Search Results (105)

The subway is crucial for urban operations, especially during holidays. Unlike traditional studies using smart card data, this research analyzes National Day holiday subway travel patterns with Hangzhou’s 2021 mobile payment data, covering 42 days from 6 September to 17 October for comprehensive comparison. Considering spatial passenger flow correlations, a Composite Weight (CW) matrix integrating network distance and time is defined and integrated into a Spatial Error Model (SEM), Spatial autoregressive model (SAR), and Spatial Durbin Model (SDM) to create CW-SEM, CW-SAR, and CW-SDM. The CW matrix innovatively considers network distance and time, overcoming traditional spatial weight matrix limitations to accurately and dynamically capture passenger flow spatial correlations. The results show the following: (1) Hangzhou saw 37% and 49% increases in average daily passenger flow during the extended holiday versus workdays and weekends, with holiday peak hour flow declining 16% compared to workdays but increasing 18% versus weekends, likely due to shifted travel purposes from commuting to tourism; (2) strong spatial passenger flow correlations existed in both workdays and weekends, attributed to urban functional zoning and transport network connectivity; (3) key factors such as population, social media activity, commercial facilities and transportation hubs show significant positive correlations with holiday passenger flow. Medical facility reveals significant negative correlations with holiday passenger flow. These findings highlight the need to incorporate spatial variations into major holiday subway travel studies for urban planning and traffic management insights. Full article

The rapid expansion of ride-hailing services has profoundly impacted urban mobility and residents’ travel behavior. This study aims to precisely identify and quantify how the built environment and socioeconomic factors influence spatial variations in ride-hailing demand using multi-source data from Haikou, China. A multi-scale geographically weighted regression (MGWR) model is employed to address spatial scale heterogeneity. To more accurately capture environmental features around sampling points, the DeepLabv3+ model is used to segment street-level imagery, with extracted visual indicators integrated into the regression analysis. By combining multi-scale geospatial data and computer vision techniques, the study provides a refined understanding of the spatial dynamics between ride-hailing demand and urban form. The results indicate notable spatiotemporal imbalances in demand, with varying patterns across workdays and holidays. Key factors, such as distance to the city center, bus stop density, and street-level features like greenery and sidewalk proportions, exert significant but spatially varied impacts on demand. These findings offer actionable insights for urban transportation planning and the design of more adaptive mobility strategies in contemporary cities. Full article

As the market share of electric vehicles (EVs) increases year by year, their charging load forecasting has become a research hotspot and a difficulty. Aiming at the shortcomings of the current research on the charging probability prediction of EVs with different power levels, this paper proposes a multi-power-level EV charging probability prediction method. Firstly, based on the characteristics of electric vehicles, the power of charging facilities, and the travel habits of owners, the SOC mathematical models of charging start time, as well as the start and end state of charge, are established, and the different charging power selection models are established in combination with the parking time. Then, the Monte Carlo simulation method is used to predict the charging probability of electric vehicles with different power levels on typical dates such as working days, weekends, and holidays. Full article

The COVID-19 pandemic altered the holiday plans of many people. Whether it was due to travel bans or the fear of contracting the infection, people modified, among other aspects, their chosen destination, travel transport, accommodations, length of stay, and activities to be undertaken during the stay. In this context, we aim to disentangle the effect of these changes on tourist satisfaction. Previous research on the effects of COVID-19 on the tourism sector has studied the shrinkage of tourism demand, changes in tourist behaviour and adaptation processes on the supply side. Nonetheless, few works have analysed changes in tourists’ plans. Two main hypotheses have been put forward. First, tourists might be dissatisfied given that they could not attain their holiday expectations. In contrast, the second hypothesis suggests that those individuals who changed their holiday plans might be more satisfied because they diminished their perceived risk of contagion. We have used data drawn from a survey of tourists (N = 2009) who visited Costa Daurada, a very popular Mediterranean coastal destination just after the end of the Spanish lockdown. Then, statistically significant differences in satisfaction levels between the groups that altered their plans and those who did not are assessed by means of Kruskal–Wallis and Wilcoxon Rank Sum tests. Results signal that tourists were not more dissatisfied when they had modified their initial holiday plans. Indeed, the overall satisfaction of those visitors who switched their initial destination to travel to Costa Daurada was slightly lower, and the difference was significant, compared to the ones who were planning to travel there from the very beginning. Satisfaction was not significantly lower for those who changed their holiday plans in the case of the rest of the items analysed (transportation, length of stay, accommodation, and overall activities). On the contrary, in the case of activities, changes apparently contributed to mitigate the risk perception and led to a better tourist experience. Results also suggest that tourists were willing to adapt to a new situation in order not to renounce their holidays. In terms of implications for destination management and stakeholders, the main conclusion is that continuous cooperation and mutual trust are key to adapting to turbulent environments in which risk perception becomes central. Full article

