Traffic Infrastructure Research Articles

Impact of Accessibility to Cities at Multiple Administrative Levels on Soil Conservation: A Case Study of Hunan Province.

The development of traffic infrastructure involves massive land use changes along the transportation routes and stimulates urban sprawl at transfer nodes, leading to a degradation in ecosystem services, including soil conservation. For developing countries, especially for China, it is very important to differentiate the influences between different standards of traffic infrastructure associated with the different administrative levels of the regions where they are constructed on soil conservation. In this study, we attempt to analyze the differences in the influence of accessibility at different levels on soil conservation, for the case study area in Hunan province in China. The results indicate that: (1) traffic conditions in Hunan province have witnessed continuous improvement, and the time taken to access mega-cities, prefecture-level cities, and county-level cities from various regions has been significantly reduced. (2) The total annual soil conservation in Hunan province is maintained at approximately 2.93 × 109 t. However, the spatial heterogeneity shows severe degradation in regions with lower accessibility, and weak enhancement in regions with higher accessibility. (3) A negative spatial autocorrelationship exists between accessibility and soil conservation at all levels, with the increase of administrative rank of the destination making it more obvious and intense, along with an increased tendency for the spatial distribution to concentrate. (4) Building more railways and highways from prefecture-level cities with LH clusters nearby as transfer nodes, instead of the construction of national roads and provincial roads that diverge from these railways and highways, will help limit the massive expansion of construction land and soil erosion within prefecture-level cities, rather than spreading to towns of LH clusters. This research provides an important scientific basis for future regional planning and traffic infrastructure construction, and also a reference for traffic infrastructure development in other geographically similar regions on a synchronous development stage in the world.

Recent advances and perspectives towards emission inventories of mobile sources: Compilation approaches, data acquisition methods, and case studies

In recent years, great efforts have been devoted to reducing emissions from mobile sources with the dramatic growth of motor vehicle and nonroad mobile source populations. Compilation of a mobile source emission inventory is conducive to the analysis of pollution emission characteristics and the formulation of emission reduction policies. This study summarizes the latest compilation approaches and data acquisition methods for mobile source emission inventories. For motor vehicles, a high-resolution emission inventory can be developed based on a bottom-up approach with a refined traffic flow model and real-world speed-coupled emission factors. The top-down approach has advantages when dealing with macroscale vehicle emission estimation without substantial traffic flow infrastructure. For nonroad mobile sources, nonroad machinery, inland river ships, locomotives, and civil aviation aircraft, a top-down approach based on fuel consumption or power is adopted. For ocean-going ships, a bottom-up approach based on automatic identification system (AIS) data is adopted. Three typical cases are studied, including emission reduction potential, a cost-benefit model, and marine shipping emission control. Outlooks and suggestions are given on future research directions for emission inventories for mobile sources: building localized emission models and factor databases, improving the dynamic updating capability of emission inventories, establishing a database of emission factors of unconventional pollutants and greenhouse gas from mobile sources, and establishing an urban high temporal-spatial resolution volatile organic compound (VOC) evaporation emission inventory.

Estimating Infection-Related Human Mobility Networks Based on Time Series Data of COVID-19 Infection in Japan

Background: Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in the 21st century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in countries where traffic and human movement data infrastructure is not yet developed. Methods: In this study, we devised a method utilizing an ordinary and partial differential equations-based mathematical model and a modified mathematical optimization method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. Furthermore, utilizing the estimated human mobility network, we predicted the spread of infection using the Tokyo Olympics as a model. Findings: We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and revealed that the estimated effective distance captured human mobility changing dynamically in the different stages of the pandemic. The model predicted that the Tokyo Olympic and Paralympic Games would increase the number of infected cases in the host prefectures by up to 80%. Interpretation: The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide.

