Highway Transportation Network Research Articles

Low Carbon Planning of PV-Charging Stations for Self-Sustained Highway Transportation Energy System Considering the Retirement of Gas Stations

With the pressure of energy crisis, how to achieve low carbon and self-sustaining operation of highway transportation network (HTN) has become an emerging research topic. In the current HTN, fuel vehicles (FVs) and electric vehicles (EVs) form a mixed traffic flow together. In this context, a novel model is proposed for the planning of energy facilities in HTN, including the construction of charging stations and the retirement of gas stations, which takes into account not only the balance of traffic flow but also environmental factors in the planning process. Meanwhile, the construction of charging stations leads to the coupling of HTN and distribution network (DN). In order to further achieve the goal of carbon reduction, a planning framework of low carbon facilities in the charging system is proposed, including the installation of photovoltaic (PV) panels and carbon capture and storage (CCS) systems. Finally, the rationality of the proposed planning method is verified by case studies.

User Opinion on Factors Affecting Drivers Frequent Lane-Changing Behavior: A National Sample of Drivers in Pakistan

Abstract: In recent years massive traffic volume increase has demanded the implementation of traffic improvement plans in Pakistan. The urban Highway transportation networks are becoming congested in developed as well as in most of the developing countries. The main causes of unmanageable heterogeneous traffic congestion on the highway are an increase in population, change in lifestyle, and economic development of a country. To recognize the characteristics of a driver who frequently changes lanes and analyze the techniques of frequent lane-changing behavior. The researchers collected data via a questionnaire survey and analyzed the influence of traffic environment and driver characteristics violation on frequent lane-changing behavior on travel speed in detail by manually Excel statistics. Results showed that the impact of a heterogeneous traffic environment of two lanes and three lanes cars, and traffic flow density was sharply associated with frequent lane-changing behavior. The drivers were the main factors affecting frequent lane-changing behavior in heterogeneous traffic conditions, which was most popular during aggressive driving. Only 8.33% of drivers were never involved in this behavior. Most liable to frequent lane-changing behavior were males as compared to females, 31–40-year-old drivers, and those with low academic qualifications and private vehicle personalities. Among various traffic factors, changes in required speed had the most significant impact on how often lane changes occurred. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency. This research recommends strict enforcement of lane discipline to counter traffic congestion and the remedial measures that are to be taken for the improvement of better and sustainable road infrastructure conditions. Overall the research study will have practical implications for improving traffic management and safety on highways, and it provides a foundation for further research in the field of traffic-related technologies.

Machine Learning-based Seismic Fragility Curves for RC Bridge Piers

A significant part of ongoing studies in the field of earthquake engineering is directed toward the seismic risk assessment of buildings and infrastructures at a territorial scale. This task is usually accomplished by grouping the structures into homogenous classes in terms of typology, for which seismic fragility curves are then obtained for different limit states via numerical simulations or from the statistical analysis of observational data when available. Particularly, the development of typological fragility curves for bridges under earthquake is useful for assessing the reliability and resilience of transportation networks in seismic areas and can be also effective decision-making support. Within this framework, the proposed study establishes a machine learning-based paradigm for the closed-form prediction of the main statistical parameters required to obtain relevant seismic fragility curves for reinforced concrete bridge piers. Initially, a huge training dataset has been obtained by Monte Carlo simulations and displacement-based bridge pier assessments by assuming data representative of the Italian highway transportation network. Next, symbolic nonlinear regression formulae for estimating the main statistical parameters of seismic fragility curves have been generated. With the aid of those formulae, the effort of calculating the seismic fragility curves is greatly reduced since the corresponding main statistical parameters can be directly calculated from a set of commonly available attributes. Therefore, the proposed study provides a helpful tool for the rapid preliminary assessment of damage and risk level of existing highway transportation networks exposed to seismic hazards.

