Construction Of Transport System Research Articles

Research on the Interactive Impact of Transportation Systems on Socio-Economic Development

This paper aims to explore the interactive impact between transportation systems and socio-economic development, employing Structural Equation Modeling (SEM) to analyze data from 31 provincial-level administrative regions in China from 2013 to 2022. It comprehensively considers key indicators from the economic, social, and transportation sectors. The paper constructs a model encompassing 5 latent variables and 15 observed variables. Through in-depth analysis, it reveals the promoting role of transportation systems on economic growth and social development, as well as the demand for transportation system construction and optimization driven by socio-economic development levels. The results indicate that an efficient transportation system can not only directly drive economic growth but also indirectly promote social development by improving social welfare and enhancing quality of life. This paper provides new insights into understanding the complex relationship between transportation systems and socio-economic development and holds significant implications for policymakers in optimizing transportation infrastructure to foster economic and social development.

Can low-carbon transportation construction improve urban green total factor productivity? A quasi-natural experiment based on 282 prefecture-level cities in China.

Total factor productivity in the green economy is a major step forward in fostering robust growth in the economy. Urban green total factor productivity (GTFP) may be increased and carbon emissions decreased through the low-carbon transformation of the transportation sector. To analyze how the pilot policy of low-carbon transportation system construction affects urban GTFP, this study builds a DID model and a spatial Durbin model using panel data from 282 Chinese cities between 2006 and 2021. The findings show that low-carbon transportation systems boost urban GTFP. In addition, the mechanism test shows that low-carbon transportation development increases urban GTFP by optimizing urban industrial structure, expediting economic agglomeration, and encouraging technological innovation. Lastly, the geographical correlation test shows that GTFP is geographically associated and that spatial spillover reduces the impact of low-carbon transportation growth on GTFP. The findings affirm the need to vigorously promote low-carbon transportation systems to aid in the achievement of the carbon neutrality target.

Ridesplitting demand prediction via spatiotemporal multi-graph convolutional network

Ridesplitting demand prediction plays an important role in vehicle scheduling and intelligent transportation system construction. Accurate ridesplitting demand prediction is crucial for alleviating supply–demand imbalance and increasing vehicle utilization. Ridesplitting refers to the sharing of a vehicle by multiple passengers with similar routes, thereby achieving a reduction in the adverse effects of increased congestion caused by ride-hailing. However, most existing works mainly address the region-based ride-hailing demand prediction, whereas few works focus on the ridesplitting demand prediction, which takes into account the correlation between shared orders. To address the issue, we develop several intersection-based graphs to character the ridesplitting demand correlations between the intersections. The multi-graph convolutional networks (MGCN) with different graph combinations are constructed to capture the spatial topologies. Furthermore, we introduce the probabilistic model structure to predict the uncertainty of ridesplitting demand. A novel deep learning model, referred to as the Multi-graph Convolutional Gated Recurrent Unit with Probabilistic prediction (MGCGRU-P) is proposed. We conduct extensive numerical experiments on the real-world ride-hailing demand dataset (from Beijing, China). The results demonstrate that the model we propose performs the best against baseline models.

Many-objective optimization of multi-mode public transportation under carbon emission reduction

The incoordination between public transportation system construction and urban infrastructure development is a challenge for the sustainable development of cities. Exploring the associations between the built environment, transportation, and carbon emissions from a macro perspective is significant for realizing the goal of low-carbon urban transportation. This study proposes a collaborative optimization framework for the built environment and public transportation structure under carbon emission reduction. The purpose is to enhance the services capacity and emission reduction potential of public transportation with limited resources. Six factors are considered to construct the many-objective optimization model, including carbon emissions, energy consumption, government subsidies, time and economic costs, and road network resource occupation. The parameters of the model are calculated based on various multi-mode travel data (including vehicle order data, vehicle global positioning system (GPS) trajectory data, and intelligent card (IC) data) and built environment data. During the process, a data processing method for identifying bus drop-off points and inferring urban functional areas is developed. Then, the NSGA–III–DE (Differential Evolution) algorithm is designed to obtain the optimal solutions. The methodological framework is validated in the experiment implemented in Shenzhen city. According to the hypervolume (HV) value, the performance of NSGA–III–DE is compared with that of NSGA-II and NSGA-III. The results show that NSGA–III–DE has better global search ability and presents stable performance for different mutation operators. Finally, the optimization results are further discussed to provide effective guidance for urban planning and low-carbon transportation.

