Out-of-home activity participation during the pandemic reduced drastically compared to the pre-pandemic period. The study explores the effect of the Stringency index, Economic support index, and Reproduction number on the mobility and activity participation of 33 Asian countries during 300 days of the pandemic. The study used a panel data model to investigate the effect of independent variables. The result shows larger Stringency index and Reproduction number were associated with lower mobility, and a larger Economic support index was associated with more mobility. The association of these variables with mobility seems to have reduced with time, and the subgroup analysis highlights the difference in the impact of variables among countries with different population density, GDP per capita, life expectancy, and extreme poverty. The findings suggest that the authorities can strengthen their efforts to provide safer activity participation by implementing effective policies and providing awareness about physical distancing.

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Hedonic pricing models show that house value capitalizes on risk factors, such as tsunami inundation zone, and properties at low risk have more value. People understood the impact of a tsunami on society after the Great East Japan Earthquake, 2011 and the subsequent tsunami caused by it. The revision of the estimated tsunami inundation after 2011 encouraged the residents of such areas to be prepared for future tsunamis. In this paper, based on the 2011 event, we examine the effects of tsunami risk on housing property values using spatial autoregressive error models. The results demonstrate that tsunami risk started negatively impacting the housing prices after the 2011 events. Considering the increasing natural disasters and population decline in Japan, citizens' housing preferences do not indicate support for compact city.

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Transportation infrastructures, as both enablers and victims of social movements, often become major political arenas of cities. In this article, we unravel the understudied role that social movements play in the formation of grassroots transport initiatives. We illustrate this role by synthesizing how grassroots efforts have created, diversified, and networked social innovations in urban transport systems in the 2019 Anti-Extradition Law Amendment Bill Movement in Hong Kong. We document the timeline of the movement and related solidarity activities, which led to various grassroots transport initiatives. We identify three lines of research that appear to be decisively informative to transport literature: a) emergency experiences, community capacities and mobility resilience, b) grassroots engagement in transport disruption management and c) community-oriented governance for a participatory smart city. These issues require particular attention in future research to guide transport policy on grassroots initiatives toward sustainable transport systems.

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Rising private vehicles use has given rise to many negative impacts on the global environment. This paper reviewed three main factors and their effects on the growth of vehicle ownership for countries with different economic levels based on a total of 60 studies published between 1997 and 2021. Income level was the most important factor alongside other socio-economic and demographic factors such as population density, household size, household employment, education level, age distribution and gender distribution. Factors related to urban developments include urbanization, road infrastructure, public transport and availability of parking space. Besides economic growth, this review also explains the effects of income inequality and road infrastructure development on the choice of using private vehicle. The findings reviewed in this paper sheds light on many of the underlying relationships among the factors that were found significant in explaining the variation of private mobility patterns from the global perspective. Several research gaps were also highlighted based on the findings.

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In March 2020 transport and economic activity in the Philippines was halted due to strict lockdown measures to mitigate the spread of COVID-19. As the economy slowly opened, safety measures included halving the occupancy of already capacity-strained public transport services. This resulted in commuters and essential workers seeking alternate modes of transportation, especially bicycles. Concurrently, national and local governments introduced pop-up bicycle lanes to accommodate the increasing demand. An online survey (255 respondents, August 2020) on awareness and perceptions of Filipinos of Environmentally Sustainable Transport (EST) was conducted to better understand possible contributions to a ″better normal″ transport situation and discussed applications of EST (active transport, walking, cycling, and e-vehicles) as a mobility response to COVID-19. Results showed 91% of respondents agree that their local government should consider implementing EST as part of ″better normal″transportation.

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The inbound tourism industry, can benefit the aging society, is very weak that can be easily affect by many kinds of reasons. Most of the past research analyzed from tourism travel intention and make improvement to the response policy to help to improve the recovery speed. However, there only few studies characteristic the length that respond policy need to have. Therefore, by totally targeting 60 target cases from WTTC's selection, this research focused on disease, natural disaster, rebellion, and terrorism in order to clarifies the recovery length and recovery probability at each time period after crisis occurred for these four types of crises. This research also came out 15 influential factors about crisis and original situation and used Weibull Distribution to examine the recovery probability. As the result, different spans of Business Continuity Planning had come out for each target types of crises.

