A car lane-changing model under bus priority-lane effects

Car lane-changing behaviour has been well investigated at merging locations or weaving sections where the lane-changes are usually due to different origin-destination trip purposes. However, the lane-changing behaviour under the effects of bus priority-lanes in urban streets has not been received much attention. This kind of behaviour is found to initially depend on the existence of oncoming buses in priority-lanes in urban streets. In this paper, a car lane-changing model under bus priority-lane effects in urban streets is proposed. This model comprises three steps: looking-back threshold determination, gap acceptance model and execution model. The model’s parameters are estimated jointly by using the Maximum Likelihood Method. The research results show that the car lane-changing behaviour under bus-priority-lane effects in urban streets is considered compulsory behaviour. The behaviour has specific characteristics with smaller critical gaps compared with those at other normal lane cases and can be modelled by the proposed model

Research and development for accuracy improvement of neutron nuclear data on minor actinides

To improve accuracy of neutron nuclear data on minor actinides, a Japanese nuclear data project entitled “Research and development for Accuracy Improvement of neutron nuclear data on Minor ACtinides (AIMAC)” has been implemented. Several independent measurement techniques were developed for improving measurement precision at J-PARC/MLF/ANNRI and KURRI/LINAC facilities. Effectiveness of combining the independent techniques has been demonstrated for identifying bias effects and improving accuracy, especially in characterization of samples used for nuclear data measurements. Capture cross sections and/or total cross sections have been measured for Am-241, Am-243, Np-237, Tc-99, Gd-155, and Gd-157. Systematic nuclear data evaluation has also been performed by taking into account the identified bias effect. Highlights of the AIMAC project are outlined

Application for developing countries: Estimating trip attraction in urban zones based on centrality

This paper introduced a network centrality-based method to estimate the volume of trip attraction in traffic analysis zones. Usually trip attraction volumes are estimated based on land use characteristics. However, executing of land use-based trip attraction models are severely constrained by the lack of updated land use data in developing countries. The proposed method used network centrality-based explanatory variables as “connectivity”, “local integration” and “global integration”. Space syntax tools were used to compute the centrality of road segments. GIS-based kernel density estimation method was used to transform computed road segment-based centrality values into traffic analysis zone. Trip attraction values exhibited significant high correlation with connectivity, global and local integration values. The study developed and validated model to estimate trip attraction by using connectivity, local integration and global integration values as endogenous variables with an accepted level of accuracy (R2 > 0.75). The proposed approach required minimal data, and it was easily executed using a geographic information system. The study recommended the proposed method as a practical tool for transport planners and engineers, especially who work in developing countries and where updated land use data is unavailable

Attitude-Based Segmentation of Residential Self-Selection and Travel Behavior Changes Affected by COVID-19

This study evaluated the effects of COVID-19 on attitudes toward residential associated with travel behavior on decisions regarding future relocation. Chi-square automatic interaction detection was used to generate tree and classification segments to investigate the various segmentations of travelers and residents around mass transit stations. The decision tree revealed that the most influential variables were the number of transport card ownerships, walking distance to the nearest mass station, number of households, type of resident, property ownership, travel cost, and trip frequency. During the COVID-19 pandemic, people have concentrated on reducing travel time, reducing the number of transfers, and decreasing unnecessary trips. Consequently, people who live near mass transit stations less than 400 and 400–1000 m away prefer to live in residential and rural areas in the future. Structural Equation Modeling was used to confirm the relationship between attitudes in normal and pandemic situations. According to the findings, attitudes toward residential accessibility of travel modes were a significant determinant of attitudes toward residential location areas. This research demonstrates travelers’ and residents’ uncertain decision-making regarding relocation, allowing policymakers and transport authorities to better understand their behavior to improve transportation services