Case Studies on Transport Policy最新文献

The advent of connected devices, such as smartphones, has had a transformative impact on the landscape of recent years. Once privacy concerns have been addressed, data can be handled and analysed in a proficient manner to gain insights into patterns and movements, thereby influencing urban policies. It is likely that mobility and transport-related topics have been the subject of the most extensive investigation in the field of cell phone big data. While the topic of commuting patterns has been extensively researched by numerous authors, there is a paucity of literature on the monitoring of attendance during major motorsport events. Despite the predictability of crowding (tickets are sold in advance and the schedule is fixed and rigid), multiday motorsport events are disruptive in terms of traffic, overcrowding and uneasiness for hosting cities. This paper aims to address the aforementioned gap by presenting a case study of monitoring attendance during the Formula One Emilia-Romagna and Made in Italy Grand Prix, held in Imola, Italy, from 22nd to 24th April 2022. The results demonstrated the potential of data to inform the prediction of mobility choices and the planning of appropriate mobility-related policies, with the aim of reducing the impact of future events. This represents a significant challenge for public administrations and stakeholders.

High-capacity trucks (HCTs) are vehicles that are heavier or larger than normally allowed and are used as a means of increasing the efficiency of road freight transport and reducing emissions. The present research analyses the economic and environmental efficiency of HCTs by comparing them with normal semitrailers. Survey-based estimates on cost structure, fuel consumption, load factor and empty running of semitrailers, road trains and HCT combinations currently operating in Finland have been used to calculate whether HCTs improve the cost competitiveness and reduce the emissions of road transport. The results indicate that HCTs have a 42% emission reduction potential in mass-based transport and a 38% potential in volume-based transport compared to normal semitrailers.

This research is aimed at developing a method for relocating wholesale markets in a city with the objective of decongesting the central area by improving the traffic efficiency and to make it pollution free. This paper proposes a bi-level optimisation framework pursuing the local authority’s objective of maximising welfare benefits relative to the spend ensuring good value for money at the upper level. The lower-level framework considers retailers’ response to the relocation of wholesale markets allowing them the choice of procurement location. The lower-level problem also models the route choice of commercial vehicle traffic as well as the private vehicle traffic to measure the resulting on-street congestion. The bi-level problem has been solved with integer Particle Swarm Optimisation algorithm for the case of Bandung, Indonesia. The results show that relocating wholesale markets improves the city centre traffic efficiency and pollution level by about 14%. Traffic speeds over the entire city also improve by up to 6.6% and the pollution levels marginally would drop too. Market relocation as a strategy would significantly improve the efficiency and pollution levels but must be carefully planned and evaluated otherwise the emissions outside of city centre could increase.

The main purpose of this paper is to show how a direct demand ride model coupled with a transfer model was able to support the choice of alternative routes for the expansion of the light rail system serving the Porto Metropolitan Area. After an overview of the literature on direct ridership models, emphasizing some key issues such as the need for a systematic assessment of their forecasting performance, the issues related to the definition of the pedestrian catchment area and the limitations of the simultaneous consideration of demand and supply effects, the paper moves into the case study, providing some background information on current occupation densities, land uses and mobility patterns, as well as on the performance of the existing LRT in the Metropolitan Area of Porto. The development of the direct ridership model, measuring the potential attractiveness of each station, and the transfer model, measuring the number of transfers at each station, are presented in detail. A justification is provided why, in this case, a two-step modelling approach was necessary. Further details about the statistical tests for model validation are also provided. After a brief characterization of the alternative routes under analysis for the expansion of the network, the modelling results are presented enabling a comparative assessment of the potential performance of each proposed route. The paper ends with a discussion of the relevance of this modelling results vis a vis the actual final decisions on investment priorities taken jointly by the Metropolitan Council and the Metro Company.

Physical road separations are normally considered cyclist-friendly, but whether they are unfriendly to the combination of bike-share (BS) and metros is seldom investigated since they might affect the cycling’s flexibility and convenience to cross streets and reach the destination. Although integrating BS and metro is thought to mitigate traffic congestion, how traffic congestion affects the usage of integration remains unknown. This study, conducted in Suzhou, China, examines new factors (road separation and traffic congestion) alongside well-studied factors influencing BS and metro integration. The survey revealed increased BS usage frequency after a new metro’s opening. Variables such as road separations, traffic congestion information, road network density, and proximity to metros are considered. They are processed in a selected area considering cyclable network and Thiessen Polygon corresponding to the selected metro stations. An ordered probit model is established to investigate significant factors. It is found that as more columns of road separations exist, the cyclists are less likely to use BS towards metros, regardless of whether they are cycling weekends or weekdays. Interestingly, after the new metro opened and a new metro hub was formed, proximity to the new metro hub is associated with lower BS transfer demand. A higher congestion level promotes more cycling toward the metro system on weekdays as well as higher parking fees. This indicates that the combination of BS and metro could attract former motor vehicle users and this finding could be instructive for urban planners and road designers.