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Origin-destination (OD) information is very useful for transportation management and planning. OD provides a detail information about traffic volume or passenger volume for each zone in the area. In developing countries, the paratransit mode produces varied OD data daily because it has stops at irregular nodes and times. This research uses Wi-Fi technology in to capture media access control (MAC) address passenger data during paratransit service. The objective of this study is to make OD or boarding alighting data based on Wi-Fi estimated. The result showed some characteristics and/or similarities with dates or routes. A high correlation is shown on all days for the Antang route (0.85) and on August 27, 2018, for the Antang route (0.79). For the accuracy of each Wi-Fi device result, A high correlation was obtained for the Antang, Cendrawasih, Daya, and Panampu routes.

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This paper considers the modeling of historical transportation accessibility and its socioeconomic influence in the long term, for mainly China Proper during the Sui-Tang period (Year 609~813). Population data of 329 preferences are obtained and the road network is reconstructed using data collected by Chu (2014) and Yen (1985). Different modes of travel and their speed are calculated. Following this, accessibilities with respect to heavy goods transport are obtained. Based on the computed accessibility, we discussed the contribution of the Grand Canal, and the influence of transportation on population dynamics. The accessibility increase induced by the Grand Canal can prove the primary construction purposes were served; and accessibility can work in both directions on population dynamics, which is to increase or decrease the population depending on the situation of wars and disasters. We conclude by discussing general lessons that can be learned from historical long-term studies

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The cost of road freight transport in the Philippines is perceived to be higher than its neighboring countries. Additionally, there is limited literature exploring the magnitude of this issue.

This study serves as a practical effort to quantify and analyze the high transport costs in the Philippines using comprehensive empirical data. It utilizes information from a Luzon-wide survey of 36 trucking and manufacturing companies operating along the country's main logistics corridors. Key findings reveal that at 9.7 US cents per ton-km, the cost of the transport of goods in the Philippines is among the highest within Southeast Asia. Further analysis indicates a possible correlation between the cost of road freight and the quality of road infrastructure, particularly the level of development of the expressway network. The results of this study aim to guide planners and policymakers in the Philippines to take the necessary steps to increase the country's trade competitiveness.

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The changes in travel-activity and well-being during the COVID-19 pandemic, as a response to mobility restriction policy implementation, has raise a question on how travel-activity participation after the mobility restriction lifted will be. This study investigated how the mobility restriction policy, through the changes of activity, travel, and well-being, influenced activitytravel participation after the policy lifted. For this purpose, we conducted an online survey during the outbreak in Indonesia (in May 2020) and performed a Structural Equation Modelling to investigate the travel behaviour changes after the mobility restriction was lifted. The model shows that increase of travel after mobility restriction policy was lifted is associated with greater reduction in travel and lower emotional well-being during the mobility restriction period. While younger people tend to increase their travel, high net worth individuals tend to increase their online activity after mobility restriction policy. This study suggested a strict health protocol for activity location, especially for social and leisure activity, increasing the quality of ICT infrastructure, and developing more green space near the residential areas.

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This study presents road users' preferences under congestion pricing as regards to alternative travel behavior responses in Hiroshima and Kumamoto, Japan. Utilizing real-time, context-aware, stated-preference survey data collected together with a two-week probe-person travel diary survey, we model the effects of congestion pricing on travel behavior responses, where explanatory variables include price, time saving, and egress distance to public transport. Using the developed model, we simulate behavioral changes to different congestion pricing schemes: cordon-based and distance-based. The results show that the distance-based scheme produced a higher level of spatial heterogeneity in the acceptance to pay the charge as compared to the cordon-based scheme, with relatively lower levels of probability for paying the charge at the center of the CBD for distance-based schemes. This indicates that distance-based schemes would be more preferable to reduce congestion in city central areas.

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The COVID-19 pandemic has adversely affected the transportation sector leading to a change in travel behaviour. One of the most important responses of countries worldwide to slow the spread of the pandemic has been to restrict the movement of people, which has had a considerable effect on transport systems and is likely to affect modal choices of commuters. To assess the likely nature of such a shift, this study attempts to understand the perception of commuters related to work trips and non-work trips before and after the lockdown phase due to COVID-19. The Ordered Logit Model is used in this study to find the association of health-related safety perceptions of commuters about private and public transport with socio-economic and travel characteristics under the COVID-19 scenario. Model results showed that significant factors for the decline in usage of public transport are age and income whereas for the increase in usage of private transport- physical distancing and hygiene are significant.

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To avoid affecting business activities from urban flooding, it is necessary to prepare for adaptation in the transport sector. In considering effective adaptation measures, firstly, it is important to understand the actual travel behaviors under flood situations. Also, it is essential for the evaluation of adaptation measures and decision of prioritization. Thus, this study focuses on the change in residents' travel behaviors and evaluation of adaptation measures in the transport sector. The data from the questionnaire-based survey was analyzed to understand the change of travel behaviors under the flooding condition. Then a traffic assignment model was developed to evaluate the raising of arterial road as an adaptation measure while reflecting the actual travel behaviors under the flood conditions. This found that the implementation in raising arterial road for the western bypass could reduce Vehicle Kilometer Traveled (VKT) by 60%, compared to flooding without taking any measure.

