Asian Transport Studies最新文献

Because road surface snow conditions are mainly monitored by road patrols, if road surface conditions can be estimated based on meteorological conditions and traffic volume, winter road management can be performed more efficiently. Therefore, this study focuses on estimating road surface snow conditions. The relationship between weather conditions, traffic volume, and road surface conditions was analyzed, and a road surface condition estimation model was constructed using random forest. In addition, because there is a relationship between road surface conditions and tire noise, we estimated the road surface condition by adding tire noise to the weather and traffic volume. As a result, we constructed a model for estimating the road surface condition from the weather and traffic volume, with an accuracy of approximately 95%. The accuracy was slightly lower when the tire noise was added.

Conventional transportation policies for railroads have primarily focused on minimizing the negative utility, such as shortening the travel time and reducing congestion. However, with the recent introduction of trains with extra fares for greater comfort and changes in work styles, there is an increasing need to focus on the positive utility of travel itself. Moreover, advances in machine learning and artificial intelligence research have facilitated highly accurate and objective analysis from vast amounts of data. The purpose of this research is to construct a new transportation choice model using inverse reinforcement learning, which is a machine learning method, and to quantify the positive utility of commuter railroads. The results of a comparison of the proposed model with conventional methods indicate the advantages and disadvantages of the model. Further, a transportation choice model for railroads was created to understand the tendency of each selected train type.

The study deals with the fleet allocation problem in public electric vehicle (EV) systems with consideration of demand uncertainty. The problem aims to determine the optimal number of EVs deployed at each station and the objective is to minimize the total system cost. We propose a genetic algorithm (GA) with an event-based simulator to solve this problem. To consider demand uncertainty, an event-based simulator is developed and embedded in the GA. This study generates and solves a number of instances based on the historical data obtained from an EV-Sharing system operator in Sun Moon Lake national park in Taiwan. We compare the solutions of the GA with those of an enumeration method. The results show that the GA is able to obtain the optimal solution for more than 70% of the instances. Even when the GA fails to find the optimum, the gaps between optimal solutions and heuristic solutions are less than 0.1%. Moreover, all solutions are found within a reasonable amount of time. The proposed solution approach provides decision support for the fleet allocation in EV-sharing systems.

Electric Vehicles being at a nascent stage in India, albeit at a slower growth rate, faces a significant barrier of economic compatibility. For countries like India, the economic aspect plays a vital role in the widespread of Electric Vehicles. Concentrating on the cost aspect, this paper contemplates the economic compatibility of battery electric vehicles (BEVs) with conventional IC Engine Vehicles (ICEVs), considering a paring vehicle approach and analysing the case study of India. As less research is carried out for India, we aim to develop an extensive total cost of ownership (TCO) model. Furthermore, the Sensitivity analysis presents the effect on TCO by considering parameters as incentives, reduction in battery prices, finance interest rates and Annual Kilometre Travelled (AKT) &EV + PV integration. For the base case, a much abysmal performance is observed for BEVs. While accounting for various Sensitivity analysis parameters, some BEVs reach cost parity while some become more economical than ICEVs.

Multi-service transport platforms (MSTPs) such as GOJEK and GRAB, providing access to many services without traveling, may lead to changes in activity and travel decisions, in turn leading to changes in the distribution of facilities, evidenced by a different distribution pattern and spatial concentration of online food merchants that only provide online food delivery services. This study empirically explores the impacts of MSTPs on food merchant distribution in Jakarta by identifying differences in distributions of dine-in and online food merchants. Our empirical results focusing on the agglomeration of facilities with different spatial scales showed that, while dine-in food merchants are more agglomerated at the metropolitan scale, online food merchants are more agglomerated at the neighborhood scale, presumably due to the necessity to attract MSTP drivers to keep the delivery service level. We also found that online food merchants tend to be agglomerated in the center and fringe area of the city.