As the transition to sustainable transportation has accelerated with the rise of electric vehicles (EVs), ensuring drivers have access to charging to maximize the electric miles driven is critical to lowering carbon emissions in the transportation sector. Limited charging station capacity and poor reliability, especially during peak travel times, long-distance travels, holidays, and events, have hindered the adoption of EVs and threaten the progress toward reducing greenhouse gas emissions. Adaptive, flexible deployment strategies combined with innovative approaches integrating mobility and renewable energy are essential to address these systemic challenges and bridge the current infrastructure gap. To address these challenges, this study proposes a self-contained, mobile charging station (MCS). Designed for rapid deployment, the proposed MCS increases charging capacity during demand surges while minimizing reliance on fossil fuels. The feasibility of integrating a solar canopy with this MCS to further reduce carbon emissions is also studied. This study weighed the pros and cons of differing cell chemistries, sized the battery using data provided by the United States’ largest public CPO, and discussed the feasibility of a solar canopy for off-grid energy. Full article

With the development of smart cities and intelligent transportation systems, path planning in multi-scenario urban mobility has become increasingly complex. Traditional path-planning approaches typically focus on a single optimization objective, limiting their applicability in complex urban traffic systems. This paper proposes a multi-objective vehicle path-planning approach tailored for diverse scenarios, addressing multi-objective optimization challenges within complex road networks. The proposed method simultaneously considers multiple objectives, including total distance, congestion distance, travel time, energy consumption, and safety, and incorporates a dynamic weight-adjustment mechanism. This allows the algorithm to provide optimal route choices across four application scenarios: urban commuting; energy-efficient driving; holiday travel; and nighttime travel. Experimental results indicate that the proposed multi-objective planning algorithm outperforms traditional single-objective algorithms by effectively meeting user demands in various scenarios, offering an efficient solution to multi-objective optimization challenges in diverse environments. Full article

Given the persistently high incidence of skin cancer, there is a need for prevention-focused information on the impact of long-term changes in ambient solar ultraviolet radiation (UVR) on human personal radiation exposure. The exposure categories of the UV Index linked to protection recommendations show long-term shifts in the frequency of occurrence with regional differences in direction and magnitude. The patterns of change for sites in the humid continental climate differ from those for sites in other climate zones such as the humid temperate or Mediterranean climate. The diversity of the individual exposures of indoor and outdoor workers can be described using probability models for personal erythema-effective UVR dose (UVD). For people who work indoors, the largest share of the total individual annual UVD is due to vacation, whereas for people who work outdoors, it is occupational exposure. The change in ambient UVDs at the residential locations is only partially reflected in the individual UVDs. For eight selected European sites between 38° and 60° northern latitude, the median of the individual annual total UVD (excluding travel) during the period 2009–2019 is 0.2 to 2.0% higher for indoor workers and 0.6 to 3.2% higher for outdoor workers compared to the period 1983–2008. Changes in the choice of an exemplary holiday destination offer both indoor and outdoor workers the potential to compensate for the observed long-term trend at their place of residence and work. Full article

