Search Results (27)

Rural communities in many low- and middle-income countries (LMICs) remain isolated from reliable access to critical sites and social services due to inadequate transport connectivity. Formal planning approaches to improve rural networked transport infrastructure (RNTI) remain limited, underfunded and deprioritized relative to urban systems. Where resources do exist, they largely emphasize roads, despite the fact that nearly one-third of the global rural population lives more than two kilometers from an all-weather road and relies primarily on walking and intermediate modes of transport (IMTs), such as bicycles, motorcycles, and animal-powered vehicles. This review examines planning approaches for RNTI with a focus on non-car-centric, multimodal mobility. It assesses prioritization frameworks, including multi-criteria analysis, that incorporate social, environmental, accessibility, and economic considerations. Long-term outcomes are strengthened by participatory methods, multimodal planning and cross-sectoral integration that align transport investments with health, education, agriculture, and renewable resource goals. Addressing persistent barriers such as funding constraints, data gaps, and maintenance challenges requires improved spatial mapping and travel-time analysis to better identify mobility needs and guide investment decisions. The limited body of formal literature on the topic of RNTI necessitates the inclusion of grey literature and practitioner sources and underscores the call for additional research. Full article

In recent years, cycling mobility in urban environments across Spain has grown significantly, driven by sustainability policies and behavioral shifts following the COVID-19 pandemic. However, this growth has been accompanied by an increase in accidents in urban areas, where more than 72.6% of cyclist accidents are concentrated, with large cities being the most affected. This study aims to explore and analyze the factors influencing cycling accidents in Spanish municipalities with populations exceeding 50,000, during the period of 2020–2023. A total of 24 variables were analyzed, encompassing not only innovative cyclist infrastructure network features (line connectivity), but also urban morphology and street infrastructure, weather conditions and mobility (all transportation modes). The methodological approach combines Principal Component Analysis (PCA) with two negative binomial regression models: one addressing all cycling accidents, and another focusing specifically on collisions between cyclists and motor vehicles. PCA shows the complex relations between urban features when comparing cyclist accidents among cities. The main results from the Negative Binomial analysis show that increased bicycle lane length significantly reduces cycling accident risk, while higher intersections with traffic signal density are associated with a greater likelihood of car–bicycle crashes. These findings emphasize the importance of cycling infrastructure provision and intersection design and regulation as key policy levers for improving urban cyclist safety. Future research should seek to corroborate these results through micro-spatial analyses and accident geolocation, assessing their severity and accounting for more detailed data on cycling infrastructure. Finally, the results’ discussion underscores the importance of implementing holistic urban mobility strategies that prioritize cyclist safety. Full article

Promoting cycling as a sustainable mode of transport is a pressing priority in contemporary urban mobility planning. This study examines the infrastructure characteristics that most strongly influence bicycle use in dense metropolitan contexts. A mixed-methods approach was adopted, combining a systematic review of current design guidelines with a large-scale empirical analysis of Barcelona’s Bicing bike-sharing system. The dataset comprised more than 54 million recorded trips, enabling the identification of the most and least frequented routes and the subsequent assessment of their infrastructural attributes. The results indicate that network configuration, continuity, and adaptation to topographic conditions have the greatest influence on cycling uptake. By contrast, factors frequently emphasized in design recommendations, such as lane width, were not decisive, as several of the city’s most intensively used corridors did not conform to these standards. These findings suggest that the expansion of network coverage and the improvement of route connectivity are more effective strategies for increasing cycling adoption than isolated design optimizations. This study contributes evidence-based guidance for urban planners and policy-makers seeking to advance cycling as a principal component of sustainable urban mobility in Barcelona and other comparable urban environments. Full article

Identifying urban functional areas increasingly relies on data-driven approaches that utilize multimodal spatial information. There is a growing focus on purpose-oriented functional area identification with greater policy relevance. This paper proposes a data-driven methodology to identify functional areas from the perspective of bicycle users. To achieve this, line-based road network units were defined around bicycle stations, and spatial–temporal data such as Origin–Destination flows and Point of Interest information were semantically integrated to delineate functional areas. An experiment was conducted on 2628 public bicycle stations in Seoul, Republic of Korea, for May 2022, and a total of five functional areas were identified via a Co-Matrix Factorization-based fusion approach. Additionally, the proposed method was validated through visual evaluation and comparison with actual bicycle usage data. The results demonstrate that by simultaneously incorporating spatial–temporal information and latent connectivity, this approach identifies bicycle-friendly areas, even with low observed usage, highlighting its potential for policy applications. Full article

