Search Results (79)

This paper deals with minimisation of fuel consumption under real driving conditions using a vehicle speed advisor. The aim is to explore the potential of speed profile optimisation in real driving conditions while assessing the suitability of an application which recommends the driver the optimal vehicle speed sequence that minimises the fuel consumption on a particular route. The speed advisor is based on solving the Optimal Control problem of covering a particular route with minimum fuel consumption with a defined time constraint. The approach presented was applied to and implemented on a real passenger vehicle to obtain a trade-off between fuel consumption and travel time for several trips on the route. Experimental results are presented with and without advisory. With speed advisor, the results approach the pareto front with lesser dispersion. On the other hand, without advisory, the dispersion is higher and largely above the pareto front. Full article

Eco-routing, as a key strategy for mitigating urban pollution, is gaining prominence due to the fact that minimizing travel time alone does not necessarily result in the lowest fuel consumption. This research focuses on the challenge of selecting environmentally friendly routes within an urban street network. Employing microsimulation modelling and a computer-generated mirror of a small traffic network, the study integrates real-world traffic patterns to enhance accuracy. The route selection process is informed by fuel consumption and emissions data from trajectory parameters obtained during simulation, utilizing the Comprehensive Modal Emission Model (CMEM) for emission estimation. A comprehensive analysis of specific origin–destination pairs was conducted to assess the methodology, with all vehicles adhering to routes recommended by Google Maps. The findings reveal a noteworthy disparity between microsimulation results and Google Maps recommendations for eco-friendly routes within the University of Pittsburgh Campus street network. This incongruence underscores the necessity for further investigations to validate the accuracy of Google Maps’ eco-route suggestions in urban settings. As urban areas increasingly grapple with pollution challenges, such research becomes pivotal for refining and optimizing eco-routing strategies to effectively contribute to sustainable urban mobility. Full article

As to the problems in current tourism recommendation, this paper proposes a tourism recommendation algorithm based on the mobile ICV service platform. Firstly, the ICV service system for the Point of Interest (POI) searching and route recommendation is designed. Secondly, the recommendation service model is set up from two aspects, namely the tourism POI clustering algorithm and the tourism POI searching and route recommendation algorithm. In the aspect of symmetrical-based matching features, the clustered POIs are matched with the tourists’ interests, and the POIs in the neighborhood of the ICV dynamic locations are searched. Then, a POI recommendation algorithm based on the tourists’ interests is constructed, and the POIs that best match the symmetrical interests of the tourists within the dynamic buffer zones of ICV are confirmed. Based on the recommended POIs, the ICV guidance route algorithm is constructed. The experiment verifies the advantages of the proposed algorithm on the aspect of the POI matching tourists’ interests, algorithm stability, traveling time cost, traveling distance cost and computational complexity. As to the iterative sum and the iterative sum average of the POI matching function values, the proposed algorithm has a performance improvement of at least 20.2% and a stability improvement of at least 20.5% compared to the randomly selected POIs in matching tourists’ interests. As to the cost of the guidance routes, the proposed algorithm reduces the average cost by 19.6% compared to the other suboptimal routes. Compared with the control group algorithms, the proposed algorithm is superior in terms of route cost, with an average cost reduction of 13.8% for the output routes compared to the control group. Also, the proposed algorithm is superior in terms of route cost compared to the control group recommendation algorithms, with an average cost reduction of 11.2%. Full article

With the acceleration of urbanization, public transport networks are an important part of urban transport systems, and their robustness is critical for city operation. The objective of this study is to analyze the topological properties and robustness of an urban public transport network (UPTN) with a view to enhancing the sustainability of urbanization. In order to present the topological structure of the UPTN, the L-Space complex network modeling method is used to construct a model. Topological characteristics of the network are calculated. Based on single evaluation indices of station significance, a comprehensive evaluation index is proposed as the basis for selecting critical stations. The UPTN cascading failure model is established. Using the proportion of the maximum connected subgraph as the evaluation index, the robustness of the UPTN is analyzed using different station significance indices and deliberate attack strategies. The public transport network of Xuzhou city is selected for instance analysis. The results show that the UPTN in Xuzhou city has small-world effects and scale-free characteristics. Although the network has poor connectivity, it is a convenient means to travel for residents with many independent communities. The network’s dynamic robustness is demonstrably inferior to its static robustness due to the prevalence of cascading failure phenomena. Specifically, the failure of important stations has a wider impact on the network performance. Improving their load capacity and distributing the routes via them will help bolster the network resistance against contingencies. This study provides a scientific basis and strategic recommendations for urban planners and public transport managers to achieve a more sustainable public transport system. Full article

