Case Studies on Transport Policy最新文献

This paper investigates the Potential for More Walking (PMW) in work tour among car commuters and proposes ways to encourage them to activate this potential. The study utilized a sample of 621 car commuters in the city of Qom, Iran, who expressed a willingness to replace their daily commute pattern with an alternative commute pattern that involves more walking time. The findings of the random effect binary logit model indicated that network patterns with improved connectivity in the residential neighborhood have a significant impact on activating the PMW. Additionally, subjective factors such as positive perceptions towards sidewalk conditions, longer distances between home and workplace, and positive attitudes towards walking were found to be associated with an increased tendency to activate the PMW. Moreover, the study assessed a set of transportation demand management measures that could influence individual behavior towards activating the PMW. The results indicated that pull measures are generally more effective than push measures in promoting walking during work commuting. Furthermore, the measures of turning streets with dense land-uses into pedestrian malls, increasing green spaces on sidewalks, and widening sidewalks were found to have the highest impact on activating the PMW, respectively.

The Covid-19 pandemic has posed significant challenges to the sector, and Türkiye’s strategic location plays a crucial role in crisis management. This study aims to explore the repercussions of the pandemic on the sector and propose appropriate solutions. To identify the most effective solutions for prioritizing the challenges posed by Covid-19, 53 sector experts participated in the study. The Stepwise Weight Assessment Ratio Analysis method was used to weigh the criteria representing these challenges. The solutions were ranked using the Weighted Aggregated Sum Product Assessment and Combined Compromise Solution methods. The results indicate that the primary challenge is the increased shipping cost, followed by the prolongation of the transport time, global-scale impacts, and port congestion. The most appropriate solution identified was alternative vessel loading, followed by mixed-mode transportation usage, reservation guarantees, long-term contracts with shipping operators, integrated logistics services, container purchases, and technological integration.

This study explores the impact of emerging vehicle technologies on direct urban traffic emissions. It investigates emission reduction potential from shifts in fleet compositions and modal choices, especially considering climate change. To achieve this, three key research areas are explored for historical, current, and future scenarios (up to 2050): mode choice, emission factors for different vehicle categories, and diverse vehicle propulsion technologies. The estimation of the modal split is pivotal, developing a methodology utilizing Stated Preference survey data, discrete choice modeling, Monte Carlo simulations, and macroscopic traffic simulations. Future scenarios derive from the reference year’s modal split, and emission factors and fleet compositions are predetermined via an extensive literature review, aiding the assessment of their respective emissions. A subsequent sensitivity analysis identifies the impact of specific parameters on emissions, guiding future research focus. Study results underscore differences in greenhouse gas emissions and primary air pollutants between base and future scenarios.

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.