No abstract

The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers' behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure -is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison

Traffic congestion is a major challenge in metropolitan areas due to economic and negative health impacts. Several strategies have been tested all around the globe to relieve traffic congestion and minimize transportation externalities. Congestion pricing is among the most cited strategies with the potential to manage the travel demand. This study aims to investigate potential travel behavior changes in response to cordon pricing in Manhattan, New York. Several pricing schemes with variable cordon charging fees are designed and examined using an activity-based microsimulation travel demand model. The findings demonstrate a decreasing trend in the total number of trips interacting with the central business district (CBD) as the price goes up, except for intrazonal trips. We also analyze a set of other performance measures, such as Vehicle-Hours of Delay, Vehicle-Miles Traveled, and vehicle emissions. While the results show considerable growth in transit ridership (6%), single-occupant vehicles and taxis trips destined to the CBD reduced by 30% and 40%, respectively, under the $20 pricing scheme. The aggregated value of delay for all vehicles was also reduced by 32%. Our findings suggest that cordon pricing can positively ameliorate transportation network performance and consequently, improve air quality by reducing particular matter inventory by up to 17.5%. The results might facilitate public acceptance of cordon pricing strategies for the case study of NYC. More broadly, this study provides a robust framework for decision-makers across the US for further analysis on the subject.

COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We propose a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and working from home. Open-source agent-based simulation models are used to evaluate the transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation is used to evaluate the air quality impacts. Finally, system-wide contact exposure on the subway is estimated from the traffic simulation output. The social distancing requirement in public transit is found to be effective in reducing contact exposure, but it has negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the negative impacts are higher due to behavioral inertia and social distancing. The findings suggest that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are followed. The proposed integrated traffic simulation models and air quality estimation model can help policymakers evaluate the impact of policies on traffic congestion and emissions as well as identifying hot spots, both temporally and spatially.

No abstract