The objective of this paper is to present a new analytical tool that predicts highway congestion in real time by utilizing a macroscopic traffic flow model, and to investigate a data collection strategy that is adaptable to the quality of traffic information. A stochastic Lagrangian traffic flow model is proposed to capture the transition into traffic jam and randomness in the traffic flow. To calibrate the model, vehicles in a traffic flow are divided into cells, and only the first and last vehicles in each cell are probed. Model parameters and traffic information are updated in real time by the unscented Kalman filter, and an advance warning is provided for stop-and-go traffic jam. Adaptive data collection is done by adjusting the probing cell size based on the variance of the prediction from the stochastic model. By validating the model with empirical highway traffic data, the proposed stochastic model shows a 20% improvement in predicting the one-step-ahead traffic state, comparing it to the result from the deterministic model. The 3-sec prediction of traffic status, which may be applied to compensate for the latency of data processing in real-time applications, can be obtained with a 15% error. The model parameter can be used to warn the drivers 6.76 sec before entering a traffic jam. The scenario with low penetration rate and longer sample time interval is also demonstrated with traffic data collected by smartphones. The results from adaptive probing suggest that it can efficiently use less data to provide higher prediction accuracy than using non-adaptive probing.

Traffic congestion in urban areas is posing many challenges, and a traffic flow model that accurately predicts traffic conditions can be useful in responding to them. With the limitation of infrastructure and the difficulty for real world experiment, traffic simulation is a good tool for the validation and future potential applications of the traffic flow model. This research presents a stochastic traffic flow model which uses a stochastic partial differential equation to describe the evolution of traffic flow on the highway. The stochastic model is calibrated and validated by real traffic data on a highway and is proved to have better predictive power than the deterministic model. Also, a microscopic traffic simulation is built and calibrated by the same highway data, and the validation result shows that the simulation is capable of representing the real traffic. By using traffic simulation to further explore the capability of the stochastic model, the validation demonstrated that the prediction works well at most locations, and the interpolation error may be improved by considering the influence of ramps and the change in the number of lanes.

Plug-in vehicles (PEVs), which include battery-only electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), have steadily grown in sales amidst various incentive programs, but much speculation exists on when PEVs would become cost-competitive without incentives. This research adopts a bottom-up approach for estimation of purchase cost, and total cost of ownership (TCO). Baseline predictions, as well as sensitivity analysis (with more favorable conditions for PEVs) are generated for 2030. Results show that the five-year TCO of some PEVs could be less than an equivalent-sized conventional internal combustion-engine (CICE) vehicle, but only in the more optimistic scenarios where the cost of batteries and motors decrease more rapidly than the baseline prediction, and when combined with either higher gasoline prices or longer annual distance travelled. However, without subsidies or incentives, purchase cost parity between PEVs and CICEs was not realized in any of the considered 2030 scenarios.

The objective of this paper is to develop an optimal planning framework for a production system utilizing multisource distributed energy supplies, in order to improve its energy efficiency and to reduce the dependency on grid energy and outage risk. The energy consumption of an automotive seat assembly plant is modeled using a discrete-event simulation, which consumes energy supplied by wind and solar, as well as traditional power grids. Machines can be halted for designed time period to avoid high peak load and operate efficiently in terms of power consumption. On the other hand, the inventory level can be planned by adjusting production goal to better utilize alternative energy and accommodate demand uncertainty using stochastic programming method. The results from case studies show that optimizing waiting time for each process can effectively reduce outage risk while meeting customer demands. Having the optimal production goal to over-produce when alternative energy supply is high can reduce the dependency on grid energy. Also, the benefit of considering predicted future customer demands would have the effect of safety inventory, which can prevent shortages during extremely high customer demand periods.

Lagrangian Traffic model which follows a platoon of vehicle has the benefit of convenient utilization of individual vehicle data and easy distribution of traffic information to the drivers. The objective of this paper is to develop a stochastic Lagrangian traffic flow model which uses probe vehicle data to predict traffic status. The proposed probing method tracks vehicles in pairs to collect speed and spacing between vehicles. The traffic flow model utilizes unscented Kalman filter (UKF) with dual estimation to update model parameters and estimated current traffic in real-time. The proposed model was validated by empirical highway traffic data, and the result showed that the stochastic model has an overall 20% improvement in estimating current traffic state comparing to the estimation from deterministic model. The predictive ability of the model with an average of 15% error for 3-sec prediction can be used to compensate for the latency of data processing in real-time application. This paper also demonstrated a new method to predict the unexpected jam traffic phase using estimated model parameter with an average lead time of 6.76 sec, which allows drivers to be prepared for potential stop-and-go traffic.

Plug-in hybrid electric vehicles (PHEVs) have powertrain architectures that seek to combine the best features of two well-known powertrains: the environmental and other benefits of electric driving of battery electric vehicles; and the fuel efficiency and, due to widely-available fueling infrastructure and quick refueling times, limitless practical range of hybrid electric vehicles (HEVs). Different regulatory organizations around the world have different standard testing procedures, and accordingly, different predictions for the degree of efficacy of PHEVs at reducing greenhouse gas (GHG) emissions. However, there is somewhat of a consensus that PHEVs have the capacity for significant GHG reduction compared to conventional internal combustion engine vehicles, yet some recent studies have claimed the real-world fuel consumption of PHEVs to be more than twice their standard ratings. A key factor to the efficacy of GHG reduction via PHEVs is the fraction of miles traveled in electric mode, also known as the utility factor (UF). In this work, we reinvestigate the data sources cited in previous studies for PHEVs in the US for the inferred real-world UF and fuel consumption via same estimation approaches as a previous study. We then compare with UF from SAE J2841 standard and fuel consumption ratings from the US environmental protection agency. While noting that it is difficult if not impossible to discern the exact reason for observed deviations given the available information in the cited data sources, we find the real-world fuel consumption of PHEVs in the US to be within 62% better to 21% worse than their standard ratings in the US, and generally, significantly better than a comparable HEV. Contrasted with reported results for other parts of the world, the results are viewed as a testimony to the importance of proper procedures for evaluation of PHEVs to reflect their correct environmental benefit value.