Urban Transport Infrastructure Research Articles

Transforming Urban Environments: Understanding the Social Implications of Metrobus (MBS) Service Development in Lahore, Pakistan

This research investigates how the Lahore Metrobus system (MBS) influences perceptions of urban space quality through the lens of urban design and transport infrastructure. Lahore is undergoing significant urban changes with the introduction of a newly constructed mass transit system, which thus necessitates a joint development strategy to preserve its cultural heritage. Recent evolutions in transport planning has heightened the interest in analyzing the way mobility factors affect the perceptions of a place’s quality. The gap in previous work lies in the limited focus on qualitative, human-centered perspectives regarding the impacts of public projects like the MBS on urban space quality. This study uniquely fills this void by examining the influence of the MBS on people’s lives and the quality of urban spaces. Comparing pre- and post-Metrobus scenarios, along with questionnaire surveys of riders and shopkeepers, exposes the neglect of the social image during design and construction. The system was implemented with a focus solely on hard, core infrastructure, thereby neglecting soft components such as area development, social and cultural value, and human-centered design. The government should unite transport agencies, stakeholders, and the public to craft a joint policy for enhancing revenue, ridership, and fostering transit-oriented development (TOD). Research findings will help in achieving social and cultural sustainability for upcoming transport lines in Pakistan, as well as directions for other developing nations looking to implement mass transit networks.

Mitigating urban heat and air pollution considering green and transportation infrastructure

Global urbanization is causing an increase in population and transportation along with a reduction of green spaces. This phenomenon is leading to the development of Urban Heat Island (UHI) and Urban Pollution Island (UPI) effects, negatively impacting the urban environment. Numerous mitigation strategies have been proposed to reduce heat and air pollution. However, the combined impact of green and transportation infrastructure and their potential to mitigate heat and air pollution remains partially understood. This review aims to contribute by systematically investigating studies on mitigation strategies for urban green and transportation infrastructure from 2003 to 2023. These strategies are classified into two types: green infrastructure-related (greenery, green roofs, and green walls) and transportation-related (transportation fuel, transportation networks, and transportation organization systems). The findings are presented under five aspects: (1) Most research on transportation infrastructure adopts qualitative research methods, whereas research on green infrastructure tends to adopt quantitative methods; (2) Developed countries prioritize upgrading their transportation fuel, while developing countries remain inclined towards developing urban green spaces; (3) In subtropical and temperate zones, green roofs and green walls may cool the local ambient (within 1 m above the surface) by up to 10℃, whereas the cooling effect in other climate zones is within 5℃; (4) More focus was put on reducing the surface temperature rather than the air temperature. Green roofs and green walls are found to cool the former 2–3 times more than the latter; (5) Green infrastructure in the subtropical zone is 1.2 to 2 times more effective at adsorbing gaseous pollutants than in the temperate zone. This review can assist policymakers, urban planners and researchers in effectively mitigating urban heat and air pollution, and guide future research endeavors.

Developing urban infrastructure constructions for increasing e-commerce sales: the moderating roles of aging population

PurposeThe aging population, a higher proportion of older adults (aged 65+), is considered a global and severe problem, while the information systems (IS) literature on detecting the relationship between the aging population and the development of electronic commerce (e-commerce) is limited and insufficient. Hence, the main objective of this paper is to examine whether an aging population can moderate the effect of infrastructure constructions on e-commerce sales and whether an aging population can affect e-commerce sales.Design/methodology/approachTo investigate the relationship between the aging population and e-commerce sales, this study proposes two potential influential mechanisms: moderating the effects of infrastructure development on e-commerce sales and direct influence. Subsequently, a sample of 31 Chinese provinces from 2013 to 2019 is utilized to conduct regression analyses in order to examine these hypotheses.FindingsThe findings suggest that the development of urban transportation infrastructure and network constructions can significantly contribute to the enhancement of e-commerce sales, and the influence cannot be affected by aging population. Furthermore, it is noteworthy that an aging population can have a positive effect on e-commerce sales.Practical implicationsThe findings can inform future infrastructure constructions by assessing the potential of infrastructure projects to boost e-commerce sales and examining whether this effect varies in an aging population context.Originality/valueThe findings substantiate the pivotal role of older adults in the e-commerce industry. Moreover, the obtained results establish a positive relationship between an aging population and e-commerce sales, thereby offering diverse perspectives on existing theories.

