Transport Infrastructure Planning Research Articles

Exploring accessibility to medical facilities for older adults: Potential vs. Revealed accessibility

IntroductionThe issue of population aging is increasingly prominent, presenting unprecedented challenges and pressures on urban medical resources. The equity and convenience of medical services for older adults have emerged as critical concerns for urban planners. While previous research on medical accessibility for older adults has yielded fruitful results, it has lacked a thorough analysis of the quantitative relationship between revealed and potential accessibility across urban spaces, as well as the influencing factors leading to the spatial heterogeneity of these two types of accessibility. MethodsThis study calculates the ratio of revealed to potential accessibility based on multi-source data, thereby identifying the allocation of medical resources in different sub-districts in Beijing. Furthermore, an extreme gradient boosting model is used to investigate the non-linear associations between the potential influencing factors and the ratio of revealed to potential accessibility. ResultsThe results revealed a significant spatial disparity between potential accessibility and revealed accessibility and identified the threshold effects of influencing factors contributing to the accessibility ratio of revealed to potential accessibility. The density of bus stops, vehicle ownership, and density of roads are the top three among the influencing factors in terms of their relative importance in influencing the accessibility ratio. ConclusionsThe threshold effect should be considered when implementing planning adjustments, as simply increasing or decreasing the values of specific built environment variables may not result in a corresponding improvement in medical accessibility for older adults. With limited resources, enhancing the planning and construction of transportation infrastructure is more efficient in reducing the discrepancy between revealed accessibility and potential accessibility compared to other methods.

Prediction of electric vehicle charging demand using enhanced gated recurrent units with RKOA based graph convolutional network

Accurate forecasting of traffic patterns plays a crucial role in the effective management and planning of urban transportation infrastructure. In particular, predicting the availability of electric vehicle (EV) charging stations is essential for alleviating range anxiety among drivers and facilitating the adoption of electric vehicles. This study proposes a novel deep learning-based predictor model to approximate the demand for charging electric vehicles over the long term. The methodology integrates the Berkeley wavelet transform (BWT) to decompose input time series data while preserving its inherent characteristics. The proposed hybrid prediction model combines an enhanced gate recurrent unit with an optimized convolution kernel within a fusion graph convolutional network (GCN). The Red Kite Optimization Algorithm (RKOA) is employed to select the convolution kernel of the GCN effectively. Additionally, the construction of the graph leverages both adjacency and adaptive graphs to accurately represent the correlations among nodes in the EV network. The model extracts multi-level spatial correlations through stacked fusion graph convolutional elements and captures multi-scale temporal correlations via an improved gated recurrent unit. Furthermore, the incorporation of residual connection units allows for the fusion of extracted spatiotemporal features with direct data, enhancing predictive performance. The proposed neural predictor is evaluated using EV charging data from Georgia Tech in Atlanta, USA. The experimental results demonstrate the effectiveness of the prediction metrics generated by the proposed model compared to existing methods reported in the literature, showcasing its capability to accurately forecast EV charging demand.

Urban Road Transport Infrastructure Planning and Development Implications on Socio-Economic Growth in the City of Douala, Cameroon

Cities are the major contributors to socio- economic growth with easy access to amenities which stem through road transport infrastructure development. This study was conducted to assess urban road transport infrastructure planning and examine development implications on socio-economic growth in the City of Douala. A descriptive research design was used with primary and secondary data collected and analyzed using descriptive and inferential statistical techniques. Results revealed that, transport infrastructure in Douala have been structured through urban development planning policy which influences surface spatial expansion of socio- economic activities. The planning policies have contributed to the rapid expansion and growth through the initiations of master plan, land use plan and sector plan. Infrastructural development on road transport depicts a physical expansion and construction of road networks which influences the socio-economic growth of the study area. Stakeholders’ actions on construction, reconstruction and rehabilitation, investments and maintenance of road infrastructure have helped to curb down some transportation challenges and creates easy access to facilities within different sectors of the study area. However, road transport infrastructure which is well-developed boasts socio-economic activities and growth. The state of road transport network, its diversity and characteristic features is defined by its prevalence within different areas in Douala. Contrarily, other transport infrastructures which are not functioning appropriately due to lapses in physical planning possess enormous implications on transportation in the study area. The conclusion drawn was that the manner in which urban planning policies are implemented determines the level of development in road transport infrastructure and socio-economic growth of Douala. The study recommends an effective application of planning policies to be integrated by implicating all the actors of urban managers and transportation planners for improved growth in the study area.