Post Evaluation of Rural Road Renovation and Upgrading Plans in Low Mountain and Hilly Areas Based on IF-VE Method

Currently, China is vigorously promoting the construction of "Four Good Rural Roads", and the planning and construction layout of urban and rural transportation, especially rural roads, can effectively promote the rapid integration of urban and rural areas and the coordinated development of small towns. However, there are still problems in the current planning and construction of rural roads, such as large inventory, low level, and chaotic layout. Post evaluation of rural road renovation and upgrading, analysis and evaluation of the effectiveness of the plan, can provide strong support for the subsequent adjustment of regional comprehensive transportation system construction. Based on various national, provincial, and municipal transportation development guidelines, this article analyzes the factors related to the planning and evaluation of rural roads in low mountain and hilly areas. Based on the IF-VE method formed by intuitionistic fuzzy theory and value engineering theory, a post evaluation study is conducted on a rural road renovation and upgrading project plan. The application results indicate that the indicator system and model constructed in the article have certain feasibility and applicability.

PAC: Partial Area Clustering for Re-Adjusting the Layout of Traffic Stations in City’s Public Transport

Now public transportation, included bus and subway occupies a greater role in city transport, and the layout of traffic stations is the most important part of planning and design. However, some unpredicted factors for construction of traffic stations results in a low utilization rate of public transportation resources, for example, the layout of bus stops is chaotic, there is no clear layout scope, and there is a lack of integration with residents’ travel hotspots. Towards these challenges modern transport faces, we firstly analyze the distribution of bus stops and subway stations to determine the area range needs to be optimized in the traffic net from the perspective of time and space. And then, we propose an optimization method, called ’partial area clustering’ (PAC), to improve the utilization by changing and renewing the original distribution. The novel method was based on the K-means algorithm in the field of machine learning. PAC worked to search the suitable bus platforms as the center and modified the original one to the subway. Experiment has shown that the use of public transport resources has increased by 20%. The study uses a similar cluster algorithm to solve transport networks’ problems in a novel but practical term. As a result, the PAC is expected to be used extensively in the transportation system construction process.

Deep multi-view graph-based network for citywide ride-hailing demand prediction

Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction. Predictable ride-hailing demand can facilitate more reasonable vehicle scheduling and online car-hailing platform dispatch. Conventional deep learning methods with no external structured data can be accomplished via hybrid models of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) by meshing plentiful pixel-level labeled data, but data sparsity of high grid granularity in spatial perspective and limited learning capabilities of long-term dependencies in temporal perspective are still two striking bottlenecks. To address these problems, we propose a novel virtual graph modeling approach to focus on significant demand regions and a novel Deep Multi-View Spatio-temporal Virtual Graph Neural Network (DMVST-VGNN) to strengthen the learning capabilities of spatial dynamics and long-term temporal dependencies. Specifically, DMVST-VGNN integrates structures of 1D CNN, Multi-Graph Attention Neural Network and Transformer Network, which correspond to short-term temporal dynamics view, spatial dynamics view and long-term temporal dynamics view respectively. In this paper, multiple experiments are conducted on two large-scale New York City datasets in higher granularity prediction scenes. And the experimental results demonstrate the effectiveness of DMVST-VGNN framework in ride-hailing demand prediction, no matter in spatial scale or the temporal scale.

Collection and transportation system construction of potentially viral municipal solid waste during the COVID-19 pandemic in China