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Accessing traffic engineering data sources has presented new challenges and opportunities when adopting cloud-based novel data mining techniques. This paper proposes the use of crowdsourced travel time data obtained from Google Distance Matrix Application Programming Interface (API) as possible mechanism to incorporate with traffic congestion monitoring. Study further reviewed its usefulness in transport planning and evaluation of congested points in the high-volume road network. Multiple traffic engineering applications have been analyzed to understand the data characteristics. Data validated by using state of art License Plate Recognition (LPR) computer system for short-distance trips in high volume By-pass Road which has high mobility in central Saitama, Japan. Results illustrate reasonable agreement between travel time data obtained from the Google and LPR method with the representative gap over time. Successful validation of methodology enabled transport planners to improve traffic management and transport decision-making in regional transport planning.

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Policymakers in India are attempting to accelerate the trend towards using Electric Vehicles through schemes, and incentives, to promote cleaner energy; nevertheless, it is also necessary to comprehensively understand customer perception. This study explores factors affecting consumer willingness to adopt EV using national-level data while considering variations among different classes of cities using machine learning method and relative weights analysis. This study also uses a deep learning technique to predict the individual's willingness to spend on purchasing an EV. While education and employment status, coupled with technological advancements and policy benefits, positively impact EV purchase, the current service-related infrastructural conditions, operation, maintenance cost, and capital cost stand out as key adoption barriers. The awareness among the individuals varies within different classes of cities in India. The results can aid automotive companies to base their products and sales strategy and the policymakers to implement forthcoming EV and related policies.

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This study describes the efforts made to design and implement a smartphone-based activity-travel survey conducted in Jakarta, Indonesia, which is different from the existing surveys and their applications in the following ways: 1) it captured rich information regarding online activities; 2) it integrated context-aware stated preference (SP) surveys based on the revealed preference (RP) information collected from users, and 3) it was able to capture the trends in the use of multi-service transport platforms (MSTPs) and its effect on activity-travel behavior in a city from the global south. The key findings include (1) access to MSTPs is pivotal for users who are making decisions on trip making and online activity participation, and (2) an analysis using a panel mixed logit model on the choice of virtual and physical activities showed that the dominant factors on the choice of online activity engagement are (unobserved) individual attributes rather than contextual factors.

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We present a novel design framework for generating personalized incentives to promote sustainable travel choices and provide a preliminary report on the empirical results of the framework applied to Hiroshima, Japan. First, this study utilized probe-person survey data collected from the region to learn the value of travel time and the effect of cost parameters for each individual using a mixed logit model. Second, these results were then used to generate personalized incentives following a two part framework. In the first part, five attributes, including total amount of incentive, congestion adjustment, health benefits, environmental benefits, and information provision were varied across different levels and were used as building blocks for the second part, the optimization problem, where the optimal values of incentives which minimize the use of private vehicles were generated. We empirically tested the proposed framework and confirmed an overall decrease in the use of cars. However, only 6 out of the 28 people who participated in the experiment again showed a reduction in the average proportion of private vehicle use.

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A five-stage methodology is presented for the safety evaluation of road construction sites. Seven performance indicators are suggested for evaluation duly considering the nature of control and way of implementation of mitigations measures. Sixty seven mitigation measures are considered under seven performance indicators to enrich the evaluation of sites against potential hazards. The overall safety score of a construction site is derived following a systematic and stepwise hierarchical approach. The methodology is applied successfully to four road construction sites in India. Major safety deficiencies are observed in the areas of barricading, temporary traffic control, and workforce management. Substantial variation is also found in the overall safety scores of the construction sites evaluated in the present work. The methodology and case specific findings, as presented in the paper, are expected to be of interest to researchers and practicing engineers working on improvement of safety of road construction sites in developing countries.

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Since the Covid-19 pandemic has affected many countries in the world, many countries have taken unusual steps of partial or full lockdowns. While grocery shopping is an essential activity, not much is known about the relationship of the pandemic to the travel behaviour of the shoppers. Therefore, an online survey was used as a data collection strategy. The data was analysed in 4 countries, namely Malaysia, Indonesia, Japan and Austria. Apart from descriptive analysis, interrelationships between different elements were exploratorily modelled with structural equation modelling. Generally, during the first phase of the Covid-19 pandemic, changes in commuting and grocery shopping behaviour were observed regardless of the age. People changed their shopping behaviour to some extent, including buying larger amount than before or using online grocery stores more often. In terms of travel mode, large extent of the people continued to use their regular travel modes to stores.