As machines gradually are introduced in human society and share public spaces with people, achieving human-machine coexistence becomes significant. Previous studies make contributions but are still insufficient to satisfy the needs of mixed traffic when mobilities increase. For dealing with the mixed traffic and ensuring safe and comfortable walking environments of pedestrians, this research proposes a new envelope theorem divided into physical and mental envelopes. The mental envelope (ME) as the focus of this research explains pedestrian perceptions from two perspectives, SME and OME, expanding the traditional interpersonal distance. To preliminarily explore the acceptance of two MEs and compare their differences, we conduct a questionnaire survey. The results show that MEs are accepted by participants, and express different pedestrian perceptions. The responses of subjective priority order showing the differences between SME and OME indicate their possible relationship. These findings would be beneficial to explore the future applications of MEs.

This paper proposes a short-term rolling horizon framework for the within-day prediction of travel times on links with and without point detectors (referred to as observed and unobserved links respectively) along a selected path covered in the Hong Kong journey time indication system (JTIS). In Hong Kong JTIS, the number of point detectors on major roads is usually limited due to the financial budget and site constraints in the densely populated urban area. However, the prediction of the travel times on urban road corridors particularly on the links without point detectors is also valuable to road users and traffic authorities. This paper proposes a 2-stage framework based on functional principal component analysis and maximum likelihood estimation method to predict the mean and standard deviation of the travel times on the study path and observed links as well as unobserved links once every 2 min for the next 30 min. An urban road network in Hong Kong is selected as a case study. The prediction results are validated using an independent dataset from JTIS, demonstrating the practical applicability of the proposed framework.

This study uses anonymous media access control address packet (AMP) collector data obtained from Wi-Fi signals to identify golden routes, i.e., routes that are most frequently followed in tourist areas. The rise of radiofrequency scanner technology has led to its potential application in the observation of people movements. This study analysed the travelling behaviour of tourists in the Higashiyama area (Kyoto, Japan) using digital footprint data collected by 20 AMP sensors. K-means clustering analysis was performed to identify the trajectory of tourists. Then, sequential pattern mining was used to extract the frequent sequence of destinations visited by tourists. As a result, we characterised the smart device users into four groups: same-day visitors, overnight visitors, commuters, and residents. Moreover, it was found that the most frequent trip patterns of tourists matched our expectations, and we conclude that the proposed method can identify golden routes.

Conflicts between pedestrians and left-turning vehicles cause pedestrian safety hazards and decrease vehicle turning capacity at signalized intersections. This commonly occurs in Japan where they share the same traffic light phase. Road capacity is influenced by the interaction between them. Autonomous vehicles (AVs) promise to improve the safety and efficiency of road networks. However, they will face the same problems as human-driven vehicles with pedestrian avoidance. Thus, this study estimated the capacity of the left-turn lane, in Japan, under AV mixed flows taking into account AV penetration rates, pedestrian flow rates, and crosswalk length. The impact of AVs on capacity was analyzed with different settings for the critical gap between vehicles and pedestrians. It was found that if AVs comply perfectly with traffic law, the capacity of the left-turn lane sharply decreases, especially in the case of long crosswalks.

The universal design of public transportation is a crucial task worldwide, especially in aging societies. For this reason, public agencies and train operators have advocated for universal architectural design for train stations, for instance, installing elevators and designing step-free routes. However, the location of elevators is often decided on an ad-hoc basis, and wheelchair users are often forced to take unnecessary detours and collision risks. This is because few methodologies are available for making an integrated evaluation of spatial structure and wheelchair accessibility.

This paper introduces an evaluation framework based on network analysis to evaluate wheelchair accessibility from the aspect of the spatial structure at train stations. Specifically, we first develop weighted network data that describe the spatial structure of train stations in detail to represent the mobility restrictions of wheelchair users and pedestrians. We next propose several indices to evaluate each station in terms of its wheelchair accessibility using these data: “reachable time indices,” which measure the time to the train doors from the ticket gate, and “level of path independence,” which describes the low risk of collisions on paths. This evaluation framework was applied to nine train stations in Tokyo, Japan, as case studies to examine the relationship between spatial structure and wheelchair accessibility. As a result, we found three important relationships. First, placing an elevator in the center of a platform narrows the range of the reachable times for wheelchair users. Second, the proximity of elevator doors to stairs or escalator entrances tends to increase the risk of collisions with pedestrians. Third, a wheelchair stairlift installed in place of elevators increases the minimum reachable time and increases the risk of collisions on designated paths. These insights will be useful for finding facilities that require improvement in the wheelchair accessibility of step-free routes.