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With the widespread application of large-section quasi-rectangular pipe-jacking tunnels in urban road traffic engineering in China, higher requirements have been put forward to control the influence of their construction on the surrounding environment. To scientifically evaluate the stability of large-section quasi-rectangular pipe-jacking tunnels under-passing existing box culverts, we proposed a novel viscoelastic–plastic model coupling Biot consolidation with non-stationary parameter shear creep (NPSCBCVPM) to fully characterize the coupling effect of consolidation and rheology of saturated soft soil. NPSCCBVPM was developed in Fortran as an ABAQUS user material subroutine. In addition, the NPSCBCVPM was compared with the creep tests of undisturbed soft soil and the generalized Nishihara creep model (GNCM). Finally, the proposed model was applied to the large-section quasi-rectangular pipe-jacking tunnel under-passing existing box culverts of Songhu Road in Shanghai. The results show that NPSCBCVPM are in good agreement with the creep tests of soft soil, and NPSCBCVPM can better reflect the nonlinear rheological characteristics of soft soil than GNCM. Furthermore, the proposed model can scientifically evaluate the viscoelastic–plastic stability analysis of large-section quasi-rectangular pipe-jacking tunnel under-passing box culvert. Further research should focus on developing three-dimensional NPSCBCVPM to better evaluate the spatial response of the box culvert structure and surrounding soil to the entire construction process of large-section quasi-rectangular pipe-jacking tunnels. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China has contributed to traffic events, such as traffic accidents and delays. It is difficult to detect and resolve highway traffic congestion in a timely manner using traditional methods because they are slow, require a large number of workers, and require the installation of a large amount of monitoring equipment. Therefore, it is imperative to introduce advanced technology to address these challenges. Recently, deep learning technology has made significant breakthroughs and has been widely applied to various fields with satisfactory results. Deep learning is among the most important technologies for the detection and evaluation of traffic congestion, enabling the accurate detection of the state of the expressway network's traffic congestion, the evaluation of traffic congestion, and the prediction of possible traffic congestion. This enables management to formulate traffic dredging strategies in advance to prevent the negative impact of traffic congestion on normal traffic flow. This paper proposes a framework based on deep learning for next-generation highway traffic management. This framework selects traffic congestion indicators to construct an index model, and then constructs a deep learning model based on self-coding. It predicts and classifies highway traffic environment data and excavates sample data based on the characteristics of traffic parameters. As soon as traffic data were classified, a prediction model based on SoftMax was established to detect and predict highway traffic congestion. We conducted a traffic congestion analysis of the Shanghai expressway network based on the speed performance data obtained from the China Traffic Management Bureau. As a result of their research, we developed an index to measure highway traffic congestion. For traffic control and management organizations to function effectively, it is crucial to have an accurate and clear picture of traffic network operations. We evaluated the proposed framework using data gathered from highway monitoring scenes, and the results indicated that 98.6% of the data could be correctly detected. Using the prediction model based on SoftMax for expressway vehicles during peak hours, the accuracy was 92%, and the misjudgment rate was 8%. This study demonstrates that detecting and evaluating the state of highway traffic environments using deep learning has high accuracy, can be applied to actual highway traffic systems, and is extremely useful for detecting highway congestion. This framework is a promising solution for next-generation highway traffic management and provides accurate and timely traffic congestion detection and evaluation. [ABSTRACT FROM AUTHOR]

Against the background of global warming, permafrost areas are facing increasing thawing, and the threat to the surface of the Qinghai–Tibet Engineering Corridor (QTEC) is serious. It is imperative to understand the current surface deformation and analyze the changes spatiotemporal characteristics for future warnings. At present, observation of a long time series and overall coverage of vertical ground deformation in QTEC are lacking. This paper takes the permafrost deformation of the QTEC as its research object. It uses the pretreated LiCSAR product and combines it with the LiCSBAS package to obtain monitoring results of the long time series deformation of the engineering corridor's surface. The SAR image acquisition date is taken as the constraint, the results covering the whole processing area are selected, and then the vertical deformation information covering the entire engineering corridor area by ignoring the north–south displacement is calculated. The results show that the surface of the study area, as a whole, slightly subsided between May 2017 and March 2022, and the vertical deformation rate was mostly distributed at −27.068 mm/yr − 18.586 mm/yr, with an average of −1.06 mm/yr. Vertical deformation dominated at 52.84 percent of the study area, of which settlement accounted for 27.57 percent and uplift accounted for 25.27 percent. According to the statistics of the normal distribution of deformation velocity per pixel, a total of 77% of the engineering corridor was stable, with a vertical deformation rate between −6.964 mm/yr and −4.844 mm/yr, and 17.7% of the region was sub-stable, with a settling rate of −12.868 mm/yr − –6.964 mm/yr. The unstable regions included areas with settlement rates greater than 12.868 mm/yr and uplift rates greater than 10.748 mm/yr, representing 4.4 percent and 0.9 percent of the total area, respectively, for a total of 5.3 percent. The results of this paper can be used as the theoretical basis and as basic data for decision making and scientific research in various departments, and they are of great significance for surface stability assessment and early warnings along engineering corridors and traffic projects. [ABSTRACT FROM AUTHOR]

Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of the data graph and extract the spatial features of road networks. Additionally, due to the capability to handle long-term dependencies, the longterm memory is used to predict the time series of traffic flow and extract the time features. The proposed model is evaluated using real-world data, which are obtained from the intersection of Liuquan Road and Zhongrun Avenue in the Zibo High-Tech Zone of China. The results show that the developed combined GCNLSTM flow direction level traffic flow prediction model can perform better than the single models of the LSTM model and GCN model, and the combined ARIMA- LSTM model in traffic flow has a strong spatiotemporal correlation. [ABSTRACT FROM AUTHOR]

The rapid global spread of the coronavirus disease 2019 (COVID-19) is now a reality. China has taken urban traffic control measures to prevent and control the epidemic, but this has prevented the flow of people between cities. This study investigates the mechanism of the impact of urban traffic control measures on the intercity population flow in China using the one-way causal measurement method. The results show that the impact of urban traffic control measures on the intercity flow of the population changes with time. Based on this, this study makes scientific suggestions for the government on how to reasonably undertake traffic control measures. [ABSTRACT FROM AUTHOR]

In order to alleviate the increasingly serious traffic congestion problem in China, realize intelligent traffic control, and provide accurate and real-time traffic flow prediction data for traffic flow guidance and traffic travel, this paper designs a GPS-based vehicle trajectory fusion optimization deep model BN-LSTM-CNN which makes full use of the temporal and spatial correlation characteristics of dynamic traffic flow to improve the accuracy of short-term traffic flow prediction. The parameters of the historical GPS dynamic trajectory of the traffic network link are converted into a two-dimensional matrix image of time and space relationship. First, the spatial features are input to the CNN network, and the spatial dependence relationship between the links is mined, then the traffic flow time series modeling is performed with a four-layer ConvLSTM network, and the BN normalization layer is added to normalize the activation value of the previous layer on each batch, so that the model can obtain higher training accuracy and quickly complete the prediction of the traffic flow state in a certain period of time in the future. The experimental results show that the prediction model is fast to optimize, the prediction error is the smallest compared with other methods, and it can meet the real-time requirements of urban traffic control. [ABSTRACT FROM AUTHOR]

With the spread of the COVID-19 pandemic and the implementation of closure measures in 2020, population mobility and human activities have decreased, which has seriously impacted atmospheric quality. Huaibei City is an important coal and chemical production base in East China, which faces increasing environmental problems. The impact of anthropogenic activities on air quality in this area was investigated by comparing the COVID-19 lockdown in 2020 with the normal situation in 2021. Tropospheric NO2, HCHO and SO2 column densities were observed by ground-based multiple axis differential optical absorption spectroscopy (MAX-DOAS). In situ measurements for PM2.5, NO2, SO2 and O3 were also taken. The observation period was divided into four phases, the pre-lockdown period, phase 1 lockdown, phase 2 lockdown and the post-lockdown period. Ground-based MAX-DOAS results showed that tropospheric NO2, HCHO and SO2 column densities increased by 41, 14 and 14%, respectively, during phase 1 in 2021 vs. 2020. In situ results showed that NO2 and SO2 increased by 59 and 11%, respectively, during phase 1 in 2021 vs. 2020, but PM2.5 and O3 decreased by 15 and 17%, respectively. In the phase 2 period, due to the partial lifting of control measures, the concentration of pollutants did not significantly change. The weekly MAX-DOAS results showed that there was no obvious weekend effect of pollutants in the Huaibei area, and NO2, HCHO and SO2 had obvious diurnal variation characteristics. In addition, the relationship between the column densities and wind speed and direction in 2020 and 2021 was studied. The results showed that, in the absence of traffic control in 2021, elevated sources in the Eastern part of the city emitted large amounts of NO2. The observed ratios of HCHO to NO2 suggested that tropospheric ozone production involved NOX-limited scenarios. The correlation analysis between HCHO and different gases showed that HCHO mainly originated from primary emission sources related to SO2. [ABSTRACT FROM AUTHOR]

Network topology is the basic for the development of traffic management and control. In a road network, bayonets with installation of surveillance facilities are key components to recognize traffic congestion from time to time. Therefore, identifying the essential bayonets in a road network becomes one of the most efficient ways to alleviate traffic congestion for traffic engineers and transport department. To do so, this paper aims to propose a novel sorting algorithm based on similarity measurements and traffic flow information to efficiently identify key bayonets in road networks. Our research results show that by analyzing the bayonet data in a fixed period of time in a medium-sized city of China, we have successfully identified the location of key bayonet points. Most of these key bayonet points are closed to residential areas and important traffic stations. The rank of these bayonet points can help the city managers better understand the topological characteristics of the road network as well as the propagation of congestion so as to make the traffic policies or control strategies for traffic congestion alleviation. [ABSTRACT FROM AUTHOR]