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In this era of total quality assurance, every organization should make efforts to give as much good service as possible. Indian Railway (IR) is one of the most used public transport in India and services provided at the railway platforms are as important as services provided inside the vehicle. This study only focuses on the service quality of services provided at platform no. 1 of Tiruchirappalli Junction, the second-largest junction in Tamil Nadu. Three hundred samples have been collected through a web-based survey and on-board data collection. The users' satisfaction towards various services is measured using 22 different indicators of 5 constructs and the importance of those services. An Importance-Satisfaction (IS) analysis was carried out to identify services/factors that require improvement on a priority basis. A structural equation model was developed to identify the relationship between various variables and the service quality.

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The Taiwanese government is planning a Mobility as a Service (MaaS) program to alleviate traffic congestion during weekends and holidays in Yilan County. In order to manage the complex features associated with MaaS markets, we apply the concept of broker and develop a bundle-pricing model to solve the MaaS package allocation problem by maximizing the MaaS broker's profit. The ultimate goal of this study is to provide different stakeholders with reasonable returns in a cost-effective manner. The empirical study results indicate that the bundle-pricing package strategy is beneficial to the MaaS broker and tourists. In addition, we conduct a sensitivity analysis by varying model parameters related to customer consumption and cost of MaaS packages. The sensitivity analysis results show that both customer consumption and cost of MaaS packages have a negative effect on the MaaS broker's profit. In contrast, customer consumption has a positive effect and cost of MaaS packages have a negative effect on the customer's surplus. These findings have policy implications for both the government and operators for a smooth deployment of related MaaS programs.

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Accessibility is an important outcome of public transport (PT) planning. Many existing studies have assessed PT accessibility based on the time impedance between stop pairs; however, few have addressed the variety of choice preference. To bridge this gap, this study applied the concept of choice complexity and network science by topological graph to compute the expected travel time from a set of attractive lines as a new type of weighted graph to evaluate accessibility. Here we introduce hyperpath-based time impedance consisting of in-vehicle time, expected wait time, and transfer time penalty costs. To confirm our study approach, we conducted a general comparison of the resulting accessibility using two methods: our proposed approach and a method presented in an earlier report. The results revealed that the proposed approach minimises the expected travel time to more realistic time frames for passengers waiting at PT stops, resulting in more accurate accessibility evaluations.

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When introducing rail-based public transport in developing countries, low fare prices are adopted from the viewpoint of public services which becomes a major operational obstacle. However, the rail transit systems in Bangkok have many passengers, despite of very high fare price. This paper examines the characteristics and behavior of public transportation users and analyzes their sensitivity to change in the fare price. For the self-elasticity, it is found that public transport users are generally inelastic. The value of the elasticity range is from -0.16 to -0.83 for various public transport modes. Various factors can affect public transit elasticities such as income level, ticket type, and access mode. The cross-elasticity of railway users in response to a price change of bus and van is 0.33, and to a change of taxi service is 0.62. Overall, the results revealed the possibility that Bangkok public transit can increase profitability by raising fares.

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With the increasing attention of population ageing, transportation services for the elderly has been one of the concerns in recent years. Among others, hospital access service (HAS) is a challenging issue in an ageing and/or aged society. Recent literature has been exploring the possibility of applying Autonomous Vehicle (AV) technologies for the HAS problem in view of their merits of manpower saving and technical advancement. The purpose of this study is to investigate the potential and financial feasibility of integrating AV into an HAS scheme. In the numerical analysis, this study developed an agent-based model to simulate the operational performance of the proposed AV-HAS scheme under a simplified grid network. The simulation results indicate that power efficiency of EVs can be negligible for an AV transportation system, but the initial cost has a great influence on the financial sustainability of an AV-based hospital access system.

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Logit-based stochastic user equilibrium (SUE) traffic assignment plays a vital role in the range of traffic assignment models. The logit-based SUE problem could be resolved by using some efficient algorithms in which the link-based approaches avoiding route enumeration are preferable. To contribute to the system of these algorithms, this paper proposes the new algorithm based on the Depth-first search algorithm with the method of successive averages for solving the logit-based SUE problem. On one hand, the proposed algorithm could keep the advantage of preventing the route variables that reduces computation storage, on the other hand, it is easy to apply in practice and will also reduce the computation time in some cases of testing. Besides, the new algorithm is more applicable in some extensions of the logit-based model. In this study, Kanazawa city urban of Japan application is adopted to demonstrate the effectiveness of the proposed method.

