Influences on bicycle use

A stated preference experiment was performed in Edmonton in Canada to both examine the nature of various influences on bicycle use and obtain ratios among parameter values to be used in the development of a larger simulation of household travel behaviour. A total of 1128 questionnaires were completed and returned by current cyclists. Each questionnaire presented a pair of possible bicycle use alternatives and asked which was preferred for travel to a hypothetical all-day meeting or gathering (business or social). Alternatives were described by specifying the amounts of time spent on three different types of cycling facility and whether or not showers and/or secure bicycle parking were available at the destination. Indications of socio-economic character and levels of experience and comfort regarding cycling were also collected. The observations thus obtained were used to estimate the parameter values for a range of different utility functions in logit models representing this choice behaviour. The results indicate, among other things, that time spent cycling in mixed traffic is more onerous than time spent cycling on bike lanes or bike paths; that secure parking is more important than showers at the destination; and that cycling times on roadways tend to become less onerous as level of experience increases. Some of these results are novel and others are consistent with findings regarding bicycle use in work done by others, which is seen to add credence to this work. A review of previous findings concerning influences on cycling behaviour is also included.     

Household Mobility Tool Ownership: Modeling Interactions between Cars and Season Tickets

This paper moves beyond traditional models of car ownership in that we propose a framework for modeling household-level decisions to acquire specific types and numbers of mobility tools to fulfill the mobility needs of household members. The framework is applied to a data set collected during the winter and spring of 2000/2001 in the German city Karlsruhe via an interactive web-based stated response survey in which respondents could optimize their household mobility tool sets through on-line feedback concerning the estimated costs of the sets. In our analysis, bivariate ordered probit models are estimated for three combinations of mobility tools: season tickets (i.e., transit passes) and cars, season tickets and small cars and season tickets and large cars. In all instances, strong substitution effects are found – that is, as the number of season tickets increases, the number of cars decreases. This finding underscores the need to move beyond simple models of car ownership to comprehensive models of mobility tool ownership. As demonstrated by our research, failure to do so is likely to lead to biased results.     

Telecommuting Suitability Modeling: An Approach Based on the Concept of Abstract job

A new approach to modeling telecommuting suitability is proposed in this paper. The approach, based on the concept of abstract job, can be employed to assess the level of suitability for telecommuting of the bundle of tasks comprising a job. By abstract job is meant a way of considering jobs on the basis of their elements and tasks, representing the general structure of the job. In this study, the basic tasks a job is composed of, pertaining to telecommuting suitability, are identified. To show the applicability of the approach, discrete choice models are calibrated, based on a sample of 245 employees in Tehran, Iran, indicating that from among the 6 tasks identified, 5 tasks are significantly associated with the level of telecommuting suitability.     

The valuation of commuter travel time savings for car drivers: evaluating alternative model specifications

The empirical valuation of travel time savings is a derivative of the ratio of parameter estimates in a discrete choice model. The most common formulation (multinomial logit) imposes strong restrictions on the profile of the unobserved influences on choice as represented by the random component of a preference function. As we progress our ability to relax these restrictions we open up opportunities to benchmark the values derived from simple (albeit relatively restrictive) models. In this paper we contrast the values of travel time savings derived from multinomial logit and alternative specifications of mixed (or random parameter) logit models. The empirical setting is urban car commuting in six locations in New Zealand. The evidence suggests that less restrictive choice model specifications tend to produce higher estimates of values of time savings compared to the multinomial logit model; however the degree of under-estimation of multinomial logit remains quite variable, depending on the context.     

Preferences of travellers for using automated vehicles as last mile public transport of multimodal train trips

In the recent years many developments took place regarding automated vehicles (AVs) technology. It is however unknown to which extent the share of the existing transport modes will change as result of AVs introduction as another public transport option. This study is the first where detailed traveller preferences for AVs are explored and compared to existing modes. Its main objective is to position AVs in the transportation market and understand the sensitivity of travellers towards some of their attributes, focusing particularly on the use of these vehicles as egress mode of train trips. Because fully-automated vehicles are not yet a reality and they entail a potentially high disruptive way on how we use automobiles today, we apply a stated preference experiment where the role of attitudes in perceiving the utility of AVs is particularly explored in addition to the classical instrumental variables and several socio-economic variables. The estimated discrete choice model shows that first class train travellers on average prefer the use of AVs as egress mode, compared to the use of bicycle or bus/tram/metro as egress. We therefore conclude that AVs as last mile transport between the train station and the final destination have most potential for first class train travellers. Results show that in-vehicle time in AVs is experienced more negatively than in-vehicle time in manually driven cars. This suggests that travellers do not perceive the theoretical advantage of being able to perform other tasks during the trip in an automated vehicle, at least not yet. Results also show that travellers’ attitudes regarding trust and sustainability of AVs are playing an important role in AVs attractiveness, which leads to uncertainty on how people will react when AVs are introduced in practice. We therefore state the importance of paying sufficient attention to these psychological factors, next to classic instrumental attributes like travel time and costs, before and during the implementation process of AVs as a public transport alternative. We recommend the extension of this research to revealed preference studies, thereby using the results of field studies.     