The influence of traffic-infrastructure factors on pedestrian accidents at the macro-level: The geographically weighted regression approach

Introduction: Walking is an active way of moving the population, but in recent years there have been more pedestrian casualties in traffic, especially in developing countries such as Serbia. Macro-level road safety studies enable the identification of influential factors that play an important role in creating pedestrian safety policies. Method: This study analyzes the impact of traffic and infrastructure characteristics on pedestrian accidents at the level of traffic analysis zones. The study applied a geographically weighted regression approach to identify and localize all factors that contribute to the occurrence of pedestrian accidents. Taking into account the spatial correlations between the zones and the frequency distribution of accidents, the geographically Poisson weighted model showed the best predictive performance. Results: This model showed 10 statistically significant factors influencing pedestrian accidents. In addition to exposure measures, a positive relationship with pedestrian accidents was identified in the length of state roads (class I), the length of unclassified streets, as well as the number of bus stops, parking spaces, and object units. However, a negative relationship was recorded with the total length of the street network and the total length of state roads passing through the analyzed area. Conclusion: These results indicate the importance of determining the categorization and function of roads in places where pedestrian flows are pronounced, as well as the perception of pedestrian safety near bus stops and parking spaces. Practical Applications: The results of this study can help traffic safety engineers and managers plan infrastructure measures for future pedestrian safety planning and management in order to reduce pedestrian casualties and increase their physical activity.

Exposure characteristics of ultrafine particles on urban streets and its impact on pedestrians.

In order to investigate the pedestrian exposure characteristics of ultrafine particles (UFPs) on urban streets, both mobile and fixed-point monitoring experiments were conducted. A generalized additive model and a respiratory deposition dose model were used to quantify the influencing factors and potential harm of UFPs, respectively. The results showed that UFPs' hotspots were more likely to manifest at places where vehicles tend to cluster, namely at road intersections and bus stops. The pedestrian bridge had the lowest number concentration of UFPs in comparison with the pedestrian crossing and underground passage at the same intersection. Aboveground, a "weekend effect" acting upon urban streets and evidence for periodicity at the intersections were found. The UFPs' number concentration was comprehensively explained-about 62.7% of its variation-by traffic volume, wind speed, temperature, and relative humidity. The UFPs were mainly deposited in the alveolar region of the respiratory system, but the deposition doses of males exceeded those of females under the same conditions. Based on these findings, the study also provides appropriate suggestions for better managing traffic pollution sources, traffic infrastructure, and traffic organization.

Analisa Pengaruh Hambatan Samping terhadap Kinerja Jalan Di Jalan Orang Kayo Pingai Kota Jambi

Increased volume of traffic flow will cause changes in traffic behavior of a road segment, especially on urban roads. This increase is caused by the increasing number of residents and the need for transportation facilities so that enough space is needed for traffic infrastructure such as roads, parking locations and so on. High road side activity has a major effect on road capacity and performance in an urban area. Among them such as pedestrians, road crossings, street vendors (Street vendors), slow-walking vehicles (rickshaws, bicycles, horse-drawn carriages), vehicles stopped carelessly (city transportation, inner-city buses), parking on the street (on street parking), and vehicles in and out of road-side land use activities. The purpose and purpose of the study conducted by this author is to find out the performance of the road against side obstacles. And the objectives can be described as follows, namely:To find out the volume of traffic, the amount of capacity and level of service on the road people kayo pingai during rush hour.To analyze the modeling of side barriers and road capacity of Kayo Pingai people using simple linear regression. As for the benefits that are expected and can be taken by the authors of the implementation of this study, including: Increase knowledge and insight into the characteristics of traffic and vehicle parking on the road body and know the various lalaulintas problems that occur in urban areas, especially on Jalan Orang Kayo Pingai.