Urban Road Transport Network Analysis: Machine Learning and Social Network Approaches

Traffic congestion is one of the most significant problems in urban transportation. It has been increasing, especially in regions close to intersections. Several methods have been developed to reduce the traffic congestion. One of the analysis methods is social network analysis (SNA). This method, which has increased use in transportation, can quickly identify the most central intersections in transportation networks. Improvements to central intersections, identified in a road network structure, speed up the traffic flow across the entire network structure. In this study, the Istanbul highway transportation network has been examined and values for a series of network centrality measures have been calculated using the SNA. The accuracy and error scales of the centrality values were compared using a machine learning algorithm. The Bonacich power centrality has been the best performance. Based on the study results the most central intersections in Istanbul have been determined.

Modeling Regional and Local Resilience of Infrastructure Networks Following Disruptions from Natural Hazards

This paper presents a framework to evaluate the regional and local resilience of infrastructure networks following disruptions from natural hazards. Herein, the regional resilience of a network relates to the accessibility of a community within a larger network, whereas the local resilience concerns the ability of a network to provide its intended service within the boundaries of a community. Using this framework, a methodology is developed to demonstrate its application to a road and highway transportation network disrupted by ground shaking and inundation under a Cascadia Subduction Zone earthquake and tsunami scenario. The regional network extents encompass the entire coast of the US state of Oregon. Embedded within this regional network are 18 local networks associated with coastal communities. Regional and local connectivity indexes are defined to identify the initial damage and then track the postdisaster recovery of the transportation network, i.e., evaluate the network resilience. The study results identify the attributes that lead to a regionally or locally resilient network and highlight the importance of considering local infrastructure networks embedded within larger regional networks. It is shown that without regional considerations, the time to recover may be severely underpredicted. The methodology is further used as a decision support tool to demonstrate how mitigation options impact the transportation network’s resilience. The importance of strategically considering mitigation options is emphasized as some communities see significant reductions in time to recover, whereas others see little to no improvement.

A study of highway logistics transportation network structure in China: From the perspective of complex network

A study of highway logistics transportation network structure in China: From the perspective of complex network

Socioeconomic impacts of resilience to seaport and highway transportation network disruption

• An integrated transportation-socioeconomic impact model is developed and applied. • Aggregate and distributional impacts of port-transportation disruptions are simulated. • Resilience tactics can substantially reduce potential GDP losses. • Losses from port disruptions increase income inequality . • Port resilience tactics reduce inequality from port disruptions. An integrated transportation-socioeconomic model is developed to analyze the economic consequences of and resilience to seaport and associated transportation network disruptions. Since such disruptions can affect people in a region unequally, the model is constructed to analyze both aggregate economic impacts and impacts across socioeconomic groups. We illustrate the model’s usefulness in a simulated earthquake scenario affecting the Ports of Los Angeles and Long Beach. Total GDP losses from port disruptions, highway transportation cost increases, and general building damages, working through regional and interregional supply-chains, are estimated at $24.2 billion in the Port Region and $30.2 billion in the U.S. as a whole. A combination of several resilience tactics is estimated to reduce GDP impacts by 41.3% in LA and 57.6% in the U.S. The distributional analysis indicates the extent to which lower-to-middle-income groups are more proportionally impacted by port disruptions and middle-to-higher-income groups are more impacted by building damages.

Complex Traffic Network Analysis Method Based on a Multiscale Aggregation Model

Identifying the aggregation characteristics of the geospatial network is an important aspect of analyzing highway traffic networks. Based on the complex network theory, this paper studies the block aggregation characteristics of the highway traffic network and proposes an Improved PageRank Spectral Clustering (IPSC) Algorithm to divide the functional blocks of highway traffic networks. Firstly, the theoretical model of a highway traffic network is constructed by adding location attribute weight, geographical distance weight, road grade weight, and dynamic traffic congestion weight. Secondly, the improved PageRank algorithm is used to get the ranking of key nodes of the highway traffic network. The clustering center and the number of clusters are determined by the ranking of key nodes and the shortest path distance. Then the improved spectral clustering algorithm is used to divide the functional blocks of the highway transportation network and identify the special common blocks of the highway transportation network. Finally, the IPSC Algorithm is used to analyze the aggregation mode of complex traffic networks at city and district scales. Crossing the limit of administrative division, the division results of special common blocks of highway transportation networks are obtained. Maintaining the connectivity between blocks can improve the overall efficiency of highway transportation networks.

