The operation of intelligent connected vehicles (ICVs) is fundamentally data-driven, continuously generating massive amounts of data. Given the significant value of ICV data to enterprises, industries, and nations, promoting data openness and sharing has become essential. However, such data often contain sensitive information, and its misuse can threaten individual privacy, corporate security, and even national interests. To address this dilemma, this paper develops the misuse risk score (MR-score), a novel quantification model and associated evaluation method for assessing the risk of ICV data misuse. The MR-score is constructed based on three core properties of ICV data: sensitivity; scale; and identifiability. The sensitivity score, information quantity, and identifiability factor are designated as the corresponding evaluation indicators, and systematic approaches for their quantification are proposed. The analytic hierarchy process is employed to measure the sensitivity score. Information entropy is adopted to evaluate the information quantity. A combination of k-anonymity-based and damage source determination-based methods is utilized to estimate the identifiability factor, considering data incompleteness, imprecision, and invalidity. Two empirical ICV data sets are utilized, and comparative analyses are conducted to demonstrate the effectiveness of the MR-score in capturing misuse risks. Higher MR-scores correspond to greater risk. The model captures the joint influence of all three data properties and reveals the marginal diminishing effect of data scale on misuse risk. This work offers valuable tools for data owners and regulatory agencies to prioritize critical data sets, implement targeted data protection measures, and enable secure data circulation while maximizing the value of ICV data.

Surrogate measures of safety (SMoS) play an important role in detecting traffic conflicts and in traffic safety assessment. However, the underlying assumptions of SMoS are different and a certain SMoS may be adequate/inadequate for different applications. A comprehensive approach to evaluate the validity and applicability of SMoS is lacking in the literature. This study proposes such a framework that supports evaluating SMoS in multiple dimensions. We apply the framework to gain insights into the characteristics of six widely-used SMoS for longitudinal maneuvers, i.e., Time to Collision (TTC), single-step Probabilistic Driving Risk Field (S-PDRF), Deceleration Rate to Avoid a Crash (DRAC), Potential Index for Collision with Urgent Deceleration (PICUD), Proactive Fuzzy Surrogate Safety Metric (PFS), and the Critical Fuzzy Surrogate Safety Metric (CFS). To ensure comparability, all measures are calibrated with the same risk detection criterion. Four performance indicators, i.e., Prediction Accuracy, Timeliness, Robustness, and Efficiency are computed for all six SMoS and validated using naturalistic driving data. The strengths and weaknesses of all six measures are compared and analyzed elaborately. A key result is that not a single SMoS performs well in all performance dimensions. S-PDRF performs best in terms of Robustness but consumes the most time for computation. TTC is the most efficient but performs poorly in terms of Timeliness and Robustness. The proposed evaluation approach and the derived insights can support SMoS selection in active vehicle safety system design and traffic safety assessment.

This paper focuses on empirically investigating the inertia effects of past behavior in commuting modal shift behavior and contributes to the current state of the art by three aspects. Firstly, this study introduces and tests the potential influences of the inertia effects of past behavior on the traveler’s preferences regarding level-of-service (LOS) variables, besides the impacts of inertia effects on the preference for the frequently used transport mode in the past. Secondly, the mode-specific inertia effects are investigated to distinguish the differences in the inertia effects for different transport modes based on posterior individual-specific parameter estimations. Thirdly, the factors contributing to the heterogeneity of inertia effects including demographics and travel contexts, are quantitatively examined. A joint random parameter logit model using a revealed and stated preference survey regarding commuting behavior is employed to unravel the three aspects. The results reveal significant interactions of inertia terms with LOS variables indicating the influences of past behavior on travelers’ evaluations on attributes of their previous choices. The mean values and variances of inertia effects for different transport modes are significantly and substantially distinct. For instance, the inertia effects of frequently using car are substantially positive representing strong stickiness to the car, while the inertia effects of frequently using the metro have large variances among travelers and mostly appear as dispositions to change. Besides, the effects of personal characteristics and travel contexts on the magnitude of the inertia effects of different transport modes are identified as well. A demand estimation analysis is utilized to investigate the influences of three aspects on predicting travel demands in various contexts. Incorporating the interactions and mode-specific inertia effects can remarkably improve the model performance. The demand estimation will be biased if they are neglected.