Toll product preferences and implications for alternative payment options and going cashless

There has been a great deal of research on deriving estimates of the value of travel time savings (VTTS) as a way of converting travel time benefits of toll roads relative to free routes into monetary units, the major user benefit in the development of forecasts of traffic and hence revenue streams. By contrast there has been almost no consideration given to identifying the role that various tollroad products play in establishing preferences for toll routes over non-tolled routes. Increasingly tollroads give users the option to pay by cash at a toll booth, by electronic tolling or by a pre- or post-paid debit and credit account system involving vehicle identification. The efficiency gained by electronic tolling for both the users and the operator have resulted in moves in many jurisdictions to eliminate cash collection entirely (or not introduce it when introducing a new tolled road facility), and to introduce a range of pre- and post-payment options. This has been accompanied by a growing move to distance-based charging in some contexts which is more cumbersome to structure with a cash option. This paper investigates current and potential travellers’ preferences for a range of toll products and how much individual’s are willing to pay for very specific toll products. Data from a stated choice experiment is used in a mixed logit model to establish the role that toll products play in the context of offered times and costs of alternative routes, in choosing between alternative ways of paying for the use of tolled routes.

Eliciting preferences for adoption of fully automated vehicles using best-worst analysis

Autonomous mobility is one of the rapidly evolving aspects of smart transportation which carries the potential of reshaping both demand and supply sides of transportation systems. While understanding public opinions about autonomous vehicles (AVs) is a compelling step towards their successful implementation, still little is known about to which extent people will embrace this new technology and how the vehicle features will affect their adoption decision. This study presents a new approach for modeling the adoption behavior of fully AVs using the profile-case best-worst scaling model. In this approach, an AV profile which is characterized in terms of the main vehicle attributes and their associated levels is presented to the decision maker and he/she is asked to select the most and the least attractive attributes. Further, a binary adoption question at the end of the choice task inquires if the respondent is willing to purchase the described AV. Utilizing this method, we can recognize the difference between the intrinsic impacts of the vehicle attributes and the impact of the attribute levels on the adoption decision. Results of the analysis indicate that people are much more sensitive to the purchase price and incentive policies such as taking liability away from the “driver” in case of accidents and provision of exclusive lanes for AVs compared to other factors such as fuel efficiency, safety, or environmental friendliness. Further, it is found that millennials with higher income, those who live in the downtown area, and the majority of people who have experienced an accident in the past have greater interests in adopting this technology.     

The influence of attitude towards individuals’ choice for a remotely piloted commercial flight: A latent class logit approach

The Remotely Piloted Commercial Passenger Aircraft Attitude Scale (RPCPAAS) was created to measure positive and negative attitudes towards a new and plausible form of air travel. This information was then used, in combination with a latent class logit model built on data generated from a stated choice experiment to gain insight into the choice behaviour between conventionally piloted aircraft (CPA) with a pilot on-board and remotely piloted aircraft (RPA) with a pilot on the ground. The results revealed that individuals, on-average, if presented a choice between a CPA and a RPA of equivalent attributes, would elect for the CPA option. However, there is variability in the passengers’ sensitivity to various flight attributes, and these sensitivities were influenced by individuals’ attitude towards the new technology (i.e., RPA). From an operational perspective, and assuming that one day passengers of commercial airlines are offered the choice between CPA and RPA, the strategies employed by airlines to encourage the use of the new technology need to be different, based on individuals’ attitude towards RPA.     

When is big data big enough? Implications of using GPS-based surveys for travel demand analysis

A number of studies in the last decade have argued that Global Positioning Systems (GPS) based survey offer the potential to replace traditional travel diary surveys. GPS-based surveys impose lower respondent burden, offer greater spatiotemporal precision and incur fewer monetary costs. However, GPS-based surveys do not collect certain key inputs required for the estimation of travel demand models, such as the travel mode(s) taken or the trip purpose, relying instead on data-processing procedures to infer this information. This study assesses the impact that errors in inference can have on travel demand models estimated using data from GPS-based surveys and proposes ways in which these errors can be controlled for during both data collection and model estimation. We use simulated datasets to compare performance across different sample sizes, inference accuracies, model complexities and estimation methods. Findings from the simulated datasets are corroborated with real data collected from individuals living in the San Francisco Bay Area, United States. Results indicate that the benefits of using GPS-based surveys will vary significantly, depending upon the sample size of the data, the accuracy of the inference algorithm and the desired complexity of the travel demand model specification. In many cases, gains in the volume of data that can potentially be retrieved using GPS devices are found to be offset by the loss in quality caused by inaccuracies in inference. This study makes the argument that passively collected GPS-based surveys may never entirely replace surveys that require active interaction with study participants.     