In this paper, a high-precision 3D object detector—Voxel–RCNN—is adopted as the baseline detector, and an improved detector named RCAVoxel-RCNN is proposed. To address various issues present in current mainstream 3D point cloud voxelisation methods, such as the suboptimal performance of Region Proposal Networks (RPNs) in generating candidate regions and the inadequate detection of small-scale objects caused by overly deep convolutional layers in both 3D and 2D backbone networks, this paper proposes a Cascade Attention Network (CAN). The CAN is designed to progressively refine and enhance the proposed regions, thereby producing more accurate detection results. Furthermore, a 3D Residual Network is introduced, which improves the representation of small objects by reducing the number of convolutional layers while incorporating residual connections. In the Bird’s-Eye View (BEV) feature extraction network, a Residual Attention Network (RAN) is developed. This follows a similar approach to the aforementioned 3D backbone network, leveraging the spatial awareness capabilities of the BEV. Additionally, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to assign dynamic weights to features, allowing the network to focus more effectively on informative features. Experimental results on the KITTI validation dataset demonstrate the effectiveness of the proposed method, with detection accuracy for cars, pedestrians, and bicycles improving by 3.34%, 10.75%, and 4.61%, respectively, under the KITTI hard level. The primary evaluation metric adopted is the 3D Average Precision (AP), computed over 40 recall positions (R40). The Intersection over IoU thresholds used are 0.7 for cars and 0.5 for both pedestrians and bicycles. Full article

Promoting alternative modes of transportation such as cycling represents a valuable strategy to minimize environmental impacts, as confirmed in the main targets set out by the European Commission. In this regard, in cities throughout the world, there has been a significant increase in the construction of bicycle paths in recent years, requiring effective maintenance strategies to preserve their service levels. The continuous monitoring of road networks is required to ensure the timely scheduling of optimal maintenance activities. This involves regular inspections of the road surface, but there are currently no automated systems for monitoring cycle paths. In this study, an integrated monitoring and assessment system for cycle paths was developed exploiting Raspberry Pi technologies. In more detail, a low-cost Inertial Measurement Unit (IMU), a Global Positioning System (GPS) module, a magnetic Hall Effect sensor, a camera module, and an ultrasonic distance sensor were connected to a Raspberry Pi 4 Model B. The novel system was mounted on a e-bike as a test vehicle to monitor the road conditions of various sections of cycle paths in Rome, characterized by different pavement types and decay levels as detected using the whole-body vibration $a_{wz}$ index (ISO 2631 standard). Repeated testing confirmed the system’s reliability by assigning the same vibration comfort class in 74% of the cases and an adjacent one in 26%, with an average difference of 0.25 m/s², underscoring its stability and reproducibility. Data post-processing was also focused on integrating user comfort perception with image data, and it revealed anomaly detections represented by numerical acceleration spikes. Additionally, data positioning was successfully implemented. Finally, $a_{wz}$ measurements with GPS coordinates and images were incorporated into a Geographic Information System (GIS) to develop a database that supports the efficient and comprehensive management of surface conditions. The proposed system can be considered as a valuable tool to assess the pavement conditions of cycle paths in order to implement preventive maintenance strategies within budget constraints. Full article

This study investigates the viability of incorporating bicycles into the Dhaka Metro system, a groundbreaking urban transit project for Bangladesh. As Dhaka’s inaugural metro rail network, the system signifies a substantial advancement in addressing urban congestion and enhancing transportation alternatives in one of the world’s most densely populated cities. The current design of the metro fails to accommodate bicycles, hindering efficient first- and last-mile connectivity. The investigation utilized data from 382 fully completed questionnaires, obtained through purposive sampling, about metro–cycle integration in Dhaka. The research employed RIDIT and TOPSIS analyses to rank the characteristics deemed most essential for bicycle–metro integration according to user opinions. Research indicates that secure bicycle parking, multi-modal ticketing, route comfort, and safety measures are the foremost objectives for commuters. The high emphasis on secure parking indicates the need for safe and accessible storage options that would make cycling a viable mode for reaching metro stations. A multi-modal ticketing system further enhances convenience, providing seamless transitions between transit modes. Journey comfort and the need to mitigate risks posed by motorized vehicles underscore the importance of safe and user-friendly commuting environments. While features like road and station design were ranked lower in priority, the study emphasizes that a well-integrated bicycle infrastructure is essential to ensure the metro system’s success. With these improvements, Dhaka’s metro system can meet the growing demands for sustainable and inclusive urban mobility, setting a precedent for future infrastructure projects in Bangladesh. Full article