Through a review of previous studies, this paper analysed the epidemiological characteristics and attempts to determine the various trends of road traffic injuries (RTIs) in China before and after the coronavirus disease 2019 (COVID-19). This paper proposed effective measures and suggestions for responding to RTIs in China. Moreover, this paper aimed to provide some references for studies on RTIs in the future. According to a reference review, 50 articles related to RTIs were published and viewed in the China National Knowledge Infrastructure (CNKI), Wanfang database, Weipu (VIP) database and PubMed/MEDLINE database. Articles were selected according to the exclusion and inclusion criteria and then classified and summarized. Regarding cases, RTIs in China were highest in summer, autumn, and in rural areas and lowest in February. Men, elderly individuals and people living in rural areas were more susceptible to RTIs. In addition, thanks to effective and proactive policies and measures, the number of RTIs and casualties in China has substantially decreased, while there has been a growing number of traffic accidents along with the increase in nonmotor vehicles. However, it is worth noting that the number of RTIs obviously fell during the COVID-19 pandemic due to traffic lockdown orders and home quarantine policies. Nevertheless, accidents related to electric bicycles increased unsteadily because of the reduction in public transportation use at the same time. The factors that cause RTIs in China can be divided into four aspects: human behaviours, road conditions, vehicles and the environment. As a result, measures responding to RTIs should be accordingly proposed. Moreover, the road traffic safety situation in developing countries was more severe than that in developed countries. RTIs in China showed a downward trend attributed to road safety laws and various policies, and the downward trend was more significant during the COVID-19 pandemic owing to traffic lockdowns and home quarantine measures. It is urgent and necessary to promote road traffic safety, reduce injuries, and minimize the burden of injuries in developing countries. [ABSTRACT FROM AUTHOR]

Microscopic traffic flow models enable predictions of traffic operations, which allows traffic engineers to assess the efficiency and safety effects of roadway designs. Modeling vehicle trajectories inside intersections is challenging because there is an infinite number of possible paths in a two-dimensional space, and drivers can simultaneously adapt their speeds as well. To date, human driver models for simultaneous longitudinal and lateral vehicle control based on the infrastructure characteristics and interactions with other drivers inside an intersection are still lacking. The contribution of this paper is threefold. First, it proposes an integrated microscopic traffic flow model to describe human-driven vehicle maneuvers under interactions. Drivers plan their heading and acceleration in the predicted future to minimize costs representing undesirable situations. The model works with a joint optimization for an interaction cost term. The weights associated with the interaction cost reflect how selfish or altruistic drivers are. Second, the proposed model endogenously gives the order of vehicles in case of crossing paths. Third, the paper develops a clustered validation method for microscopic traffic flow models with interacting vehicles, which account for interdriver variations. Results show that the model can accurately describe vehicle passing orders of interacting maneuvers, paths, and speeds against empirical data. The model can be applied to assess various intersection designs. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71971140 and 52122215] and the Natural Science Foundation of Shanghai [Grant 20ZR1439300]. [ABSTRACT FROM AUTHOR]

COVID-19 has become a major public health emergency in the world, which seriously affects the normal operation of cities. Epidemic prevention and control is not only needed in big cities, but also in small and medium-sized cities. In view of this, the paper takes Beian city, China as the research area. This study establishes a street network model through spatial syntax, and predicts the crossing potential and arrival potential of its street network. This will play a reference role for traffic flow control in Beian city. The article uses emerging data. Through GIS spatial analysis method, we identify the hidden danger space of city. Therefore, this summarizes the places where people are easy to gather and some problems of the current situation of the city. The results show that: (1) Beian bridge and Wuyuer street have a good traffic potential. The intersection of Longjiang Road and Beidahuang street and the intersection of Tianyuan North Road and Baocheng road have good accessibility. (2) The intersection of Ping'an Street and Shanghai road is a potential hidden danger space of the city, and the focus of epidemic prevention and control. (3) The coverage rate of urban community medical services to residential land is 58.61%, and the existing medical infrastructure is insufficient. Under public health emergencies, the paper will argue a new development ideas for health and safety small town planning by visualizing the hidden danger space of the city. [ABSTRACT FROM AUTHOR]

Traditional penetration indices cannot accurately evaluate the performance of asphalt binders at high and low temperatures. A study was therefore undertaken to determine temperature conversion formulae for designing asphalt pavements using the US Strategic Highway Research Program's Superpave performance grading (PG) method. The research involved the observation of climate and pavement temperatures in north-east China, the determination of temperature conversion formulae and classification of climatic zones and Superpave PG standards. The results of this study showed that the proposed formulae are more accurate than penetration indices for predicting actual asphalt pavement temperatures and their performance at low and high temperatures, which will deliver economic and social benefits. [ABSTRACT FROM AUTHOR]

With the development of aviation industry, a series of problems have appeared in aviation and airspace, among which the most prominent problem is the congestion of aviation and airspace. Airspace congestion has become a major problem in the development of civil aviation in China. Especially in the central and eastern regions of China, airspace congestion is becoming more and more serious. To better solve the problem of airspace congestion, rough set theory and the Fuzzy C-means (FCM) model are first analyzed. By analyzing the temporal and spatial characteristics of traffic congestion in the control sector, a multisector traffic congestion identification model is established based on radar track data. Four multisector congestion characteristics including equivalent traffic volume, proximity, saturation, and traffic density are established. FCM and rough set theory are used to classify and identify sector congestion. Finally, the model based on FCM-rough set theory is compared with other methods based on the data of the regional control sector in northwest China. The experimental results show that the congestion recognition rate of the model is 92.6%, 93.5%, and 94.2%, and the congestion misjudgment rate is 1.5%, 1.2%, and 1.3%, respectively. Hence, the multisector congestion recognition model has a high recognition rate and a low misjudgment rate, and the overall discrimination result is relatively stable. By comparing the proposed method with other methods, it is concluded that the recognition accuracy of the model based on FCM theory is superior to other methods. In summary, the congestion situation of the sector is affected by a variety of macro- and micro-characteristics of the sector, and the congestion identification model is feasible and efficient. Multisector traffic congestion identification has certain application value for airspace planning, air traffic control-assisted decision making, and air traffic flow management. This work can optimize the aviation and airspace management system and provide relevant suggestions for the study of aviation and airspace congestion. [ABSTRACT FROM AUTHOR]

