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• Risk compensation effect of helmet use is studied based on selected bicyclists. • Targeted maximum likelihood estimator and ensemble learning are applied in this study. • Our results suggest the existence of risk compensation effect due to helmet use. Introduction: Safety is a critical factor in promoting sustainable urban non-motorized travel modes like bicycles. Helmets have shown to be effective in reducing injury severity in bicycle crashes, however, their effects on bicyclists' behaviors still requires deeper understanding, especially amid the emerging trend of using shared bicycles. Risk compensation effects suggest that bicyclists may offset perceived gains in safety from wearing a helmet by increasing risk-taking behaviors. A better understanding of these compensation effects can be useful in assessing various bicycle safety related programs. Method: Using a sample of 131 bicyclists from the San Francisco Bay area, this research studies how bicyclists respond with respect to risk-taking behaviors under various urban-street conditions, as a function of helmet use. Study participants were each shown 12 videos, shot in Berkeley, California, from the perspective of a bicyclist riding behind another bicyclist. A fractional factorial experiment design was used to systematically vary contextual attributes (e.g., speed, bike lane facilities, on-street parking, passing vehicles) across the videos. After each video, participants were asked to indicate if they would overtake the bicyclist in the video. With the help of data adaptive estimation techniques, targeted maximum likelihood estimation (TMLE) was applied to estimate the average risk difference between helmeted users and non-users, controlling for self-selection effects. Individual-based nonparametric bootstrap was performed to assess the uncertainty associated with the estimator. Results: Our findings suggest, on average, individuals more likely to wear a helmet are 15.6% more likely to undertake a risky overtaking maneuver. Practical Applications: This study doesn't try to oppose mandatory helmet laws, but rather serves as a cautionary warning that road safety programs may need to consider strategies in which unintended impact of bicycle helmet use can be mitigated. Moreover, our findings also provide additional evaluation component when it comes to the cost-benefit assessment of helmet-related laws. [ABSTRACT FROM AUTHOR]

On-demand transport (ODT) refers to adaptive transport services that use a fleet of vehicles to provide shared flexible transport to consumers, when and where they need it. This study surveyed 3,985 geographically and demographically representative Australians nationwide, to understand consumer demand and willingness to pay for ODT in Australia. Our analysis finds that the current market for ODT services in Australia is small. For example, for an ODT service that costs roughly the same as UberX's ridesharing service, and offers comparable level-of-service, our analysis predicts that only 17 per cent of the Australian population can be expected to use the service a few times a week or more. However, shared electric autonomous vehicles could significantly change the business case for ODT services, by enabling on-demand door-to-door transport services at a fractional cost of similar existing services. Our analysis finds that while consumers are willing to pay, on average, 0.28$/km more to avoid sharing a vehicle with other passengers, 0.17$/km more for door-to-door service, and 0.10$/km to be able to book the service in real time, cost is the most important determinant of ODT use (of the service attributes included in our analysis). For an ODT service that provides the same level-of-service as UberX, but at a fractional cost of $0.30 per km, 31 per cent of the Australian population can be expected to use the service a few times a week or more. And this figure is likely to be larger once we account for more long-term changes in lifestyles that might accompany the introduction of these services. We find that frequency of ODT use is strongly correlated with age and lifecycle stage: young individuals who are employed full-time are likely to use ODT most frequently; older adults who have retired from the workforce and whose children have left home are likely to be infrequent users. Overall, our analysis indicates that ODT services have the ability to both increase public transport use among existing public transport customers, and to draw new customers to public transport services. [ABSTRACT FROM AUTHOR]