In the last few decades, the promotion of active transport has been a viable solution recommended by transportation researchers, urban planners, and policymakers to reduce traffic congestion and improve public health in cities. To encourage active transport, it is important for cities to provide safe and accessible infrastructure for pedestrians and cyclists, as well as incentives for individuals to choose active modes of transportation over private vehicles. In this research, we focused on the suburb of Point Cook, located within the City of Wyndham in Melbourne’s west, owing to its rising human population and private vehicle ownership. The primary aim of this research is to examine the barriers in the interconnectivity of active transport networks for pedestrians and cyclists and to determine the segments of the transportation network that are not accessible to Point Cook residents. Our methodology is enshrined in the use of Social Pinpoint, which is an online interactive survey platform, and ground surveys (face-to-face interviews). In our assessment of the suburb of Point Cook, we utilised the concept of 20-min neighbourhoods to evaluate the accessibility of many important places within an 800-metre walking distance from residents’ homes. Based on our online interactive survey findings, approximately one-third of the individuals engaged in regular walking, with a frequency ranging from once a day to once every two days. One-third of the participants engaged in walking trips once or twice a week, whereas the remaining two-thirds conducted walking trips less frequently than once a week. Almost 89% of the participants expressed varying levels of interest in increasing their walking frequency. The findings showed that improving pedestrian and cycling networks that are easily accessible, well-integrated, inclusive, and safe is a prerequisite for achieving active transport and create neighbourhoods in which everything is accessible within a 20-min walking distance. Full article

The increase in fire accidents caused by indoor charging of electric bicycles has raised concerns among people. Monitoring EBs in elevators is challenging, and the current object detection method is a variant of YOLOv5, which faces problems with calculating the load and detection rate. To address this issue, this paper presents an improved lightweight method based on YOLOv5s to detect EBs in elevators. This method introduces the MobileNetV2 module to achieve the lightweight performance of the model. By introducing the CBAM attention mechanism and the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5s neck network, the detection precision is improved. In order to better verify that the model can be deployed at the edge of an elevator, this article deploys it using the Raspberry Pi 4B embedded development board and connects it to a buzzer for application verification. The experimental results demonstrate that the model parameters of EBs are reduced by 58.4%, the computational complexity is reduced by 50.6%, the detection precision reaches 95.9%, and real-time detection of electric vehicles in elevators is achieved. Full article

Public bicycle systems (PBSs) serve as the ‘last mile’ of public transportation for urban residents, yet the problem of the difficulty in borrowing and returning bicycles during peak hours remains a major bottleneck restricting the intelligent and efficient operation of public bicycles. Previous studies have proposed reasonable models and efficient algorithms for optimizing public bicycle scheduling, but there is still a lack of consideration for actual road network distances between stations and the temporal characteristics of demand at rental points in the model construction process. Therefore, this paper aims to construct a public bicycle dispatch framework based on the spatiotemporal characteristics of borrowing and returning demands. Firstly, the spatiotemporal distribution characteristics of borrowing and returning demands for public bicycles are explored, the origin–destination (OD) correlation coefficients are defined, and the intensity of connections between rental point areas is analyzed. Secondly, based on the temporal characteristics of rental point demands, a random forest prediction model is constructed with weather factors, time characteristics, and rental point locations as feature variables, and station bicycle-borrowing and -returning demands as the target variable. Finally, bicycle dispatch regions are delineated based on actual path distances between stations and OD correlation coefficients, and a public bicycle regional dispatch optimization method is established. Taking the PBS in Ningbo City as an example, the balancing optimization framework proposed in this paper is validated. The results show that the regional dispatch optimization method proposed in this paper can achieve optimized dispatch of public bicycles during peak hours. Additionally, compared with the Taboo search algorithm (TSA), the genetic algorithm (GA) exhibits a 11.1% reduction in rebalancing time and a 40.4% reduction in trip cost. Full article