Развитие подходов к оценке эффективности функционирования пассажирских транспортно-пересадочных узлов

Objective: the article proposes a method for assessing the efficiency of passenger transport hubs, based on the key principles of operation management. Methods: to achieve the goal of the study, methods of calculating coefficients evaluating compliance with the basic principles of operations management, methods of statistical analysis and data processing, and the method of simulation are used. To carry out the research, materials from scientific papers on the chosen topic, periodicals, as well as Internet resources, including open data from the St. Petersburg Transport Committee, were used. Results: the main scientific result is a method developed by the author to evaluate the efficiency of passenger transport hubs, based on the key principles of operations management. The proposed method takes into account the specifics and characteristics of passenger transport hubs and can be used at any stage of their life cycle, including at the design stage. Unlike the available methods, the proposed methodology allows timely detection and elimination of “bottlenecks” in the planning solutions of a passenger transport hub. Practical significance: as a result of testing the methodology on the example of the Volkovskaya transport interchange hub project, practical recommendations were formed to eliminate bottlenecks and increase the capacity of the facility. The author’s developments can be used in the executive bodies of state power responsible for the development of urban transport infrastructure, and in design organizations in the planning processes for the construction and modernization of passenger transport hubs, as well as in evaluating the effectiveness of their work.

Technoeconomic analysis of rooftop PV system in elevated metro station for cost-effective operation and clean electrification

Metro system constitutes an essential component of urban transportation infrastructure, simultaneously serving as a significant energy consumer and carbon emitter. Integration of rooftop PV systems in elevated stations can effectively mitigate carbon emissions and enhance the utilization rate of clean energy. Nevertheless, current research rarely explores the application and feasibility analysis of rooftop PV systems in elevated metro stations. To address this research gap, the study investigates potential of rooftop PV systems applied in the elevated stations, and optimizes the PV scale and battery capacity for achieving the maximum net present value. The results show that the optimized photovoltaic-battery (PVB) system can bring about a PV self-sufficiency (SS) of 0.54, in the baseline scenario. For the entire line, SS can achieve 0.26–0.59 with the integration of PVB systems. Furthermore, the impact of skylights on energy consumption for illumination and PV power generation is discussed, and the PV potential of skylights is investigated. The results show that PV skylights lead to an increase a rise in the SS of 8.6%–15.3%. This study contributes reference for the potential of PV systems on elevated stations and for cost-effective operation and clean electrification of the urban rail transit sector.

How does the opening of high-speed rail drive energy restructuring? New micro evidence from China

Against the dual backdrop of China vigorously promoting high-speed railways (HSR) construction and establishing an ecological civilization system, HSR as urban transportation infrastructure, is currently gaining growing attention from the academic community due to its environmental benefits as well as its effects in energy conservation and emission reduction. In this context, this research treats the initiation of HSR as a quasi-natural experiment, meanwhile empirically examining the effect of HSR on energy structure restructuring and exploring the micro-level channels through which it operates. The empirical results validate the reduction effect of HSR opening on fuel energy consumption of industrial enterprises, especially the usage of fuel coal. This highlight remains valid across a series of robustness tests. Moreover, it is evident that the “driving effect” of enterprise technological innovation capability and the “industrial upgrading effect” of enterprise relocation are effective transmission pathways in the process of HSR opening influencing the adjustment of energy consumption structure. Moreover, the unique characteristics of enterprise, industry, and region introduce a certain degree of heterogeneity. The low-energy-consumption effect of HSR is more pronounced in high-carbon industry enterprises, technology-intensive enterprises, firms engaged in innovation pilot cities, small to the medium-sized city and resource-based city. This paper provides a new perspective on energy structure adjustment, contributing to offering solid experiences and references for environmental governance in China and other emerging economies.

