Asian Transport Studies最新文献

The public transportation system in densely populated areas is often managed as a self-financing system due to fare passenger revenues. In a self-financing system, the network structure and the monetary fare mutually interact; therefore, it is efficient to design both simultaneously. We developed an optimization model that endogenously determines the bus network and fares to maximize the total social surplus of the region. In this study, we conducted numerical applications of the model to a public transportation system around a railroad station in a metropolitan suburb area. The results showed that the optimal network topology changes with the scale of demand, and network shape significantly alters the optimal fare structure. Three types of virtual regions were examined to illustrate optimal fares.

This study proposes two tourist excursion behavior models. One is the trip chain-based model, in which the tourist behavior is represented by a trip chain and the duration of stay at each tourist spot in the trip chain. The model is based on the tourist's utility maximization, where a tourist allocates the time budget along the trip chain generated from our proposed algorithm formulated as dynamic programming. The proposed model assumes that travel data obtained from Wi-Fi sensors are available. We also proposed the Trip-based model, which estimates the transition ratio of a tourist trip between tourist spots based on the tourist's utility maximization. The trip-based model only partially captures the excursion behavior but is available when only tip data between two nodes in the network is available. To demonstrate the models presented in the paper, we calibrated parameters and predicted the tourist demand in a test and real networks.

With the increasing importance of sustainability, warehouse sharing arises as a possible way to improve the logistics system efficiency. This paper studied the warehouse sharing platform systems (WSPS) and proposed an enhanced modelling approach for the WSPS design problem (WSPSDP) using the multi-allocation hub location routing problem framework. New elements, such as inter-warehouse transportation and multi-allocation scheme, were considered compared to the existing WSPS model. Then, an adaptive large neighbourhood decomposition search heuristic was applied to solve our problem. Computational experiments were conducted on different-sized instances to compare the WSPS model without inter-warehouse transportation (WSPSDP-WI) and the WSPS model with single-allocation scheme (WSPSDP-SA). The results suggested that our proposed WSPSDP model is more cost-efficient than the existing WSPS models, reducing 15.38% and 1.43% operation costs compared to the WSPSDP-WI and WSPSDP-SA models. Finally, our proposed WSPSDP model also has the potential to promote the utilisation of existing cheap idle warehouses.

Japan has used the Area Division Policy to control the disorderly spread of urban space for over 50 years. As a key component of area division, “urbanization promotion areas” are expected to promote land development, concentrate population within it, and prevent or slow down land and population expansion outside. To review the implementation of this system and provide a reference for future spatial optimization, quantitative methods to analyze the influence of urbanization promotion areas are important. This paper applies a regression-discontinuity design to study the impact of an urbanization promotion area on land development and population density in the Kofu urban area. The results indicate that the urbanization promotion area retains control over land and population, but development pressure remains on the urban periphery. To achieve orderly urban development, area division should be adjusted according to residential demands and coordinated with other recent land control policies.

In recent years, there has been an increasing demand for “Michi-no-Eki.” These roadside stations in Japan help solve food desert problems and enhance social interaction. Accordingly, the number of Michi-no-Eki with various urban amenities (e.g., supermarket, restaurant, and playground) has been increasing. However, there is no statistical model that can calculate the impact of a Michi-no-Eki with urban amenities on shopping difficulty or social interaction. Consequently, it can be difficult for local authorities to assess the need, the location, and the design of a Michi-no-Eki with urban amenities to ease shopping difficulty and enhance social interaction. This study aimed to (1) confirm the influence of Michi-no-Eki with urban amenities on shopping difficulty and social interaction, and (2) identify the factors influencing the impact of such Michi-no-Eki to support development of statistical models for shopping difficulty and social interaction.

Bus bunching is a critical issue in bus operation management. This study investigated the impacts of potential factors affecting bus bunching on a city-wide bus route network, using Taipei City as an example with its 267 bus routes and 4741 bus stops. Frequency of bus bunching was measured on an hourly basis using Automatic Vehicle Location data. Multilevel count models based on Conway-Maxwell Poisson distributions were developed, and the spatiotemporal distributions of random intercepts and residuals were examined. The results showed that the effects of bus lane design, bus route network and operation, and traffic characteristics were consistent with those reported in prior studies. However, a quadratic relationship between bus route length, best measured by number of stops, and bus bunching was identified, which explains the conflicts between the theories of bus bunching propagation and self-repair. Bus route design often faces a dilemma between extending route length to collect additional passengers and reducing in-vehicle travel time to enhance service quality. The findings of the study could be a useful reference for bus route design to minimize bus bunching.

Mobile spatial statistics from across Japan are used to analyze the vitality of stations over the COVID period. Time series population information of 500m × 500m meshes that include major stations are extracted. We analyze the demand loss patterns of 69 train stations during the first COVID wave. We firstly discuss the correlation of this data with annual ridership information. We then conduct a clustering analysis of the time series data and distinguish five impact patterns which we try to explain with a multinomial logistic regression. Stations in large cities had higher ridership but were also more affected than smaller cities. We also find that cities with less dense populations and more local population frequenting the station appear to be more robust to the pandemic. Our results can be used to help cities forecasting the impact of future pandemics on the local economy.

Since the detection of the first COVID-19 case in March 2020, the Indonesian government has implemented various mobility restrictions as a policy response to address the pandemic. To date, violations of mobility restrictions have been discussed in relation to public health risk, but rarely analyzed in terms of understanding the transport policy-practice gap. Using content analysis of news media from March 2020 to May 2021, this article identifies individual actions and institutional factors enabling violations of mobility restrictions. Our findings infer a policy-practice gap regarding operationalization, institutional issues, and lack of consideration of target groups’ behavior. These findings provide insights for transport policy formulation in uncertain times, such as the post-pandemic, especially in the context of rapidly growing Asian cities.

We used a Bayesian structural time series (BSTS) model to evaluate the short- and long-term impacts of the coronavirus disease 2019 (COVID-19) pandemic on transit ridership. We accessed smart-card data from Miyazaki City, Japan. We defined attributes based on card types (commuters, students and elders) and aggregated attributes (high-frequency users and “frequently used bus-stop pairs”) and analyzed the differences between all users and the extracted groups. Among card types, the short-term impact on elders was almost identical to that of all users, however, the short-term impact of the pandemic on commuters was much smaller and that of students was much larger than that of all users. The long-term trend of commuters was less fluctuated than that of all users. The long-term ridership recovery of students was higher than that of all users. Among aggregated attributes, the short-term impact was smaller on “high-frequency users” than on all users: the decrease in ridership immediately after the appearance of COVID-19 was smaller among “high-frequency users” than among all users. The long-term recoveries in the riderships of the extracted subsets were slower than the recoveries of riderships of all users.