This study develops a hierarchical latent class choice model that captures the concurrent influence of lifestyles on household residential neighbourhood location and individual transport mode use decisions. The model is empirically evaluated using data from the 2010–12 California Household Travel Survey. The model identifies six household-level classes that differ in terms of their preferences for different neighbourhood attributes when deciding where to live and their household characteristics. Coincidentally, the model also identifies six individual-level classes that differ in terms of the travel modes that they consider when deciding how to travel, their sensitivity to different level-of-service attributes, and their individual characteristics. Household preferences for neighbourhood types and individual preferences for travel modes show expected patterns of correlation. In general, households that prefer to live in suburban neighbourhoods are more likely to consist of individuals that are car-dependent, and households that prefer to live in inner-city neighbourhoods are more likely to consist of individuals that are multimodal. However, our analysis also reveals interesting patterns of deviation. For example, high-income migrant households and median-income white households display strong preferences for suburban neighbourhoods, but individuals belonging to these households also have a high likelihood of being multimodal, with a strong preference for bicycling. We discuss how these patterns of correlation can be used to inform transport and land use policy in novel ways. [ABSTRACT FROM AUTHOR]

This study proposes a flexible and scalable single-level framework for origin–destination matrix (ODM) inference using data from IoT (Internet of Things) and other sources. The framework allows the analyst to integrate information from multiple data sources, while controlling for differences in data quality across sources. We assess the effectiveness of the framework through a real-world experiment in Greater Adelaide (GA), Australia. We infer car OD flows within the region using four separate data sources: site-level traffic counts from loop detectors, vehicle trajectories recorded by roadside Bluetooth sensors, partial OD flows based on data from in to vehicle navigation systems, and journey-to-work OD data collected by the Australian Census. We compare our OD inferences with those from the current version of the Strategic Adelaide Model (SAM), calibrated using data from traditional household travel surveys. We find remarkable consistency between our inferences and those from SAM, despite differences in input data and methodologies. For example, for the morning peak period, we predict the total number of trips made within GA to be equal to 556,000, while the corresponding prediction from SAM is equal to 484,000. The two predictions are within 15 per cent of each other. When we compare the spatial distribution of trips, in terms of origins and destinations, we find that our inferred OD matrix has an 86 per cent cosine similarity to the corresponding SAM matrix. Travel time predictions based on our OD inferences correspond closely to those predicted by the Google Directions API, lending further confidence in our inferences. In summary, our results show that the proposed framework can produce highly comparable ODM, trip production and trip attraction patterns to those inferred from traditional household travel survey-based transportation demand modelling methods. [ABSTRACT FROM AUTHOR]

This study proposes a mixed logit model with multivariate nonparametric finite mixture distributions. The support of the distribution is specified as a high-dimensional grid over the coefficient space, with equal or unequal intervals between successive points along the same dimension; the location of each point on the grid and the probability mass at that point are model parameters that need to be estimated. The framework does not require the analyst to specify the shape of the distribution prior to model estimation, but can approximate any multivariate probability distribution function to any arbitrary degree of accuracy. The grid with unequal intervals, in particular, offers greater flexibility than existing multivariate nonparametric specifications, while requiring the estimation of a small number of additional parameters. An expectation maximization algorithm is developed for the estimation of these models. Multiple synthetic datasets and a case study on travel mode choice behavior are used to demonstrate the value of the model framework and estimation algorithm. Compared to extant models that incorporate random taste heterogeneity through continuous mixture distributions, the proposed model provides better out-of-sample predictive ability. Findings reveal significant differences in willingness to pay measures between the proposed model and extant specifications. The case study further demonstrates the ability of the proposed model to endogenously recover patterns of attribute non-attendance and choice set formation. [ABSTRACT FROM AUTHOR]