The transmission route of COVID-19 through municipal solid waste (MSW) has been confirmed and receives increasing attention. Potentially viral municipal solid waste (PVMSW) refers to the domestic waste generated by risky areas and epidemic-related populations under a major epidemic in their daily lives or in activities that provide services for their daily lives. For its potential infectivity, PVMSW should be properly collected and transported. This study aimed to standardize the collection and transportation of PVMSW and proposed specific construction schemes of PVMSW collection and transportation systems for three situations which were city-wide lockdown status, medium and high-risk area, and home quarantine separately. In the cases of city-wide lockdown status and home quarantine, PVMSW collection and transportation systems were constructed qualitatively with the examples of Wuhan and Shanghai respectively, and in the case of medium and high-risk area, the systems were constructed quantitatively through the development of a waste collection and transportation costs model. To reduce the risks of virus transmission during the collection and transportation process, the collection and transportation links should be minimized. For the disposal of PVMSW, medical waste treatment facilities and MSW incineration plants should be prioritized. Furthermore, the results showed that the total number of people and the transfer capacity of MSW transfer facility were the two main influencing factors for the selection of PVMSW collection and transportation systems in medium and high-risk area. This article could help manage MSW for preventing virus transmission during the COVID-19 pandemic or similar future epidemics.

A New Perspective on Traffic Flow Prediction: A Graph Spatial-Temporal Network with Complex Network Information

Traffic flow prediction provides support for travel management, vehicle scheduling, and intelligent transportation system construction. In this work, a graph space–time network (GSTNCNI), incorporating complex network feature information, is proposed to predict future highway traffic flow time series. Firstly, a traffic complex network model using traffic big data is established, the topological features of traffic road networks are then analyzed using complex network theory, and finally, the topological features are combined with graph neural networks to explore the roles played by the topological features of 97 traffic network nodes. Consequently, six complex network properties are discussed, namely, degree centrality, clustering coefficient, closeness centrality, betweenness centrality, point intensity, and shortest average path length. This study improves the graph convolutional neural network based on the above six complex network properties and proposes a graph spatial–temporal network consisting of a combination of several complex network properties. By comparison with existing baselines containing graph convolutional neural networks, it is verified that GSTNCNI possesses high traffic flow prediction accuracy and robustness. In addition, ablation experiments are conducted for six different complex network features to verify the effect of different complex network features on the model’s prediction accuracy. Experimental analysis indicates that the model with combined multiple complex network features has a higher prediction accuracy, and its performance is improved by 31.46% on average, compared with the model containing only one complex network feature.

A GAN framework-based dynamic multi-graph convolutional network for origin–destination-based ride-hailing demand prediction

Ride-hailing demand prediction plays an important role in ride-hailing vehicle scheduling, traffic condition control and intelligent transportation system construction. Accurate and real-time ride-hailing demand prediction is crucial for improving supply–demand imbalance, vehicle utilization and traffic conditions. However, most existing works mainly address the region-based demand prediction whereas only a few works focus on the origin–destination (OD)-based demand prediction. To address the issue, we develop several dynamic OD graphs to character the ride-hailing demand transactions between the origin and destination. We propose a novel deep learning model, referred to as the Dynamic Multi-Graph Convolutional Network with Generative Adversarial Network structure (DMGC-GAN), to investigate the challenging problem. Different from previous studies, we develop the temporal multi-graph convolutional network (TMGCN) layer with different dynamic OD graphs to capture the spatial topologies contained in the dynamic OD graphs in terms of time, and exploit GAN structure to overcome the high sparsity of OD demand. We conduct extensive numerical experiments on the real-world ride-hailing demand dataset (from Manhattan district, New York City). The results demonstrate that the model we propose performs the best against to nine baseline models.

A Two-Stage Scheduling Model for the Tunnel Collapse under Construction: Rescue and Reconstruction

In the process of transportation system construction, the tunnel is always an indispensable part of the traffic network due to terrain constraints. A collapse of the tunnel under construction may give rise to a potential for significant damage to the traffic network, complicating the road conditions and straining relief services for construction workers. To cope with the variety of vehicle types during the rescue effort, this paper divides them into small, medium, and large sizes, herein correcting the corresponding speed considering six road condition factors on account of the previous research. Given the influence of different special road conditions on the speed of different sized vehicles, a multi-objective model which contains two stages is presented to make decisions for rescue vehicle scheduling. Under the priority of saving human life, the first-stage objective is minimizing the arrival time, while the objective of the second stage includes minimizing the arrival time, unmet demand level, and scheduling cost. To solve the currently proposed model, a non-dominated sorting genetic algorithm II (NSGA-II) with a real number coding method is developed. With a real tunnel example, the acceptability and improvement of the model are examined, and the algorithm’s optimization performance is verified. Moreover, the efficiency of applying real number coding to NSGA-II, the multi-objective gray wolf algorithm (MOGWO), and the traditional genetic algorithm (GA) is compared. The result shows that compared with the other two methods, the NSGA-II algorithm converges faster.

