Walking is an essential sustainable mode of transportation. Encouraging to increase walking trips can bring various social and economic benefits to our society. Since the policy paradigm has been shifting from car-oriented to pedestrian-oriented, interest in securing pedestrian rights and improving walking environments is increasing significantly. This study aims to examine factors affecting pedestrian satisfaction according to land use and street type. A pedestrian satisfaction survey was conducted in an industrial city with a mid-size population in the city of Changwon, South Korea. Based on the survey data from 500 respondents, factors affecting pedestrian satisfaction were analyzed by land use (commercial or residential areas) and street type (non-separated or separated sidewalks). The analysis results, using binary and ordered logit models, showed that the less illegal parking, the more pedestrian space, pedestrian guidance facility, and green space, the higher the pedestrian satisfaction. Factors positively affecting the satisfaction of pedestrian paths according to land use were physical environmental variables, such as the separated sidewalk variable. In commercial areas, pedestrian guidance facilities and street cleanliness were included as major influencing factors, implying differences in land use influencing factors. A common factor affecting the satisfaction of separated or non-separated sidewalk cases was also identified as the sufficiency of walking space. Therefore, the most urgent policy measure for improving pedestrian satisfaction for the city was to install a sidewalk or expand the pedestrian space. In the pedestrian-vehicle separation models, green space and cleanliness were included as significant variables, and in the non-separated models, variables of pedestrian guidance facilities and sidewalk conditions were included.

Recently, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns or policy decision support due to missing information on alighting stops of users. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. In order to overcome these limitations, various methods for estimating alighting stops have been developed. This study classifies trips with missing alighting stop information into trip four types and then applies appropriate alighting stop estimation methodology for each trip type in stages. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checking the accuracy for each standard of allowable error for sensitivity analysis. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can estimate users’ destinations more accurately than the methodologies of previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies.

As public perception about the shared economy evolves, peer-to-peer ridesharing has been gaining increased attention worldwide. Both private and public sector entities have launched mobile app-based ridesharing services, while a range of methodologies and system architectures have been proposed in academia. Whereas traditional ridesharing methods match drivers and riders when their origin and destination are similar, recently proposed algorithms often feature multi-hop and multimodal properties that allow riders to be connected by multiple modes. Such algorithms can reduce travel time and/or travel cost; however, they may also add other travel impedances, such as requiring multiple transfers. Understanding user behavior toward such new ridesharing systems is essential for successful service design. For policymakers and service planners, identifying factors that impact traveler choices can lead to better design and improved services. This research involved a web-based survey to capture traveler preferences using a conjoint analysis framework. A choice-based method was adopted to identify factors for the estimation model and to analyze traveler willingness to pay. Among the proposed factors, the number-of-transfers was shown to be the most important, as was expected. When a multimodal ridesharing system provides less travel time, low travel cost, and sufficient ridesharing incentive, people are more likely to pay for the service.

With the accelerated commercialization of autonomous vehicles, new accident types and complex risk factors have emerged beyond the scope of existing traffic safety management systems. This study aims to contribute to sustainable safety by establishing a quantitative basis for early recognition and response to high-risk situations in urban traffic environments where autonomous and conventional vehicles coexist. To this end, high-risk factors were identified through a combination of literature meta-analysis, accident history and image analysis, autonomous driving video review, and expert seminars. For analytical structuring, the six-layer scenario framework from the PEGASUS project was redefined. Using the analytic hierarchy process (AHP), 28 high-risk factors were identified. A risk prediction model framework was then developed, incorporating observational indicators derived from expert rankings. These indicators were structured as input variables for both road segments and autonomous vehicles, enabling spatial risk assessment through agent-based strategies. This space–object integration-based prediction model supports the early detection of high-risk situations, the designation of high-enforcement zones, and the development of preventive safety systems, infrastructure improvements, and policy measures. Ultimately, the findings offer a pathway toward achieving sustainable safety in mixed traffic environments during the initial deployment phase of autonomous vehicles.

In recent years, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns, policy decision support, etc. because the information on alighting stops of users is missing. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. Various studies to overcome this limitation have been conducted in South Korea and other countries to develop es-timation methodologies of alighting stops. Even existing studies introduce an advanced method, we found the margin for better accuracy by combining various estimation methodologies for estimating alighting stops. This study reviewed previously conducted studies to classify data with missing alighting stop information into trip types and then applied an appropriate alighting stop estimation methodology for the characteristics of each trip type by stage. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checked the accuracy for each standard of allowable error for sensitivity analysis. Furthermore, the number of trips, accuracy, and valid tag rate were checked for each type to examine the need for classifying the trip types. Finally, our evaluation also examines the impact of classifying trip types on estimation accuracy. The evaluation criteria are accuracy of the number of trips and valid tag rate. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can es-timate users’ destinations more accurately than previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies.

Conventional Safety Performance Functions (SPFs) primarily rely on static exposure measures such as Annual Average Daily Traffic (AADT), often failing to capture real-time, individual-level risky driving behaviors. To address this gap, this study proposes a Risky Driving Indicator (RDI) that integrates large-scale smartphone-based hard braking event data with traffic detector occupancy measures. The RDI was evaluated against traditional models across three specific aggregation levels: AADT, Annual Average Weekday Daily Traffic (AAWDT), and AAWDT excluding the overnight period. A case study was conducted using data from 2021 to 2022, a period coinciding with the COVID-19 pandemic, on South Korea’s busiest freeway to evaluate RDI-based SPFs. The results showed that models using the COM-Poisson framework outperformed traditional volume-based versions, showing superior performance across Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Akaike Information Criterion (AIC) values. These findings confirm that integrating crowdsourced behavioral data enhances predictive accuracy, offering transportation agencies a cost-effective, scalable solution for proactive hotspot identification and dynamic safety monitoring. By improving safety management through scalable and cost-effective mobile sensing, this study contributes to the development of more sustainable highway transportation systems.