Major technological and infrastructural changes over the next decades, such as the introduction of autonomous vehicles, implementation of mileage-based fees, carsharing and ridesharing are expected to have a profound impact on lifestyles and travel behavior. Current travel demand models are unable to predict long-range trends in travel behavior as they do not entail a mechanism that projects membership and market share of new modes of transport (Uber, Lyft, etc.). We propose integrating discrete choice and technology adoption models to address the aforementioned issue. In order to do so, we build on the formulation of discrete mixture models and specifically Latent Class Choice Models (LCCMs), which were integrated with a network effect model. The network effect model quantifies the impact of the spatial/network effect of the new technology on the utility of adoption. We adopted a confirmatory approach to estimating our dynamic LCCM based on findings from the technology diffusion literature that focus on defining two distinct types of adopters: innovator/early adopters and imitators. LCCMs allow for heterogeneity in the utility of adoption for the various market segments i.e. innovators/early adopters, imitators and non-adopters. We make use of revealed preference (RP) time series data from a one-way carsharing system in a major city in the United States to estimate model parameters. The data entails a complete set of member enrollment for the carsharing service for a time period of 2.5 years after being launched. Consistent with the technology diffusion literature, our model identifies three latent classes whose utility of adoption have a well-defined set of preferences that are significant and behaviorally consistent. The technology adoption model predicts the probability that a certain individual will adopt the service at a certain time period, and is explained by social influences, network effect, socio-demographics and level-of-service attributes. Finally, the model was calibrated and then used to forecast adoption of the carsharing system for potential investment strategy scenarios. A couple of takeaways from the adoption forecasts were: (1) placing a new station/pod for the carsharing system outside a major technology firm induces the highest expected increase in the monthly number of adopters; and (2) no significant difference in the expected number of monthly adopters for the downtown region will exist between having a station or on-street parking. [ABSTRACT FROM AUTHOR]

This study examines changes in observable patterns of travel mode choice behavior over time, and attempts to explain these changes in terms of possible shifts in latent modal preferences, while controlling for the confounding influence of concurrent changes in the socioeconomic environment and transportation infrastructure. Using repeated cross-sectional travel diary data collected from individuals residing in the San Francisco Bay Area in 2000 and 2012, we develop a latent class choice model of travel mode choice behavior. Estimation results reveal ten segments across the pooled sample populations that differ from one another in terms of their demographic composition, the travel modes that they consider, and the relative importance that they attach to different level-of-service attributes, namely travel times and costs. Findings indicate shifts in latent modal preferences that exceed analogous changes in observable travel mode choice patterns. For example, private (motorized) vehicle mode shares decreased from 85.0% in 2000 to 81.2% in 2012, but the proportion of the population that only considers private vehicle when deciding how to travel is found to decline from 41.7% to 23.5% during the same period. Changes in economic and social factors and changes in the level of service of different travel modes are found to have had a marginal effect. Had modal preferences not changed between 2000 and 2012, over and above changes in the socioeconomic environment and the transportation infrastructure, our framework predicts that private vehicle mode shares would have increased to 88.3% by 2012. Finally, shifts in modal preferences are not found to be limited to any one generation but to have cut across the entire population, reflecting broader cultural shifts that have transcended generational differences. [ABSTRACT FROM AUTHOR]

Integrated Choice and Latent Variable (ICLV) models are an increasingly popular extension to discrete choice models that attempt explicitly to model the cognitive process underlying the formation of any choice. This study was born from the discovery that an ICLV model can in many cases be reduced to a choice model without latent variables that fits the choice data at least as well as the original ICLV model from which it was obtained. The failure of past studies to recognize this fact raised concerns about other benefits that have been claimed with regards to the framework. With the objective of addressing these concerns, this study undertakes a systematic comparison between the ICLV model and an appropriately specified reduced form choice model. We derive analytical proofs regarding the benefits of the framework and use synthetic datasets to corroborate any conclusions drawn from the analytical proofs. We find that the ICLV model can under certain conditions lead to an improvement in the analyst's ability to predict outcomes to the choice data, allow for the identification of structural relationships between observable and latent variables, correct for bias arising from omitted variables and measurement error, reduce the variance of parameter estimates, and abet practice and policy, all in ways that would not be possible using the reduced form choice model. We synthesize these findings into a general process of evaluation that can be used to assess what gains, if any, might be had from developing an ICLV model in a particular empirical context. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling choice behaviour and is increasingly drawing interest in our field. Cross-pollination of machine learning models, techniques and practices could help overcome problems and limitations encountered in the current theory-driven modelling paradigm, such as subjective labour-intensive search processes for model selection, and the inability to work with text and image data. However, despite the potential benefits of using the advances of machine learning to improve choice modelling practices, the choice modelling field has been hesitant to embrace machine learning. This discussion paper aims to consolidate knowledge on the use of machine learning models, techniques and practices for choice modelling, and discuss their potential. Thereby, we hope not only to make the case that further integration of machine learning in choice modelling is beneficial, but also to further facilitate it. To this end, we clarify the similarities and differences between the two modelling paradigms; we review the use of machine learning for choice modelling; and we explore areas of opportunities for embracing machine learning models and techniques to improve our practices. To conclude this discussion paper, we put forward a set of research questions which must be addressed to better understand if and how machine learning can benefit choice modelling. • Clarifies the similarities and differences between theory and data-driven paradigms. • Reviews the use of machine learning for choice modelling. • Explores opportunities for embracing machine learning to benefit choice modelling. • Puts forward research agenda. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a new latent class choice model framework that captures preference endogeneity. [•] The formulation allows a decision-maker’s preferences to be a function of the decision-making environment. [•] The framework is tested using travel mode choice data from the Bay Area Travel Survey (BATS) 2000. [•] Estimation results identify six segments in the sample population that differ in terms of their preferences. [•] The distribution of individuals across segments is shown to be sensitive to changes in level-of-service attributes. [ABSTRACT FROM AUTHOR]