A Probabilistic Approach to the Evaluation of Seismic Resilience in Road Asset Management

Road networks are classified as critical infrastructure systems. Their loss of functionality not only hinders residential and commercial activities, but also compromises evacuation and rescue after disasters. Dealing with risks to key strategic objectives is not new to asset management, and risk management is considered one of the core elements of asset management. Risk analysis has recently focused on understanding and designing strategies for resilience, especially in the case of seismic events that present a significant hazard to highway transportation networks. Following a review of risk and resilience concepts and metrics, an innovative methodology to stochastically assess the economic resources needed to restore damaged infrastructures, one that is a relevant and complementary element within a wider resilience-based framework, is proposed. The original methodology is based on collecting and analyzing ex post reconstruction and hazard data and was calibrated on data measured during the earthquake that struck central Italy in 2016 and collected in the following recovery phase. Although further improvements are needed, the proposed approach can be used effectively by road managers to provide useful information in developing seismic retrofitting plans.

A data‐driven approach for regional bridge condition assessment using inspection reports

Bridges play an important role in the highway transportation network. There is an increasing concern that highway bridges have been suffering from structural degradation and deficiency due to severe environment, overloading, initial structural defects, and other factors. Ensuring the safety of a large number of regional bridges at the network level becomes a major challenge. This study proposes an entire data-driven condition assessment framework for network-level bridges, including data integration, condition assessment, and maintenance management. The periodic bridge inspection reports in China could provide quite a few condition-related information to reveal the time-variant bridge condition deterioration and effect of maintenance behaviors. The proposed framework is applied to a real highway bridge network located in Hebei province, China. Inspection data are obtained from thousands of bridges over quite a few years. The established regional deterioration model could effectively predict the bridge future condition based on the extracted hidden features. The regional maintenance strategies are optimized to satisfy the economic and performance constraints, which allows bridge managers to select the most appropriate one for implementation. It is shown that the proposed data-driven approach can provide a guideline to bridge managers to estimate the future condition and allocate the maintenance resources.

Research on Highway Slope Disaster Automated Monitoring Method Based on Video Image Processing

With the rapid development of China highway transportation network, a lot of highways have risen in the mountainous areas. And the risk of geological disasters is also increasing. Due to the instability of the geological structure and geological changes, slope landslides and collapses frequently occur. However, there is a lack of effective means and tools in the field of highway slope disaster monitoring. For large and important slopes, various information-based monitoring methods are used, but the effects and practicability are not satisfactory. This paper proposes a highway slope disaster automated monitoring research method based on multi-camera video image processing to solve the previous engineering problems of slope video monitoring. It can quickly identify slope disaster events with centimeter-level monitoring accuracy, which can meet the application requirements of highway engineering. Correspondingly, the disaster emergency response capabilities of highway operators can also be supported and improved.

Predicting highway freight transportation networks using radiation models.

Highway freight transportation (HFT) plays an important role in the economic activities. Predicting HFT networks is not only scientifically significant in the understanding of the mechanism governing the formation and dynamics of these networks, but also of practical significance in highway planning and design for policymakers and truck allocation and route planning for logistic companies. In this work we apply parameter-free radiation models to predict the HFT network in mainland China and assess their predictive performance using metrics based on links and fluxes, which can be done in reference to the real directed and weighted HFT network between 338 Chinese cities constructed from about 15.06 million truck transportation records in five months. It is found that the radiation models exhibit relatively high accuracy in predicting links but low accuracy in predicting fluxes on links. Similar to gravity models, radiation models also suffer difficulty in predicting long-distance links and the fluxes on them. Nevertheless, the radiation models perform well in reproducing several scaling laws of the HFT network. The adoption of population or gross domestic product in the model has a minor impact on the results, and replacing the geographic distance by the path length taken by most truck drivers does not improve the results.