The carbon emitted by vehicles traveling on curved roads is greatly affected by the alignment of the route, yet the mechanism behind this is not yet clear, leading to current horizontal alignment designs being unable to avoid this problem. To clarify the principles and indicator thresholds of low-carbon design for planar geometry, this study takes the carbon emission of traveling on curved routes as the research object, and establishes a relationship model between carbon emissions and design indicators based on the principles of vehicle dynamics and kinematics. Field tests were conducted to validate the quantitative relationship model. The model shows that both radius and superelevation are negatively correlated with carbon emissions, while the lateral force coefficient is positively correlated with carbon emissions. The contribution of radius to carbon emissions is greater than that of superelevation. This study clarifies the recommended values of low-carbon design indicators by assessing carbon emissions according to the current route design specification, outlines the principles of superelevation settings, and proposes a methodology to deal with the relationship between superelevation and the lateral friction coefficient. The research findings promote the quantification and standardization of low-carbon highway design, contributing to the early mitigation of high-carbon emissions from curved traffic during the design phase. Full article

Households with limited financial resources often struggle with inadequate access to healthy, affordable food. Community supported agriculture (CSA) has the potential to improve access to fresh fruits and vegetables, yet low-income households seldom participate due to cost and other barriers. Cost-offset (or subsidized) CSA reduces financial barriers, yet engagement varies widely among those who enroll. This scoping review explored factors associated with CSA participation among low-income households in the United States. Eighteen articles met the inclusion criteria, quantitative and qualitative data were extracted, the evidence was synthesized, and themes were developed. The findings suggested that women may be more likely than men to enroll in CSA. A lack of familiarity with CSA may hinder enrollment, whereas more education and self-efficacy for food preparation may facilitate participation. In terms of share contents, high-quality produce, a variety of items, more fruit, a choice of share contents, and a choice of share sizes may facilitate participation. In terms of CSA operations, a low price, good value, acceptance of Supplemental Nutrition Assistance Program (SNAP) benefits, close pick-up locations on existing travel routes, delivery of shares, clear communication, fostering a sense of belonging and trust, and educational support may support participation. Together these findings support 13 recommendations for cost-offset CSA implementation to engage low-income households. Full article

Coffee production has become increasingly technified in order to optimize the use of inputs and the sustainable use of natural resources. In this context, one way that farmers are investing in their coffee plantations is in the use of precision agriculture techniques, termed precision coffee farming. Over the last few years, research has been conducted to facilitate the application of this technology, and sampling grids with two points per hectare have been recommended by several studies. These georeferenced demarcations in a plot are generally shaped as equidistant squares or rectangles, and the sampling points are located at the centers of these areas. Coffee farmers typically plant their crops following the level line, which greatly hinders the navigation of equidistant points within the field. Thus, the objective of this study was to develop an optimized walking route method to reduce the distance for sampling soil, leaf, and yield attributes. The experimental plots were established in 2000 at Samambaia Farm, located in Santo Antônio do Amparo, Minas Gerais, Brazil, with coffee the cultivar Acaia IAC 479-19, totaling 56.65 ha. The 111 sampling points were distributed in the land following the new method proposed in this study, and, after walking simulations using Farm Works Mapping Software, the new method was compared with the conventional method using the mean displacement between points. The new optimized walking routes method reduced the mean distance traveled to sample the points by 50.1%. Full article

In view of the problems in planning and recommending tour routes, this paper constructs a feature text mining (FTM) method and spatial accessibility model (SAM) as the key factors for scenic spot recommendation (SSR) and tour route recommendation (TRR). The scenic spot clustering algorithm (SSCA) based on FTM was constructed by tourists’ text evaluation data mining. Considering the spatial attributes of scenic spots, the scenic spot topology tree algorithm (SSTTA) based on dynamic buffer spatial accessibility (DBSA) was constructed. The optimal scenic spots were recommended based on interest matching and spatial accessibility optimization. As to the recommended scenic spots, this paper proposes an optimal tour route recommendation algorithm (TRRA) based on SSTTA, which aims to determine the optimal adjacent section path structure tree (ASPST) with the lowest cost under travel constraints and transportation modes. The experiment verifies that the proposed algorithm can recommend scenic spots that match tourists’ interests and have optimal spatial accessibility, and the optimal tour routes with the lowest costs under certain travel constraints. Compared with the searched sub-optimal tour routes, the optimal tour route recommended by the proposed algorithm produces the lowest travel costs, and all the scenic spots in the tour route meet the tourists’ interests. Compared with the commonly used BDMA and GDMA methods, the proposed algorithm can determine the optimal routes with lower travel costs. Full article

