In this study we aim to identify how the adoption of autonomous vehicles (AVs) affects the value of travel time savings (VOTTS) for commuting trips under different trip distance scenarios (local and inter-city travel). Using a stated preference survey administered to Korean commuters in 2019, two multinomial logit (MNL) mode choice models are developed. These models are stratified by trip length and include four alternatives: AV with a manual driving option (AVMD), AV with a self-driving option (AVSD), shared AV (SAV), and public transit (PT). The results show that the value of in-vehicle travel time savings for AVSD (on average, $7.61/hr) is lower than for the other two AV modes (AVMD = $10.26/hr, SAV = $13.67/hr). It suggests that travelers tend to pay less to reduce travel time while using a private hands-free mode because it allows travelers to use their travel time more productively and/or relieve stress for driving. In addition, changes in the VOTTS for each mode vary by trip length. As travel distance increases, VOTTS for shared modes (SAV and PT) tends to decrease. The value of in-vehicle travel time for SAV in the local travel scenario is the highest among the four modes but is considerably lower than that in the inter-city scenario. These differences suggest that travelers require a certain amount of time to fully use their in-vehicle time, highlighting the importance of trip length when determining the VOTTS. Interestingly, the VOTTS for PT is lower than three AV modes, implying that it might be too early to expect substantial reduction in the VOTTS for AVs due to potential risks of accidents, unfamiliarity with AVs, or immature technology of AVs.

With increasing concerns about urban pollution, electric vehicles (EVs) have offered an alternative mode of transportation that reduces urban pollution levels. Previous studies have sought to identify the various factors influencing EV charging patterns to deploy an appropriate charging infrastructure. However, limited attention has been paid to the investigation of different charging patterns identified in different regions at different phases of the EV program. This study aims to fill this research gap in the literature by developing binary logistic models that account for the factors influencing charging demands in different regions of Korea, i.e., Jeju-do and Gangneung-si. To this end, we collected historical data on EV charging transactions in these study regions and analyzed them to evaluate the difference in charging demands. The developed models suggest that the charging demand varies with charger characteristics and charging time. Moreover, different charging patterns in different regions can be explained by the different travel behaviors of those who use EVs for different trip purposes. These findings provide an important implication suggesting that policymakers should consider a stepwise strategy to construct charging infrastructure at the appropriate scale and configuration, depending on the phase of the EV program.

The purpose of this study is to analyze the impact of lowering speed limit on an urban transportation network. A microscopic traffic simulation model, Vissim was utilized to measure the impact. Also, various traffic inputs were tested with different signal coordination scenarios to investigate the impact in different traffic conditions. It was found that during early morning hours with very light traffic, the impact of lowering speed limit was significant. During congested time periods, including level of service E and F, the travel speed reduction from lowering speed limit was not significant. As suggested in other studies, the results demonstrated that lowering the speed limit does not have a significant impact on average travel speed in congested traffic networks. Also, different signal coordination was tested. As expected, signal coordination based on the lowered speed limit performed better than the case with signal coordination based on the previous higher speed limit. The results of this study are expected to provide insights when considering lowering speed limit for existing traffic networks.

To monitor air pollution on roads in urban areas, it is necessary to accurately estimate emissions from vehicles. For this purpose, vehicle emission estimation models have been developed. Vehicle emission estimation models are categorized into macroscopic models and microscopic models. While the calculation is simple, macroscopic models utilize the average speed of vehicles without accounting for the acceleration and deceleration of individual vehicles. Therefore, limitations exist in estimating accurate emissions when there are frequent changes in driving behavior. Microscopic emission estimation models overcome these limitations by utilizing the trajectory data of each vehicle. In this method, the total emissions in a road segment are calculated by adding together the emissions from individual vehicles. However, most research studies consider the total vehicle emissions in a road section without considering the difference in vehicle emissions at different locations of a selected road section. In this study, a road segment between two intersections was divided into sub-sections, and energy consumption and emission generation were analyzed. Since there are unique driving behaviors depending on the section of the road segment, energy consumption and emission generation patterns were identified. The findings of this study are expected to provide more detailed and quantitative data for better modeling of energy consumption and emissions in urban areas.