Highlights: [•] Operationalizes the construct of modality styles, or behavioral predispositions towards different travel modes within the framework used by traditional travel demand models. [•] Synthesizes recent advances in discrete choice analysis in the fields of taste heterogeneity, choice set generation, and simultaneous choice models. [•] Applies the framework to six-week travel diary data from Karlsruhe, Germany. [•] Results indicate the presence of auto-oriented individuals who display a strong bias for using the automobile and multimodal individuals who exhibit variation in their modal preferences. [•] Multimodal behavior is further distinguished by those who appear to be sensitive to travel times and those who appear to be insensitive. [Copyright &y& Elsevier]

• This study examined the impact of communication channels on AV consideration. • Men are more likely to belong to segments with higher AV consideration. • Social media sentiment has the largest positive effect on AV consideration for 90% percent. • Family members, men, and contacts with fulltime job and college education are trusted more. • Interactions with social contacts on social media increases the trust. The emerging technology of autonomous vehicles (AVs) is envisaged to substantially alter transport systems and travel behaviour. However, this impact is conditional on the level and rate of AV market penetration. This study examines consumers' consideration to adopt AVs, with a particular focus on the impact of informational cues from different communication channels. Data for our analysis is collected through an online survey conducted in December 2019, and our sample comprises 862 residents from Sydney, Australia. The survey asks respondents about their social networks, and their use of different mass and social media platforms. Respondents are subsequently presented a discrete choice experiment where we systematically vary informational cues about AVs from each of these communication channels, and respondents are asked to indicate their willingness to consider AVs under each scenario. This data is used to develop a latent class choice model of AV consideration as a function of the informational cues received from different communication channels. Our model also considers the level of trust that respondents associate with their social contacts when modelling the effect of word-of-mouth from these social contacts. The results reveal eleven segments in the sample that differ from one another in terms of sociodemographic attributes, evaluation of social contact trust, and the relative importance they attach to different communication channels. The findings show social media sentiment has the highest effect on AV consideration for 90 per cent of the sample. Also 3.8 per cent of the sample never consider adopting AVs, regardless of the informational cues. [ABSTRACT FROM AUTHOR]