Impact of 2022 Winter Olympics Transportation System Construction on Beijing-Tianjin-Hebei Transportation Integration

Impact of 2022 Winter Olympics Transportation System Construction on Beijing-Tianjin-Hebei Transportation Integration

Jointly Modeling Spatio–Temporal Dependencies and Daily Flow Correlations for Crowd Flow Prediction

Crowd flow prediction is a vital problem for an intelligent transportation system construction in a smart city. It plays a crucial role in traffic management and behavioral analysis, thus it has raised great attention from many researchers. However, predicting crowd flows timely and accurately is a challenging task that is affected by many complex factors such as the dependencies of adjacent regions or recent crowd flows. Existing models mainly focus on capturing such dependencies in spatial or temporal domains and fail to model relations between crowd flows of distant regions. We notice that each region has a relatively fixed daily flow and some regions (even very far away from each other) may share similar flow patterns which show strong correlations among them. In this article, we propose a novel model named Double-Encoder which follows a general encoder–decoder framework for multi-step citywide crowd flow prediction. The model consists of two encoder modules named ST-Encoder and FR-Encoder to model spatial-temporal dependencies and daily flow correlations, respectively. We conduct extensive experiments on two real-world datasets to evaluate the performance of the proposed model and show that our model consistently outperforms state-of-the-art methods.

Postdisaster Labor-Demand Surge in the US Highways, Roads, and Bridges Construction Sector

The swift and cost-efficient postdisaster reconstruction of highways, roads, and bridges is a necessary component of resilient transportation infrastructure systems. However, the US construction industry often reports temporary increases in construction labor wages following large-scale disasters due to a gap between labor supply and demand, a concept known as demand surge. Utilizing publicly available big data related to the construction labor market and postdisaster infrastructure repair expenditures, this study seeks to (1) measure construction labor demand surge in the highways, roads, and bridges construction sector following federally-declared disasters in US metro-areas and (2) identify when such postdisaster demand surge occurs. A total of 118 US metro-areas were analyzed for demand surge following federally-declared disaster events from 2004 to 2017. Findings highlight inflated postdisaster construction labor wages in metro-areas, with the demand surge spiking within six months after a disaster. This data-driven study is the first to quantitatively measure postdisaster demand surge in the US highways, roads, and bridges sector following multihazard disaster events. Knowledge of the demand surge can guide predisaster planning and decision-making processes for local, state, and national agencies and inform postdisaster best practices for contractors and engineers coordinating transportation system construction projects.

Assessment of the stability and loading indicators of two-axle mine cars and rail tracks

The paper presents the research results of the stability and loading of the two-axle mine cars and rail tracks with various design for calculation schemes construction of transport systems of underground rail haulage. The dependences of changes in loading, stability and operation safety of rolling stocks from the parameters of irregularities, curvature radii of the rail track and the movement speed of rolling stocks at various technical solutions of the transport system, have been studied. The research results of the interaction between the two-axle mine cars and rail track with the gauge of 750 mm for underground rail haulage are presented. The parameters have been determined of the rail track irregularities in the horizontal and vertical planes, which cause the highest values of vertical and horizontal loads, acting on the rail track and the two-axle rolling stock. The recommendations are proposed for improving the study of the rail vehicles and regulatory documents development for the construction and operation of the track structure. The results can be applied for the underground rail haulage when creating the new vehicles for mines.

Strengthening green transportation and implementing sustainable urban environmental development in the new period

This article studies the relationship between green transportation and urban sustainable development from the perspective of urban sustainable development, and discusses in detail the theory, design and implementation of green transportation system construction based on urban sustainable development, and believes that green transportation is a city. An important component of sustainable development plays an important role in promoting its development. In the analysis of existing research materials, it discusses how urban development should create an environment and conditions for the development of green transportation from the aspects of planning, policy, awareness, technology, management and internal and external coordination. It also proposes scientific planning of urban development and improving urban land utilisation, and other suggestions to better promote the development of urban green transportation, improve the role of green transportation in the process of urban sustainable development, and put forward corresponding countermeasures.

