The accuracy of proxy responses in a stated choice setting: A re-examination and some controversial conclusions

Data is typically gathered from an individual respondent who represents the group or the household. This individual is often identified as the “primary decision maker” and is asked to provide responses as a proxy for the group given that the cost of interviewing each member individually is impractical and/or expensive. The collection of joint preferences is rarely undertaken, with the use of proxy responses not uncommon in travel behaviour research. Under such a framework, there exists an assumption that the primary decision maker has perfect knowledge of other group member preferences, and bargaining behaviour, and is able to synthesise this information when providing a response on their behalf. The validity of such an assumption however remains an open question, with recent research calling the reliability of proxy responses into account (Bateman and Munro, 2009). In this paper, using three models estimated in willingness to pay space, we examine the accuracy of proxy responses in a stated choice experiment. We find that there is overlap between a proxy response and the own preferences of the individual providing the proxy choice, but while the proxy responses fail to represent the full preference heterogeneity that exists in the actual choices made by individuals, the proxy responses in aggregate provide a suitable replacement for actual data, subject to a number of caveats.     

大连新建地铁对公交出行选择的影响

为了分析城市新建地铁项目对居民公共交通出行的影响,改善公交服务水平,提高公共交 通的分担率,针对大连市新建地铁线路展开研究。以大连市地铁1 号线沿线站点作为研究区域, 采用SP （Stated Preference） 调查方法,收集了300 名地铁沿线居民对常规公交以及新建地铁两种 出行方式的选择数据。通过建立二项Logit 模型,分析出行成本、换乘时间等选择方案特性变 量,出行特性变量以及出行者特性变量对于居民出行方式选择行为的影响,并计算弹性值及模型 精度。结果表明,通过调整出行成本及换乘时间可有效提高地铁分担率。由此提出相关政策建 议：对于出行成本,可从出行者年龄及出行次数两方面制定不同的优惠政策;对于换乘时间,可 从提高可达性、完善购票系统以及调整发班频率三方面进行改善。     

Experimental design influences on stated choice outputs: An empirical study in air travel choice

Discrete choice experiments are conducted in the transport field to obtain data for investigating travel behaviour and derived measures such as the value of travel time savings. The multinomial logit (MNL) and other more advanced discrete choice models (e.g., the mixed MNL model) have often been estimated on data from stated choice experiments and applied for planning and policy purposes. Determining efficient underlying experimental designs for these studies has become an increasingly important stream of research, in which the objective is to generate stated choice tasks that maximize the collected information, yielding more reliable parameter estimates. These theoretical advances have not been rigorously tested in practice, such that claims on whether the theoretical efficiency gains translate into practice cannot be made. Using an extensive empirical study of air travel choice behaviour, this paper presents for the first time results of different stated choice experimental design approaches, in which respective estimation results are compared. We show that D-efficient designs keep their promise in lowering standard errors in estimating, thereby requiring smaller sample sizes, ceteris paribus, compared to a more traditional orthogonal design. The parameter estimates found using an orthogonal design or an efficient design turn out to be statistically different in several cases, mainly attributed to more or less dominant alternatives existing in the orthogonal design. Furthermore, we found that small designs with a limited number of choice tasks performs just as good (or even better) than a large design. Finally, we show that theoretically predicted sample sizes using the so-called S-estimates provide a good lower bound. This paper will enable practitioners in better understanding the potential benefits of efficient designs, and enables policy makers to make decisions based on more reliable parameter estimates.     

Carpooling and carpool clubs: Clarifying concepts and assessing value enhancement possibilities through a Stated Preference web survey in Lisbon, Portugal

The increase of urban traffic congestion calls for studying alternative measures for mobility management, and one of these measures is carpooling. In theory, these systems could lead to great reductions in the use of private vehicles; however, in practice they have obtained limited success for two main reasons: the psychological barriers associated with riding with strangers and poor schedule flexibility. To overcome some of the limitations of the traditional schemes, we proposed studying a carpooling club model with two main new features: establishing a base trust level for carpoolers to find compatible matches for traditional groups and at the same time allowing to search for a ride in an alternative group when the pool member has a trip schedule different from the usual one. A web-based survey was developed for the Lisbon Metropolitan Region (Portugal), including a Stated Preference experiment, to test the concept and confirm previous knowledge on these systems’ determinants. It was found through a binary logit Discrete Choice Model calibration that carpooling is still attached with lower income strata and that saving money is still an important reason for participating in it. The club itself does not show promise introducing more flexibility in these systems; however, it should provide a way for persons to interact and trust each other at least to the level of working colleagues.     