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Urban rezoning and redevelopment may change the type and amount of activities and travel demand, furthermore, it may affect the performance of transportation systems. Therefore, it is essential to understand the effect of land use on travel demand. Based on this, this study aims to explore the hourly travel demand patterns under various land uses. Additionally, this study uses cellular signal data for hourly origin-destination matrices, based on the zoning of villages. Three models for trip departures and arrivals are developed and compared. A case study based in Taipei City shows that the daily model (daily trip and time-of-day clustering patterns) performs best in terms of the accuracy of predictions of travel demand. Model results show that travel pattern and demand are highly related to land use. These results provide an evidence base upon which operators, planners and government sectors will be able to plan for future cities and to predict travel demand. This can be achieved by taking land use impacts into consideration.

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In the Philippines, public transport terminals are seldom located based on housing data. As a significant trip generator, universities require sustainable transport options such as shuttle services. This study aimed to analyze the off-campus UP Diliman population to identify potential shuttle terminals through cluster analysis. These were then subjected to a user preference survey to see how good an indicator the population distribution is. For areas near campus such as Teacher’s Village and Loyola Heights, the cluster analysis was accurate as they were among the respondents’ top choices. This was also true for areas far from campus but with moderate UPD population such as Novaliches and Antipolo. However, user preference reveals that areas without high UPD populations can also be effective terminals if they have good access to public transportation. The same can be said for CBDs due to more work-to-campus trips instead of home-based ones.

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Research involves collection of Revealed and Stated preference data and analyze intercity mode choice behavior using an econometric model. Discrete choice models, multinomial logit model and nested logit model, are used to analyze SP & RP data. Mode choice modeling studies provide reliable estimates on mode shift into a new mode and establish a strong basis for setting fare structure and service attributes. This study first explores the mode choice scenario after railway is introduced and then builds up evidence for railway, a mode, for intercity travel sustainability. It is found that the mode choice behavior of intercity travelers in Nepal is influenced by time, cost, distance, travel partner, cost bearer, age, income, car ownership, and family size. Moreover, the utility of travel mode is found more sensitive towards time than cost. Finally, it is concluded that demand and preference for railways is relatively higher than existing modes.

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In pavement management, traffic data is important. Time series-based traffic projection method, which is used widely by the Indonesian Road Agency, requires a lot of data. Consequently for the purpose of network-wide pavement management system, traffic data to be collected are in each particular link roads in every single years. These activities require large of resources. However, time-series projection method are not sensitives to the changes of physical and operational road network and the change in land use pattern. It is important therefore to improve the traffic projection method. One method that is sensitive to changes in the land use area and road network is a four-stage transportation modeling, where data requirements are a) road network characteristics, b) land use pattern and c) Origin Destination Matrix (ODM). The advantage of this method is that the results are more accurate and the amount of data required is less. The main problem is in providing data ODM for the base year. This paper attempts to determine the optimal number of traffic survey locations, using a case study of East Java Road Network, Indonesia. The results show that reducing the number of traffic data points can be done while improving the accuracy of traffic projections.

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The 2019 opening of the main facility of a redevelopment project in Kumamoto City should have a significant effect on the number of visitors to the center of Kumamoto City as well as their excursion behavior. In this study, we investigated the actual state of excursion behavior in the city center by conducting surveys and developing an excursion behavior model according to the survey data. Then, we tested the temporal transferability of the model estimated according to past survey data to predict future excursion behavior. The results indicated that the number of visitors entering this area significantly increased and that the pattern of excursion behavior dramatically changed after the facility opened. The hypothesis of temporal transferability was statistically rejected.

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The paper introduces a method for acquiring trip behaviors within walking distance by means of multiple big data. First, an optimal set of big data is selected from possible sets of big data in the transport sector to estimate the trip behaviors. Second, the authors propose a method of estimating trip volume and trip modes. Finally, the proposed method is applied to a case study that has been carried out at Tachikawa Station of the Japan Railway Central Line in Tokyo in order to validate the proposed method. A field test with the use of the Wi-Fi packet sensor was conducted at 11 locations including stores and traffic nodes on 1st of September 2018．The estimated trips were nearly the same as the actual situations. The authors have demonstrated the possibility of using Wi-Fi packet sensor data and Mobile Phone Location Data to acquire trip behaviors within walking distance.