With China's fast expansion of urban traffic and railway engineering, a great number of subway and railway lines have been erected in subterranean tunnels in recent years. When the tunnel is in the process of construction, it will encounter the weak fracture zone, and the surrounding rock of this part of the tunnel is often poor stability and deformation is difficult to control, which will lead to the smooth progress of the project, delay and other problems. Therefore, to facilitate the timely treatment of tunnel soft surrounding rock, the study of image recognition of its stability is particularly important. Based on this, an improved local binary mode algorithm (ILBP) is proposed in this paper. By adjusting the weight of binary polynomial in local binary mode (LBP) operator, the target of ILBP is to extract the characteristics of weak surrounding rock of tunnel at a specific location, hoping to provide reference for the follow-up study of tunnel engineering. [ABSTRACT FROM AUTHOR]

In recent years, urban traffic congestion has seriously affected the healthy development of urbanization in China. And many measures to combat congestion have had little effect. The purpose of this paper is to find out the most reasonable and sustainable measures to control traffic congestion. Based on the theory of system dynamics, this study constructs a model of the formation mechanism of urban traffic congestion in China, and analyzes the thinking error of the traditional strategy of "building roads to eliminate traffic congestion" This study includes the current policy measures to control traffic congestion in the system dynamics model and analyzes the influence of each measure on the formation mechanism of urban traffic congestion. Then, it critiques the unsustainability of rigid policies, such as the vehicle number limit and the "similar road building to control traffic congestion" policies. This study reveals that of the five policies adopted by the government to alleviate traffic congestion, and come to the conclusion: the "sparse block collocation" policy is the most sustainable and fundamental congestion control measure. To achieve efficient traffic congestion control and support the healthy development of urbanization in China in the future, the government should increase the balance of infrastructure investment to improve the slow environment of public transport, adhere to public transport-oriented land development policies, raise the cost of motor vehicle travel, and promote urban traffic. [ABSTRACT FROM AUTHOR]

Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the traffic dynamics of the network regions are first transformed into MFAPC data models, and then, the derived MFAPC data models instead of mathematical traffic models serve as the prediction models in the distributed control design. The formulated control problem is finally solved with an alternating direction method of multipliers (ADMM)-based approach. The simulation results for the traffic network of Linfen, Shanxi, China, show the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background: With increasing urbanization in developing countries, sanitation workers are frequently involved in road traffic collisions. Our purpose was to study specific collisions involving sanitation workers and provide decision-making suggestions and reference measures for the sanitation industry and urban managers to reduce the occurrence of collisions.Methods: We obtained online news data about sanitation worker road traffic collisions in China between 2013 and 2017 and analyzed occurrence time and location, victim characteristics, and causes of collisions.Results: In China, between 2013 and 2017, 511 road traffic collisions were reported, with the fewest in February and July. Most occurred around 5:00 a.m. in Eastern regions and in urban areas. Victims were mainly over 50 years old, with more females than males. Collisions usually resulted in death at the scene. The ambiguity of laws, the exploitation of workers through industry outsourcing, and the difficulty of processing claims may be the main factors preventing victims from obtaining legal compensation.Conclusions: The most common cause of collisions was drivers' speeding, but workers also regularly risk death by crossing the road in pursuit of their duties. The absence of legal controls for environmental protection, the excessive pursuit of efficiency in urban governance, and the lack of basic education of sanitation workers are underlying causes of collisions. Raising awareness about sanitation worker road traffic collisions will help protect the work safety rights of this vulnerable group. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to understand how the contextual factors of health crisis information needs are different from a general health context and how these factors work together to shape human information needs. Design/methodology/approach: This study collected the COVID-19-related questions posted on a Chinese social Q&A website for a period of 90 days since the pandemic outbreak in China. A qualitative thematic approach was applied to analyze the 1,681 valid questions using an open coding process. Findings: A taxonomy of information need topics for a health crisis context that identifies 8 main categories and 33 subcategories was developed, from which four overarching themes were extracted. These include understanding, clarification and preparation; affection expression of worries and confidence; coping with a challenging situation and resuming normal life; and social roles in the pandemic. The authors discussed the differences between a health crisis and a normal health context shaping information needs. Finally, a conceptual framework was developed to illustrate the typology, nature and triggers of health crisis information needs. Research limitations/implications: First, only the Baidu Zhidao platform was investigated, and caution is advised before assuming the generalizability of the results, as the questioners of Baidu Zhidao are not representative of the whole population. Furthermore, since at the time of writing the COVID-19 is still in an emerging and evolving situation (Centers for Disease Control and Prevention, 2020), the collected data included only a relatively small sample size compared to the post-pandemic period, and this might have impact on the interpretation of the study's findings. Practical implications: The study's taxonomy of information needs provides a reference for indexing and organizing related information during a disaster. Social implications: The study helps authoritative organizations track and send information in social media and to inform about policies related to the pandemic (e.g., quarantine and traffic control policies in our study) to the right people in the right regions and settings when the next disaster emerges. Originality/value: The taxonomy of information need topics for a health crisis context can be used to index and organize related information during a disaster and support many information agents to enhance their information service practices. It also deepens the understanding of the formation mechanism of information needs during a global health crisis. [ABSTRACT FROM AUTHOR]