An Information Gradient Approach to Optimizing Traffic Sensor Placement in Statewide Networks

Traffic sensors are vital to the development and operation of Intelligent Transportation Systems, providing essential data for traffic monitoring, management, and transportation infrastructure planning. However, optimizing the placement of these sensors, particularly across large and complex statewide highway networks, remains a challenging task. In this research, we presented a novel search algorithm designed to address this challenge by leveraging information gradients from K-nearest neighbors within an embedding space. Our method enabled more informed and strategic sensor placement under budget and resource constraints, enhancing overall network coverage and data quality. Additionally, we incorporated spatial kriging analysis, harnessing spatial correlations of existing sensors to refine and reduce the search space. Our proposed approach was tested against the widely used Genetic Algorithm, demonstrating superior efficiency in terms of convergence time and producing more effective solutions with reduced information loss.

Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling

AbstractA two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO‐v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO‐v7's detection speed of 7.8 ms per frame further validates the feasibility of real‐time data construction. The findings indicate that the combination of YOLO‐v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance.

Bicycle Infrastructure Safety Assessment From the Perspective of Urban Development Specialists and Engineers

The safety of bicycle infrastructure is a primary factor influencing bicycle travel. While cyclists’ perspectives on infrastructure safety are extensively studied, they are merely the end users. Decisions on infrastructure design are made by engineers and urban development specialists. Therefore, it is crucial to determine if these professionals’ safety assessments align with those of cyclists. A qualitative survey was conducted with 5 expert engineers and 5 urban development specialists, each having 5 to 20 years of experience in transportation infrastructure planning. Kendall’s coefficient of concordance W was used to assess the compatibility of their opinions. The results showed significant compatibility: W = 0.697 for engineers and W = 0.511 for urban development specialists. Seventeen cycling infrastructure installation schemes were evaluated. Both engineers (M = 10.0, SD = 0.0) and urban development specialists (M = 9.8, SD = 0.44) indicated the DT_2 option as providing the greatest sense of security, where the bicycle path is physically separated from both the carriageway and pedestrian path. The key findings reveal agreement on the safety of straight-street segments of bicycle infrastructure but diverging opinions at intersections zones. Urban development specialists are influenced by existing practices and legal frameworks lacking detailed cycling infrastructure guidelines at intersections. Engineers align more closely with cyclists’ perceptions, emphasising physical separation and speed reduction measures. The study concludes that urban development specialists need to better understand cyclists’ needs and prioritize safer infrastructure solutions.

Understanding the influence of environmental factors on driver speed: A structural equation modeling analysis

This study examined the influential roadway and contextual factors affecting drivers' speed selection through a comprehensive investigation employing structural equation modeling. Roadway and contextual variables including road curvature, presence of roundabouts, adverse weather conditions, and access were investigated using infrared speed sensors. The analysis revealed that specific roadway and contextual factors such as field variables, uphill and downhill road inclines, particular road curvature, and rainy weather significantly influence drivers' chosen speeds, while factors such as road access, nighttime conditions, larger road curvature, and signage exhibit a lesser impact. Notably, the study also found a positive correlation between road curvature radius and driver-chosen speed. The study's implications for transportation infrastructure planning and road safety interventions are underscored, with potential applications in road design, signage improvement, and weather-responsive measures to regulate driver speed choices in specific roadway and contextual conditions.

