Case Studies on Transport Policy最新文献

Distracted Driving Behavior is one of the leading factors responsible for road accidents. Using a case study of Pakistan, this study aimed to observe the effect of road type and conditions, trip timing, law enforcement, passenger type, and sociodemographic indicators on distracted driving behavior. The effect of driving experience on distracted driving behavior was also studied as a mediating variable. A questionnaire was prepared, and 501 responses were selected for this study. The PLS-SEM was employed to test the hypotheses of the study. The results showed that all the factors significantly influenced distracted driving behavior except passenger type. Trip timing had the strongest effect, suggesting drivers were more distracted during the daytime, possibly due to the ease of driving enabling environment, followed by significant effects of road type and conditions, age, and law enforcement. However, passenger presence did not significantly impact distracted driving in this study. The results indicate a need for suitable interventions with various stakeholders (policymakers, police, mental health experts, advocates, and community leaders) to raise awareness, change attitudes, and increase risk perception related to distracted driving behavior and its imperilments.

Marginalized communities usually experience inadequate public transport services in North America. Within these communities, buses play a vital role in everyday activity participation. Unfortunately, transport services that may advance equity by improving coverage and service for disadvantaged population groups and areas are often overlooked in transit planning since they usually underperform in ridership-based metrics.

To address this problem, the Toronto Transit Commission (TTC) introduced an equity service policy in 2019 to enhance bus services in vulnerable neighbourhoods known as Neighbourhood Improvement Areas (NIAs). This policy involves augmenting observed ridership counts for buses serving NIAs to improve or protect services for these communities. Although this proposed measure can improve bus services in NIAs, its current form might exclude other equity-deserving riders (EDRs) living outside NIAs, increasing their accessibility barriers.

Our study uses quantitative and spatial analytical methods to better understand the equity impacts of this service policy change. We propose moving from the aggregate neighbourhood-based approach using NIAs to a more comprehensive and disaggregated person-based approach. First, we create an alternate method that identifies EDRs at the bus stop level using a fusion of census and travel survey data. We then explore the differences in augmented ridership levels created by the two approaches and evaluate whether the augmented ridership levels are likely to result in meaningful service changes using existing TTC service standards.

We found that 2 out of 3 transit riders in Toronto are equity-deserving, with 72% residing outside NIAs. Our approach reveals increased ridership on routes with many EDRs, which receive minimal augmentation under the NIA-based method. Conversely, if all riders in NIAs are considered EDRs, our approach shows a 19% ridership overestimation equivalent to 35,245 users. Based on these results, we recommend creating a dedicated equity tool with an individual-centric focus, identifying areas with low transit service and high EDR percentages. This tool could then be integrated as an equity perspective into annual bus service planning.

The current study suggests a user satisfaction-based approach to prioritise attributes influencing walk accessibility to metro stations in Delhi, India. The target user group of the present study includes metro users who access the metro stations by walking. Responses from 466 such users are collected using smart tablets through face-to-face interviews. User satisfaction ratings towards twelve walk accessibility influencing attributes identified in the study are recorded on a six-point Likert-type ordinal rating scale. The study engages a comparative Multi-Attribute Decision Making (MADM) approach comprising three prominent techniques, viz., Relative to an Identified Distribution Integral Transformation (RIDIT), Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for analysing the collected perception database and prioritising the attributes based on user satisfaction. The study outcomes identify ‘Extreme Weather Conditions’, ‘Illumination’, ‘Universal Design Considerations’ and ‘Safety and Security’ as the poorly performing attributes based on user perception and require immediate interventions. Spearman’s rank-order correlation analysis is performed to compare the attribute priority ranks derived from the three methods. The research findings can be useful to transportation planners, policymakers and enforcement officials in formulating and implementing strategies to allocate funds based on user-identified priorities for improving walk accessibility to metro stations.

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.