Deaths and injuries resulted from road traffic crashes remain a serious problem globally, and current trends suggest that this will continue to be the case in the foreseeable future mainly in developing countries. Among diverse cause of traffic safety challenges, traffic violation has been considered as one of the noticeable contributing factors. The main aim of the study is to identify and evaluate the major traffic violation with related risk factors using multinomial logit model. Traffic violation data of Luzhou were collected from Sichuan Province Public Security Department, China. The study result revealed six major traffic violations, including traffic light violation, illegal parking, wrong-way driving, speeding, and NOT wearing a seat belt. Urban roads classified with congested driving and severe weather conditions were the major risk factors. Among different vehicle types and use, those small car/automobile categories with private purpose use exhibit statistically significant association (p-value < 0.05) with the aforementioned traffic violations. Taking into consideration these risky contributing factors during the development of traffic regulations and enforcement will help to reduce traffic violations and create a smooth/healthy driving condition with improved traffic safety and will also increase the performance of driving in general. [ABSTRACT FROM AUTHOR]

In this study, the accumulated plastic strain (APS) of granite residual soil under cyclic loads is investigated by a series of undrained cyclic triaxial tests. Granite residual soil is a special kind of soil that is widely distributed in areas of rapid economic development. Owing to a lack of understanding of the cyclic deformation of granite residual soil, the effects of several essential stress conditions, such as cyclic shear amplitude, initial static deviatoric stress, and effective consolidation stress, are explored and analyzed on the APS growth with loading cycles. The cyclic loads represent the typical traffic load acting on the foundations of subgrade works. Based on the results of tests under various stress conditions, a simple and easily adopted accumulated plastic strain growth (APSG) model is correlated to predict the evolution of APS with loading cycle number. Through comparing the results of separate tests, the APSG model is validated and exhibits its ability to predict the accumulated plastic behavior with satisfactory accuracy. Furthermore, the APS growth with a loading cycle is examined under various stress conditions by the APSG model. The influences of those important stress factors on the APS behavior can be successfully described by two often-used parameters in soil dynamics, that is, cyclic stress ratio and static deviatoric stress ratio, which are involved in the APSG model. As a result, the characteristics of plastic deformation of granite residual soil under cyclic loading conditions can be further analyzed by the new model. Although this model is easy to adopt with some accuracy, a more powerful model based on rigorous theory is still needs to be developed in the future. The finding of this study will provide a deeper insight into the cyclic loading behavior of granite residual soils that are often encountered in the subgrade of traffic engineering in China. [ABSTRACT FROM AUTHOR]

Abnormal weather and natural disasters have become more frequent and serious around the world in recent years. Heavy rain, ice, snow, fog and haze impacts transport systems seriously and differently. Based on reviewing the relevant research on the prevention and control of traffic meteorological disasters at home and abroad, this study firstly illustrates the micro mechanism of different abnormal weathers' influences on transport, and then analyzes cases of damage to transport system due to different abnormal weather conditions in China in recent years. Lastly, it discusses several key strategies, especially policy recommendations, for prevention of transport congestion and traffic accidents in abnormal bad weather. • Classifying different weather conditions and pre-arranged plans are very critical. • To create a reliable transportation system is a priority to disaster prevention. • Mitigation and adaptation policies can ease the impacts of abnormal bad weather. [ABSTRACT FROM AUTHOR]

This article analyses the effectiveness of the Chinese road traffic liability system in terms of both deterrence and compensation. We utilize the neo-classical economic model of accidents to assess the key features of the system, such as the basis of liability, the level of the benefits, the impacts of liability insurance and regulation, as well as the capacity of riskspreading. The analysis shows that the road traffic liability system in China can only achieve partial deterrence. Undercompensation and insufficient risk-spreading seem to be serious problems, at least in the economically underdeveloped regions. Therefore, we propose several legislative changes that the policymaker could implement to improve the system. [ABSTRACT FROM AUTHOR]

Copyright of Policy & Internet is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the improvement of our country's economic level and quality of life, the numbers and scales of highway networks and motor vehicles are constantly expanding, which makes the current road traffic burden more and more serious. As an important means of traffic automation management, license plate recognition (LPR) technology plays an important role in traffic surveillance and control. However, the recognition rate and accuracy of the traditional license plate recognition methods still need to be improved. In the case of poor surrounding environment, it is prone to localization failure, vehicle license plate recognition errors or unrecognizable phenomena. Wavelet transform, as another landmark signal processing method after Fourier transform, has been widely used in the field of image processing. In China, the number of horizontal lines is usually larger than that of vertical lines. If the two vertical boundaries of the license plate can be detected successfully, the four angles of the license plate can be determined efficiently to complete the license plate positioning. In view of the advantages of wavelet transform technology and the characteristics of vehicle license plate, in this paper, a vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching is proposed. The edge of the license plate is detected by wavelet transform technology, and then the license plate is located by vertical edge matching technology. After the location is realized, the characters are segmented by vertical projection method and the characters are recognized by improved BP neural network algorithm. The experimental results show that the proposed vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching performs well in algorithm performance, which provides a good reference for the development of vehicle license plate recognition system. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Speed, acceleration and tracking are used to analyzed mixed traffic flow safety. • 3 conflict regions are divided based on Haar-Adaboost algorithm. • Traffic characteristics with and without conflicts in three regions are compared. • New safety factors of mixed traffic flow at intersection are proposed. Mixed traffic is the typical characteristic of urban traffic in China. Especially on the crosswalks at signalized intersection, mixed traffic flow is large, traffic conflicts happen frequently. So, the traffic safety analysis is one of the research directions at signalized intersection. The main conflicts on the pedestrian crosswalk include the conflict among pedestrians and non-motorized vehicles as well as the conflict between the right-turning motor vehicles and pedestrians. Several impacts are imposed on the order of mixed traffic flow on the crosswalk by the above conflicts. In this paper, on the basis of on-the-spot filming and investigation, 3 regions are categorized based on Harr-Adaboost algorithm according to traffic characteristics of mixed traffic flows. Typical parameters such as speed and trajectory of pedestrian, non-motorized vehicle and right-turning motor vehicle as well as the variation of each characteristic results from the traffic conflicts in 3 regions including the pedestrian crosswalk and its vicinity are analyzed. The concluded characters could be used to optimize the intersection design and traffic control and improve the safety of mixed traffic individuals. [ABSTRACT FROM AUTHOR]