In recent years, passenger holiday travel momentum continues to increase, which proposes a challenge to the refined transportation organization of China’s high-speed railway. In order to save the cost of transportation organization, this paper proposes a collaborative optimization method using a high-speed railway train diagram and Electric Multiple Unit (EMU) routing considering the coordination of weekdays and holidays. Based on the characteristics of the train diagram and EMU routing, this method optimizes the EMU routing synchronously when compiling the train diagram. By constructing a space–time–state network, considering the constraints of train headway, operation conflict, and EMU maintenance, a collaborative optimization model of the train diagram and EMU routing considering the coordination of weekdays and holidays is established. This research combines the actual operation data to verify the model and algorithm. Based on five consecutive days of holidays, a seven-day transportation plan covering before and after the holidays and during the holidays is designed, and a case study is carried out. The results show that the proposed collaborative optimization theory has practical significance in the application scenarios of high-speed railway holidays. Full article

The paper highlights the potential of cross-docking to reduce storage time and costs. The study addresses evolving market demands that push logistics providers to adopt new technologies for operational efficiency, emphasizing the often-overlooked importance of optimizing cross-docking layouts. The research, conducted in two phases, first analyzed the current warehouse layout (Variant I) to identify inefficiencies and then designed a new layout (Variant II) that was simulated using FlexSim 2022 software. The results showed significant improvements with the new layout, including a 35% increase in deliveries and a 3.23% reduction in forklift travel distances, leading to lower operational costs. Even minor adjustments in the warehouse design proved to enhance logistics efficiency, particularly during peak demand periods like holidays. The study demonstrates how FlexSim software can be applied in cold chain logistics to optimize warehouse operations, underscoring the benefits of cross-docking for cost-effective logistics management. Full article

Based on our interest in properties of human movement, we investigated Japanese GPS data, and arrived at the following three observations: (1) there is a strong correlation between the area of polygons created by human movement trajectories and their perimeters; (2) short-distance movement trajectories less than 5 km tend to enclose a large area like a circle; and (3) long-distance movement trajectories over 5 km tend to be straight. We also clarified the following four observations on individual attributes and external factors related to long-distance movements: (1) women tend to travel more linearly than men; (2) linearity is stronger on weekends and national holidays in areas with a large theme park; (3) linearity is weaker on weekends and holidays in areas with many historical tourist attractions; and (4) these variations are created by people visiting such areas. These properties should be incorporated when modeling the movement trajectories of humans. Full article

Traffic anomaly detection is crucial for urban management, yet current research is often confined to small-scale endeavors. This study collected 9 months of real-time Wuhan traffic-monitoring data from Amap. We propose Traffic-ConvLSTM, a multi-scale spatial-temporal technique based on long short-term memory (LSTM) networks and convolutional neural networks (CNNs) to effectively achieve long-term anomaly detection at the city level. First, we converted traffic track points into an image representation, which enables spatial correlation between traffic flow and roads and correlations between traffic flow and roads, as well as the surrounding environment, to be captured. Second, the model utilizes convolution kernels of different sizes to extract spatial features at road-, regional-, and city-level scales while incorporating the temporal features of different time steps to capture hourly, daily, and weekly dynamics. Additionally, varying weights are assigned to the convolution kernels and temporal features of varying spatio-temporal scales to capture the heterogeneous strengths of spatio-temporal correlations within patterns of traffic anomalies. The proposed Traffic-ConvLSTM model exhibits improved performance over existing techniques in the task of identifying long-term and large-scale traffic anomaly occurrences. Furthermore, the analysis reveals significant traffic anomalies during holidays and urban sporting events. The diverse travel patterns observed in response to various activities offer insights for large-scale urban traffic anomaly management, providing recommendations for city-level traffic-control strategies. Full article

The prediction of bike-sharing demand plays a pivotal role in the optimization of intelligent transportation systems, particularly amidst the COVID-19 pandemic, which has significantly altered travel behaviors and demand dynamics. In this study, we examine various spatiotemporal influencing factors associated with bike-sharing and propose the Local-Global Dynamic Multi-Graph Convolutional Network (LGDMGCN) model, driven by multi-source data, for multi-step prediction of station-level bike-sharing demand. In the temporal dimension, we dynamically model temporal dependencies by incorporating multiple sources of time semantic features such as confirmed COVID-19 cases, weather conditions, and holidays. Additionally, we integrate a time attention mechanism to better capture variations over time. In the spatial dimension, we consider factors related to the addition or removal of stations and utilize spatial semantic features, such as urban points of interest and station locations, to construct dynamic multi-graphs. The model utilizes a local-global structure to capture spatial dependencies among individual bike-sharing stations and all stations collectively. Experimental results, obtained through comparisons with baseline models on the same dataset and conducting ablation studies, demonstrate the feasibility and effectiveness of the proposed model in predicting bike-sharing demand. Full article

