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．     

Analysing Demand for Suburban Trips: A Mixed RP/SP Model with Latent Variables and Interaction Effects

We analyse mode choice behaviour for suburban trips in the Grand Canary island using mixed revealed preference (RP)/stated preference (SP) information. The SP choice experiment allowed for interactions among the main policy variables: travel cost, travel time and frequency, and also to test the influence of latent variables such as comfort. It also led to discuss additional requirements on the size and sign of the estimated model parameters, to assess model quality when interactions are present. The RP survey produced data on actual trip behaviour and was used to adapt the SP choice experiment. During the specification searches we detected the presence of income effect and were able to derive willingness-to-pay measures, such as the subjective value of time, which varied among individuals. We also studied the systematic heterogeneity in individual tastes through the specification of models allowing for interactions between level-of-service and socio-economic variables. We concluded examining the sensitivity of travellers’ behaviour to various policy scenarios. In particular, it seems that contrary to political opinion, in a crowded island policies penalising the use of the private car seem to have a far greater impact in terms of bus patronage than policies implying direct improvements to the public transport service.     

Managing Beach Amenities to Reduce Exposure to Coastal Hazards: Storm Water Pollution

Despite posted warnings and educational campaigns warning about the health risks associated with storm water pollution, swimmers continue to swim in coastal areas polluted by storm water runoff. This study uses a simple spatial model of beach visitation to show how beach amenities and storm drains influence the way in which beach goers choose to locate themselves at beaches in Santa Monica Bay, California. The estimated models indicate that passive means of preventing exposure to marine pollution (e.g., posted signs) could be made significantly more effective if combined with the active management of other beach amenities. Similar methods also could be used to reduce risks to swimmers from other hazards including riptides and boat traffic.     

基于GIS平台的地铁客流预测模型构建

借助GIS平台,对公交线网进行编码,并利用ArcGis的缓冲和叠加分析工具,建立公交站点和地铁站点的空间对应关系。根据调查得到的公交线路站间OD矩阵,利用地铁意向调查标定的参数结果,得到公交客流转移到地铁的比例。基于GIS平台构建的地铁客流预测模型,可以有效地提高建模速度,并能对公交线路调整进行快速反应。     

Choice of parking: Stated preference approach

Over recent years, parking policy has become a key element of transport policy in many countries. Parking policy measures can affect many different dimensions of travel behaviour but are likely to be most significant in terms of travellers' choice of parking type and location. This dimension of travel choice has, to date, received comparatively little attention, yet is of vital importance if we are to properly understand and predict the effects of parking policy measures.This paper presents two studies addressing this issue carried out in the United Kingdom and Germany. Both studies used a stated preference approach in order to collect disaggregate data on travellers responses to changes in parking attributes and used these data to build simple logit models of parking type choice. The studies were designed in order to allow comparable choice models to be estimated from the two datasets. The results obtained strongly indicate the need to separately identify the costs associated with different components of the parking activity (e.g., general in-vehicle time, parking search time, egress time) and also point to the existence of significant differences in the relative valuation of these components across different journey purposes. Where possible, the results of the choice modelling exercises are also compared with existing revealed and stated preference results and are found to be generally in line with prior expectations.     

Sampling of alternatives for route choice modeling

This paper presents a new paradigm for choice set generation in the context of route choice model estimation. We assume that the choice sets contain all paths connecting each origin–destination pair. Although this is behaviorally questionable, we make this assumption in order to avoid bias in the econometric model. These sets are in general impossible to generate explicitly. Therefore, we propose an importance sampling approach to generate subsets of paths suitable for model estimation. Using only a subset of alternatives requires the path utilities to be corrected according to the sampling protocol in order to obtain unbiased parameter estimates. We derive such a sampling correction for the proposed algorithm.Estimating models based on samples of alternatives is straightforward for some types of models, in particular the multinomial logit (MNL) model. In order to apply MNL for route choice, the utilities should also be corrected to account for the correlation using, for instance, a path size (PS) formulation. We argue that the PS attribute should be computed based on the full choice set. Again, this is not feasible in general, and we propose a new version of the PS attribute derived from the sampling protocol, called Expanded PS.Numerical results based on synthetic data show that models including a sampling correction are remarkably better than the ones that do not. Moreover, the Expanded PS shows good results and outperforms models with the original PS formulation.     

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.     

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城市公共交通乘客出行成本的量化研究,对于科学合理地评价公共交通系统的社会效益意义重大,而出行时间价值是进行乘客出行成本估计的一个重要环节. 本文以北京市市区居民SP调查数据为依托,通过分析公共交通出行时间价值的影响因素,选取Logit模型为基础模型,引入“出行者收入”作为模型新增变量,并针对工作商务、社会娱乐两种出行目的在不同换乘情况下出行时间价值模型进行参数标定,建立了基于SP调查数据的改进型时间价值求解模型,并计算得到北京市公共交通出行者在不同出行目的和有无换乘情况下的换乘时间价值、候车时间价值以及运行时间价值. 研究发现,工作商务出行目的出行时间价值普遍高于社会娱乐的出行时间价值,出行者在不同出行目的和有无换乘情况下的候车时间价值,大于换乘时间价值以及运行时间价值,并且有换乘情况下的候车时间价值大于无换乘情况下的候车时间价值.