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Over the years, various studies have been made to determine how a person’s travel behavior and its driving forces. Precedent studies stated that psychological mechanism combined with space-time variables contributed to people’s travel behavior. Integration between social psychological mechanism and spatiotemporal variables is supposed to describe the difficulty in shifting people to public transportation. Within severe traffic congestion particularly in cities of developing countries, the presence of public transportations has been mentioned in numerous studies to be one of the solutions for traffic congestion. Therefore, initiatives to increase its usage has been promoted diligently. However, research in travel behavior has been focusing on shifting motorized mode users to public transportations without considering the effect of psychological mechanism and spatiotemporal variables. This paper showed evidence supporting relations of spatiotemporal variables and psychological mechanisms in public transportations usage.

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Road networks are among the social capitals that were constructed during a period of high economic growth in Japan; however, they have recently deteriorated, and the countermeasures have created challenges. Therefore, an increase in future long-term road facility rehabilitation is to be expected. During rehabilitation, lane closure and link closure will occur, and traffic capacity will be reduced over a long period. Therefore, it is necessary to consider the impact on users of the entire road network in terms of detours and congestion. In this study, it is assumed that long-term road facility rehabilitation is planned at multiple road sections in the network. Considering the combination of long-term road facility rehabilitation and regulation methods, this problem is described as a network design problem, and a model can solve the traffic restriction schedule of long-term road facility rehabilitation to minimize its impact on users.

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Long-term traffic regulations with the reduction of the vehicle travel space, such as regulations for update of the aging infrastructure and bus exclusive lane regulations, are widely introduced all over the world. Due to such long-term traffic regulations, drivers take the corresponding behaviors, and the corresponding behaviors as well as the capacity reduction due to the traffic regulation will have an influence on the transportation network. This paper evaluates the impact of bus exclusive lane regulation onto both drivers’ behavior and the traffic network using a transportation network analysis framework. As a result, it was confirmed that the uncertainty that drivers perceived about the road congestion was increasing immediately after the introduction of the bus exclusive lane. It was also confirmed that the network capacity after the introduction of the regulation is drastically reduced.

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Transportation planning models play an important role in development of transportation systems. These models are calibrated before they are used. Usually, calibration cannot be done accurately in practice because, first, the collected data may be sparse and noisy, hence, insufficient for an accurate model calibration; second, there might be some inconsistency due to the disjoint calibration of the models. This study aims to address both issues by simultaneously calibrating demand and supply models using real-world multi-source data. To illustrate the performance of the method, it is applied to Tuen Mun network, which is a corridor network of Hong Kong transportation system. It is shown that the proposed approach can fit the observed data well (flow error 1.00% and time error 5.95%). Potentially, the procedure described in this study may facilitate policy makers with a structured and robust way of assessing traffic demand and improving supply services.

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Philippine maritime transport serves as an important corridor for international and domestic trade. In order to support the growing demand for trade, port authorities have been under pressure to improve port efficiency and make services more competitive to accommodate future demand. This paper studies the performance of different forecasting methods for the cargo throughput demand of the Manila International Container Terminal (MICT). Three forecasting methods were implemented and compared, namely Autoregressive Integrated Moving-Average model (ARIMA), Artificial Neural Network (ANN) model, and Vector Autoregression (VAR) model. Macro-economic variables were used as exploratory variables. The predicted results were compared using the coefficient of determination (R²), and root mean squared error (RMSE). Results show that all three forecasting models exhibit goodness of fit and accuracy of results according to their R² and RMSE values. In comparison, the VAR model is the most accurate among the three followed by the ANN and ARIMA models, respectively.

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The ridesharing is discussed in the wide range of road network policy: efficient road space usage and smooth evacuation under limited drivers. This study proposed a ridesharing traffic flow model based on the dynamic network loading (DNL) problem, formulated as a system of differential algebraic equations following Han et al. (2019). The ridesharing behavior in this study is represented by using the pick-up link and the through link set at each point in a network where travelers wait for vehicles picking them up. The proposed model minimizes the total travel time by optimizing the path choice probabilities dynamically across the whole road network. The model is formulated based on the link transmission model; thus, the calculation process is efficient. Finally, the numerical calculation is performed in a test network to verify the proposed model.

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The built environment could influence travel behaviour, but studies related to the influence of the built environment on travel behaviour in the periphery area and city centre have not been widely explored. To fill this gap, in this study, to what extent the influence of the built environment on travel behaviour in the periphery area and the city centre was explained using multinomial logit and linear regression. Travel behaviour is defined by choice of mode and trip length. Mode choice in this study consisted of motorbikes, cars, paratransit, transit, and active transport. The built environment is measured by density, diversity, design, destination accessibility, and distance to transit. The results showed that density and destination accessibility had a significant effect on the probability of using transit and the length of the trip. We further found that respondents located in the periphery area tend to travel longer than their counterpart.