PARALLEL AND DISTRIBUTED COMPUTING TECHNOLOGIES FOR AUTONOMOUS VEHICLE NAVIGATION

Context. Autonomous vehicles are becoming increasingly popular, and one of the important modern challenges in their development is ensuring their effective navigation in space and movement within designated lanes. This paper examines a method of spatial orientation for vehicles using computer vision and artificial neural networks. The research focused on the navigation system of an autonomous vehicle, which incorporates the use of modern distributed and parallel computing technologies.
 Objective. The aim of this work is to enhance modern autonomous vehicle navigation algorithms through parallel training of artificial neural networks and to determine the optimal combination of technologies and nodes of devices to increase speed and enable real-time decision-making capabilities in spatial navigation for autonomous vehicles.
 Method. The research establishes that the utilization of computer vision and neural networks for road lane segmentation proves to be an effective method for spatial orientation of autonomous vehicles. For multi-core computing systems, the application of parallel programming technology, OpenMP, for neural network training on processors with varying numbers of parallel threads increases the algorithm’s execution speed. However, the use of CUDA technology for neural network training on a graphics processing unit significantly enhances prediction speeds compared to OpenMP. Additionally, the feasibility of employing PyTorch Distributed Data Parallel (DDP) technology for training the neural network across multiple graphics processing units (nodes) simultaneously was explored. This approach further improved prediction execution times compared to using a single graphics processing unit.
 Results. An algorithm for training and prediction of an artificial neural network was developed using two independent nodes, each equipped with separate graphics processing units, and their synchronization for exchanging training results after each epoch, employing PyTorch Distributed Data Parallel (DDP) technology. This approach allows for scalable computations across a higher number of resources, significantly expediting the model training process.
 Conclusions. The conducted experiments have affirmed the effectiveness of the proposed algorithm, warranting the recommendation of this research for further advancement in autonomous vehicles and enhancement of their navigational capabilities. Notably, the research outcomes can find applications in various domains, encompassing automotive manufacturing, logistics, and urban transportation infrastructure. The obtained results are expected to assist future researchers in understanding the most efficient hardware and software resources to employ for implementing AI-based navigation systems in autonomous vehicles. Prospects for future investigations may encompass refining the accuracy of the proposed parallel algorithm without compromising its efficiency metrics. Furthermore, there is potential for experimental exploration of the proposed algorithm in more intricate practical scenarios of diverse nature and dimensions.

Assessing the Transformative Potential: An Examination of the Urban Mobility Impact Based on an Open-Source Microscopic Traffic Simulator for Autonomous Vehicles

Integrating autonomous vehicles (AVs) into urban areas poses challenges for transportation, infrastructure, building, environment, society, and policy. This paper goes beyond the technical intricacies of AVs and takes a holistic, interdisciplinary approach by considering the implications for urban design and transportation infrastructure. Using a complex methodology encompassing various software types such as Simulation of Urban Mobility (SUMO 1.17.0) and STREETMIX, the article explores the results of a simulation that anticipates the implementation of AVs through different market penetration scenarios. We investigate how AVs could enhance the efficiency of transportation networks, reducing congestion and potentially increasing the throughput. However, we also acknowledge the dynamic nature of the scenarios, as new mobility patterns emerge in response to this technological shift. Furthermore, we propose innovative urban design approaches that could harness the full potential of AVs, fostering the development of sustainable and resilient cities. By exploring these design strategies, we hope to provide valuable guidance for urban planners and policymakers as they navigate the challenges and opportunities presented by the integration of these advanced technologies.

STUDY OF THE DENSITY OF LOCATION OF CAR SERVICE ENTERPRISES IN NAMANGAN AND THE LIST OF SERVICES OFFERED

The problem of urban transport infrastructure is considered in terms of ensuring the rational organization of vehicle maintenance and repair based on the principle of optimal placement of car service enterprises in urban areas and the list of services offered. Factors have been identified that make it possible to solve the problem of the chaotic location of such enterprises.