Deep learning accurate predictions of house prices are essential for prospective homeowners, investors, appraisers, and insurers. However, some studies lack accuracy as they overlook critical factors like accessibility and economic attributes that influence house prices. This paper aims to predict house prices by considering structural, locational, accessibility, and economic attributes, while also exploring the effect of accessibility on housing prices. The dataset contains 2,019,663 real estate transaction records from 1975 to 2018 in the Washington metropolitan area, obtained from the Zillow website. In this study, the accessibility index is calculated using Distance, Cumulative Opportunities, and Gravity measures, with the gravity measure surpassing others due to its consideration of both land use and transportation aspects. Economic attributes are then utilized to predict the average monthly house price using deep learning algorithms such as LSTM, GRU, and Simple RNN, with the Simple RNN demonstrating superior performance. Following the amalgamation of structural and locational attributes with the accessibility index and average house prices, various machine learning algorithms—including Linear Regression, Lasso, Ridge, Random Forest, GBM, LightGBM, XGBoost, Decision Tree, AdaBoost, Artificial Neural Network, and Stacked Generalization—are employed for prediction. Subsequent evaluation reveals that Stacked Generalization (ANN + LightGBM) provides the best performance, with an R-squared value of 0.96 and RMSE of $23,290. Moreover, this paper identifies accessibility index thresholds (80,003 for large buildings and 160,103 for small buildings) and demonstrates that a higher accessibility index leads to lower housing prices, attributed to noise pollution, decreased privacy, and increased supply responses.

Vehicle sharing is one of the most innovative ways to transport business and management. The purpose of this study was to determine factors affecting the perceived usability of vehicle-sharing mobile applications by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT), pro-environmental behavior (PEB), and system usability scale (SUS). 206 Taiwanese filled out an online questionnaire with 65 questions which was shared using a convenience sampling approach. Partial Least Squares-Structural Equation showed that perceived usability was found to be significantly influenced by price value, environmental concern, authority support, behavioral intention, and actual use. Interestingly, authority support was found to have the highest significant indirect effect on perceived usability, indicating that vehicle-sharing mobile applications must be supported by the national government in order for people to consistently use them. This study is one of the first studies that analyzed vehicle sharing, particularly the mobile application. The findings of this study may be used as a guideline or strategy for the national government to reduce environmental risks caused by thousands of vehicles in the country, for vehicle-sharing companies to further boost their profit, and for other investors who intend on utilizing mobile applications for their market.

Concern is growing regarding the increasing use of private car in many developing countries, including Thailand. Car usage is rising in Thailand in part because the public transit system is inadequate and unreliable, especially in rural and suburban communities. Often, children’s route to school is unsafe due to the increasing number of cars around the school. This prompts parents to worry about their child’s safety when in transit. The model of travel by public transportation is an alternative for reducing road congestion and improving children’s safety. This study seeks to better understand the factors that influencing parents’ decisions regarding the use of public transportation for their children’s school commutes. Data were gathered through a questionnaire from a sample of 750 parents with children between the ages of 6 and 18 years in Nakhon Ratchasima Province, Thailand. The findings of the structural equation model analysis showed a relationship between service users’ loyalty and the components of satisfaction, trust, expectation, and perception of service quality. The outcomes derived in this study can serve as practical guidelines for public and private transportation system operators to improve service efficiency, meet consumer demand, and foster positive interactions between service users and operators. In this way, this study can lead to important internal and external benefits for the continued development of the public transportation system and motivate more service users to transition to the use of public transportation.

Road traffic crashes (RTC) are the main cause of mortality, morbidity, and disability for children globally as well as in the United Arab Emirates (UAE). Although vehicle occupants usually account for the majority of children involved in RTC in developed countries and in the UAE, injuries sustained by child vulnerable road users (VRUs) are usually more severe due to their lack of protection compared to child occupants. Such injuries are known to result in long-term suffering for children including disabilities in some cases, thereby posing a severe public health burden and economic losses to the population. However, despite the severity of injuries to child VRUs involved in RTC, studies in the UAE have mostly focused on vehicle occupants for both children and adults. To the best of our knowledge, this is the first dedicated study on child VRU RTC injuries in the UAE. Additionally, the UAE government is promoting an active transportation policy among children in a bid to curb childhood obesity. Hence, this study examined the factors contributing to RTC injury severity for child VRUs in the UAE. The results of this study will help in enhancing the safety outcomes of child VRU RTC injuries as well as providing policy recommendations for safe active transport among children in the country.

Network Data Envelopment Analysis (NDEA) has been extensively applied to evaluate the air transportation sector. NDEA provides a tool for evaluating the internal processes of Decision-Making Units (DMUs). Optimistic Network Cross-Efficiency (ONCE) has recently been extended to the basic two-stage system. However, there are still two main shortcomings that need to be addressed. First, the ONCE evaluates DMUs based only on the optimistic viewpoint, neglecting the pessimistic viewpoint. The optimistic viewpoint assumes that there is only one set of reference points, which includes the best practice DMUs. The first contribution of this study is to develop a new Pessimistic Network Cross-Efficiency (PNCE) method. This method is based on a new set of reference points, which includes the worst-performing DMUs. The PNCE is developed as an extension of the ONCE. Second, both the ONCE and newly developed PNCE methods may lead to unrealistic results because they neglect the subjective preferences of Decision Makers (DMs). These NDEA models employ the Arithmetic Mean (AM) as the cross-evaluation aggregation method, which not only underestimates the importance of self-evaluation but also overestimates the importance of peer evaluations. Consequently, ONCE and PNCE may lead to biased efficiency results. To address this drawback, the second contribution of this study is to develop a new Aggregation method based on the Regret theory and Consensus (ARC). This method aims to reflect the psychological preferences of DMs when estimating cross-evaluation weights. To achieve this goal, we obtained new optimistic and pessimistic efficiencies by utilizing the newly developed ONCE-ARC and PNCE-ARC methods. Subsequently, a Double-Frontier Network Cross-Efficiency with ARC (DFNCE-ARC) is developed as a more comprehensive NDEA. Finally, a practical application is conducted to assess the performance of a set of Iranian airlines, demonstrating the usefulness and applicability of DFNCE-ARC.