Suggestions on Over-the-horizon Cloud Warning System for Collision Avoidance of Two Intersections

As the highway transportation network expands and modern technologies in the automobile industry develops, automobiles contribute a lot to human life, which have also led to many problems such as traffic accidents, traffic jams, environmental pollution, and energy waste. Take traffic accidents as an example. According to the China’s website of the State Administration of Work Safety, in 2011, about 211,000 people died or injured in traffic accidents, with the death toll hitting 62,000. Among all those accidents, rear-end collisions account for a large proportion, as the freeway rear-end accidents was about 33.4% of the total accidents in China and 24% in America. These traffic accidents have not only resulted in huge economic losses, but have also made highways more congested and occupied medical resources.

Estimation of Regional Economic Development Indicator from Transportation Network Analytics

With the booming economy in China, many researches have pointed out that the improvement of regional transportation infrastructure among other factors had an important effect on economic growth. Utilizing a large-scale dataset which includes 3.5 billion entry and exit records of vehicles along highways generated from toll collection systems, we attempt to establish the relevance of mid-distance land transport patterns to regional economic status through transportation network analyses. We apply standard measurements of complex networks to analyze the highway transportation networks. A set of traffic flow features are computed and correlated to the regional economic development indicator. The multi-linear regression models explain about 89% to 96% of the variation of cities’ GDP across three provinces in China. We then fit gravity models using annual traffic volumes of cars, buses, and freight trucks between pairs of cities for each province separately as well as for the whole dataset. We find the temporal changes of distance-decay effects on spatial interactions between cities in transportation networks, which link to the economic development patterns of each province. We conclude that transportation big data reveal the status of regional economic development and contain valuable information of human mobility, production linkages, and logistics for regional management and planning. Our research offers insights into the investigation of regional economic development status using highway transportation big data.

Gravity law in the Chinese highway freight transportation networks

The gravity law has been documented in many socioeconomic networks, which states that the flow between two nodes positively correlates with the strengths of the nodes and negatively correlates with the distance between the two nodes. However, such research on highway freight transportation networks (HFTNs) is rare. We construct the directed and undirected highway freight transportation networks between 338 Chinese cities using about 15.06 million truck transportation records in five months and test the traditional and modified gravity laws using GDP, population, and per capita GDP as the node strength. It is found that the gravity law holds over about two orders of magnitude for the whole sample, as well as the daily samples, except for the days around the Spring Festival during which the daily sample sizes are significantly small. Accordingly, the daily exponents of the gravity law are stable except during the Spring Festival period. The results also show that the gravity law has higher explanatory power for the undirected HFTNs than for the directed HFTNs. However, the traditional and modified gravity laws have comparable explanatory power.

Deformation Analysis by Geomatic and Geotechnical Methods in Highway Tunnels

In the highway transportation network, the tunnel has an important place. Tunnels are used to provide safe, comfortable and economical transportation in areas where topography is not available. In addition to the facilities provided by the tunnels, there are various risks during and after the manufacturing phase. These risks need to be anticipated and manufacturing must be shaped to address these risks. Therefore, deformation analysis in tunnel projects has an important place. During and after manufacture, it is necessary to determine and follow the deformations. In this study, Ayvacık - Küçükkuyu highway in Çanakkale will be dealt as T1 Tunnel between km 10 + 700 - 12 + 400. Due to the fact that the region is located on the earthquake zone and because of this reason it has a mixed lithology, it is very important to follow the deformations. In this context, it is aimed to analyze the relationship and dimensions of deformation factors in the structure by using geodetic and geotechnical measurement methods. The relationship between these data and the load, pressure and tension values observed on horizontal and vertical movements will be examined during the manufacturing process. Moreover, due to the fact that the zone is located on the earthquake zone, earthquakes that occur in the region during the manufacturing process and the manufacturing effects of these earthquakes will be investigated. With this study, it is aimed to give guidance to manufacturing by predicting deformation and external effects that may occur and arise in engineering constructions. The required revisions should be made in the fortification systems in line with the predetermined parameters. In this way, it is aimed to ensure that the manufacturing process continues in the most economical and safest manner.

