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Autonomous vehicles (AVs) hold great promise for increasing the capacity of existing roadways and intersections, providing more mobility to a wider range of people, and are likely to reduce vehicle crashes. However, AVs are also likely to increase travel demand which could diminish the potential for AVs to reduce congestion and cause emissions of greenhouse gases (GHG) and other air pollutants to increase. Therefore, understanding how AVs will affect travel demand is critical to understanding their potential benefits and impacts. We evaluate how adoption of AVs affects travel demand, congestion and vehicle emissions over several decades using an integrated travel demand, land-use and air quality modeling framework for the Albuquerque, New Mexico metropolitan area. We find that AVs are likely to increase demand and GHG emissions as development patterns shift to the region's periphery and trips become longer. Congestion declines along most roadways as expanded capacity from more efficient AV operation outpaces increasing demand. Most of the population can also expect a reduction in exposure to toxic vehicle emissions. Some locations will experience an increase in air pollution exposure and traffic congestion from changes in land-use and traffic patterns caused by the adoption of AVs.
Razieh Nadafianshahamabadi; Mohammad Tayarani; Gregory Rowangould. A closer look at urban development under the emergence of autonomous vehicles: Traffic, land use and air quality impacts. Journal of Transport Geography 2021, 94, 103113 .
AMA StyleRazieh Nadafianshahamabadi, Mohammad Tayarani, Gregory Rowangould. A closer look at urban development under the emergence of autonomous vehicles: Traffic, land use and air quality impacts. Journal of Transport Geography. 2021; 94 ():103113.
Chicago/Turabian StyleRazieh Nadafianshahamabadi; Mohammad Tayarani; Gregory Rowangould. 2021. "A closer look at urban development under the emergence of autonomous vehicles: Traffic, land use and air quality impacts." Journal of Transport Geography 94, no. : 103113.
Understanding the temporal and spatial variation of air quality (AQ) impact due to congestion pricing is important since the health and economic benefits of air quality improvements depend on the distribution of traffic-related air pollution. Aiming to improve our knowledge of the AQ impacts from congestion pricing, this study integrates a disaggregate agent-based travel demand model with a hyper-local air quality model to examine emissions, air quality, and exposure. Studying congestion pricing schemes in NYC, we find that daily single-occupancy-vehicle trips to the charging area decreases by 14.5% and 24.3% under the low and high charging schemes, respectively. Correspondingly, the PM2.5 concentration decreases by 5–25% in the Central Manhattan areas in the low-toll scenario, and by more than 10% across almost all of New York City areas in the high-toll scenario. Our results indicate non-linear relations between the adaptation of travel behavior and the resulting air quality/exposure impacts.
Mohammad Tayarani; Amirhossein Baghestani; Mahdieh Allahviranloo; H. Oliver Gao. Spatial/temporal variability in transportation emissions and air quality in NYC cordon pricing. Transportation Research Part D: Transport and Environment 2020, 89, 102620 .
AMA StyleMohammad Tayarani, Amirhossein Baghestani, Mahdieh Allahviranloo, H. Oliver Gao. Spatial/temporal variability in transportation emissions and air quality in NYC cordon pricing. Transportation Research Part D: Transport and Environment. 2020; 89 ():102620.
Chicago/Turabian StyleMohammad Tayarani; Amirhossein Baghestani; Mahdieh Allahviranloo; H. Oliver Gao. 2020. "Spatial/temporal variability in transportation emissions and air quality in NYC cordon pricing." Transportation Research Part D: Transport and Environment 89, no. : 102620.
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.
Amirhossein Baghestani; Mohammad Tayarani; Mahdieh Allahviranloo; H. Oliver Gao. Evaluating the Traffic and Emissions Impacts of Congestion Pricing in New York City. Sustainability 2020, 12, 3655 .
AMA StyleAmirhossein Baghestani, Mohammad Tayarani, Mahdieh Allahviranloo, H. Oliver Gao. Evaluating the Traffic and Emissions Impacts of Congestion Pricing in New York City. Sustainability. 2020; 12 (9):3655.
Chicago/Turabian StyleAmirhossein Baghestani; Mohammad Tayarani; Mahdieh Allahviranloo; H. Oliver Gao. 2020. "Evaluating the Traffic and Emissions Impacts of Congestion Pricing in New York City." Sustainability 12, no. 9: 3655.
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 depicted the robustness of the proposed methodology. The proposed method reduces the aggregators’ financial loss approximately by 16 $/PEV per year compared to the conventional methods. The findings underline the importance of applying more accurate methods to forecast PEV-TB to gain the most benefit of vehicle electrification in the years to come.
Hamidreza Jahangir; Hanif Tayarani; Ali Ahmadian; Masoud Aliakbar Golkar; Jaume Miret; Mohammad Tayarani; H. Oliver Gao. Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach. Journal of Cleaner Production 2019, 229, 1029 -1044.
AMA StyleHamidreza Jahangir, Hanif Tayarani, Ali Ahmadian, Masoud Aliakbar Golkar, Jaume Miret, Mohammad Tayarani, H. Oliver Gao. Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach. Journal of Cleaner Production. 2019; 229 ():1029-1044.
Chicago/Turabian StyleHamidreza Jahangir; Hanif Tayarani; Ali Ahmadian; Masoud Aliakbar Golkar; Jaume Miret; Mohammad Tayarani; H. Oliver Gao. 2019. "Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach." Journal of Cleaner Production 229, no. : 1029-1044.
The discussion of whether, and to what extent, telecommuting can curb congestion in urban areas has spanned more than three decades. This study develops an integrated framework to provide the empirical evidence of the potential impacts of home-based telecommuting on travel behavior, network congestion, and air quality. In the first step, we estimate a telecommuting adoption model using a zero-inflated hierarchical ordered probit model to determine the factors associated with workers’ propensity to adopt telecommuting. Second, we implement the estimated model in the POLARIS activity-based framework to simulate the potential changes in workers’ activity-travel patterns and network congestion. Third, the MOVES mobile source emission simulator and Autonomie vehicle energy simulator are used to estimate the potential changes in vehicular emissions and fuel use in the network as a result of this policy. Different policy adoption scenarios are then tested in the proposed integrated platform. We found that compared to the current baseline situation where almost 12% of workers in Chicago region have flexible working time schedule, in the case when 50% of workers have flexible working time, telecommuting can reduce total daily vehicle miles traveled (VMT) and vehicle hours traveled (VHT) up to 0.69% and 2.09%, respectively. Considering the same comparison settings, this policy has the potential to reduce greenhouse gas and particulate matter emissions by up to 0.71% and 1.14%, respectively. In summary, our results endorse the fact that telecommuting policy has the potential to reduce network congestion and vehicular emissions specifically during rush hours.
Ramin Shabanpour; Nima Golshani; Mohammad Tayarani; Joshua Auld; Abolfazl (Kouros) Mohammadian. Analysis of telecommuting behavior and impacts on travel demand and the environment. Transportation Research Part D: Transport and Environment 2018, 62, 563 -576.
AMA StyleRamin Shabanpour, Nima Golshani, Mohammad Tayarani, Joshua Auld, Abolfazl (Kouros) Mohammadian. Analysis of telecommuting behavior and impacts on travel demand and the environment. Transportation Research Part D: Transport and Environment. 2018; 62 ():563-576.
Chicago/Turabian StyleRamin Shabanpour; Nima Golshani; Mohammad Tayarani; Joshua Auld; Abolfazl (Kouros) Mohammadian. 2018. "Analysis of telecommuting behavior and impacts on travel demand and the environment." Transportation Research Part D: Transport and Environment 62, no. : 563-576.