Asian Transport Studies最新文献

Since floods caused by overflowing rivers are increasing in Southeast Asia cities, reducing urban mobility, adaptation measures are required to cope with this situation. One possible measure would be to develop transportation facilities to make them passable even during flooding conditions. Another measure is to encourage people to relocate from high-risk areas to low-risk areas. Implementing an appropriate combination of these two different adaptation measures is necessary. However, there is limited research to assess the impacts of adaptation measures on flood risk and urban activity. Therefore, this study developed an urban adaptation model that could assess mobility and land use for Ubon Ratchathani, Thailand, and analyzed to what extent the decrease in urban mobility can be alleviated by combining these adaptation measures. It showed that a combination of both adaptation measures could ensure mobility under flood conditions, with benefits from travel time reduction estimated at up to 230.44 million baht (6.36 million USD) over 20 years.

This study addresses the lack of knowledge regarding the diversity in commuter travel behavior, particularly the effects of different types of ride-hailing services (RHS). It focuses on the distinct impacts of motorcycle-based RHS (MRHS) and car-based RHS (CRHS) on user behaviors. By applying multinomial logit and bivariate probit regression analyses to data from 500 RHS users gathered in March 2023, the study reveals that increases in income and personal vehicle ownership have contrasting effects on MRHS and CRHS usage. Notably, MRHS usage is positively associated with employing RHS as a feeder mode to metro stations, while CRHS usage demonstrates a negative correlation. Furthermore, the study establishes a user preference for MRHS over CRHS during peak traffic times. These insights highlight the importance of creating specific policies for different types of RHS, which is vital for the development of successful urban transportation strategies.

Understanding driver behavior is crucial for introducing roundabouts. This study focuses on calibrating the parameters of the car-following model using naturalistic data and analyzing the appropriateness of different car-following models on the roundabout. We utilize rounD trajectory dataset. This dataset allows for the precise definition of lead and follow vehicles, enabling the calibration of model parameters accordingly. We compared the calibration results for roundabouts with those obtained for signalized intersections from CitySim. Our results show that the Krauss and intelligent driver models (IDM) achieve mean absolute percentage errors of 10.09% and 23.21%, respectively. Furthermore, IDM exhibited higher errors in the circulation segment of the roundabout, while in the exit segment, the Krauss model showed elevated errors. It contrasted with the homogenous results obtained in the signalized intersection. These findings provide valuable insights into driver's behavior on roundabouts.

This study aims to investigate the relationship between the numbers of pedestrian-vehicle crashes (PVCs) and the traffic and built environments from a macroscopic perspective. A binomial distribution model has been developed to represent the occurrence of PVCs. To calibrate the model, Bayesian analysis using the Markov chain Monte Carlo method has been employed. The results identify thirteen variables representing urban activities and traffic conditions, including land uses, degrees of mixed use, points of interest, various passage widths, and street hierarchy that significantly impact PVCs. Additionally, the study unveils the spatial distribution of PVC probabilities and exposures. This research contributes to the field by developing an analytical framework for comprehending PVCs from a macroscopic viewpoint, introducing innovative methods for uncovering latent variables, integrating diverse types of data into the analysis, and creating a model for simulating the effects of urban planning revisions and traffic management strategies.

The aviation industry has a key role in international trade activities. Most studies measure aviation performance based on airlines or airports. However, the frontline operation is delivered via air routes and more operating characteristics can be revealed with route-based performance evaluation. Further, the traditional data envelopment analysis (DEA) approach cannot rank performance in a particular context which represents an efficiency frontier built by decision making units (DMUs). Therefore, this study uses a context-dependent DEA approach to measure and rank the performance of air routes with a case study of 112 international routes from two airlines in Taiwan. Model results identify eight performance levels with most air routes at efficiency performance level 2 and level 3 and most route destinations in East Asia (e.g., Japan). The results of this study can inform operators about a clear relationship between performance and potential markets as well as improvement strategies.

Renewing feeder transit services is crucial for maximizing the potential of the public transport system and advancing toward a decarbonized Bangkok. Herein, we propose a smart feeder transit service using small electric vehicles for limited areas (sois) and target facilities in central Bangkok. A 1-year proof of concept was conducted to verify the feasibility and effectiveness of this service and assess its potential for deployment as a new mobility service. We found that 1) the introduction of a small mobility service in sois can considerably reduce both road congestion levels and CO₂ emissions owing to the improved capacity utilization rate, downsizing, and the use of electric vehicles, 2) advantages of scaling could be garnered by expanding the number of facilities to be serviced, and 3) providing a mobility service that is cooperatively operated by neighboring facilities is more convenient and profitable than providing a shuttle service for each facility.

The growing utilization of autonomous cars may affect people's residential location choice behavior and change the population distribution in a city, contrary to compact city policy. This paper proposes a model to estimate the time-series impacts of autonomous cars on population distribution in a city. The model basically consists of the primary factors cohort method and a housing land location equilibrium model. The location equilibrium model considers the residential location choice behavior of individuals, while prior models have been based on the behavior of households. We applied the model to Niigata City, Japan and analyzed the impact of permission to utilize autonomous cars (an increase in car availability to non-drivers) in the whole area of the city and only in the suburbs. Results indicated that permission to utilize autonomous cars only in the suburbs may lead to urban sprawl.

The efficient operation of container terminals facilitates the seamless flow of goods across borders. New technologies such as big data, data mining, and simulation models, have emerged in the maritime industry, enabling optimization and performance evaluation. This study investigated how data science using operational data can improve container terminal operations, drive efficiency, enhance throughput, and bolster competitiveness in the shipping sector. Decision-making within container terminals, particularly in determining optimal container stacking locations, poses a significant challenge owing to the multitude of factors at play. By analyzing the datasets, new strategies and policies can be simulated to minimize container rehandling operations. This study focuses on the Hakata International Container Terminal in Japan, where daily operational data from rubber-tired gantry equipment are used to investigate container movements. A significant reduction in the total number of movements required to perform container-handling operations was demonstrated by implementing data-driven strategies and simulation modeling.

The East Japan Railway Company is planning a new access railway line to the Tokyo International (Haneda) Airport. This paper proposes an evaluation method for the development of the new airport access line considering the socioeconomic characteristics of the Greater Tokyo Area and indicates what is needed to increase its social value. As the evaluation method, we develop an econometric model that considers commuters’ trip behaviors across prefectures and the relationship between the increased number of Meeting, Incentive Travel, Convention, Exhibition and Event (MICE) visitors and the regional economy. We also conduct a simulation analysis using an empirical econometric model and a cost-effectiveness analysis based on multiple scenarios. The results indicate that a 50% increase in MICE visitors is needed to make the present discounted value of the total increase in the gross regional product larger than the construction cost of the line.