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In Japan, many facilities such as Michi-no-eki, convenience stores, shopping centers, and parks on ordinary roads are used for stopping for rest during long-distance trips. Michi-no-eki, in particular, serves the function of stopping for rest and time adjustment in the transportation network; hence, they should be accurately located. However, there has been little discussion on their placement, and there are no guidelines for their placement as rest facilities. In this study, we conducted a questionnaire survey to obtain information on travelers’ preferences and strategies for stopping at roadside facilities, to develop an index for evaluating the resting function of all facilities that can be candidates for resting and stopping at roadside facilities. Through the analysis of the results of the questionnaire survey, we aim to elucidate the decision-making process for the use of roadside facilities by ordinary road users and to obtain useful knowledge for creating a model that defines the strategic establishment of Michi-no-eki.

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This paper formulates a nonlinear shared taxi model of the ridesharing sharing money considering the passengers' benefits and drivers. The model explores the concept of a shared taxi to minimize the total trip fare of passengers and the number of taxis utilized while maximizing customer satisfaction, thereby increasing the taxi demand and driver’s income. The problem solves the Branch and Cut algorithm employed for estimating the parameters. The computational results obtained by the Gurobi solver show that the proposed mathematical model can find the shortest route of taxi routing decisions that can minimize the customers' total trip cost. This study is shown, based on real taxi valid data for the Sanjo city, Japan. Moreover, it evaluates the effect of various taxi-sharing practices, e.g., waiting time and fare decrease. Furthermore, the sensitivity of significant parameters such as time window, initial fare, and travel distance in the fixed fare is analyzed.

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An efficient mass rapid transit system in metropolitan cities is believed to be able to solve many transport-related issues such as congestion and emissions. However, some of the developing cities in Southeast Asia have to develop mass rapid transit systems effectively under a limited budget constraint to meet the ever-increasing travel demand. In this study, the demand and supply gaps in Jakarta, Indonesia, and Bangkok, Thailand were calculated by utilizing OD data and network data of respective cities using a normalized index approach. The results show that the proposed calculation method is appropriate, a comparison between each city with the proposed new network plan was done. In conclusion, this study has clarified the importance of estimating the demand and supply gaps in transportation planning. Nevertheless, further study is to be expected for this analysis to be able to evaluate a more detailed policy.

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A heterogeneous mixture of vehicle types and the absence of lane discipline are common elements of traffic congestion in developing countries. The foremost objective of this study is to exercise the road space reassignment technique in case of mixed traffic to mitigate traffic congestion while emphasizing the importance of dedicated lanes. The methodology is based on these steps: First, determined the relationship between traffic demand and travel time through regression analysis using micro-simulation results for five different road-lanes cases. Second, a user equilibrium mode assignment model was performed to calculate each passenger’s travel time and also witnessed a possible shift of personal vehicle users to the bus lane based on the analogy of the user equilibrium traffic route assignment model. The dedicated bus lane resulted in 62% reduction in travel time, and almost 78% of passengers shifted their traveling mode from personal vehicles to buses.

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Hyperaging and depopulation are two of the main pressing issues due to sustained low fertility and mortality in Japan as well as in many other countries. The dramatic growth of the elderly population is expected to affect the urban and transport system significantly. A better understanding on daily mobility patterns of seniors is important for aging society. This study investigates the determinants of daily travel time expenditure and travel mode choice for the elderly as compared with young adults. A duration model and a multinomial logit model are respectively estimated by applying the Ome personal trip survey data. The results indicate not only common determinants but also highlights a significant influence of the built environment on the elderly travel patterns. Meanwhile, the results provide some insights for policymakers into promoting public transport use and active travel mode such as cycling and walking.

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This study analyses the factors influencing the behaviour of travellers before and after the construction of a new bridge in the Jayapura, Indonesia. This study used panel data obtained from the stated preference experiment and applied the latent class discrete model to determine the route choice. The latent class model results revealed that the survey respondents were divided into three clusters (groups/cohorts): convenient choice free traveller, a group of regular daily travellers such as commuters (45 per cent); captive travellers, most of whom depend on public transport or only orient with their existing vehicles (47 per cent); and conservative travellers, a group of travellers whose trips were casual and/or irregular (8 per cent). This research showed that there was a structural change between classes before and after the opening of the new bridge. The results of the study will assist local authorities to formulate appropriate land use and transport policies.