Fatigue fractures can be frequently observed in welded joints in orthotropic steel decks (OSDs) after just a few decades of operation, which become the major deterioration mechanism deterring the serviceability of OSDs. In this paper, a novel dynamic Bayesian network (DBN) model has been established for the fatigue reliability analysis of OSDs at system-level. The exact inference algorithm is applied in the DBN model with discrete variables. Special modifications have been made on the existing algorithm to improve the computational efficiency in dealing with the deck system consisting of a considerable number of joints. Using the DBN model, the fatigue reliability of welded joints can be predicted and updated with the inspection and monitoring results at system-level. At the same time, a framework is established for the system-level reliability considering the fatigue fracture of rib-to-deck (RD) joints, the dominant cracking pattern affecting the serviceability of OSDs. For illustration, a typical OSD bridge in China has been selected to carry out a case study. To derive the stress spectrum required by the DBN model, the stochastic traffic model is employed, and the influence-based Monte Carlo simulations have been carried out. As a result, the fatigue reliability can be predicted at both component- and system-levels. Meanwhile, the observation of the traffic and the inspection result has been fused into the DBN model to update the deteriorating state of the deck system. Besides, the effect of enhancement and maintenance has been highlighted, including the enhancement in fatigue strength at the construction stage, and the repair and traffic control during the operation stage. • A novel dynamic Bayesian network (DBN) model is established for the fatigue reliability of orthotropic steel decks (OSDs). • Special modifications are made on the inference algorithm of the DBN model to improve computational efficiency. • A framework is established for the system-level fatigue reliability of OSDs based on serviceability. • The DBN model is illustrated with a case study, including prediction, information-based fusion and evidence-based updating. • The effects of enhancement and maintenance are investigated using the DBN model. [ABSTRACT FROM AUTHOR]

Screw-pile composite foundations have been widely implemented on multiple high-speed railways in China. Their distinctive design with variable sections makes the bearing behaviour of screw piles far more complex than that of common smooth piles. The bearing behaviour of a screw-pile composite foundation was analysed in silt soil on a national key construction project. A comparative analysis of the bearing characteristics of composite foundations of smooth piles and screw piles was performed through model tests. The results showed that, in a single-pile composite foundation and with the same pile diameter and length, the ultimate bearing capacity of the screw pile was 67% higher than that of the smooth pile. The smooth pile showed an area of negative frictional resistance at the top of the composite foundation. The screw pile had a negative friction area below the junction of the smooth section and the screw section in the screw-pile composite foundation. [ABSTRACT FROM AUTHOR]

Bicycle-metro integration is theoretically considered to be an effective solution for improving public transportation efficiency of "last mile" between home and metro station in cities. However, this proposition has not been fully proved in practice. In recent years, the emerging dockless bike-sharing system makes it possible to examine the spatial integration between flexible bicycle traffic and rail transit. This study mapped the bicycle traffic on an equal population cartogram of Shanghai to distinguish overall patterns within the center of Shanghai. The research result indicates the sharing bike usage frequency from metro station to outlying area is gradual declining, which demonstrates that bicycle-metro integration has already become the basic model for daily transport in Shanghai. [ABSTRACT FROM AUTHOR]

• Latent class cluster and random parameters model were integrated to explore factors influencing motorcyclists' injury levels. • Homogeneous clusters help to reveal new information such as important contributing factors hidden in the general model. • Although observations in each cluster are homogeneous in some aspects, heterogeneity was still found in each subgroup. • The proposed model is proved to have great potential for accounting for the source of heterogeneity. Due to the wide existence of heterogeneous nature in traffic safety data, traditional methods used to investigate motorcyclist rider injury severity always lead to masking of some underlying relationships which may be critical for the formulation of efficient safety countermeasures. Instead of applying one single model to the whole dataset or focusing on pre-defined crash types as done in previous studies, the present study proposes a two-step method integrating latent class cluster analysis and random parameters logit model to explore contributing factors influencing the injury levels of motorcyclists. A latent class cluster approach is first used to segment the motorcycle crashes into relatively homogeneous clusters. A mixed logit model is then elaborately developed for each cluster to identify its unique influential factors. The analysis was based on the police-reported crash dataset (2015–2017) of Hunan province, China. The goodness-of-fit indicators and the Receiver Operating Characteristic curves show that the proposed method is more accurate when modeling the riders' injury severities. The heterogeneity found in each homogeneous subgroup supports the application of the random parameters logit model in the study. More importantly, the results demonstrate that segmenting motorcycle crashes into relatively homogeneous clusters as a preliminary step helps to uncover some important influencing factors hidden in the whole-data model. The proposed method is proved to have great potential for accounting for the source of heterogeneity. The injury risk factors identified in specific cases provide more reliable information for traffic engineers and policymakers to improve motorcycle traffic safety. [ABSTRACT FROM AUTHOR]