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

Urban traffic accidents pose significant challenges to the sustainability of transportation infrastructure not only in Vietnam but also all over the world. To decrease the frequency of accidents, it is crucial to analyze accident data to determine the relationship between accidents and causes, especially for serious accidents. This study suggests an integrated approach using Geographic Information System (GIS) and Data Mining methods to investigate the features of urban traffic accidents in Hanoi, Vietnam aiming to solve these challenges and enhance the safety and efficiency of urban transportation. Firstly, the dataset was segmented into homogenous clusters using the two-step cluster method. Secondly, the correlation between causes and traffic accidents was examined on the overall dataset as well as on each cluster using the association rule mining (ARM) technique. Finally, the location of accident groups and high-frequency sites of accidents (hotspots) were determined by using GIS techniques. As a result, a five-cluster model was created, which corresponded to five common accident groupings in Hanoi. Moreover, the results of the study also identified the types of accidents, the main causes, the time, and the surrounding areas corresponding to each accident group. In detail, cluster 5 depicted accidents on streets, provincial, and national roads caused by motorbikes making up the highest percentage within the groups, accounting for 29.2%. Speeding and driving in the wrong lane in the afternoon and at night were the main causes in this cluster (Cf ≥ 0.9 and Lt ≥ 1.22). Next, cluster 2 had the second-highest proportion. Cluster 2 presented accidents between a truck/car and a motorbike on national and provincial roads, accounting for 27.8%. Cluster 1 presented accidents between a truck/car and a motorbike on local streets, accounting for 22%. Cluster 3 illustrated accidents between two motorbikes on the country lanes, accounting for 12.3%. Finally, cluster 4 depicted single-vehicle motorbike crashes, with the lowest rate of 8.8%. More importantly, this study also recommended using repeatability criteria for the same type of accidents or causes to determine the location of hotspots. Also, suggestions for improving traffic infrastructure sustainability were proposed. To our knowledge, this is the first time in which these three methods are applied simultaneously for analyzing traffic accidents.

DEVELOPMENT OF A MACHINE VISION SYSTEM FOR DAMAGE AND OBJECT DETECTION IN TUNNELS USING CONVOLUTIONAL NEURAL NETWORKS

Abstract. Tunnel inspection, i.e. detection of damages and defects on concrete surfaces, is essential for monitoring structural reliability and health conditions of transport facilities, thus providing safe and sustainable urban transportation infrastructures. In this study, an innovative visual-based system is developed for damage and object detection tasks in roadway tunnels based on deep learning techniques. The main components of the developed Machine Vision System such as industrial cameras, flash-based light sources, controller, the synchronization unit and corresponding software programs are designed to collect high-resolution images with sufficient quality from dimly lit tunnel environments in normal traffic flows with an operating speed of 30–50 km/h. Unlike recent studies, the training data includes multiple types of damage such as cracks, spalling, rust, delamination and other surface changes. Furthermore, 10 classes of common tunnel objects including traffic signs, traffic cameras, traffic lights, ventilation ducts, various sensors and cables are labeled for object detection. As state-of-the-art Convolutional Neural Networks, DeepLab and U-Net are trained and evaluated using accuracy metrics for image segmentation. The results highlight the most important parameters of the discussed Machine Vision System as well as the performance of DeepLab and U-Net for object and damage detection.

Assessment of Pedestrians Crossing Behaviour at Mid-Blocks Under Mixed Traffic Conditions in Urban Contexts

Pedestrians are an integral part of urban transportation infrastructure systems, which is also sustainable mode of transportation system. But they are particularly at risk in isolated mid-block sites that have different levels of traffic. The purpose of this study is to investigate, analyze, and simulate pedestrian behavior at Uncontrolled Midblock Crossings in Hyderabad (Hitech city to Habsiguda) under Mixed Traffic Conditions. Particularly, the Mid-Block Crossing Behavior and Pedestrian Crossing Speed. A field survey needs to be conducted using during the project by using any kind of survey techniques, such as Video-graphic technique, manually. Different locations are to be identified, and to be chosen in accordance to Pedestrian traffic and Pedestrians Crossing at Midblock. The Survey should be carried out as per the standards and done during the peak hours. The overall Pedestrian Speed should be determined by using the pedestrian field survey. An Interview method Survey was done to determine the exact Age of Pedestrians at the Study Locations. The data that is required for analysis of pedestrian behaviour at the Mid-Block Crossings are included with Speed of vehicles, Crossing Speeds of Pedestrians, individual characteristics (Gender, Age etc.) as well as the type of Crossing Behaviour. The Pedestrian Crossing Speeds and the Cumulative Speeds were determined and the variations in accordance to Age and Gender were obtained for the selected locations. And further it can be extended to predict the Gap size of the locations by using the log Normal Regression Model and A Binary Logit Model can be examined to find the effects of behavioural aspects of parameters on the Gender (Male and Female) coded as a discrete variable.