The Road Ahead: A Survey of Autonomous Vehicle Challenges and Opportunities

This comprehensive study delves into the rapidly advancing field of autonomous vehicles (AVs). We explore the core technologies that enable AVs to navigate, perceive their surroundings, make informed decisions, and control their movements. The intricate interplay between sensor fusion, localization and mapping, planning algorithms, and control systems is examined. A critical focus of this research is on the ethical and societal implications of AV deployment. We analyze the crucial considerations surrounding safety, security, legal frameworks, and ethical guidelines that must be addressed to ensure responsible adoption of AVs. The potential social and economic impacts of widespread AV adoption are examined, including implications for transportation infrastructure, job markets, and urban planning .Furthermore, this study explores the exciting future directions of AV technology. We discuss emerging trends and innovations, such as the integration of artificial intelligence and machine learning, advancements in sensor technology, and the development of advanced communication networks. These developments hold the promise of further enhancing the capabilities and reliability of AVs, paving the way for a future where autonomous transportation becomes a ubiquitous part of our daily lives. Keywords and Phrases: Autonomous Vehicles, Sensor Fusion, Localization and Mapping, Planning Algorithms, Control Systems, Safety, Security, Legal Frameworks, Ethical Considerations, Social Impact, Economic Impact, Future Directions.

CAG-NSPDE: Continuous adaptive graph neural stochastic partial differential equations for traffic flow forecasting

Traffic flow forecasting, which aims to predict future traffic flow given historical data, is crucial for the planning and construction of urban transportation infrastructure. Existing methods based on discrete dynamic graphs are unable to model the smooth evolution of spatial relationships and usually have high computational cost. In addition, they ignore the effect of stochastic noise on the modeling of temporal dependencies, which lead to poor predictive performance on complex intelligent transportation systems. To address these issues, we propose continuous adaptive graph neural stochastic partial differential equation (CAG-NSPDE). A continuous graph is constructed based on learnable continuous adaptive node embeddings for the modeling of evolution of spatial dependencies. To explicitly model the effect of noise, a novel architecture based on stochastic partial differential equation (SPDE) is proposed. It can simultaneously extract features from the temporal and frequency domains by reducing SPDEs to a system of ODEs in Fourier space. We perform extensive experiments on four real-world datasets and the results demonstrate the superiority of CAG-NSPDE compared with state-of-the-arts.

Spatial imaginaries and the politics of inter-regional transport infrastructure development in Northern England

ABSTRACT This article explores how relationships between policy actors at different scales, and their changing character over time, shape the production of spatial imaginaries and their use in transport infrastructure planning. Based on research on the planning of a major rail project in Northern England between 2011 and 2022, the article illustrates how spatial imaginaries reflect interactions between national, sub-national, regional and local institutions. Drawing on a programme of interviews with policy actors, the research highlights the tensions manifested in planning for transport infrastructure about which places are prioritized in policy, and the institutional influences shaping these decisions. The article develops understanding of how inter-scalar politics influences the production of spatial imaginaries and how these imaginaries are in turn utilized in support of transport infrastructure plans by policy actors at different spatial scales.

Optimization Strategy for the Spatiotemporal Layout of E-Bike Charging Piles from the Perspective of Sustainable Campus Planning: A Case Study of Zijingang Campus of Zhejiang University

With the expansion of Chinese university campuses, electric bikes (E-bikes) have become the most sustainable and effective commuting option because they are a flexible and energy-saving travel mode. Consequently, campus E-bike charging piles have become one of the most essential public service facilities on campuses. However, since most Chinese campuses are closed and independent, the principles of urban public service facilities cannot be simply applied to the layout and use of campus charging facilities. Thus, this study focuses on Zijingang Campus at Zhejiang University, and proposes an optimization strategy for the spatial and temporal layout of E-bike charging piles on the campus. First, trip chain demand models are constructed to examine the travel patterns of E-bike users on campus and the demands for charging areas and time. Second, a space location model is constructed to locate the charging piles in areas with high demand. Finally, according to the charging times of different users, user charging time is integrated into the strategy. This study enhances the layout and utilization system of campus E-bike charging facilities by considering both temporal and spatial dimensions. Overall, this study contributes to the advancement of sustainable transportation infrastructure planning on a campus-wide scale, offering theoretical insights for the design and utilization of functional facilities in large-scale, semi-enclosed environments (e.g., university campuses).

