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• A simulation based bi-level model minimizes network delays due to inundations. • Budget constraints, hydrodynamic and traffic interactions are considered. • Findings are applied to a real world network in California for 0.5 m sea level rise. • The optimal strategies vary according to the available budget. • There exist critical shorelines to protect, to reduce the traffic disruptions. Sea level rise predictions have motivated research towards the protection of shoreline infrastructures, including transportation systems. Transportation network interactions in cases of inundation can lead to severe disruptions that cause capacity and accessibility reduction and thus considerable delays, especially due to congestion feedback, because congestion occurring due to an inundated link or a link that becomes isolated through inundation of one of its nodes can lead to delays in other parts of the network, due to queue spillback or traffic rerouting. This paper describes a decision tool to support infrastructure protection planning against sea level rise. A simulation-based optimization model is designed to minimize delays occurring in a transportation system under inundation. The model considers budget constraints, hydrodynamic interactions within the shoreline, as well as traffic assignment in the network. The case study focuses on San Francisco Bay area, for a 0.5 m sea level rise that is expected in 2054 and may increase highway users' travel time by 37%. The results show that the optimal strategies vary according to the available budget, and that there exist relatively critical shorelines to protect in order to reduce the traffic disruptions. We anticipate our research to provide a general framework for transportation infrastructure protection planning against sea level rises. [ABSTRACT FROM AUTHOR]

• Framework for modeling well to wheels real-world plug-in vehicles CO 2 using big data. • Simulations conducted using open-source code and public data sets. • Considered sensitivity to fuel mix in electric grid as well as charging behavior. • Examined several electric range designs for three plug-in powertrain architectures. • Too long electric range can be detrimental to equivalent CO 2 emissions. This paper presents a modeling and simulation study of the greenhouse gas (GHG) emissions of plug-in vehicles subject to real-world driving conditions. The simulations utilize open-source software tools for estimation of second-by-second energy and/or fuel consumption in more than 65 thousand real-world recorded trips in the California household travel survey (CHTS) data set. Statistical distributions of the equivalent well-to-wheels (W2W) GHG emissions corresponding to CHTS trips are generated for three baseline public-domain plug-in vehicle models of: (i) parallel-drive plug-in hybrid, (ii) serial-drive plug-in hybrid, and (iii) pure electric vehicle. In addition to the baseline models, several derived vehicle models are created by upsizing of the battery and powertrain via an iterative procedure to attain the new all-electric range (AER). The emissions model also considers days when a pure electric vehicle cannot meet the travel demand by assuming that the driving on those days is done on a conventional vehicle. For each of the considered powertrain architectures, optimizing the AER for minimum GHG emissions becomes a trade-off between reducing the gasoline driven miles and maintaining reasonably sized (lightweight) powertrain components. As such, the equivalent GHG emissions of a vehicle are shown to have diminishing returns in terms of AER, and the optimum AER depends on both the carbon content in the electric grid, as well as the charging behavior. Within considered modeling assumptions and several vehicle usage conditions, the optimum AER is estimated to be between 30 and 60 miles for PHEVs, and between 120 and 200 miles for BEVs. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyzes personal and car-sharing characteristics of commuters at university in Los Angeles, California. These commuters do not hold an on-campus parking permit and commute by an alternative mode other than driving alone. Each month, the university offers them 8h free use of shared vehicles across the campus. University employee car-sharers’ housing distribution is significantly different from that of their counterparts who drive to work. Commuter benefits influence not only the participation rate of a car-sharing program but also the program participants’ frequency, time and quantity of car-sharing consumption. Car-sharing is most popular among bus commuters, university students and female employees. [Copyright &y& Elsevier]

Abstract: This paper presents the results of a survey testing whether California residents would support the concept of “green” transportation taxes and fees. Green taxes and fees would be set at variable rates, with higher rates for more polluting vehicles and lower rates for those that pollute less. The results show that the concept of green transportation taxes and fees strongly appeals to Californians. The survey data were also analyzed to identify if sub-groups within the state were particularly likely to support or oppose green transportation taxes and fees. Support for the green taxes and fees held at 50% or higher across most population sub-groups. Bivariate analysis showed that demographic factors were generally poor predictors of support, but that some attitudinal and knowledge variables did correlate with increased support for the green taxes and fees. Multivariate analysis confirmed that pro-environment and pro-government attitudes are significant and strong predictors of support for increasing transportation revenues. [Copyright &y& Elsevier]

Abstract: This paper assesses the potential energy profile impacts of plug-in hybrid electric vehicles and estimates gasoline and electricity demand impacts for California of their adoption. The results are based on simulations replicating vehicle usage patterns reported in 1-day activity and travel diaries based on the 2000–2001 California Statewide Household Travel Survey. Four charging scenarios are examined. We find that circuit upgrades to 240V not only bring faster charging times but also reduce charging time differences between PHEV20 and PHEV60; home charging can potentially service 40–50% of travel distances with electric power for PHEV20 and 70–80% for PHEV60; equipping public parking spaces with charging facilities, can potentially convert 60–70% of mileage from fuel to electricity for PHEV20, and 80–90% for PHEV60; and afternoons are found to be exposed to a higher level of emissions. [Copyright &y& Elsevier]

• Workplace charging is a major driver of the pattern of use of infrastructure. • Female and older PEV owners prefer home over non-home charging locations. • Non-home charging is important for PEV owners in multi-unit dwellings. • Owners of long- and short-range BEVs have different mixed use of infrastructure. • Commute distance is an important factor explaining charging behavior of PHEV owners. This paper examines the charging behavior of 7,979 plug-in electric vehicle (PEV) owners in California. The study investigates where people charge be it at home, at work, or at public location, and the level of charging they use including level 1, level 2, or DC fast charging. While plug-in behavior can differ among PEV owners based on their travel patterns, preferences, and access to infrastructure studies often make generalizations about charging behavior. In this study, we explore differences in charging behavior among different types of PEV owners based on their use of charging locations and levels, we then identify factors associated with PEV owner's choice of charging location and charging level. We identified socio-demographic (gender and age), vehicle characteristics, commute behavior, and workplace charging availability as significant factors related to the choice of charging location. [ABSTRACT FROM AUTHOR]