Due to the inappropriate path caused by traffic control, climatic conditions, and inconsistent regional economic development, etc., the transportation efficiency of emergency relief supplies is low in mining areas in China. To improve the transportation efficiency, a road impedance function model was proposed based on the uniqueness of emergency relief supplies' transportation after mine disasters. The model, which aimed to solve the optimal transport time conforming to practical traffic conditions, was designed in accordance with China's road design norms and actual traffic flows while considering random delays at road junctions. The weight of path was calculated from the road impedance function model and traffic allocation simulation data. The feasibility and universality of the model were proved by performing simulations under normal traffic, rainfall and traffic control conditions. Results indicate that the optimal path selection by the Dijkstra algorithm achieves a high accuracy. Time cost under rainfall and traffic control conditions increases by 3.3 % and 1 %, respectively, compared with the normal condition. This study provides new ideas to select transport path for emergency relief supplies. [ABSTRACT FROM AUTHOR]

Violation probability of taxi drivers in metropolis is far more than that of normal drivers because they are labor-intensive, overconfident of self-driving skill, and always searching potential customers, sometimes even picking up or dropping off passengers randomly. In this paper, four types of violated behavior of taxi drivers in metropolis were first summarized, based on which corresponding scale table was initial designed with social statistical method. Furthermore, with certain samples, relative item analysis, exploratory factor analysis, validity analysis and reliability analysis were conducted to verify validity of the initial scale table, based on which some improvements were made, and we can see that the modified scale table in the paper has high fitness degree, good reliability and validity to detect violated behavior of taxi driver accurately. Finally, large area survey data of taxi driver questionnaire from Shanghai was collected with the modified scale table above, the analysis results showed that among four types of violated behavior of taxi drivers in metropolis, the probability over-speed is top to 89.57%, in which probabilities of behaviors of "driving over-speed at mid-night" and "accelerating to across the intersection during the yellow signal" are top to 64.2% and 58.2% respectively, which is meaningful for the improvement of taxi drivers' behaviors specification and traffic safety regulation. [ABSTRACT FROM AUTHOR]

Rolling stock connections and route conflicts of trains at terminal stations are critical to train delay propagation and prediction on the railway networks. Previous studies primarily focused on delay prediction/propagation from a macro perspective, without considering the two factors. To address this problem, a hybrid neural network architecture, called TLF-net, is proposed to predict departure delays at original stations (DDOSs), considering the rolling-stock connection and potential route conflicts in the railway network. TLF-net consists of a transformer, a long short-term memory (LSTM), and a fully-connected neural network (FCNN) block, to separately address variables with different characteristics. Based on the real-world data from two terminal stations in China, the experimental results show that the consideration of the potential route conflicts from the network can considerably improve TLF-net's prediction performance over the model that only considers train interactions on a single railway line. Also, it is proven that arrival/departure routes of consecutive trains are crucial for delay prediction, while using the transformer block can efficiently reveal the route conflict severity between arrival/departure routes. The sensitivity analysis to influence factors demonstrates the significance of considering the rolling stock connection and potential route conflicts. Finally, to support real-time railway traffic management, a train arrival delay prediction model from our previous study is integrated to predict the input of TLF-net (i.e., train arrival delays), enabling the proposed model to dynamically predict the DDOSs. This dynamic updating lengthens the prediction horizon (the prediction time ahead), making it better support real-time train traffic control and management. [ABSTRACT FROM AUTHOR]

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications. • A statistical framework to infer the fine-grained traffic arrival pattern is proposed. • The signal cycle parameters are inferred by exploiting the features of the time headway sequences. • The valid travel time is extracted at the selected time interval based on the Gaussian mixed model. • The proposed framework could achieve satisfactory performance in different traffic scenarios. [ABSTRACT FROM AUTHOR]

The urban green wave traffic control system is one of the effective means to solve the problems of urban traffic jams and reduce vehicle delay. Aiming at the problems of traditional green wave traffic control in optimization computation, this paper introduces the adaptive mechanism and crossover, mutation operators to artificial fish swarm algorithm (AFSA) in order to adjust the evolution group. Through setting bulletin board measure and retention strategy, the individual status of the optimal artificial fish is recorded. In that way, the adaptive genetic AFSA is established. Finally, the paper applies this algorithm to green wave traffic control in five continuous intersections on Jianning Road, Lanzhou, China, gains one traffic control program better than traditional method through calculation, and verifies the feasibility and effectiveness of adaptive genetic-AFSA to optimize green wave traffic control system. [ABSTRACT FROM AUTHOR]