Analysing intermodal connectivity for the functionality of Namibia’s regional logistics hub

Background: In today’s interconnected global economy, logistics and supply chain management are pivotal in facilitating the movement of goods and services across regions. Supply chains have become increasingly complex, involving multiple stakeholders, diverse transportation modes, and intricate global networks. The efficient management of these supply chains has emerged as a critical determinant of competitiveness and economic growth for businesses and nations.Objectives: The study aims to analyse the level of intermodal connectivity and how it enhances the functionality of a regional logistics hub using Namibia as a case study.Method: The research study adopted a mixed-method design, collecting data through interviews, questionnaires, and documentary analysis to examine intermodal connectivity and its impact on the functionality of the Namibian regional logistics hub.Results: The study’s key findings reveal a fragmented railway system and a significant reliance on road transportation within the Namibian transport system. The level of intermodality is found to be low, indicating limited connectivity and integration between different modes of transport in Namibia.Conclusion: The study highlights the need to address the fragmentation within the railway system, reduce the heavy reliance on road transportation, and improve intermodal connectivity to enhance the functionality and efficiency of the Namibian transport system.Contribution: This research contributes to the existing knowledge of transportation and logistics hubs within the Namibian context. By exploring the role of intermodal connectivity, the study builds upon the theoretical frameworks of transport geography, logistics management, and infrastructure planning.

Enhancing Roadway Efficiency through Comprehensive Studies on Travel Time, Delays, and Spot Speeds

This study integrates travel time and delay with spot speed study, utilizing the floating car method. The correlation between increased traffic flow and elevated delay times is underscored. Notably, a significant portion of vehicles operating below anticipated speeds emerges as a pivotal factor contributing to congestion. The average journey time is approximately 4 minutes, with an average stopped delay ranging from 0.17 to 0.11 minutes. Spot speed study in four directions reveals nuanced speed dynamics. In the East-West direction, the 15th, 85th, and 98th percentile speeds are 39.14 km/h, 62.27 km/h, and 85.00 km/h and similar patterns are observed in the other directions. These results transcend routine traffic studies, offering actionable insights for urban planning, traffic regulation, and economic assessments. The study delves beyond statistical compilations, providing concrete evidence of the intricate interplay between traffic flow, delay times, and speed dynamics. It aspires to guide traffic engineers, urban planners, and decision-makers into the realms of future urban development. The comprehensive understanding derived ensures informed decision-making in transportation management and infrastructure planning.

Системный анализ экологических факторов при проектировании прибрежных транспортных объектов в арктических морях

Рост промышленной деятельности на акваториях российского нефтегазового арктического шельфа сопровождается созданием различных специализированных многофункциональных грузоперевалочных комплексов, основу которых на трассе Северного морского пути представляют морские порты. Развитие портовой и прибрежной инфраструктуры неразрывно связано с необходимостью обеспечения бесперебойного и безаварийного функционирования порта, а в суровых условиях российской Арктики одним их важнейших вопросов является вопрос обозначения навигационных и экологических рисков в процессе эксплуатации планируемого объекта. В работе проведен детальный анализ непосредственно устьевой акватории реки Индига, выбранной в качестве наиболее подходящего места для возведения нового транспортного объекта. Обозначено значение специфических параметров окружающей природной среды при проектировании прибрежных морских объектов в зоне арктических морей. Представлена оценка влияния прибрежных волновых потоков как наносодвижущего фактора, и основания рассматривать их динамические процессы в комплексе с морфологическими особенностями исследуемой акватории как потенциальную угрозу блокировки будущей судоходной трассы песчаными перемычками. Обоснованы предложения по установке инженерных конструкций волногасящего типа для перекрытия потока наносов и защиты причальных линий от прямого волнового воздействия. The growth of industrial activity in the waters of the Russian Arctic oil and gas shelf accompanied by the creation of various specialized multifunctional cargo complexes. Seaports represent the basis of cargo complexes on the route of the Northern Sea Route. The development of port and coastal infrastructure closely linked to ensuring trouble-free operation of the port. In the harsh conditions of the Russian Arctic, one of their most important issues is the designation of navigational and environmental risks of the new facility. The paper provides a detailed analysis of the mouth of the Indiga River. This water area has been chosen as the most suitable place for the construction of a new transport facility. The importance of specific environmental parameters in the design of coastal marine facilities in the Arctic seas zone indicated. The paper presents an assessment of the influence of coastal wave flows as a factor creating the shoal. The reasons are present to consider the dynamic processes and morphological features of the studied water area as a potential threat of blocking the future shipping route by sand bridges. The proposals for the installation of engineering wave-damping structures are substantiated. Their purpose is to block the flow of sediment and protect the marine berths from direct wave action.