Willingness-to-pay for alternative fuel vehicle characteristics: A stated choice study for Germany

In the light of European energy efficiency and clean air regulations, as well as an ambitious electric mobility goal of the German government, we examine consumer preferences for alternative fuel vehicles (AFVs) based on a Germany-wide discrete choice experiment among 711 potential car buyers. We estimate consumers’ willingness-to-pay and compensating variation (CV) for improvements in vehicle attributes, also taking taste differences in the population into account by applying a latent class model with 6 distinct consumer segments. Our results indicate that about 1/3 of the consumers are oriented towards at least one AFV option, with almost half of them being AFV-affine, showing a high probability of choosing AFVs despite their current shortcomings. Our results suggest that German car buyers’ willingness-to-pay for improvements of the various vehicle attributes varies considerably across consumer groups and that the vehicle features have to meet some minimum requirements for considering AFVs. The CV values show that decision-makers in the administration and industry should focus on the most promising consumer group of ‘AFV aficionados’ and their needs. It also shows that some vehicle attribute improvements could increase the demand for AFVs cost-effectively, and that consumers would accept surcharges for some vehicle attributes at a level which could enable their private provision and economic operation (e.g. fast-charging infrastructure). Improvement of other attributes will need governmental subsidies to compensate for insufficient consumer valuation (e.g. battery capacity).     

Preferences for shared autonomous vehicles

Shared autonomous vehicles (SAVs) could provide inexpensive mobility on-demand services. In addition, the autonomous vehicle technology could facilitate the implementation of dynamic ride-sharing (DRS). The widespread adoption of SAVs could provide benefits to society, but also entail risks. For the design of effective policies aiming to realize the advantages of SAVs, a better understanding of how SAVs may be adopted is necessary. This article intends to advance future research about the travel behavior impacts of SAVs, by identifying the characteristics of users who are likely to adopt SAV services and by eliciting willingness to pay measures for service attributes. For this purpose, a stated choice survey was conducted and analyzed, using a mixed logit model. The results show that service attributes including travel cost, travel time and waiting time may be critical determinants of the use of SAVs and the acceptance of DRS. Differences in willingness to pay for service attributes indicate that SAVs with DRS and SAVs without DRS are perceived as two distinct mobility options. The results imply that the adoption of SAVs may differ across cohorts, whereby young individuals and individuals with multimodal travel patterns may be more likely to adopt SAVs. The methodological limitations of the study are also acknowledged. Despite a potential hypothetical bias, the results capture the directionality and relative importance of the attributes of interest.     

Preference-based evolutionary algorithm for airport surface operations

In addition to time efficiency, minimisation of fuel consumption and related emissions has started to be considered by research on optimisation of airport surface operations as more airports face severe congestion and tightening environmental regulations. Objectives are related to economic cost which can be used as preferences to search for a region of cost efficient and Pareto optimal solutions. A multi-objective evolutionary optimisation framework with preferences is proposed in this paper to solve a complex optimisation problem integrating runway scheduling and airport ground movement problem. The evolutionary search algorithm uses modified crowding distance in the replacement procedure to take into account cost of delay and fuel price. Furthermore, uncertainty inherent in prices is reflected by expressing preferences as an interval. Preference information is used to control the extent of region of interest, which has a beneficial effect on algorithm performance. As a result, the search algorithm can achieve faster convergence and potentially better solutions. A filtering procedure is further proposed to select an evenly distributed subset of Pareto optimal solutions in order to reduce its size and help the decision maker. The computational results with data from major international hub airports show the efficiency of the proposed approach.     

Errors in dynamical fields inferred from oceanographic cruise data: Part I. The impact of observation errors and the sampling distribution

Diagnostic studies of ocean dynamics based on the analysis of oceanographic cruise data are usually quite sensitive to observation errors, to the station distribution and to the synopticity of the sampling. Here we present an error analysis of the first two sources. The third one is evaluated in Part II of this work (J. Mar. Sys. (2005), this issue). For observed variables and those linearly related to them, we use the Optimal Statistical Interpolation (OI) formulation. For variables which are not linearly related to observed variables (e.g., the vertical velocity), we carry out numerical experiments in a consistent way with OI statistics. Best results are obtained when some kind of scale selection or spatial filtering is applied in order to suppress small scales that cannot be properly resolved by the station distribution.The formulation is first applied to a high resolution (SeaSoar) sampling aimed to the recovery of mesoscale features in a region of large spatial variability (noise-to-signal fraction of the order of 0.002). Fractional errors (rms error divided by the standard deviation of the field) are estimated in about 2% for dynamic height and between 4% and 20% for geostrophic vorticity and vertical velocity. For observed variables, observation errors and sampling limitations are shown to contribute in similar amounts to total errors. For derived variables, sampling errors are by far the dominant contribution. For less dense samplings (e.g., equally spaced CTD stations), fractional errors are about 6% for dynamic height and between 15% and 30% for geostrophic vorticity and vertical velocity. For this sampling strategy, errors of all variables are mostly associated with sampling limitations.     

既有线测量新技术

研究目的:解决老既有线测量技术的安全性差、效率低、精度低等问题。研究方法:用坐标法测量既有线。该既有线测量新技术坚持以人为本、安全生产,尽量减少线上工作人数为前提。置镜任意点,每个曲线建立相对独立的直角坐标系,同时完成里程丈量、线路方向、小偏角、轨面高程的测量。研究结果:用坐标法测量既有线,由原来5道顺序流水作业流程缩减成2道顺序作业流程,作业人数由31人减少到13人。完成同样的工作量,减少18个生产人员。线上作业人数由原来的23人减少到4人;最危险的轨道中心作业人数从8人减少到0人,大幅度降低危险系数。研究结论:既有线测量新技术在安全生产、测量精度控制、效率等各方面都有优势。技术更新的目标在于实现电子记录,减少人为错误,实现勘测设计一体化,安全生产,提高效益。