Adapting interactive stated response techniques to a self-completion survey

We report here on how we adapted Interactive Stated Response Methods to use in a self-completion survey of 454 California households to measure the market for electric vehicles in California. Electric vehicles are a novel product and have many features unfamiliar to consumers, in particular their home recharging capability and limited range. Reflexive techniques were designed to draw households into a deeper exploration of the lifestyle implications of electric vehicles than can be done in more typical self-completion surveys, and to stimulate key decision processes previously observed in detailed gaming interviews with 51 households. Reflexive self-completion techniques provide a middle course between typical large sample quantitative surveys and small sample, detailed gaming interviews. One of the benefits of the more intensive techniques was that participants reported finding the surveys interesting and return rates were high for a self-completion approach – over sixty percent. We review previous transportation and sociological methods which inspired our design, describe the design goals and features of our research, and summarize research results pertinent to testing the validity of our approach.     

Passengers’ willingness to pay for airport ground access time savings

There are cases when passengers are willing to pay a premium to reduce the travel time, in particular when the trip has to be made. This paper aims to provide insight into factors that determine passengers’ willingness to pay to reduce travel time for their ground access to an airport. A methodology is developed that comprises two steps: the identification of the passengers with zero willingness to pay and from the rest the estimation of the additional price they are willing to pay to reduce their travel time. For the first step a Probit model was formulated and for the second a linear regression model. To this purpose, data has been collected employing stated preference from passengers at the Athens International Airport. It has been found that a high percentage of passengers have zero willingness to pay, and of the remaining ones those using public transport have a significant willingness to pay to reduce access travel time. The methodology and the models are structured in such a way that their transferability to any airport environment is possible, thus providing a useful tool for decisions relating to airport ground access measures.     

Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: Application to intended purchase of electric cars

Recently, policy makers’ expectations about the role of electric cars in reducing emissions have risen substantially. In parallel, academic research on purchase intentions has dramatically increased. Originally, most studies have focused on utility attributes and price. More recently, several hybrid choice models have been estimated to include the impact of attitudes on choice probabilities. In addition, a few studies have caught the attention to social influence. In contributing to this line of research, this paper reports the results of an expanded hybrid choice, which simultaneously estimated all these different effects in a single integrated model of purchase intention. Results indicate that the model performs well. Costs considerations contribute most to the utility of electric cars. Social influence is less important, but there is also evidence that people tend to take it into consideration when there are positive public opinions about electric cars and the market share becomes almost half of friends of their social network. The intention to purchase an electric car is also influenced by attitudes about environmental concerns and technology acceptance.     

Using repeated cross-sectional travel surveys to enhance forecasting robustness: Accounting for changing mode preferences

Transportation system capacity and performance, urban form and socio-demographics define the influences and constraints conditioning the preferences of urban residents for different transport modes. Changes in characteristics of urban areas are likely to lead to changes in preferences for alternative modes of transport over time; as a consequence, statistical models to forecast mode choice need to be sensitive to both purposeful changes to urban systems as well as exogenous shocks. We make use of the 1996, 2001 and 2006 household surveys conducted in the Greater Toronto and Hamilton Area to study mode preference evolution and model forecasting performance. These repeated cross-sectional household surveys provide an opportunity to investigate aggregate structural changes in commuting mode preferences over time, in a manner sensitive to changes in the urban area. We focus on commuting mode choices because these trips are prime determinants of peak period congestion and peak spreading. We then address how to combine the three cross-sections econometrically in a robust way that allows for use of a single mode choice model across the entire period. Using independent data from 2012, we are able to compare the individual year and combined models in terms of forecasting performance to demonstrate the combined model’s more robust forecasting performance into the future.     

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.     

Impact of telecommuting and intelligent transportation systems on residential location choice

The impacts of telecommuting and intelligent transportation systems (ITS) on urban development patterns were investigated in terms of households' residential location choice decisions. A discrete choice modelling approach framework was used. A stated preference (SP) logit analysis was carried out to estimate the parameters of the utility function. An attitude survey of employees of selected public and private sector organizations in the Ottawa-Carleton Region (Canada) yielded the required data for model estimation. In addition to obtaining background information, the survey elicited SP responses by presenting a number of hypothetical residential choice scenarios defined according to the principles of SP experimental design. Results show that telecommuting and ITS measures are highly significant factors in the residential choice model. This leads to the conclusion that these reinforce dispersed residential patterns and encourage moves towards outlying sites. Implications of this conclusion for urban land development planning are noted.