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With the rapid development of information and communication technologies in recent years, developing better ways to utilize the large amount of data has become an important research agenda in urban and transportation planning. In this study, we (1) develop a method for estimating the role of each station/bus stop in a city by quantifying the regularity of public transport usage at the station/bus stop level using smart card data, (2) calculate the diversity index using the regularity information, which is defined as “the degree to which there is a mixture of people who use public transportation at various frequencies”, and (3) analyze the relationship between the diversity index with land use patterns and accessibility of stations/bus stops. Our empirical analysis confirms that improved accessibility encourages diversified use of public transport systems, that may contribute to the stabilization of travel demand and hence the stabilization of public transport services.

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Travel demand surges related to long-weekend holidays have clogged the entire national highway system in Taiwan, resulting in excessively prolonged travel times. As such, a large-scale simulation-based dynamic traffic assignment (DTA) was developed to evaluate various strategies and more accurately analyze their effect on system congestion. For a large-scale nationwide DTA model, obtaining demand data that is contextually relevant to long-weekend scenarios is challenging. To address this challenge, the use of cellular signaling data was explored. This paper first discusses converting the latest-generation cellular signaling data to high-fidelity trip chain data. Secondly, the process of extracting trip chain data for specific periods to develop time-dependent origin-destination matrices required for the DTA model. Model validation results indicate mean absolute percentage error (MAPE) of volume, travel time, travel speed ranging between 3.54% to 45.25%, which is deemed satisfactory for such large-scale network and data variability. A cast study on Freeway No. 5 illustrates the application of the model, and the result shows the ability to travel demand management during long-holiday.

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Transportation system sustainability is important in the transportation planning process. Recently, the emergence of ridesharing has reshaped people’s travel choice behavior. However, it is unclear that ridesharing can contribute to sustainable transportation, particularly in cities with a multi-modal transportation system. Two questions need us to answer: (1) how the ridesharing service affects the choice-making of travellers in a multi-modal transportation system, and (2) whether ridesharing is a sustainable alternative mode to transit. To answer these two questions, a network equilibrium approach with ridesharing option is developed. For network performance assessment, emission and total travel cost indicators are adopted to evaluate the transportation system sustainability. Numerical analysis is provided to demonstrate the features of the proposed model. The results show that we cannot simply determine that ridesharing is more or less sustainable than transit for general cases as they strongly depend on the spatial distribution of driver and passenger travel demand.

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The BRT system is widely introduced in developing cities and becoming very popular. It is the most affordable and relatively fast solution for city governments to provide mass public transport system services. This paper discusses a paradigm change towards Semarang BRT service, from previously corridor-based to direct service-oriented. This approach is conducted by developing its current service line by considering the urban spatial patterns, structure, and land use development to optimize service coverage. Moreover, an exclusive lane for BRT operation is attached to ensure the system will run effectively. Finally, real demand surveys were conducted to estimate changes in BRT ridership. The BRT operation improvement can attract more passengers with more excellent service coverage based on the demand forecast.

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In this study, a panel data survey covering multiple time points and analysis, by applying the technology acceptance model based on structural equation modeling, were conducted to understand the changes in the psychological mechanisms of using electronic commerce (EC) by Japanese consumers before and after the COVID-19 outbreak. Evidently, compared with the number of people before the outbreak, those who considered EC essential immediately after the COVID-19 outbreak increased significantly. The percentage of people who considered EC essential did not decrease even after a certain period since the COVID-19 outbreak. On the other hand, the total effect of the COVID-19 related latent variable on the EC usage decreased after a certain period compared with that immediately after the outbreak. Furthermore, after a certain period since the COVID-19 outbreak, the necessity of EC decreases for those who are not aware of its convenience. Some people use EC not only because of its convenience, but also because of the subjective norm of whether other people around them are using it. According to the analysis by the city size of the residential area, subjective norm significantly influences the intention to use EC, especially in areas with small populations.

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The transportation sector and travel behavior have a significant role in the cities’ economic functioning and growth. Jaipur has been the economic and political centre of Rajasthan. Urbanization, increasing population and travel demand, and a surge of private vehicles necessitate studying travel behavior in Jaipur, India. The study focuses on understanding the travel behavior and to analyze the scope of public transport in commute to the workplace. Data has been collected through an online questionnaire survey having sections pertaining to demographics, residential and workplace location, availability of social and physical infrastructure, neighborhood quality, and travel diary were. In addition, data related to travel time has been collected through Google maps. The binary logistic regression has been estimated to showcase the relationship of public transport mode with significant variables. The study suggests incorporating an appropriate framework to implement informed policy decisions for integrated growth and to boost public transportation usage.

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