Case Studies on Transport Policy最新文献

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.

This study examined the long-term impacts of the COVID-19 pandemic on mobility patterns in Indonesia, focusing on the transition from pre-pandemic norms to the endemic phase. Using quantitative methods and retrospective data analysis, we explored changes in travel behaviour, including mode choice, trip frequency, and travel time. We identified travel behaviour in three phases: pre-pandemic, pandemic (March 2020 to the end of January 2022), and transition to endemic (the data collection period: July – August 2022). Our investigation of the shifts in travel behaviour across these periods enabled us to distinguish between temporary fluctuations and enduring, potentially long-term changes in behaviour. The empirical investigation revealed significant alterations in trip frequency that persisted during the endemic phase, suggesting that these changes in mobility are likely to continue. Factors influencing these shifts include socioeconomic characteristics, work modalities, and attitudes toward health protocols. This study contributes to the understanding of pandemic-induced travel behaviour transformations and provides insights for policymakers and transportation planners to adapt to these changes in the Southeast Asian context.

Over the last two decades and a half, the Ethiopian government has been involved in extensive road construction as part of successive road development plans that have significantly increased road network coverage. However, the tendencies that worsen congestion show no signs of abating. As a result, drivers and passengers waste time in long traffic lines, rendering modern travel unreliable and increasing travel costs due to delays. To curb the problem, the remedies proposed by practitioners or researchers are mostly centred on the supply side. This study aims to determine drivers’ willingness to pay (WTP) (from the demand side) for a hypothetical congestion reduction project. A stated preference survey was utilised on the population identified in nine segments in Addis Ababa’s Kolfe Keraniyo sub-city. The data was gathered from 1012 respondents using a survey questionnaire based on the contingent valuation method (CVM). Tobit and Heckman two-step selection models were used to estimate factors determining WTP for congestion reduction. Accordingly, the analysis showed that the average amount of willingness to pay by drivers was estimated to be birr 2.7 ($0.05) per km. Education, marital status, income, segment use, and trip frequency significantly determined the outcome equation, while age, gender, income, occupation, work experience, and regularity and frequency of segment use were found to determine the decision to participate in WTP to accept congestion pricing. Identifying the willingness to pay and the desired price can assist the Addis Ababa City Administration in establishing a baseline for any future congestion reduction strategy based on congestion pricing.

In response to the imperatives outlined in SDGs 7, 11, and 13 and the Paris Agreement, folks increasingly focus on transportation to mitigate climate change. Uganda’s capital, Kampala, lacks ratified passenger land transportation plans. This study employs the KLAP-TIMES model, a bottom-up engineering framework, to examine Kampala commuters’ behaviors using three scenarios. Leveraging TIMES-VEDA, the study explores a baseline scenario (BAU) without time travel financial (TTF) limitations or distinctive Value of Travel Time (VTT) levels but with model permissions for competition among dissimilar trip modes, technologies, and fuels, a Kampala Accessibility and Priority Options (KAPO) Scenario with specific VTT levels under TTF limitations, and the Kampala Sustainable scenario (KSS), with a 50% carbon emissions bargain in the system by 2060, contingent upon the persistence of VTT and TTF over the planning horizon. The study utilizes the VTT purposively to measure infrastructure investments as a policy shock on travel time. By exogenously inputting travel demand, the model elucidates the endogenous preferences of individual commuters among various trip modes, including Boda-boda, Kampala sedans, and mass rapid transit (MRT) with an electrified Metro at its core, for both short—and long-distance journeys. The findings reveal significant shifts in consumer behavior, particularly in the Kampala Sustainable scenario, where commuters prefer the electrified Kampala Metro for both short—and long-distance travel, signaling a notable departure from traditional Kampala sedan usage. The study suggests prioritizing the value of travel time (VTT) alongside a carbon reduction trajectory (KSS) is a strategic route for sustainable mobility in Kampala. The study provides valuable insight to policymakers, aiding them in formulating and enacting transportation policies that effectively support Kampala’s sustainability objectives.