiment to the next, an effect they related to respondent fatigue. While the work by Bradley and Daly has become a standard reference in this context, it should be recognised that not only was the fatigue part of the work based on a single dataset, but the state-of-the-art and the state-of-practice in stated choice survey design and implementation has moved on significantly since their study. In this paper, we review other literature and present a more comprehensive study investigating evidence of respondent fatigue across a larger number of different surveys. Using a comprehensive testing framework employing both Logit and mixed Logit structures, we provide strong evidence that the concerns about fatigue in the literature are possibly overstated, with no clear decreasing trend in scale across choice tasks in any of our studies. For the data sets tested, we find that accommodating any scale heterogeneity has little or no impact on substantive model results, that the role of constants generally decreases as the survey progresses, and that there is evidence of significant attribute level (as opposed to scale) heterogeneity across choice tasks. 相似文献