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Accurately understanding driving behaviour is of crucial importance for advanced driving assistant systems such as adaptive cruise control system and intelligent forward collision warning system. To understand different driving styles, this study employs the clustering method and topic model to extract latent driving states, which can elaborate and analyse the commonness and individuality of driving behaviour characteristics with the longitudinal driving behaviour data collected by the instrumented vehicle. To handle the large set of data and discover the valuable knowledge, the data mining techniques including ensemble clustering method based on the kernel fuzzy C-means algorithm and the modified latent Dirichlet allocation model are employed in this study. The 'aggressive', 'cautious' and 'moderate' driving states are discovered and the underlying quantified structure is built for the driving style analysis.

Short-term traffic prediction is vital for intelligent traffic systems and influenced by neighboring traffic condition. Gradient boosting decision trees (GBDT), an ensemble learning method, is proposed to make short-term traffic prediction based on the traffic volume data collected by loop detectors on the freeway. Each new simple decision tree is sequentially added and trained with the error of the previous whole ensemble model at each iteration. The relative importance of variables can be quantified in the training process of GBDT, indicating the interaction between input variables and response. The influence of neighboring traffic condition on prediction performance is identified through combining the traffic volume data collected by different upstream and downstream detectors as the input, which can also improve prediction performance. The relative importance of input variables for 15 GBDT models is different, and the impact of upstream traffic condition is not balanced with that of downstream. The prediction accuracy of GBDT is generally higher than SVM and BPNN for different steps ahead, and the accuracy of multi-step-ahead models is lower than 1-step-ahead models. For 1-step-ahead models, the prediction errors of GBDT are smaller than SVM and BPNN for both peak and nonpeak hours.

This paper presents an effort to develop a market penetration model for autonomous vehicle (AV) technology adoption on the basis of similar technologies and previous trends in the United States. Generalized Bass diffusion models are developed on the basis of data from earlier technologies, including sales and price data on conventional automobiles and hybrid electric vehicles, and the use of Internet and cell phones. On the basis of the adoption patterns of earlier technologies, two values that represented the innovation factor (i.e., risk-taking capacity) and the imitation factor (i.e., culture and lifestyle preferences) were selected for AV market penetration. In addition, external variables (e.g., price of AVs compared with price of conventional vehicles) and economic wealth were incorporated into the model. The market size for AV adoption was determined on the basis of the use of the Internet, and a household was considered as the unit. Given the uncertainties in market size and the price of AVs, sensitivity analyses were conducted to understand the possible impacts of these factors on user adoption. In general, a larger market size led to a higher adoption rate, although the initial cost of AVs compared with that of conventional vehicles did not seem to influence the diffusion process much. This paper contributes to the literature through the addition of a quantitative analysis of AV market penetration on the basis of earlier technology adoption experience. The study results provided valuable insights in terms of possible market diffusion patterns and the impacts of different factors on user adoption.

How and to what extent telecommuting engagement affects time allocation among nonmandatory activities are examined to help understand the impacts of telecommuting on daily activity–travel patterns. Five categories of nonmandatory activities are considered: shopping, maintenance, discretionary, escort, and in-home shopping. The hypothesis is that telecommuting relaxes the temporal and spatial constraints related to work activities at the regular workplace, and telecommuters may allocate some of the time budget to other nonmandatory activities, which may or may not lead to additional travel. The structural equations model approach is applied to capture the impacts of telecommuting as well as the interactions among the nonmandatory activities. The activity durations by type along with the number of total daily trips are considered as endogenous (dependent) variables. By incorporating work hours at the regular workplace and daily telecommuting hours as exogenous variables, the models can reveal how people may reallocate their time among different nonmandatory activities given different levels of telecommuting engagement (either part-day or full-day). All types of telecommuting arrangements increased nonmandatory activity durations (compared with those of nontelecommuters). Full-day telecommuters have higher durations of discretionary activities, while part-day telecommuters have higher durations of maintenance and out-of-home shopping errands. Telecommuting also increased total daily trip rates for both telecommuters and their household members. This study used data obtained from the 2010–2011 Regional Household Travel Survey in the New York metropolitan region.

Here we present the highly effective cleavage of C–C bonds in lignin model compounds for the production of N-substituted aromatics in up to 96% total yield, including benzonitriles and amides, via oxime formation followed by Beckmann rearrangement (BR).

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