When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations. Full article

The aim of this research is to address the challenge of transforming car-oriented industrial parks into pedestrian- and bicycle-friendly environments. Through the implementation of a multi-criteria decision-making (MCDM) approach, the study aims to evaluate alternative pathway connections and assess their potential impact on bicycle and pedestrian traffic volumes. By enhancing the connectivity of the cycling pathway network, the research seeks to demonstrate the potential for substantial increases in cycling and walking within industrial zones. This research leverages a multi-criteria decision-making framework, specifically the ARAS-G method, and integrates geographic information system analysis alongside Python scripting to project future bicycle usage and assess alternative pathway connections. The study underscores the potential for substantial increases in cycling and walking by augmenting the connectivity of the cycling pathway network. The findings hold practical significance for urban planners and industrial zone developers, advocating a holistic approach to sustainable transportation. The research contributes a comprehensive set of criteria encompassing connectivity, safety, accessibility, efficiency, integration within the urban fabric, and cost-effectiveness to evaluate sustainability and prioritize actions and measures for reestablishing industrial zones as bicycle-friendly spaces. Full article

Cooperative intelligent transport systems (C-ITSs) are being deployed all around the world. Shortly, in addition to vehicles, bicycles, pedestrians, buses, and all moving equipment will be compatible with C-ITS. These systems are connected through wireless local area networks based on WIFI IEEE 802.11p. The large number of C-ITSs and services will lead to a glut in the bandwidth of wireless networks. To overcome this limitation, we propose in this paper a new approach using the information-centric networking (ICN) paradigm which allows vehicles to communicate with the cloud environment. This scheme is denoted by vehicular central data networking (GeoVCDN). Our approach aims to reduce bandwidth consumption and improve data freshness by taking benefit from the existing application beacons and the geographical routing used by C-ITS actors. We have compared the performances (in terms of the network overhead and data freshness) of our solution to two other well-known ICN-based solutions. Each of them represents one of ICN categories, in particular, rendez-vous network (RENE) and named data networking (NDN). To do so, we have proposed a probabilistic model that allows us to evaluate the freshness and the load of the network. Furthermore, we have implemented these methods in a simulator. Our proposal outperforms the other methods in terms of network overhead and data freshness. Full article

This paper deals with a variant of the multifacility location-routing problem in urban areas. The distribution network is modelled by an undirected graph, in which the nodes are split into a set of pickup-delivery stations, a depot, and a set of customers. The arcs represent the minimum-cost connections between nodes. A customer is assigned to a pickup-delivery station if he or she can reach it at the lowest sustainable cost, i.e., on foot or by bicycle, without exceeding a predefined maximum distance. The goal is to minimise the goods’ total delivery cost, including pollutant emissions. In this perspective, both travel distance and means of transport play a key role. We present an exact novel approach based on partitioning the research space of the solutions of a Mixed Integer Linear Programming model. In the model, Boolean decisional variables, representing the selection of the locations for the pickup-delivery stations, are fixed simultaneously with the solution of the classical Travelling Salesman Problem. A branching constraint allows us to determine the route that serves the selected pickup-delivery stations and the route, if any, that serves customers who do not go to any pickup-delivery station. We conduct extensive experimentation to test the proposed approach’s computational efficiency and analyse the optimal solution’s robustness with respect to the maximum distance of customers from the stations, their activation cost and the pollutant emissions. The effectiveness of the proposed approach in terms of solution quality and computation time is certified by a set of computational tests based on randomly generated instances with up to 150 customers and 30 pickup-delivery stations. The application of the proposed exact method to a case study related to a district of the city of Genoa (Italy) confirms its validity also for sustainably addressing real-size urban delivery problems. An evaluation of incentives for customers using pickup-delivery stations, possibly by implementing discount policies on orders, is also proposed. Full article

A topological index is a number that is connected to a chemical composition in order to correlate a substance’s chemical makeup with different physical characteristics, chemical reactivity, or biological activity. It is common to model drugs and other chemical substances as different forms, trees, and graphs. Certain physico-chemical features of chemical substances correlate better with degree-based topological invariants. Predictions concerning the dynamics of the continuing pandemic may be made with the use of the graphic theoretical approaches given here. In Networks, the degree entropy of the epidemic and related trees was computed. It highlights the essay’s originality while also implying that this piece has improved upon prior literature-based realizations. In this paper, we study an important degree-based invariant known as the inverse sum indeg invariant for a variety of graphs of biological interest networks, including the corona product of some interesting classes of graphs and the pandemic tree network, curtain tree network, and Cayley tree network. We also examine the inverse sum indeg invariant features for the molecular graphs that represent the molecules in the bicyclic chemical graphs. Full article