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• This is one of the earliest efforts to identify residency and detailed worker types from passively collected location data. • The study features a large-scale national-level mobile device location dataset (MDLD) covering the 50 U.S. states and D.C. • The paper identifies normal commuters, professional drivers, mobility-for-work workers, and nonworkers/home-based workers. • The paper addresses the passive data sample biases and evaluated the travel demand patterns by worker types. • The unique innovations of this study contribute to the development of MDLD-based passenger travel demand estimation. Mobile device location data (MDLD) have been widely recognized for their rich human mobility information and thus considered as a supplementary data source for the current travel data bank consisting of travel survey data and traffic monitoring data. However, the lack of ground truth information about the device owners raises concern about the biases and representativeness of the nonprobability MDLD sample and significantly limits the applications of MDLD. This paper focuses on identifying two important socio-demographic characteristics for the MDLD sample devices: residency and worker status, including four worker types (normal commuters, professional drivers, mobility-for-work workers, and nonworkers/home-based workers). Based on the spatial–temporal patterns of location sightings and derived trips from MDLD, a comprehensive imputation framework is proposed with parameters calibrated against public domain ground truth data. A national-level case study in the U.S. based on a commercial MDLD dataset further evaluates the performances of the proposed heuristic rules. The multi-level validation results indicate a satisfying match against the ground truth data and prove the effectiveness of the proposed methods. As one of the earliest efforts to identify the residency and worker status information for a large-scale national-level MDLD dataset, mobile workers—including professional drivers and mobility-for-work workers—are also identified from MDLD for the first time. [ABSTRACT FROM AUTHOR]

• Developed an integrated and personalized traveler information and incentive scheme. • Developed an empirical model to quantify the personalized monetary incentives. • The scheme integrated behavioral modeling and optimization of incentive allocation. • Demonstrated the effectiveness of control in a large-scale transportation system. Recently, the employment of different types of incentives in transportation systems to form advanced transportation congestion management solutions has garnered significant attention. Instead of using presumed or fixed-amount incentives, this paper develops an integrated and personalized traveler information and incentive scheme to incentivize toward a more energy-efficient travel and mobility decisions. We have developed a behavior research and empirical modeling system to quantify the personalized monetary incentives. Then, it is integrated with a control optimizer for optimized incentive allocation. This scheme innovatively integrates behavioral modeling and optimization for travel incentive design. Through a demonstrative case study for a large-scale transportation system in the Washington D.C. and Baltimore regions, the capability of the proposed scheme is highlighted with significant system-level energy savings, reasonable insights on individual travel behavior responses, as well as superior computational efficiency. [ABSTRACT FROM AUTHOR]

Highlights • A dynamic sensor network optimization model for network travel time prediction. • Effects of both direct and indirect measurements are incorporated in the model. • Appropriate sensor relocation can improve network-level surveillance benefit. • Marginal benefits of relocation and adding new sensors are comparably investigated. Abstract Optimally deploying sensors into a complex highway network can significantly enhance the socio-economic benefits through accurately providing system users and operators with the latest traffic state information. This paper presents a linear optimization framework for designing a sensor network for travel time surveillance. The presented framework consists of a traditional static network model and a novel dynamic network model as well as an integer cut-based speed-up solution approach. The aims of the study are twofold. First, the proposed optimization model maximizes the network-level travel time surveillance benefits by taking into account the values of real-time measurements induced from both temporal and spatial traffic state patterns. Second, the mechanism of sensor relocation operations is designed and incorporated into the proposed model. This mechanism enables one to evaluate whether appropriately relocating sensors can optimize the utilization of the surveillance resources and further enhance the surveillance benefits over a given time horizon. We applied the proposed optimization framework to a mixed arterial-freeway commuting network located in Washington. D.C.-Baltimore metropolitan area. The study empirically revealed and illustrated two interesting findings: (1) relocation operations do bring additional surveillance benefits on travel time prediction, and the marginal benefit is highly correlated with the sensor fleet size; (2) spatial measurement is not a trivial part in travel time prediction, and one should fully recognize the effectiveness of temporal and spatial measurements when designing a traffic sensor network for real-time travel time surveillance. [ABSTRACT FROM AUTHOR]

• The first quantitative system impact analysis that recognizes that vehicle users may override the automation mode. • Jointly utilizes previous research on the trend of autonomous vehicle (AV) technology installation and the macroscopic fundamental diagram augmented with the AV technology usage. • Considers the dynamic interactions among vehicle users, planning agencies, construction, maintainance, law enforcement, and public sentiment. • Provides general policy and investment implications on traffic law enforcement, AV technology regulation, planning, and programming, raised from the differentiation between the AV technology installation and the usage. Existing research that forecasts market penetration of installed connected and autonomous vehicle (AV) technologies is often confused with the traffic composition in roadway networks. Users may override AV mode due to arrival time pressure, facility constraint (e.g., "I will have to make a U-turn a mile away if I do not cross the solid double-yellow lines here"), drug and alcohol influence, pleasure, envy (e.g., "why the front car can surpass that slow truck but I can't?"), insufficient law enforcement, driving culture, media and public sentiment, etc. Therefore, the installation and the usage of AV technologies should not be instantaneously assumed ignorable in planning and policy studies. This paper is dedicated to clarifying this confusion by demonstrating that ignoring the difference between the installation and the usage of AV technologies might lead to systematic bias in evaluating policy and investment decisions. Through a system dynamics (SD) model, the complex interactions of relevant factors are captured so that the mixed traffic condition influences traffic law enforcement adjustment effort and system investment decisions, which, in turn, influence the AV technology usage share and the system performance. The case study applies to the greater Washington, D.C. area for demonstrating the feasibility and advantages of the proposed model and for studying policy implications. This paper does not attempt to forecast; instead, it proposes a modeling framework for studying the conditions under which differentiating the installation and the usage of AV technologies might be critical in forecasting the traffic composition trend and system performance for public policy and investment decisions. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes the notion of anticipatory (dynamic) pricing, and investigates the advantages of using predicted traffic conditions over the use of prevailing and/or historical conditions in setting time-varying link tolls along a freeway corridor to maintain target level of service (LOS) and avoid traffic breakdown on toll links. This is accomplished through an anticipatory toll generator intended to operate in tandem with a real-time traffic estimation and prediction system. Using a calibrated network model of the Baltimore – Washington, DC corridor as test bed, simulation experiments are performed to compare the proposed anticipatory pricing strategies to reactive as well as static pricing schemes. The results indicate that setting prices on the basis of predicted conditions can make a substantial difference in terms of achieving the objectives of pricing in managed-lane situations. [Copyright &y& Elsevier]