In this study, we investigate the impact of online review characteristics on consumers’ purchasing decisions in the context of spatial distance. We consider the product experience of online travel routes, geographical location characteristics, and price adjustment factors, as well as the dynamics between consumers and businesses during the booking of travel routes. Through empirical research and large-scale data simulation experiments, we have found that the variability in attributes of tourist routes significantly influences the user recommendation rate, while the overall rating has a positive moderating effect. Furthermore, the number of reviews negatively moderates the relationship between them. Additionally, the product information and service quality of tourist routes also significantly affect the recommendation rate. Finally, we propose a management strategy for tourism route managers to enhance user recommendation rates and achieve greater benefits. Full article

One of the main methods of the path localization of moving objects is positioning using Global Navigation Satellite Systems (GNSSs) in cooperation with Inertial Navigation Systems (INSs). Its basic task is to provide high availability, in particular in areas with limited access to satellite signals such as forests, tunnels or urban areas. The aim of the article is to carry out the testing and analysis of selected navigation parameters (3D position coordinates (Northing, Easting, and height) and Euler angles (pitch and roll)) of the GNSS/INS system for Unmanned Surface Vehicle (USV) path localization during inland hydrographic surveys. The research used the Ellipse-D GNSS/INS system working in the Real Time Kinematic (RTK) mode in order to determine the position of the “HydroDron” Autonomous Surface Vehicle (ASV). Measurements were conducted on four representative routes with a parallel and spiral arrangement of sounding profiles on Lake Kłodno (Poland). Based on the obtained research results, position accuracy measures of the “HydroDron” USV were determined using the Ellipse-D GNSS/INS system. Additionally, it was determined whether USV path localization using a GNSS/INS system working in the RTK mode meets the positioning requirements for inland hydrographic surveys. Research has shown that the Ellipse-D system operating in the RTK mode can be successfully used to position vessels when carrying out inland hydrographic surveys in all International Hydrographic Organization (IHO) Orders (Exclusive, Special, 1a/1b and 2) even when it does not work 100% correctly, e.g., loss of RTK corrections for an extended period of time. In an area with limited coverage of the mobile network operator (30–40% of the time the receiver operated in the differential mode), the positioning accuracy of the “HydroDron” USV using the Ellipse-D GNSS/INS system working in the RTK mode was from 0.877 m to 0.941 m for the R95(2D) measure, depending on the route travelled. Moreover, research has shown that if the Ellipse-D system performed GNSS/INS measurements using the RTK method, the pitch and roll error values amounted to approx. 0.06°, which is almost identical to that recommended by the device manufacturer. However, when working in the differential mode, the pitch and roll error values increased from 0.06° to just over 0.2°. Full article

The commonly used POI route recommendation methods usually ignore the effects of tourists’ interests and transportation geographical conditions, and so may not output the optimal results. To solve the problems, we propose a POI route recommendation model based on symmetrical Naive Bayes classification spatial accessibility (NBCSA) and an improved cockroach swarm optimization algorithm (ICSOA), aiming to recommend POI routes that satisfy tourists’ interests and have the lowest travel costs under tourism transportation geographical conditions. Using the historical POIs visited by tourists as the training set, we construct an improved symmetrical Naive Bayes classification algorithm (NBCA), and the POIs in the destination city are divided into categories by tourists’ preferences. Then we propose an improved NBCSA model to calculate the spatial accessibility field strength (SAFS) for each category’s POIs. Based on the recommended POIs, we propose the ICSOA to recommend optimal POI routes. The experiment verifies that the proposed algorithm can effectively classify the POIs and recommend POIs that best match the tourists’ interests and produce the lowest travel costs. Compared with the TCA and GDA method, the proposed algorithm can output the POI routes with lower travel costs and has higher algorithm execution efficiency. Among the output optimal routes, the proposed algorithm can reduce costs by 5.62% at the lowest and 52.25% at the highest. Full article

Moving is an indispensable component of travelling. This paper discusses the experiences of persons with disabilities when moving around cities on foot or wheels, based on research conducted during the EU-funded project TRIPS. Findings comprise participants’ vignettes from 49 interviews in seven European cities, views on smart assistive technologies (e.g., Augmented Reality) from a pan-European quantitative survey, and design concepts related to walking based on a co-creation workshop that actively engaged persons with various types of disabilities in ideation. Findings suggest that people need reliable and clear wayfaring information on accessible travel routes featuring the coordinated design of streets, pavement, stops, stations, and vehicles to ensure seamless, step-free, and obstacle-free access, as well as disability-sensitive management of disruptions such as maintenance works, for example. Findings also suggest that users are open to using any assistive technology that can enable them to live more independently, assuming it is accessible, and are keen to co-innovate. Finally, we make recommendations for policy changes that can facilitate the redesign of urban infrastructure to make cities more accessible for people with disabilities and drive structural changes in urban planning. Full article