THE STRATEGIC PLANNING OF TRANSPORT INFRASTRUCTURE AND MANAGEMENT OF LOGISTICS SOLUTIONS IN CONDITIONS OF WAR

This article examines the challenges faced by Ukraine, which is embroiled in a military conflict within its borders, particularly the imperative to ensure the efficiency of its transport infrastructure and logistics processes. It has been identified that military actions result in damage to transport networks, restricted access to critical resources, and alterations in logistics operations. It has been established that in wartime conditions, strategic planning of transport infrastructure and effective management of logistics solutions become particularly pertinent as they dictate the country's capacity to meet its own needs and accomplish strategic objectives. Furthermore, it has been determined that Ukraine's future integration into Europe necessitates reforming its transport and logistics system to align with EU standards. Strategic approaches to restoring the transport network and addressing logistics challenges during conflict are discussed, primarily focusing on alternative routes, the development of effective supply chain management systems, and enhancing system redundancy. Strategies for adaptation and response to changes aimed at ensuring the reliability and efficiency of transport and logistics processes in Ukraine have been explored.

An integrated urban water resources management approach for infrastructure and urban planning

Transportation projects are crucial for the overall success of major urban, metropolitan, regional, and national development according to their capacity by bringing significant changes in socio-economic and territorial aspects. In this context, sustaining and development of economic and social activities depend on having sufficient Water Resources Management. This research helps to manage transport project planning and construction phases to analyze the surface water flow, high-level streams, wetland sites for development of transportation infrastructure planning, implementing, maintenance, monitoring and long-term evaluations to better face the challenges and solutions associated with effective management and enhancement to deal with Low, Medium, High levels of impact. A case study was carried out using the Arc Hydro extension within ArcGIS for processing and presenting spatially-referenced Stream Model. Geographical information systems have the potential to improve water resources planning and management. The study framework would be useful for water resources problem solving by enabling decision makers to collect qualitative data more effectively and gather them into the water management process by a systematic framework.

Strava Metro Data

The widespread adoption of mobile phone and other location-tracking devices, and the enormous amounts of data they produce, has provided municipalities with the opportunity to automate previously time-consuming and labour-intensive data collection processes. Municipal planners, in particular, have begun to integrate the aggregated data sets of private urban technology platforms into active transportation and broader infrastructure planning initiatives. To date, however, there has been limited research on the implications of this integration for municipal decision-making and governance processes. Using the Strava Metro data stream and its free-access model as a case study, this paper explores both the motivations behind municipal adoption of the Strava platform and the benefits that accrue from its usage. Through the application of a mixed methods approach, including the building of a use case database via a search of internet and academic literature sources and qualitative interviews with municipal planning staff, our research examines how Strava data is used to support the work of municipal planners and evaluates the strengths and weaknesses of that use. Our study finds that Strava Metro data aided municipal staff in the planning of cycling and pedestrian infrastructure, complementing available in-house data sets; helped spur new active transportation initiatives; and enabled innovation and professional curiosity on the part of planners. The paper concludes by exploring the ramifications of Strava data for community wellness and broader public realm improvements, as well as extending a discussion with respect to the platform’s sociodemographic representativeness and related limitations.