Laboratory study on the compressibility of sand–rubber mixtures under one dimensional consolidation loading conditions

The continuous increase of road traffic needs in both developed and developing countries is accompanied by an important growth of highway transportation networks and consequently produce a huge amount of waste tires, and causes numerous environmental issues. On the other hand, due to their lightweight and high damping performance, several researchers are interested in the use of the soil–rubber mixtures for the attenuation of earthquake motions and ground vibrations by their capacity to absorb the external solicitations. However, several questions arise regarding the mechanical behaviour of the soil–rubber mixtures. This paper presents a thorough study on the impact of scrap tire addition on physical properties of Chlef sandy soil. A series of laboratory tests are conducted on sand–rubber mixtures with various percentage of crumb rubber: 0, 0.5, 1.0 and 1.5% by a dry weight. Oedometric tests are carried out in order to explore the influence of rubber particles on the sand compaction and consolidation with focusing on the compressibility parameter. The experimental tests show that the increase of the percentage of crumb rubber leads to an increase of both the compression index Cc and the swelling index Cs of sand–rubber mixtures, contrariwise, a slight decrease in the preconsolidation pressure and the oedometric modulus is observed.

Seismic fragility assessment of highway bridges using D-vine copulas

Seismic vulnerability of highway bridges is crucial to the seismic risk assessment of highway transportation networks. A new framework for developing the fragility function at the system level utilizing the D-vine copula theory is proposed. In this methodology, first, the joint probabilistic seismic demand model (JPSDM), which represents the conditional joint distribution of various engineering demand parameters (EDPs) under each intensity measure (IM) level, is constructed using the D-vine approach; then, sample realizations of both the established JPSDM and capacity models of these considered components are compared to derive the system-level fragility curves. This methodology is illustrated in a typical two-span RC continuous girder-box bridge case, where pier columns, elastomeric pad bearings, the sliding bearings, and the abutments in both passive and active directions are considered as the major components. The results demonstrate that the performance of the optimal D-vine for characterizing the dependence structure among various EDPs is relatively superior in comparison with commonly-utilized multivariate standard copulas. The combination of the quantitatively-identified optimal D-vine copula and their conditional lognormal marginal distributions is adequate to construct the JPSDM of these five EDPs. Furthermore, the effects of different copula selections on the overall system vulnerability are well captured, which highlight the necessity for quantitative identification of the optimal D-vine copula for modeling the correlations among various EDPs.

PEV Fast-Charging Station Siting and Sizing on Coupled Transportation and Power Networks

Plug-in electric vehicle (PEV) charging stations couple future transportation systems and power systems. That is, PEV driving and charging behavior will influence the two networks simultaneously. This paper studies optimal planning of PEV fast-charging stations considering the interactions between the transportation and electrical networks. The geographical targeted planning area is a highway transportation network powered by a high voltage distribution network. First, we propose the capacitated-flow refueling location model (CFRLM) to explicitly capture PEV charging demands on the transportation network under driving range constraints. Then, a mixed-integer linear programming model is formulated for PEV fast-charging station planning considering both transportation and electrical constraints based on CFRLM, which can be solved by deterministic branch-and-bound methods. Numerical experiments are conducted to illustrate the proposed planning method. The influences of PEV population, power system security operation constraints, and PEV range are analyzed.

Evaluation of the Turkish Highway Network Analysis With Traffic Data

As a complex geospatial structure, Turkish national highway transportation network is studied by the means of network science. We used the dataset retrieved from the KGM (Karayollari Genel Mudurlugu) maps with a hand-driven process. The dataset labels the junctions in the map as nodes, and the roads between these junctions as edges. We outlined the statistical properties of the Turkish highway transportation network by the means of eigenvector, betweenness, closeness centrality, modularity and eccentricity measures, while comparative percentile plots between these measures are also performed. We investigated the correlation of these parameters with the traffic volume, and outlined that only eccentricity measure is correlated with the traffic volume. We also investigated the degree correlations of the network and found that the network displays disassortative mixing behavior, meaning that nodes with high degrees tend to connect with lower degree nodes, and vice versa. This property is consistent with the recent studies of transportation networks, as well as various types of real networks like Internet, World-Wide Web, protein interactions, neural network etc.