Case Studies on Transport Policy最新文献

Efficient use of transport systems can increase well-being and ensure benefits for society. Reducing the costs involved in transport operations can occur through management, investments, or using more efficient infrastructures. Well-being gains are weighted based on economic theory, particularly with the classical welfare function that measures consumer surplus gains. Therefore, this article aims to analyse the economic well-being of freight transport companies with disaggregated data for Brazilian states. Data from a stated preference survey were used for the entire Brazilian territory. We estimate logit models to determine the value of travel time, and a logsum measure was used to calculate differences in well-being for freight transport companies. The results show the heterogeneity of Brazilian shippers when choosing the mode of mode of transport for freight transport companies. Research deepens the investigations underway in Brazil, disaggregating the analysis at the state level and simulating different scenarios to describe well-being and the value of travel time. The results show significant differences in the choice of road modes compared to their competing modes by region in Brazil. The findings indicate that states may have greater marginal benefits in well-being with changes in transport costs, transport time, delivery reliability, and flexibility.

This study critically examines the influence of freeway toll pricing on the travel behavior of intercity commuters in Iran, focusing on mode choice, route selection, and departure times. A survey of 921 commuters was conducted to gather data, which was then analyzed using a mixed logit model. The key findings reveal that a one percent increase in toll prices leads to a 1.5101 percent decrease in the likelihood of commuters maintaining their usual travel mode. Conversely, the probability of selecting alternative travel options rises by 2.5129 percent. A notable change is observed in route choice, with a 1000 toman increase in tolls raising the likelihood of route change by 0.320 percent. Furthermore, increased tolls are associated with a higher probability of changes in travel mode (0.1188%) and departure time (0.1078%). This research highlights the significant behavioral shifts among commuters in response to toll pricing adjustments and underscores the need for strategic toll management in transportation planning.

Sun Moon Lake is a famous tourist attraction in Taiwan and around the world. However, as Sun Moon Lake is surrounded by mountains and has limited land to develop, traffic congestion around the lake area is commonplace during peak holiday hours. This study focuses on the parking choices of visitors to Sun Moon Lake and develops a stated preference (SP) instrument with multiple scenarios to evaluate parking preferences under various financial and service level scenarios. We estimate Multinomial Logit (MNL) and Mixed Logit (ML) models, accounting for the panel nature of the data (PDML)to identify preferences for parking choices of visitors to Sun Moon Lake. The focus is on understanding how parking price, travel time, walking time, scenery, and transfers between public transport affect visitors’ parking choices. Unlike the findings of studies in metropolitan areas, which often find that parking price was the deciding factor, visitors’ parking decisions in the tourist area were more concerned with time factors, such as the time to search for places to park and traffic congestion, possibly due to the less frequent use of tourist venues. Although raising parking price can suppress parking demand in the scenic area, other parking management mechanisms work better, such as the construction of new and suitable outer parking lots with transfer buses to relieve the heavily congested traffic in the scenic area. In addition, we find that using the parking space in the area can be improved by beautifying the landscaping between the parking lots and the tourist spots, enhancing the pleasure of traveling along the routes, introducing multiple transfer modes, and providing real-time traffic information to tourists.

Rapid urbanization and globalization have resulted in intolerable congestion and traffic, necessitating the investigation of Intelligent Transportation Systems (ITS). ITS employs advanced technologies to address modern transportation challenges, aiming to create smarter, faster, and safer transportation networks. Increased data availability and the emergence of Artificial Intelligence (AI) and Big Data have enabled ITS gain significant attention in recent years. The integration of AI and Big Data contributes significantly to ITS development, optimizing traffic planning, forecasting, and management, and concurrently reducing transportation costs by enhancing the performance of public transportation, ride-sharing, and smart parking. This survey paper performs a systematic study and comprehensive exploration of the synergistic integration of Big Data and Artificial Intelligence (AI) in Intelligent Transportation Systems (ITS). By elucidating the underlying principles, the paper emphasizes the transformative potential of these technologies in addressing contemporary challenges in transportation. It innovatively delves into specific ITS application domains, including traffic flow forecasting, congestion management, and intelligent routing, offering a detailed analysis of how the amalgamation of Big Data and AI enhances efficiency across various facets of modern transportation systems. The survey not only highlights the benefits of this integration in terms of efficient traffic planning and reduced transportation costs but also delves into the associated challenges, including data collection, data privacy, security, computational complexity, and algorithmic scalability. Furthermore, it contributes valuable insights by proposing potential solutions and suggesting future research directions to enhance effectiveness of big data and AI algorithms in the realm of ITS.

Public transport information imbalances are rife in cities with hybrid systems composed of scheduled and unscheduled modes, hindering users’ ability to access mobility. Though private and public entities alike are seeking information-based technological solutions to aid users to navigate these systems, there is still little understanding of what information users need to navigate these complex hybrid systems. Particularly for captive public transport users who do not have access to private alternative means of travel, access to relevant information across all modes could enable access to information on trips that better suit their needs and preferences. Through semi-structured interviews followed by a best-worst scaling survey with captive public transport users in Cape Town, South Africa, this study investigates what information users need to plan non-routine hybrid journeys. Information needs are extensive, ranging beyond that which is publicly offered, not only on available transport services in isolation, but also across these collective services. This paper provides a method for investigating the information needs of users to enable policy makers to better align information and data strategies to support the integration of hybrid public transport systems through passenger information.