The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments

The objective of the study is to show that landslide conditioning factors derived from different source data give significantly different relative influences on the weight factors derived with statistical models for landslide susceptibility modelling and risk analysis. The analysis of the input data for large-scale landslide hazard assessment was performed on a study area (20.2 km2) in Hrvatsko Zagorje (Croatia, Europe), an area highly susceptible to sliding with limited geoinformation data, including landslide data. The main advantage of remote sensing technique (i.e., LiDAR, Light Detection and Ranging) data and orthophoto images is that they enable 3D surface models with high precision and spatial resolution that can be used for deriving all input data needed for landslide hazard assessment. The visual interpretation of LiDAR DTM (Digital Terrain Model) morphometric derivatives resulted in a detailed and complete landslide inventory map, which consists of 912 identified and mapped landslides, ranging in size from 3.3 to 13,779 m2. This inventory was used for quantitative analysis of 16 input data layers from 11 different sources to analyse landslide presence in factor classes and thus comparing landslide conditioning factors from available small-scale data with high-resolution LiDAR data and orthophoto images, pointing out the negative influence of small-scale source data. Therefore, it can be concluded that small-scale landslide factor maps derived from publicly available sources should not be used for large-scale analyses because they will result in incorrect assumptions about conditioning factors compared with LiDAR DTM derivative factor maps. Furthermore, high-resolution LiDAR DTM and orthophoto images are optimal input data because they enable derivation of the most commonly used landslide conditioning factors for susceptibility modelling and detailed datasets about elements at risk (i.e., buildings and traffic infrastructure data layers).

A multi‐attention dynamic graph convolution network with cost‐sensitive learning approach to road‐level and minute‐level traffic accident prediction

Traffic accident prediction on road levels and minute levels plays an important role in optimizing public safety and improving traffic infrastructure. However, there are still some challenges in this work. Firstly, the dynamic factors (e.g. traffic flow) affecting traffic accidents make the road network have dynamic spatio-temporal dependency, which leads to biased prediction results. Secondly, the occurrence of traffic accidents is a small probability event, which brings about zero-inflation problem. To address aforementioned problems, the authors propose a Multi-Attention Dynamic Graph Convolution Network with Cost Sensitive Learning approach (MADGCN). Specifically, in the spatial dimension, MADGCN calculates the attention scores of different types of dynamic factors through attention mechanism to simulate the different influence degrees of different factors, and models dynamic inter-road spatial correlation through Graph Convolution Network (GCN). In the temporal aspect, MADGCN adaptively models the dynamic temporal correlations through self-attention blocks. In addition, MADGCN improves the loss function based on cost-sensitive learning strategy to increase the cost of false classification of positive samples, so as to accurately mine sparse positive samples. Experimental results on two real-world traffic accident datasets demonstrate the superiority of MADGCN. Compared with the existing state-of-the-art methods, F1-score of MADGCN on two real-world traffic accident datasets is improved by 11.61% and 9.15%, respectively.

Assessing the management of road traffic infrastructure in form of BOT in Tien Giang province from the perspective of traffic safety and investment efficiency

Road traffic infrastructure management and exploitation is a material production industry with both service and technical attributes, directly related to human life, means of transport and other assets. Traffic safety criteria are expressed through the ability to smooth traffic and the safety when traveling. To meet the above requirements, the road traffic infrastructure system must ensure quality in all aspects such as: design - construction - operation management. The Trung Luong - My Thuan expressway through Tien Giang province, invested in BOT form, phase 1, does not meet traffic safety criteria. The route does not have an emergency stop lane for rescue activities in the event of an accident. This is due to the issues in investment project planning, surveying and designing. For that reason, the article evaluates the management and operation of roads invested in the form of public-private partnership (BOT) in Tien Giang province from the perspective of traffic safety and investment efficiency. The, the article points out the gaps of state management in the field of road exploitation, as a basis for proposing solutions to improve the efficiency of BOT road traffic operation in Tien Giang province, Vietnam.