• Farmers surveyed to examine motivations to participate in forestry in Ireland. • While expected economic returns are important, they are not the primary motivator. • irreversibility of the planting decision major barrier to investment in forestry. • Forestry is perceived as reducing descendants' flexibility and farming options. • Desire for descendants to inherit an agricultural property a key consideration. Forest policy in Ireland has focused on the expansion of forest cover on private lands. Despite financial support to incentivize farmers to afforest agricultural land and the high returns of farm forestry relative to the agricultural alternatives, the rate of annual afforestation is falling well below targets. This paper presents results from a survey of farmers that examined the differences between the characteristics and attitudes of farmers that have planted trees and those that have not, with interest in the role of bequest value among other socio-economic determinants. We explore two decisions (a) the decision to enter forestry and (b) how much land is planted. The results show that while farmers consider a broad range of factors along with the expected economic returns in deciding whether to afforest land, the irreversibility of the planting decision and a desire to bequeath to descendants a property consisting of agricultural enterprises plays a significant role in the investment decision. Incentives for afforestation have underperformed in Ireland as they largely focus on the provision of economic benefits. Our results suggest demographic factors including those relating to inheritance and bequests are significant factors influencing afforestation decisions. Future incentive policies may be more effective in achieving increased afforestation, if policy makers can better target policies in order to address issues around farm inheritance, specifically the perceptions of irreversibility and intergenerational burden of land use change to forestry. [ABSTRACT FROM AUTHOR]

This paper i) compares parametric and semi-parametric representations of unobserved heterogeneity in hierarchical Bayesian logit models and ii) applies these methods to infer distributions of willingness to pay for features of shared automated vehicle (SAV) services. Specifically, we compare the multivariate normal, the finite mixture of normals and the Dirichlet process mixture of normals (DP-MON) mixing distributions. The latter promises to be particularly flexible regarding the shapes it can assume, and unlike other semi-parametric approaches does not require that its complexity is fixed before estimation. We evaluate the different mixing distributions, using simulated data and real data from a stated choice study on preferences for SAVs in New York City. In the considered data settings, the DP-MON mixing distribution provides an excellent data fit and performs at least as well as the other methods at out-of-sample prediction. The DP-MON mixing distribution also offers substantive behavioural insights into the adoption of SAVs. We find that preferences for in-vehicle travel time by SAV with ride-pooling are strongly polarised. Whereas one-third of the sample is willing to pay between 10 and 80 USD/h to avoid pooling a vehicle with strangers, the remainder of the sample is either indifferent to ride-pooling or even desires it. We also estimate that vehicle automation and powertrain electrification are relatively unimportant to travellers. Consequently, travellers may primarily derive indirect, rather than immediate benefits from these new technologies through increases in operational efficiency and lower operating costs. • Comparison of parametric and semi-parametric representations of unobserved heterogeneity in hierarchical logit models. • Dirichlet process mixture of normals (DP-MON) mixing distribution is particularly flexible and easy to use. • Application to simulated data and stated choice data on shared automated vehicle (SAV) services in New York City. • Preferences for in-vehicle travel time by SAV with ride-splitting are strongly polarised. • Vehicle automation and drivetrain electrification are relatively unimportant to most travellers. [ABSTRACT FROM AUTHOR]

• Up to 46 per cent of Australian population willing to adopt MaaS. • Consumers prefer pay-as-you-go MaaS schemes to bundled MaaS schemes. • Public transport and taxis are the most popular transport services. • Willingness to use MaaS is negatively correlated with age and lifecycle stage. Mobility-as-a-Service, or MaaS, offers potential consumers access to multiple transport modes and services, owned and operated by different mobility service providers, through an integrated digital platform for planning, booking and payment. We surveyed 3985 geographically and demographically representative Australians nationwide, to understand consumer demand and willingness to pay for MaaS in Australia. Our analysis reveals that there is definitely a market for MaaS in Australia. Depending on the service offering, we find that up to 46 per cent of the Australian population would be willing to adopt MaaS. On average, consumers prefer pay-as-you-go schemes to bundled schemes that offer unlimited access to one or more transport modes and services at fixed monthly costs. Local public transport, taxis and long-distance public transport are the most popular transport services; bikeshare is the least popular. Willingness to use MaaS is strongly correlated with age and lifecycle stage: young individuals who are employed full-time are most likely to use MaaS; older adults who have retired from the workforce and whose children have left home are least likely to use MaaS. Our analysis identifies Melbourne, Canberra and Sydney as good markets for MaaS trials and early launches. [ABSTRACT FROM AUTHOR]