This paper develops an integrated urban modelling framework (IUMF) to predict how work from home (WFH) decision affects travel behavior. First, it conducts a questionnaire survey among working professionals in Halifax, Canada, to collect data on their socio-demographic characteristics, mode choice, vehicle ownership, and work-arrangement. Bayesian Belief network models are developed using the collected responses to calculate the cumulative probability tables (CPTs) of variables associated with the decision to WFH. Next, the ascertained CPTs are used as input to extend an integrated urban modelling framework (IUMF) that is further utilized to simulate individuals’ work from home choices and travel behavior up to 2025 for Halifax, Canada. Results indicate that around 57% of the workers would like to WFH and 7% wants to relocate closer to workplace. The model forecasts a significant preference for remote work among individuals with offices in the urban core. Results also show that auto mode share is increased to 79% in 2024, whereas transit, walking and biking trips decreased. Average travel distance is higher in the post-pandemic compared to the pre-pandemic, while travel distance of telecommuters is found to be higher than non-telecommuters. Statistically significant differences are observed between telecommuters and non-telecommuters for ‘number of activities’ and ‘distance travelled’ in a day. The outcomes of this study will offer policy makers a better understanding of long-term impacts of WFH on transport and land-use systems and help to develop effective travel demand management strategies.

Recent research has reported travel behaviour changes during the COVID-19 pandemic. Speculatively, these short-term disruptions in travel may lead to new habit formation and longer-term travel behaviour changes among young adults belonging to generations Y and Z. Focusing on post-secondary students within Greater Toronto and Hamilton Area, Canada and using longitudinal data collected in fall 2019 and spring 2022, our exploratory study examined the post-COVID-19 travel behaviour changes and analyzed whether these changes are associated with their socio-demographic characteristics and life events experienced over the course of pandemic. Results show that many public transit users and active travellers (pedestrians and cyclists) switched to cars for commuting post-pandemic. The post-pandemic retention of public transit use was lower compared to cars, while active transportation modes had the lowest post-pandemic retention rate. Some socio-demographic characteristics such as age, living situation, work hours and access to cars were significantly associated with these changes. In terms of life events, students who joined workforce after completion of education between 2019 and 2022 were more likely to shift their commute mode from public transit to cars, implying some influence of this life event on commute mode changes, in addition to pandemic-induced changes. Our findings suggest that the post-pandemic commute mode changes observed among young adults in the GTHA may not be a result of only COVID-19 pandemic and may also be partly associated with important life events that they experienced over the course of pandemic. Future transportation planning and policy implications, and directions for future research have been discussed.

This study aims to examine the relationships between the Liner Shipping Connectivity Index and foreign trade volumes in Brazil, China, India, Russia, Türkiye, and South Africa. In pursuit of this objective, causality relationships among the variables were examined using the bootstrap panel causality test with data 2006–2021. The analysis findings indicate a positive and bidirectional causality relationship between the connectivity to global maritime networks and exports in Brazil and a positive and unidirectional causality relationship from the connectivity to global maritime networks and exports in Türkiye. Regarding the relationships between the connectivity to global maritime networks and imports, the analysis findings reveal a negative and unidirectional causality relationship from imports to the connectivity to global maritime networks in China, India, and Russia. However, in Türkiye, a positive and unidirectional causality relationship was identified from the connectivity to global maritime networks to imports.

India’s transition to electric vehicles has entered its second decade. The government has set a target of having EV sales accounting for 30 % of private cars and 80 % for two-wheelers by 2030. However, despite several efforts of government and industry, the penetration of electric vehicles till-date has not been as per the set targets. This study aims to estimate the end-user demand and adoption timeframe of electric 4-wheelers (e-4 W) and 2-wheelers (e-2 W) in India’s four large metropolitan areas. Binary logit choice models are developed based on a discrete choice experiment carried out by utilizing 2,400 face-to-face interview responses. In addition, ordered logit models are developed to assess the adoption timeframe of the EVs. The study results show a significant geographic variation in demand for e-4Ws and e-2Ws within India. This demand is also driven by vehicle attributes, demographics, infrastructural elements, and user attitudes. Existing vehicle owners are more likely to purchase an EV in the future, and are also likely to drive/ride it more. In addition, consumers who are young and wealthy, and living in homes with dedicated parking spaces are more likely to be early adopters of EVs. These findings would assist policymakers in designing a tailormade and phased EV implementation scheme in India.

The insight into origin–destination (OD) demand patterns aids transport planners in making the public transit system more efficient and attractive. This may encourage individuals to shift from private vehicles to public transit, easing the burden on traffic and its negative impacts. Hence, to know how OD demand is going to vary in future, a state-of-the-art OD demand prediction model needs to be developed. Previously, studies have developed zone-based prediction models which may not be appropriate for predicting OD demand within a route of public transit. Additionally, spatial correlations between the stops of public transit must be included in the model for improved forecasting accuracy. Hence, in an effort to fulfil these gaps, a Graph Convolutional Neural Network (GCN) is developed to forecast the OD demand of public bus transit with nodes being the bus stops and links between them representing the passenger flow between the stops. Land use around the bus stops is retrieved as a node feature and included in the model to account for the spatial correlation between the stops. The model is trained using a real-life dataset from the public bus service of Davangere city located in India. Land use around the bus stops is extracted from the Davangere city master plan, procured from the urban development authority. The developed model is compared with conventional models and the findings show that the GCN model performs better in terms of prediction accuracy than the baseline models. Additionally, at the stop level, the performance of the model remained stable due to the inclusion of land use data compared to conventional models where land use data was not considered.