The intelligent connected vehicle (ICV) decision-making system needs to match tourist interests and search for the route with the lowest travel cost when recommending POIs (Points of Interest) and navigation tour routes. In response to this research objective, we construct a navigation route-planning model for tourism intelligent connected vehicles based on symmetrical spatial clustering and improved fruit fly optimization algorithm. Firstly, we construct the POI feature attribute clustering algorithm based on the spatial decision forest to achieve the optimal POI recommendation. Secondly, we construct the POI spatial attribute clustering algorithm based on the SA-AGNES (Spatial Accessibility-Agglomerative Nesting) to achieve the spatial modeling between POIs and ICV clusters. On the basis of POI feature attribute and spatial attribute, we construct the POI recommendation algorithm for the ICV navigation routes based on the attribute weights. On the basis of the recommended POIs, we construct the tourism ICV navigation route-planning model based on the improved fruit fly optimization algorithm. Experiments prove that the proposed algorithm can accurately output POIs that match tourists’ interests and needs, and find out the ICV navigation route with the lowest travel cost. Compared with the commonly used map route-planning methods and traditional route-searching algorithms, the proposed algorithm can reduce the travel costs by 15.22% at most, which can also effectively reduce the energy consumption of the ICV system, and improve the efficiency of sight-seeing and traveling for tourists. Full article

On the basis of analyzing the problems concerning hotel accommodation recommendation (HAR), this paper constructs a tourism HAR algorithm based on the CS-IDIANA clustering model (cellular space-improved divisive analysis). The algorithm integrates the cellular space model with DIANA, and takes the tourist attractions and the travel route costs as the research background and constraint conditions. Considering the feature attributes and spatial attributes of the tourist attractions, the tourist attraction recommendation algorithm based on the CS-IDIANA clustering model is established, then the HAR algorithm based on the spatial accessibility and route cost is constructed, with the constraints of the spatial accessibility field strength (SAFS) between the hotels and attractions and the travel route costs between the hotels and attractions. The experiment selects the tourism city Zhengzhou as the research object, and the experimental results are analyzed in four dimensions: the clustering results, the recommendation field strength of the tourist attractions, the hotel SAFS and the HAR results. The experiment proves that the proposed algorithm can find the best matched tourist attractions for tourists and the hotels with the lowest tour route cost based on the constraint conditions. Compared to the suboptimal hotels, the route costs are reduced by 5.67% and 9.63%, respectively. Compared to the hotel with the highest route cost, it reduces the travel costs by 29.23%. Compared with the two commonly used recommendation methods, the UCFR (user-based collaborative filtering recommendation) and ICFR (item-based collaborative filtering recommendation), the proposed CSIDR (CS-IDIANA recommendation) has a higher accuracy and recall rate. Full article

In machine learning, classifiers have the feature of constant symmetry when performing the attribute transformation. In the research field of tourism recommendation, tourists’ interests should be mined and extracted by the symmetrical transformation in founding the training dataset and creating the classifier, so as to ensure that the recommendation results meet the individualized interests and needs. In this paper, by applying the feature of constant symmetry in the classifier and analyzing the research background and existing problems of POI tour routes, we propose and construct a tour route recommendation model using improved symmetry-based Naive Bayes mining and spatial decision forest search. First, the POI natural attribute classification model is constructed based on text mining to classify the natural attributes of the destination POIs. Second, the destination POI recommendation model based on the improved symmetry-based Naive Bayes mining and decision forest algorithm is constructed, outputting POIs that match tourists’ interests. On this basis, the POI tour route recommendation model based on a spatial decision tree algorithm is established, which outputs the optimal tour route with the lowest sub-interval cost and route interval cost. Finally, the validation and comparative experiments are designed to output the optimal POIs and tour routes by using the proposed algorithms, and then the proposed algorithm is compared with the commonly used route planning methods, GDM and 360M. Experimental results show that the proposed algorithm can reduce travel costs by 4.56% and 10.36%, respectively, on the optimal tour route compared to the GDM and 360M and by 2.94% and 8.01%, respectively, on the suboptimal tour route compared to the GDM and 360M, which verifies the advantages of the proposed algorithm over the traditional route planning methods. Full article