“Financial appraisal of cement industry in India-A comparative study of ultra tech cement & J.K. cement”

Being the 3rd largest economy in the world, Indian economy, is going to touch new heights in the coming years. Indian Cement Industry will play a significant role in the economic development of the country. The cement industry in India is one of the oldest sectors in India. The industry is driven by the immense growth in the housing sector, the infrastructure development, and construction of transportation systems. Average cement prices are expected to rise by 6% year-on-year (YoY) and 7% on quarter-on-quarter (QoQ) basis across the country despite volume decline in the southern and central regions, a report said. The present research has been aimed at appraising the financial health of cement Industry in general and of the sampled units in particular. The units selected for the present study are Pioneer in their field. Financial management and Accounting techniques have been used. Data is collected from the published financial reports of the company. The major conclusion drawn is that both the companies are using earnings to maximize shareholder's wealth as the market price of both the units under study has increased to a great extent. The inferences drawn from the report could be of immense help to investors.

Xi ‘an Intelligent Transportation System Construction Platform Research

The field of intelligent transportation has pointed out the goal for the future development of transportation and brought people convenient, scientific and standardized modes of transportation. In recent years, with the emergence of Internet of things, mobile Internet, cloud computing and other new technologies, large and medium-sized cities at home and abroad are developing rapidly in the construction of intelligent transportation and gradually evolving into intelligent transportation system. In this paper, starting from how to build the intelligent transportation system platform, starting from the establishment of the five layers, one main body and three departments of the Internet of things, some solutions and Suggestions are put forward from the technical level. It is also hoped that this will be a theoretical and practical exploration for the construction of intelligent transportation in China’s megacicities (such as xi ‘an).

Key Issues and Challenges in Developing Integrated Road Networks Infrastructure With Regional Connectivity: A Study Case of Porto-novo in the Republic of Benin

In recent years, with the rapid and continuous increase in traffic demand, a new upsurge in the construction of transport system is being made in Porto-novo. Thus, the problem of how to make modal transport system in this area play its full role and make its helpful to the development of integrated multimodal transportation networks needs to be urgently put forward. Recognizing these needs, this paper systematically addresses key issues and challenges toward developing integrated multimodal national transportation networks in the banded development axis of Abomey-Calavi-Pkota, Cotonou and Porto-novo with an emphasis to regional connectivity. Particularly, the paper addresses issues with the planning process, institutions, governance, strengths and weaknesses, funding and integrated multimodal development.

Prerequisites for the Creation of a High-Speed Container Transport System

The new scope of application for vehicles equipped with magnetic suspension is the fright container transportation. In order to realise the transit potential of the country, the increase in mean speed of the container trains is required. Objective. The purpose of the work is to explore the possibilities to develop the highspeed transport system equipped with magnetic suspension for container transportation along Eurasian land bridge. Methods. The following methods and analysis have been used as research instruments: case study, computer modeling, transport geography and technical and economic analysis. Results. The market analysis results of the transit container transportation have shown that the major challenge for Russian transit development is the constrained traffic capacity of the existing transport corridors. The drastic solution of the problem can be the construction of a new high-speed transport system. The following factors determining the conditions for the creation of the new transport system have been identified: the use of the Azov-Black sea basin transportation facilities, provision of the accessibility of the northern areas; the development of technical solutions ensuring the design of the transport system with low cost infrastructure. The combined traction levitation system has been developed based on the extremely simple design of the linear switched reluctance motor. The experimental researches of the full-functional physical model of the transport platform have been performed. The assessment of investment project efficiency has shown that despite large start-up investment in the track infrastructure, the project has positive economic effect. Practical importance. The computer model of the combined traction levitation system based on the linear switched reluctance motor has been designed. The concept of the new transport system construction has been introduced taking into account the characteristics of the supposed operating test sites in poorly developed areas. The high-speed route has been proposed connecting the Azov-Black sea basin with the Pacific coastline and completely passing through the territories of Russia including some northern regions. The transit potential assessment has shown that due to the transit time reduction it is possible to attract the container freights with traffic volume of 1.52 million in twenty-pounds equivalent.