Our study introduces an enhanced version of the Importance–Performance Analysis (IPA) method, a powerful tool that can be applied across various domains. This method plays a crucial role in our research, aiding in making well-informed decisions about smart tourism destination attributes. We achieved this by evaluating how 911 consumers from four different generations (Baby Boomers, Generation X, Millennials, and Generation Z) rated these attributes based on their most recent tourist destination visit. Unlike traditional methods that often rely on subjective opinions or complex statistical models, the Improved IPA (IIPA) method offers a clear approach to decision-making. It enables decision-makers to focus on the most crucial attributes that drive consumer interest, thereby optimizing resource allocation and marketing efforts. Specifically, to remain competitive, decision-makers for smart tourist destinations should focus on queuing-time forecast and applications, websites, and content accessible for travelers with disabilities for Baby Boomers; e-complaint handling for Generation X; smart emergency response system for Millennials; and tourist-flow forecast, real-time traffic broadcast, electronic-entrance guard systems, and accessible data about physical design features of accommodation, restaurants, and tourist attractions for Generation Z. Theoretically, this study advances the research on managerial decision-making by demonstrating the effectiveness of the IIPA as a clear and straightforward method for making optimal decisions about product or service attributes. In practice, the study provides decision-makers with valuable insights into the importance of different categories of smart attributes in shaping the overall holiday experience at a tourist destination for Baby Boomers, Generation X, Millennials, and Generation Z tourism consumers. Full article

Decarbonising holiday travel is crucial for climate change mitigation: policy interventions need to encourage less frequent trips, closer destinations, and travelling on the ground. To increase effectiveness, interventions should fit with the specific ways holidays are perceived and performed in each context. We explore the holiday behaviour of people living in a medium-sized city in Southern Switzerland (Lugano, 70,000 inhabitants), with the aim of identifying key intervention strategies for a future “community challenge” encouraging the population to take low-carbon holidays. We combine a literature review with n = 15 qualitative, semi-structured interviews that allow us to understand the reasons for taking a holiday, the favourite destination and activity types, and the transport mode choices. As Switzerland is characterised by high cultural and linguistic diversity providing the feeling of being abroad even at a short distance from home, it could be a valuable holiday destination for Swiss people themselves. Located at the centre of Europe, it is also well-connected by train with many holiday destinations abroad. Gaps between pro-environmental attitudes and holiday behaviour suggest leveraging digital carbon trackers showing how carbon emissions compare between holiday and everyday life. Also, interventions could leverage social norms via social networks, local influencers, and travel agencies. Full article

Going on holiday is often associated with taking the car or plane. Even for short distances, and where alternative, sustainable transportation modes would be available, we frequently choose the more unsustainable options. Affordability, comfortability, and time savings led to an increase in transportation, which in turn, negatively contributed to greenhouse gas emissions. The reduction in those emissions can be achieved by choosing public transportation. However, since transportation choices are often made unconsciously and habitually, it is crucial to transform those unsustainable habits into more sustainable ones. Contextual changes can serve as a catalyst. This research investigates whether pre-COVID-19 and pre-inflation unsustainable travel habits can be broken through the perceived impact of COVID-19, financial hardship, and green consumption values, increasing the intention for sustainable transportation modes using a survey design. We found that the context change, as such, does not predict future intentions to travel sustainably, but existing green consumption values do. Building on the self-activation theory, the results show that habits and the perceived impact of COVID-19 and financial hardship activate a person’s green consumption values. Consumers’ green values mediate the relationship between unsustainable habits and the intentions to use sustainable transportation modes, combining the habit discontinuity and self-activation hypotheses. Full article