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The potential effects of autonomous vehicles (AVs) on greenhouse gas (GHG) emissions are uncertain, although numerous studies have been conducted to evaluate the impact. This paper aims to synthesize and review all the literature regarding the topic in a systematic manner to eliminate the bias and provide an overall insight, while incorporating some statistical analysis to provide an interval estimate of these studies. This paper addressed the effect of the positive and negative impacts reported in the literature in two categories of AVs: partial automation and full automation. The positive impacts represented in AVs’ possibility to reduce GHG emission can be attributed to some factors, including eco-driving, eco traffic signal, platooning, and less hunting for parking. The increase in vehicle mile travel (VMT) due to (i) modal shift to AVs by captive passengers, including elderly and disabled people and (ii) easier travel compared to other modes will contribute to raising the GHG emissions. The result shows that eco-driving and platooning have the most significant contribution to reducing GHG emissions by 35%. On the other side, easier travel and faster travel significantly contribute to the increase of GHG emissions by 41.24%. Study findings reveal that the positive emission changes may not be realized at a lower AV penetration rate, where the maximum emission reduction might take place within 60–80% of AV penetration into the network.

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Sustainable transportation systems play a key role in the socio-economic development of a country. Microscopic simulation models are becoming increasingly useful tools in designing, optimizing, and evaluating the sustainability of transportation systems and concerned management strategies. VISSIM, a microscopic traffic simulation software, has gained rapid recognition in the field of traffic simulation. However, default values for different input parameters used during simulation need to be tested to ensure a realistic replication for local traffic conditions. This paper attempts to model driving behavior parameters using the microscopic simulation software VISSIM through a case study in the Khobar-Dammam metropolitan areas in Saudi Arabia. VISSIM default values for different sensitive parameters such as lane change distances, additive and multiplicative parts of desired safety distances, the number of preceding vehicles spotted, amber signal decisions, and minimum headway were identified to be most sensitive and significant parameters to be calibrated to precisely replicate field conditions. The simulation results using default values produced higher link speed, larger queue length, and shorter travel times than those observed in the field. However, measures of effectiveness (MOEs) obtained from calibrated models over desired simulation runs were comparable to those obtained from field surveys. All compared MOEs used to validate the model matched within a range of 5-10% to the field-observed values.

Vehicle automation and communication technologies are considered promising approaches to improve operational driving behavior. The expected gradual implementation of autonomous vehicles (AVs) shortly will cause unique impacts on the traffic flow characteristics. This paper focuses on reviewing the expected impacts under a mixed traffic environment of AVs and regular vehicles (RVs) considering different AV characteristics. The paper includes a policy implication discussion for possible actual future practice and research interests. The AV implementation has positive impacts on the traffic flow, such as improved traffic capacity and stability. However, the impact depends on the factors including penetration rate of the AVs, characteristics, and operational settings of the AVs, traffic volume level, and human driving behavior. The critical penetration rate, which has a high potential to improve traffic characteristics, was higher than 40%. AV’s intelligent control of operational driving is a function of its operational settings, mainly car-following modeling. Different adjustments of these settings may improve some traffic flow parameters and may deteriorate others. The position and distribution of AVs and the type of their leading or following vehicles may play a role in maximizing their impacts.