ТЕНДЕНЦИИ РЕШЕНИЯ ТРАНСПОРТНЫХ ПРОБЛЕМ В МЕГАПОЛИСАХ И ЭВОЛЮЦИЯ ПРОЕКТНОГО МЫШЛЕНИЯ

Congestion in megacities due to extreme population density and insufficient transport infrastructure leads to serious consequences. Traffic jams not only cause inconvenience for passengers, but also have a negative impact on the economy and the environment. In order to stabilize the situation and eliminate the negative impact of inefficient traffic on the life of megacities, it is necessary to review previously used techniques and identify new measures to solve the problem. Research methods include a review and systematization of information sources related to economic, urban planning and social aspects of transport infrastructure. By summarizing and comparing the results of domestic and foreign experience in eliminating traffic congestion, outdated methods were identified and trends in the development of design thinking in the field of construction of road spaces were identified. Current ratings of transport systems make it possible to fix a list of leaders with an effective policy for the development of public transport. Conceptual modeling demonstrates modern features of the placement of road networks within the city. To improve urban mobility, an integrated approach is required: active implementation of innovations in the transport structure, changing urban planning thinking, reviewing management impacts, changing priorities in favor of the needs of pedestrians and informing the public, involving it in the modernization process. These techniques will create modern and smart urban spaces that meet the needs of modern society.

Life and death of Poland’s landscape – are we living in landscape “hell”?

The author’s thesis assumes that the degree of landscape degradation in many parts of Poland is so high that the use of the term “landscape hell” is justified. This denotes such a state of visual and aesthetic, structural and functional landscape degradation that leads or has already led to the landscape’s death. This condition existed in many areas during the communist period, but now, 34 years after the political transformation and eight years of the development of the “mafia” state, it is also occurring with increasing frequency. Its origin lies in the attitude of some politicians and the public towards space and nature as resources that can be exploited to an extreme extent and that must be subordinated to people. This is due, among others, to the Poles’ civilizational lag in relation to many other nations, Polish society’s developmental history, and the recurring compensation for economic shortcomings. In the first quarter-century of transformation, the development of consumption, the dominance of economic development criteria, and the politicians’ ignorance and bias have introduced phenomena such as strip farm urbanisation, “concreteosis”, architectural “gigantomania”, “urban blight”, “railingeosis”, and “advertosis” into the landscape. After 2015, the above have been overlaid by the landscape-eroding activities of the representatives of the “mafia” state, resulting from, among others, excessive deforestation and tree clearing in open and urban areas, continued operation of the coal-based energy sector, including open-cast mines, river diversions, construction of hydro-technical facilities, mass legalisation of arbitrary construction, and transport infrastructure planning. The various recommendations on landscape conservation, formulated for many years, have, for the most part, not been implemented, despite Poland’s ratification of the European Landscape Convention. We can thus gradually kill off more landscape, which will continue to linger in our memories and documents for some time, until eventually, virtual reality becomes the main way of admiring it.

A novel framework for road vectorization and classification from historical maps based on deep learning and symbol painting

Road networks in the past are imperative for understanding evolution of transportation infrastructure, urban sprawl, and route planning, etc. Various approaches have been developed for road extraction from historical maps, among which deep learning techniques stand out as the most effective ones. However, little attention has been paid to investigating road vectorization and classification from historical maps. Moreover, road classification via machine learning methods usually requires large amounts of dedicated training data. To address these issues, this paper proposes a novel and comprehensive framework for road vectorization and classification on the basis of road segmentation from historical maps. First, deep learning is used to get pixel-wise raster road segmentation results, which are further skeletonized using morphological operations. Then, considering that each road class is represented with a certain symbol, a painting function is defined for each class able to paint the corresponding symbol. These painting functions are then used to draw road segments along the skeletons. Since the start and end points in each painting function are used to vectorise the segment, this method achieves vectorization and classification at the same time. Our method is validated on four Siegfried map sheets in Switzerland, and evaluated via both visual and quantitative assessments. The results indicate that the method is capable of classifying roads accurately. In particular, two evaluation metrics completeness and correctness achieve 90.69% and 72.71% respectively for road class 2 which accounts for the highest portion in the map. Moreover, the results of this method avoid the saw-toothed issue of vectorised road lines. This research is beneficial for creating complete vector road network datasets with class information to support decision-making in urban planning and transportation.