Gas anxiety and the charging choices of plug-in hybrid electric vehicle drivers

Plug-in hybrid electric vehicles (PHEVs) can provide many of the benefits of battery electric vehicles (BEVs), such as reduced petroleum consumption and greenhouse gas emissions, without the “range anxiety” that can accompany driving a vehicle with limited range when there are few charging opportunities. However, evidence indicates that PHEVs are often plugged in more frequently than BEVs in practice. This is somewhat paradoxical: drivers for whom plugging in is optional tend to do so more frequently than those for whom it is necessary. This has led to the coining of a new term – “gas anxiety” – to describe the apparent desire of PHEV drivers to avoid using gasoline. In this paper, we analyze the variables influencing the charging choices of PHEV owners, testing whether drivers express preferences consistent with the concept of gas anxiety. We analyze data collected in a web-based stated preference survey using a latent class logit model. The results reveal two classes of decision-making patterns among the survey respondents: (1) those who weight the cost of gasoline and the cost of recharging approximately equally (the cost-minimizing class), and (2) those who weight the cost gasoline more heavily than the cost of recharging (the gas anxiety class). Respondents in the gas anxiety class expressed a willingness to recharge at a charging station even when doing so would cost approximately four times as much as the cost of the gasoline avoided. While the gas anxiety class represents the majority of our sample, more recent PHEV adopters are more likely to be in the cost-minimizing class. Looking forward, this suggests that public charging station operators may need to price charging competitively with gasoline on a per-mile basis to attract PHEV owners.     

Understanding suburban travel demand: Flexible modelling with revealed and stated choice data

We analyse the choice of mode in suburban corridors using nested logit specifications with revealed and stated preference data. The latter were obtained from a choice experiment between car and bus, which allowed for interactions among the main policy variables: travel cost, travel time and frequency. The experiment also included parking cost and comfort attributes. The attribute levels in the experiment were adapted to travellers’ experience using their revealed preference information. Different model specifications were tested accounting for the presence of income effect, systematic taste variation, and incorporating the effect of latent variables. We also derived willingness-to-pay measures, such as the subjective value of time, that vary among individuals as well as elasticity values. Finally, we analysed the demand response to various policy scenarios that favour public transport use by considering improvements in level-of-service, fare reductions and/or increases in parking costs. In general, demand was shown to be more sensitive to policies that penalise the private car than those improving public transport.     

Stated choice: a study in predictive validity using an aggregate truth set

Stated Choice models expand the ability of transportation planners to forecast future trends. The Stated Choice approach can forecast demand for new services or policies. However, Stated Choice models are subject to a range of experimental error not found within Revealed Preference (RP) designs. Primary among the concerns facing researchers is the ability of respondents to understand and operate upon hypothetical choice scenarios in a manner that will reproduce choices made under actual situations. These concerns are specified in the magnitude of a scaling factor. Efforts to estimate the scaling factor has proceeded by linking real decisions taken from a revealed preference survey with comparable decisions made under hypothetical conditions. However, where the alternative is new, actual decision data is not available. This study examines the level of error incorporated in a study where no RP data is available. The test of predictive validity focuses on the switching behavior of commuters at a single employment site. The actual data used to test the forecast is limited to company wide or aggregate ridership levels on the public transit mode taken two years after estimation of the SC model. The Fowkes and Preston hypothesis is examined and shown to bound the future actual value between forecasts derived from probabilistic and deterministic methods. The results show that with the passage of time, the probabilistic method approaches the reported ridership levels within 15 percent error.     

Stakeholder-specific data acquisition and urban freight policy evaluation: evidence,implications and new suggestions

Cities, characterised by scarce resources and facing increasing citizens’ requests for more liveable and attractive environments, need to define and implement more efficient urban freight transport policy interventions. It is strategically relevant to perform an ex-ante in-depth policy evaluation. The paper aims at investigating the relevance, the needs and the implications of the data acquisition process by combining a focused review with an original research study. It critically compares five categories of methodological approaches adopted for policy evaluation purposes with an explicit focus on the stated preference method given its behavioural peculiarity and robustness. The review, following a systematic procedure where scientific quality is complemented by relevance and coverage, underlines a high variability in the level of sophistication for data acquisition. While a stakeholder-specific approach seems natural in all those cases where local authorities want to adopt distinct policy instruments for each stakeholder, this is also needed when homogeneous policies impacting the various stakeholders are tested. Stakeholder-specific data acquisition translates into the creation of multiple experimental designs, explicitly conceived for each stakeholder considered. A case study, intended to compare two alternative strategies for data acquisition, provides a quantitative measure of potential distortions in policy evaluation due to the adoption of a stakeholder-generic approach. Results, in terms of willingness to pay measures, show that a stakeholder-specific approach is needed not only when modelling but also in the data acquisition process.     

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针对摩托车交通的快速发展对城市交通状况的负面影响,本研究选取典型城市佛山市南海区进行了居民出行调查和公交意向调查。从摩托车出行行为入手,分析了人们选择摩托车出行的原因、摩托车方式对出行行为的影响、摩托车出行方式与其替代方式的联系和区别,并在此基础上建立了出行方式选择的Multinomial Logit模型,深入分析了影响出行方式选择的主要因素。研究还结合摩托车车主对公共交通的评价意见和限摩政策可能带来的出行方式的调整,评价了大力发展公共交通和限制摩托车的策略对改善现有交通状况的效果,对类似城市解决摩托车交通问题有借鉴作用。