Case Studies on Transport Policy最新文献

Road crashes continue to be the leading cause of death across all age groups despite several efforts being taken by the government and non-government organizations. The brunt of traffic fatalities affects males and females differently. Studies on assessing gender-wise exposure to road safety are limited in the Indian context. Therefore, an attempt is made to understand the role of gender in fatality risk assessment. The aim is to evaluate the change in exposure to different motorization growth scenarios for males and females. The fatality-risk model is developed based on the interaction between the victim (pedestrian, bicyclist, two-wheeler, car, and bus) and threat (pedestrian, bicyclist, two-wheeler, car, bus, and environment) road users combined with the at-risk distance traveled by modes for Bangalore Metropolitan Region, India. Male and female traffic fatalities are estimated based on the interaction severity between victim and threat modes. The results show that pedestrians, bicyclists, and two-wheeler users are more vulnerable. Both males and females demonstrate an increased risk of traffic fatalities with the rise in the motorization mode share due to the severity of interaction of vulnerable modes with another motorized mode such as two-wheelers and cars. The most significant reduction in road crashes for both genders is observed for the High Bus scenario, which assumes that 80 % of the total motorized distance is traveled by bus. Maximum traffic deaths are estimated for the High Car scenario; the fatalities remained high even with 100 % motorization indicating higher risk with high motorization rates. The study outcomes would help practitioners and decision-makers to make informed policy decisions. Further, the fatality risk is assessed considering the interaction of at most two road users, and the study can be extended to the simultaneous evaluation of multiple road users.

Participating in activities can significantly enhance the life satisfaction of older adults, aged 60 and above. Limited access to transportation can have several negative effects on older adults. The present-day public transit system primarily serves young and middle-aged individuals, which results in a lack of comfort and safety for the elderly. This study aims to model elderly users’ perceived level of satisfaction with bus transit service using the ordered probit model. The study area for our research is the city of Patna, which is the capital of Bihar, India. An extensive perception survey of 416 elderly users populates the database for the present study. According to model estimation results, increasing the number of seats reserved for the elderly and improving the condition of bus stops will increase the perceived level of satisfaction and will lead to attract more elderly passengers to bus transit service. Marginal effects were calculated to evaluate the impact of each independent variable on the perceived level of satisfaction. The overall prediction success rate is 60%. The result of the study will be helpful in better understanding the elderly users’ perception of bus transit service and accordingly will be helpful in planning and management of the bus transit service to meet the expectations of the elderly.

The construction of a new rail infrastructure to connect airports with cities plays a vital role in enhancing the accessibility of airport ground access and mitigating negative externalities resulting from automobiles. To identify air travelers’ preferences for airport ground access by considering a new metro line in Taiwan, we develop nested logit models, which combine revealed and stated preference data. The results reveal significant effects of travel time, travel cost, and individual characteristics; the metro and taxis are highly substituted for airport access. Consumer surplus or accessibility is not equally distributed across air travelers. Travelers who are female, young adults, lower income, and without vehicles in the household obtain considerable consumer benefits from using the new metro. Policy incentives to time and cost have a small impact on the increase in metro share, but consumer surplus surges considerably.

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.