Solutions to complete state management for road traffic infrastructure investment in BOT form

This article recommends resolutions to improve the state management of road transport infrastructure investment in the form of BOT (Building - Operate - Transfer) in Vietnam, on the basis of Vietnam's practice and international experience. Accordingly, the group of authors solves 05 basic issues affecting the state management of PPP (Public - Private - Partnership) projects in Vietnam, including: State management’s regulatory documents; State management model, content and management method; Personnels participating in PPP; Financial instruments and management; Investment environment. The authors used qualitative and quantitative research methods to identify factors affecting state management and eliminate duplicate factors and factors with small impacts. Through the multivariable regression equation, there is the general formula Y= ?0 + ?1 X1 +…+ ?n Xn as the basis for the solution system. The conclusions drawn from the study help the state management to get a comprehensive view of the BOT projects that have been implemented in the past time to make adjustments and gradually improve the investment model in the form of PPP in general and BOT in particular.

Conceptualizing an individual full-trip tradable credit scheme for multi-modal demand and supply management: The MobilityCoin System

Economic instruments are considered promising to achieve the urgently needed reduction of greenhouse gas emissions in the transportation sector. In this context, tradable credit schemes have received more and more attention in recent years. These cap-and-trade systems have the primary goal of limiting congestion, but they can also incorporate emission reduction goals. In this paper, we present the conceptual extension of a tradable credit scheme from a congestion and emission-oriented to a holistic, full-trip, multi-modal mobility traffic management system. In addition to the demand it also includes the management of the supply side. The integration of all existing modes into one holistic scheme ensures that the overall system goals are reached as all behavioral responses remain within the system boundaries. The system comprises two key innovations. First, a central agency has the possibility to provide full-trip incentives across modes to support the overall system’s goal. Second, users of the system can spend parts of their allocated mobility budget for transport infrastructure upgrades as an addition to paying for mobility or monetizing it on the market. Those innovations are a distinct extension to the idea of tradable credits. Commonly used smartphones would serve as the enabling technology of the proposed system. They offer all technical requirements and almost every citizen has access to one. Smartphones are affordable compared to dedicated traffic management infrastructure and they are flexible to accommodate system changes, e.g., new modes, through software updates. Besides the potential technical implementation, overall design questions, social aspects as well as general implications of the concept are covered.

The Risk Model of Traffic Engineering Investment and Financing by Artificial Intelligence.

This study aims to analyze the influencing factors and mechanisms of investment and financing risks in transportation projects so that regions do not restrict the transportation investment and financing risk models in all areas to achieve intelligent transportation financial risk assessment. Firstly, the investment and financing modes are studied and analyzed. According to the analysis of intellectual investment and the financing report of traffic engineering infrastructure, a traffic engineering investment and a financing model based on intelligent computing is established, which is based on artificial intelligence (AI) big data analysis technology. Secondly, the investment and the financing risk model of traffic engineering is established based on multimodal learning. Finally, the urban traffic engineering of Xi'an is taken as the research object. Based on its investment and financing data in the construction of urban roads, the risk assessment is carried out. Combined with risk influencing factors, the accuracy of the intelligent calculation in the risk assessment model is calculated. Different grades of urban transportation projects have different risks in the investment and financing of transportation projects. The results show that different levels of urban transport projects have different risks in the investment and financing (IAF) performance of transport projects. Among them, the risk index of the first-class project is the highest, reaching 0.55. The risk index of the second-class project is 0.49. The results before and after using the flow engineering IAF risk model are compared. In the test results of traffic engineering risk, all target risks did not increase after the AI-based traffic engineering IAF is tested. The model test results for credit risk and financial risk are the highest at 70 and 60, respectively. Combined with the actual urban development situation, this study can provide investment and financing risk models for urban transportation projects in different regions and provide a reference for the resource control of transportation projects. This study uses AI to learn and analyze traffic engineering investment and financing data and more accurately provide data references for traffic engineering investment and financing risk models.