Development of the transport network considering the specifics of Lviv’s planning structure (compared to Leipzig and Krakow)

The relevance of this study is determined by the need to adapt the transport network of Lviv to the requirements of modern urban traffic. The planning structure of Lviv was formed based on radial roads, later developing ring connections, which is typical for most European cities, but eventually acquired its own characteristics. The purpose of this study was to analyse the specific features of the current planning structure of Lviv, which affect the development of urban transport infrastructure and mobility of each mode of transport. The methodology of this study involves analysing the modern street and road network as the formative basis of the planning structure of the city of Lviv and assessing transport mobility based on geospatial data from open sources. Comparison with similar European cities, such as Leipzig and Krakow, helps to highlight the specific features and rational ways of developing Lviv’s transport network. The results of the study point to the main feature of Lviv’s planning structure, which is its compactness and high population density – twice as high as in comparable cities. This makes it impossible to adapt the transport infrastructure for car travel and highlights the need to improve public transport networks and cycling infrastructure as more efficient and environmentally friendly ways of urban transport. However, the study of urban transport mobility found that the means of limiting the use of private cars in Lviv are inferior to those implemented in comparable cities, and the public transport network and bicycle infrastructure are underdeveloped and do not provide a quality alternative to car travel as of 2023. The practical value of the study lies in highlighting the most problematic aspects of Lviv’s transport infrastructure that require urgent decisions to function in the context of the specific planning structure of the city

РОЛЬ ГЕНДЕРНОГО ФАКТОРА ПРИ ВЫБОРЕ СРЕДСТВА ПЕРЕМЕЩЕНИЯ ПО ГОРОДУ

Modern studies of approaches to urban development in general and to urban transport in particular pay great attention to the interests and needs of various population groups. In particular, the factor of gender identity becomes important in the formation of urban transport policy, namely, understanding the need to take into account the different requirements of men and women in the formation and development of urban infrastructure. In this paper, based on the results of empirical data collection, we show that the transportation behavior and factors of transportation mode choice are different for men and women. This allows us to conclude that developing urban transport infrastructure, city authorities have the opportunity not just to meet the needs of representatives of different gender, but also effectively influence the peculiarities of transportation behavior of citizens.

Short-term traffic flow prediction based on weather factors analysis and neural network

With the progress of urban transportation infrastructure and the construction of intelligent transportation system, sufficiently accurate traffic flow prediction is of increasing importance for urban planning and management. This paper based on traffic flow data in a section of highway in Los Angeles, USA, we combine external qualitative data such as weather and work calendars, analyze and process the factors significantly influencing traffic flow first, then train and test the traffic flow data by building machine learning Support Vector Regression (SVR) model and the Back Propagation neural network (BP neural network). The test results show that the BP neural network traffic flow prediction model incorporating the external environment is more accurate, and that the external environment has a significant impact on the traffic flow prediction problem. If more significantly influencing factors can be taken into account, or if the current algorithm can be optimized, the prediction accuracy will have further improved, thus effectively enhancing the efficiency and safety of traffic systems.

Exploring the Relationships Between Urban Form, Transportation Infrastructure, and Travel Behaviour: A Cross-City Comparison of Singapore, Beijing, Shanghai, Mumbai, Delhi, and Tokyo

This paper combines traffic statistics of six typical Asian cities, including population density, GDP per capita, mode share, etc., and provides an in-depth analysis of the cities traffic situation. We summarized relationships between income, population density, transportation infrastructure, transportation policies in each city, and the mode of transportation in each area. We find that the population density in a town dramatically impacts the public transit systems, and there are significant relationships between the local travel policies and the mode share. In addition to the conclusions, we summarized the existing problems in this paper and made suggestions for future research in the field.

