Case Studies on Transport Policy最新文献

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.

Toll roads with fully dynamic pricing schemes could play a key role in addressing the economic and environmental tolls imposed by increasing congestion levels in major metropolitan areas. Transportation professionals usually argue that they could provide additional revenue streams to accelerate the delivery of much-needed highway projects and serve as a tool for travel demand management. However, the number of dynamic pricing toll roads only increased recently in the U.S., and their performance has not been empirically evaluated systematically. This study provides a comprehensive review of U.S. dynamic pricing toll roads. Pricing is reactive to demand, and additional travel demand management strategies such as incentives for high occupancy vehicles, clean vehicles, and transit are commonly included. Both conventional project delivery methods and private–public partnerships have been used for such projects. Financial statements from a few P3 dynamic toll road projects suggest they are taking full advantage of TIFIA loans and are performing well during their short history. Equity issues are commonly raised, and many projects address them specifically through subsidies to vulnerable groups or re-investment in the community. Further quantitative analyses are needed to test if dynamic toll roads are an efficient tool to address the financial, economic, and environmental challenges major metropolitan areas face.

Airport noise assessment and mitigation have been recognized as major challenges in the current civil aviation context. This paper aims to provide a general, simple, and flexible methodology to approximate airport noise-influenced zones and quantify the social cost of noise pollution. The proposed methodology performs this assessment without the need for specific software, monitoring stations, and sophisticated data. Airport noise-influenced zones are estimated by relying on publicly available aircraft certification data, while the social cost of such estimation is computed by taking into account the distribution of residential units located within zones affected by noise. We present an application of this method to a group of Italian and Spanish airports, as well as possible beneficial policy interventions in terms of minimization of noise impact on the population living in the airport neighborhoods. In addition, possible mitigation policies are presented in the form of noise surcharges applied to different aircraft categories.

The international outbreak of the new coronavirus (COVID-19) at the end of 2019 has caused an indelible impact on the global economy. However, it has grown against the trend in unmanned distribution and online consumption. In recent years, the maritime industry has gradually introduced artificial intelligence, blockchain, the Internet of Things, and big data. Through literature review and expert interviews, this research figures out the development status of smart ports at home and abroad and the digital development barriers of ports and then uses the Fuzzy Delphi and the Fuzzy Analytic Hierarchy Process method to identify three dimensions and thirteen key factors target companies. The survey targets shipping companies, shipping agencies, and port management companies. The paper conducted the questionnaires to identify the critical success factors of port digitization. It was found that the “digital solutions” dimension had the highest weight value among the three dimensions and the top five critical factors in the overall ranking were based on the order of “carbon emission management,” “enhancing port safety”, “technology standardization,” “digital asset management,” and “optimization of the supply chain.” The result of this study can help port operators and shipping companies identify critical success factors of smart ports and adopt port digital development strategies for operation efficiency improvement and climate change mitigation.