Cooperative Critical Turning Point-Based Decision-Making and Planning for CAVH Intersection Management System

The intersection is a critical traffic problem from the perspective of safety and traffic efficiency. As wireless communication technology advances, vehicle infrastructure cooperative approaches have received increased attention. In this paper, we propose a cooperative critical turning point method to help the cooperation between vehicles and infrastructures to improve the traffic efficiencies. The idea of cooperative critical turning point improves the cooperation between connected automated vehicles, the surrounding traffic and roadside infrastructures in order to provide high efficiencies of the intersection. An intersection management system using such a method is implemented based on the framework of the connected automated vehicle highway system. Such system can efficiently allow a roadside infrastructure receives state information from vehicles, reserve the associated intersection time-space occupancy, and then provide decision-making and planning feedback to the vehicles. The vehicles covered by the system then adjust their trajectories to meet their assigned time slot. The study validates the proposed system that considers the uncertainties of the driving environment by formulating the problem into a POMDP problem and solves it using an online solver. Based on preliminary simulation experiments, the proposed strategy can significantly reduce travel delays, decrease stops and improve the sustainability of the traffic system.

Attack Modeling Methodology and Taxonomy for Intelligent Transportation Systems

With newer technologies, the embedded hardware and software in traditional vehicles and traffic control infrastructure continue to become more interconnected and more vulnerable. To assist in dealing with existing and potential vulnerabilities, we present a novel attack modeling methodology, taxonomy, and metrics (relative average waiting time, average network flow, impacts and rate of changes) to model, simulate, and meaningfully evaluate the security of Intelligent Transportation Systems. We implement our work in two different architectures: 1) Newell’s Car-Following Model with Bounded Acceleration (the BA-Newell Model) in Matlab and 2) Intelligent Driver Model in Veins. Our code is entirely open-sourced and will be maintained so that the ITS community may use it as a tool. We observe that the architectural-related metric values for sample attack simulation results are similar and transferable; where, for example, the rate of change values have range of average distances 1.8-3.5% for network flow impact and 3.3-9.6% for wait time impact.

Teenage bicyclists' perceptions toward automated vehicles and their associated traffic infrastructure: A focus group discussion

Automated vehicles (AVs) can improve traffic safety for vulnerable road-users like bicyclists. It is most likely that current young people will be the ones to use these vehicles and interact with them. However, very few studies have focused on bicyclist-AV interaction, with little to no inclusion of teenage bicyclists. This research used a virtual focus group study with 25 teenage bicyclists in six groups. Participants were presented with potential designs of infrastructures and communicating interfaces, they discussed pros and cons for each design, and answered multiple surveys. Overall, teenage bicyclists showed positive perceptions toward AVs. They preferred spacious lanes for bicyclists and physical barriers separating AVs and bicyclists. Teenagers chose visual interfaces with familiar icons; nevertheless, they recommended both visual and audible interfaces to ensure bicyclists’ attention and address the need of visually and audibly impaired populations. Future researchers and stakeholders will be benefited from the methodology and outcomes of this research.

Influence of cyclic deviator stress and consolidation degree on permanent strain of “under-consolidated” marine sediment

Rapid rates of clay sedimentation produce a highly hazardous marine foundation condition: “under-consolidated” marine sediments. Numerous traffic infrastructure facilities have been constructed on this type of clay. In this study, a series of original cyclic oedometer tests were performed to investigate the drained long-term permanent strain of soft marine clays. The stress–strain relationships and permanent strain accumulations of the clay specimens were evaluated and compared under different cyclic stress ratios (CSR s) and consolidation degrees (U s). First, when CSRs increased, the total strain also increased; however, its rate of increase decreased at higher CSR values. Thereafter, we derived a new threshold value as the upper boundary of the CSR for pavement designs on soft clay foundations. When the CSR was below this threshold value, the accumulated permanent strain increased rapidly with an increase in the CSR; when the value was surpassed, the strain accumulated slowly and was approximately linear as the CSR increased. Finally, equations for predicting the long-term permanent strain of under-consolidated clays were established. The findings of this study will serve as a useful technical reference for researchers and engineers in the field of land reclamation, thereby helping ensure the safety and durability of current and future public infrastructure facilities constructed on such lands.

How to Promote Urban Intelligent Transportation: A Fuzzy Cognitive Map Study.

