This paper develops a signal optimization algorithm that aims to equalize queue growth rates across links in oversaturated urban roadway networks and thus postpones queue spillbacks that form at the localized sections of networks. The performance of this algorithm is evaluated by simulating traffic conditions with optimized signal settings on an idealized 3 by 3 roadway network under various oversaturated demand scenarios. The simulation experiments show that the algorithm can delay queue spillbacks by distributing queues over upstream links that would otherwise be underused. The findings from the experiments also show that the signal settings optimized by the queue growth equalization (QGE) algorithm outperform those optimized using the conventional signal optimization software, TRANSYT-7F, for all the performance measures examined in this paper, i.e., compared with TRANSYT-7F, the QGE results in higher outflows, higher vehicle miles traveled, shorter delays, less sensitivity to various demand scenarios, and delayed queue spillbacks. In addition, the algorithm is computationally light to provide a promising groundwork for large-scale signal optimization.

Shared mobility is a service that allows users to share various transportation modes and use them with reservations when necessary. It started with private automotive car-sharing and ride-sharing services. Currently, it operates on a wider range, including personal mobility devices such as electric bicycles and scooters. The purpose of this study is to derive a direction for providing future shared mobility services through analysis of factors affecting the usage intention of both current and prospective users. The survey targets 753 citizens living in Gyeonggi Province, Korea. The survey period is from February 12, 2020, to February 26, 2020. In this study, a logistic regression analysis is conducted to investigate the factors affecting the use intention of shared mobility. The analysis results show that gender, car ownership, and education, among variables reflecting socio-demographic characteristics, have significant effects on intention to use shared mobility. Moreover, we find that experience factors, including mainly used transportation modes, ownership of shared mobility device, past experience in similar services, satisfaction of existing shared mobility services, and distance from the home to the nearest bus stop, are also statistically influential. The analysis results are expected to lay the foundation for the introduction of shared mobility services and can be used as data for planning smart mobility services in the future.

Mobility as a service (MaaS) integrates various transportation modalities and connects these modalities to enable users to travel from departure points to destination points. The objectives of this study were to analyze the factors influencing the intention to use MaaS and determine a direction for the implementation of this service. Hence, this study utilized an ordered probit model and marginal effect analysis. Specifically, a survey was conducted based on citizens living in Gyeonggi-do, South Korea, to prepare logical MaaS operation measures customized in this province. Subsequently, variables related to MaaS were classified, and statistical analysis was performed based on these variables. The need for integrated transportation service, intention to use shared mobility, gender, household income, and region are found to have significant effects. Moreover, demographic groups with high levels of intention to use MaaS were identified. Finally, the intention to use MaaS was found to be mainly affected by whether public transportation was the primary means of transportation or whether the subjects spent a comparatively long time commuting. It is expected that these findings will serve as the basis for MaaS implementation in the future and will also be used as fundamental data for transportation service planning.

The Korean government has announced plans to supply electrified buses to achieve decarbonization in the transportation sector and to create next-generation growth engines. Although a multitude of technical and political studies have been conducted to support the successful introduction of electrified buses, studies on the attitudes and perceptions of passengers toward electrified buses remain insufficient. To evaluate the perceptions and preferences of potential passengers toward the specific attributes of electrified buses, this study performed an online survey (N = 586) that includes people who had experienced travel on public buses. Values of the relative importance of eight different attributes—safety, ride comfort, environmental friendliness, exterior design, cleanliness, crowding, seat comfort, and convenience getting on/off—were evaluated using the best-worst scaling method. The results showed that safety (share of preference: 41.3%) was the most important attribute when using electrified buses. This was followed by eco-friendliness (14.3%) and ride comfort (13.6%). On the other hand, the least important attribute was exterior design (1.8%). Gender differences were also observed in the valuation of certain attributes among the passenger preferences toward electrified buses. The results of this study contribute to the development of strategies for the wide-spread adoption of electrified buses and provide a stepping-stone to a more sustainable public transportation system.

The unstructured-textual crash descriptions recorded by police officers is rarely utilized, despite containing detailed information on traffic situations. This lack of utilization is mainly due to the difficulty in analyzing text data, as there is currently no innovative methodology for extracting meaningful information from it. Given limitations and challenges in analyzing traffic crash descriptions, this study developed a methodology to classify significant words in unstructured data that describe traffic crash scenarios into standardized data. Ultimately, a natural language processing technique, specifically a bidirectional encoder representation from transformer (BERT), was used to extract meaningful information from crash descriptions. This BERT-based model effectively extracts information on the exact collision point and the pre-crash vehicle maneuver from crash descriptions. Its practical approach allows for the interpretation of traffic crash descriptions and outperforms other natural language processing models. Importantly, this method of extracting crash scene information from traffic crash descriptions can aid in better comprehending the unique characteristics of traffic crashes. This comprehension can ultimately aid in the development of appropriate countermeasures, leading to the prevention of future traffic crashes.

The introduction of Connected and automated vehicles (CAVs) promises various social and economic benefits, but safety issues are encountered when CAVs are mixed with human‐driven vehicles (HDVs). This study presents three scenarios as lane operation alternatives for situations in which CAVs and HDVs are mixed. The proposed lane operation scenarios for CAVs are the operation of dedicated lanes (DLs) for CAVs, operation of a DL for CAVs and buses, and the operation of DLs for buses. For each scenario, the effects of the market penetration rate (MPR) on traffic efficiency and safety were evaluated. The following findings were obtained from the results: CAVs had a potential effect on each lane according to the lane operation scenario; traffic efficiency and safety improved in a traffic flow situation where CAVs and HDVs were mixed; traffic efficiency and safety were higher when operating DLs for CAVs than when operating a mixed traffic flow; and efficiency and safety were optimal when operating Scenario 2 (DL for CAVs and Buses) in the early stages of introducing CAV (MPR 10–30%) and operating Scenario 1 (DL for CAVs) in the mid‐ to long‐term stages (MPR 40–80%). These findings provide a basis for preparing new policy alternatives to the current policy of adopting DLs for buses. Furthermore, the evaluation of the effects of each DL operation in the transition period where CAVs and HDVs are mixed will contribute to the operation of an efficient and safe CAV‐based road traffic system.