Patterns of Urban Transport Infrastructure in China

China has focused on heavily investing in transportation infrastructure for many decades, and this study has aimed to conduct a detailed discussion of Chinas transportation infrastructure and has also described the evolution of the transportation system over the past few decades. This report has examined that China has been aggressively focusing on transportation infrastructure to build stronger economic growth in the changing environment. The data was collected and extracted from secondary research, such as general articles and books. Results show that the Chinese government has heavily invested in different sectors, such as railways, airways, seaways and other commuting methods, to transform the infrastructure of the country's transportation system. The highly transformed transportation system also minimizes the gap between the urban and rural regions, helps the country to influence the economic growth, and opens a new source of business with international markets. Further findings of this report reveal that from the past few years, China government is also exceptionally accentuating the development of metropolitan public transportation foundations to limit the hole. The government has clarified that urban public transportation also requires financial investment to improve traffic conditions and encourage sustainable societal development.

Switching intention, WOM and quality of public transport services: A case of the Kuala Lumpur conurbation

Despite spending billions of dollars on urban transport infrastructure, Malaysia has a poor reputation for its public transit service. Consequently, the majority of urban residents continue to rely on their cars. Hence, this paper aims to understand commuters’ intention to switch from private vehicles to public transit and spread word-of-mouth (WOM) about its services and assess the roles of satisfaction and service quality as antecedents. A cross-sectional survey of 421 rail and bus passengers was undertaken in Kuala Lumpur's urban and sub-urban areas, and the data was analyzed using SmartPLS. According to the data, empathy is the strongest predictor of satisfaction with transit services. Except for reliability, the service quality characteristics influence satisfaction and yield positive indirect effects on both switching intention and WOM. Transit operators and planners should take into account that providing a pleasant and hassle-free riding experience has a substantial impact on commuter satisfaction and long-term commitment. This study contributed to the body of knowledge by exploring the intervention role of satisfaction in the relationship between service quality and switching intention, as well as WOM.

Identifying critical urban intersections from a fine-grained spatio-temporal perspective

With the rising travel demand of crowds, urban road networks are under increasing pressure, which makes them progressively more fragile. Accurate identification of critical intersections is essential for urban management, resource allocation and traffic supervision. However, existing intersection evaluation methods fail to fully exploit transportation infrastructure, travel behavior and their synergistic relationships, resulting in coarse-grained and low-dynamic evaluation results. In this study, we develop an urban Intersection Evaluation framework from a Fine-grained Spatio-Temporal perspective (IE-FST). The framework is divided into three parts: fine-grained extraction of intersection information, multi-perspective assessment of node importance, and dynamic perception of spatio-temporal patterns in importance ranking. Specifically, IE-FST upgrades the intersection evaluation scale to turn-level, proposes the concept of Turning Sub-Node (TSN) and constructs a refined TSN topology network. Then, by finely extracting the network properties and mobility patterns of TSNs and mining the association patterns among TSNs, IE-FST proposes a Critical Fine-grained node Identification (CFI) algorithm to achieve a refined and dynamic evaluation of urban intersections. Finally, IE-FST analyzes the temporal characteristics and spatial distribution of TSN importance rankings, and provides traffic management measures for different types of TSNs to improve urban transport efficiency. Experiments are performed in Wuhan, China, and the results indicate that the proposed IE-FST framework achieves an accurate perception of urban intersections with high spatial and temporal resolution. This study promotes a better understanding of the synergistic relationships between crowd travel movements and urban transportation infrastructure, and provides theoretical support for infrastructure siting, refined traffic supervision, and rational resource allocation, thus contributing to the resilient and sustainable development of transportation systems and society.

Incorporating hybrid networks into urban transportation infrastructures for improved COVID-19 transmission forecasting

In this study, we focus on exploring the propagation characteristics of particle swarms in social networks and analyze the diffusion process of viruses among populations based on system dynamics. The article mainly discusses three propagation influence mechanisms, including individual attributes, group attributes, and particle swarm attributes, and delves into the modeling of diffusion processes based on network structures. Firstly, we adopt the main roads in the transportation network (hub nodes) as the initial network backbone. On this basis, by introducing branch networks with small-world characteristics and scale-free characteristics, we construct a transportation network that integrates multiple properties. Using this network, we conducted a detailed simulation and analysis of the COVID-19 transmission process and compared and verified it with the infection dynamic data of COVID-19 in Shanghai from March to September 2022. The verification results reveal that our proposed model can significantly improve prediction accuracy. Compared with other existing dynamic models, our model demonstrates excellent performance, possessing high practical application value. This study provides robust theoretical support for the propagation characteristics of particle swarms in social networks and lays the foundation for further research and application in related fields.