As an important part of smart city, intelligent transportation is an critical breakthrough to solve urban traffic congestion, build an integrated transportation system, realize the intelligence of traffic infrastructure and promote sustainable development of traffic. In order to investigate the construction of intelligent transportation in cities, 20 initial affecting variables were determined in this study based on literature analysis. A questionnaire collected from professionals in intelligent transportation was conducted, and a total of 188 valid responses were received. Then the potential grouping was revealed through exploratory factor analysis. Finally, a causal model containing seven concepts was established using the practical experience and knowledge of the experts. A root cause analysis method based on fuzzy cognitive map (FCM) was also proposed to simulate intelligent transportation construction (ITC). The results indicate:(1) The 20 variables can be divided into six dimensions: policy support (PS), traffic sector control (TSC), technical support (TS), communication foundation (CF), residents’ recognition (RR), and talent quality (TQ); and (2) In the FCM model, all six concept nodes (PS, TSC, TS, CF, RR, and TQ) have a significant positive correlation with the target concept node ITC. The rank of the six dimensions according to correlation strength is TS, CF, PS, TSC, RR, and TQ. The findings of this paper can help academics and practitioners understand the deep-seated determinants of urban intelligent transportation construction more comprehensively, and provide valuable suggestions for policy makers. And thus, the efficiency of intelligent transportation construction can be improved.

Multi-Agent Trajectory Prediction With Heterogeneous Edge-Enhanced Graph Attention Network

Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for safe and efficient operation of connected automated vehicles under complex driving situations. Two main challenges for this task are to handle the varying number of heterogeneous target agents and jointly consider multiple factors that would affect their future motions. This is because different kinds of agents have different motion patterns, and their behaviors are jointly affected by their individual dynamics, their interactions with surrounding agents, as well as the traffic infrastructures. A trajectory prediction method handling these challenges will benefit the downstream decision-making and planning modules of autonomous vehicles. To meet these challenges, we propose a three-channel framework together with a novel Heterogeneous Edge-enhanced graph ATtention network (HEAT). Our framework is able to deal with the heterogeneity of the target agents and traffic participants involved. Specifically, agents’ dynamics are extracted from their historical states using type-specific encoders. The inter-agent interactions are represented with a directed edge-featured heterogeneous graph and processed by the designed HEAT network to extract interaction features. Besides, the map features are shared across all agents by introducing a selective gate-mechanism. And finally, the trajectories of multiple agents are predicted simultaneously. Validations using both urban and highway driving datasets show that the proposed model can realize simultaneous trajectory predictions for multiple agents under complex traffic situations, and achieve state-of-the-art performance with respect to prediction accuracy. The achieved final displacement error (FDE@3sec) is 0.66 meter under urban driving, demonstrating the feasibility and effectiveness of the proposed approach.

A lattice hydrodynamic model integrating the velocity limit effect under V2X environment

The velocity limit signal often occurs because of bad traffic road infrastructure. Therefore, a novel lattice hydrodynamics model is provided by combining the velocity limit effect under V2X environment. The linear stability analysis reveals that the stability conditions are closely related to the velocity limit effect. Moreover, the evolution of density and hysteresis loop have been deeply explored in this paper via simulation. Interestingly, it is valuable to correctly consider the impact of velocity limit on traffic flow, which can play a positive role on traffic stability.

팬데믹 대응형 도시계획의 방향성 및 기법에 관한 연구

This study aims to propose urban planning directions and strategies that enable building urban space safe from transmissive disease like COVID-19. Through reviewing published articles and interviewing experts of various urban fields, this study derived fourteen planning considerations and reassembled them into five urban planning directions: accssibility, self-sustainability, smart technology, health, and prevention. Also, eighteen strategies covering the following seven urban planning areas were proposed: land use, traffic infrastructure, green infrastructure, public facility, medical facility, residential environment, and urban environment. This study contributes to providing comprehensive urban planning directions and strategies necessary for planning resilient cities in POST-COVID-19 era. Specifying the proposed strategies into different approaches, implementing necessary policy improvements, and conducting pilot projects are required to applicate the result of this study into actual urban planning stage.