Two-stop-line signalized roundabouts (TSLSR) are widely utilized in China. To calculate the capacity and optimal cycle length accurately, a model considering internal space constraint of the roundabout is developed in this paper. Firstly, the operational principle of TSLSR is analyzed. TSLSR is modeled as being equivalent to a four-phase intersection with left-turn protection phases, and the left-turn lane is further simplified as the "short-lane model". Secondly, based on the above analysis, the capacity of TSLSR is modeled and additionally, the accuracy and sensitivity of the model are also analyzed. Finally, the optimal cycle length of TSLSR which maximizes the capacity is put forward. Results show that the accuracy of the capacity model is low without considering the internal space constraint of the roundabout. However, the model developed in this paper is shown to improve the accuracy (about 20%) and control the relative error to be within 10%. The parameter sensitivity analysis demonstrates that the cycle length and radius of the central island have a significant influence on the capacity of the left-turn lane and results in an optimal value of signal cycle length maximizing the capacity. [ABSTRACT FROM AUTHOR]

Abstract Network-wide traffic control strategies (e.g. perimeter control and route guidance) in urban networks have recently been mainly studied to relieve or postpone congestion based on the theory of macroscopic fundamental diagram (MFD). Nevertheless, these studies are mostly applied to the statically partitioned networks or the dynamic networks that fail to fully consider traffic state prediction, conflicting with strongly spatiotemporal variability of traffic congestion or objective of active traffic management (ATM). This paper proposes a methodology to dynamically identify critical congestion warning areas from heterogeneous urban road networks, which aids to design efficient perimeter control approaches. In the methodology, a dynamic directed weighted network is built on the base of the link connectivity and the real-time traffic loads, and a link travel time prediction method based on Kalman filter is developed to calibrate directed weight values and undirected input values for links. With the undirected link input information, a dynamic congestion warning community detection method which consists of three consecutive steps is developed. Firstly, it could capture emergence of new congestion areas based on the definition of congestion seed intersection. Secondly, expansion and regression of each congested area could be achieved with the objectives of spatial compactness and traffic condition homogeneity. Thirdly, a two-level merging algorithm of adjacent different congestion areas is designed utilizing modularity model in community detection of complex networks. The proposed methodology is validated using ground truth data from downtown network of Jinan City in China. The results show that the proposed algorithms can efficiently track congestion evolutionary processes and effectively detect congestion warning areas from the test real network. Highlights • First, we use community detection theory to identify traffic congestion warning areas. • Second, three consecutive steps are proposed to describe the entire process of traffic congestion propagation. • Third, homogeneous sub-areas with different levels of congestion are extracted from heterogeneous urban network. • Fourth, our method performs more effectively in real-word urban road networks. [ABSTRACT FROM AUTHOR]

China's rapid urbanization and high traffic accident frequency have received many researchers’ attention. It is important to reveal how urban infrastructures and other risk factors affects the traffic accident frequency. A growing amount of research has examined the local risk factors impact on traffic accident frequency at certain time. Some studies considered these spatial influences but overlooked the temporal correlation/heterogeneity of traffic accidents and related risk factors. This study explores risk factors’ influence on urban traffic accidents frequency while considering both the spatial and temporal correlation/heterogeneity of traffic accidents. The study area is split into 100 equally sized rectangle traffic analysis zones (TAZs), and the urban traffic accident frequency and attributes in each TAZ are extracted. The linear regression model, spatial lag model (SLM), spatial error model (SEM) and time-fixed effects error model (T-FEEM) are established and compared respectively. The proposed methodologies are illustrated using ten-month traffic accident data from the urban area of Guiyang City, China. The results reveal that the time-fixed effects error model, which considers both spatial and temporal correlation/heterogeneity of traffic accidents, is superior to other models. More traffic accidents will happen in those TAZs that have more hospitals or schools. Moreover, hospitals have a greater influence on traffic accidents than schools. Because of the location in the margin of the city, those TAZs that have passenger stations have more traffic accidents. This study provides policy makers with more detailed characterization about the impact of related risk factors on traffic accident frequencies, and it is suggested that not only the spatial correlation/heterogeneity but also the temporal correlation/heterogeneity should be taken into account in guiding traffic accident control of urban area. [ABSTRACT FROM AUTHOR]

Highlights • A bi-objective stochastic simulation-based optimization (BOSSO) method is proposed. • Equitable and efficient signal timing stochastic SO problem is solved well. • Network-wide microscopic traffic simulation model is well calibrated from two aspects. • The proposed BOSSO method can address other similar problems. Abstract The equity of right-of-way is an important topic in traffic management and control. With the balance consideration of traffic equity and efficiency, which are respectively evaluated by the Atkinson index and average travel time, this study proposes a bi-objective signal timing simulation-based optimization (SO) model under uncertainties, and solve it by a bi-objective stochastic simulation-based optimization (BOSSO) method. In this method, two types of surrogate models (i.e., regressing Kriging model and quadratic regression model) are used to capture the complicated mapping relationship between decision variables and bi-objectives, respectively in the whole variable domain and in the local trust-region. Meanwhile, the incorporation of the global regressing Kriging model and an adaptive selector helps to predict bi-objective values of untested samples and re-estimate simulated samples in the local trust-region, which can save great computational costs and smooth stochastic noises. Moreover, the non-interactive role of a decision maker is taken to generate more Pareto optimal solutions around his/her desired bi-objective values. Through the algorithm comparison for a benchmark bi-objective stochastic optimization problem, the proposed BOSSO method is validated to outperform three other counterparts (i.e., NSGA-II, BOTR and BOEGO) under the same simulation costs. In real-field experiments, an urban road network with 15 signalized and five non-signalized intersections in Changsha, China is modeled by VISSIM. After the well calibration of the microscopic traffic simulation model, the network-wide bi-objective signal timing stochastic SO problems with and without coordination are solved by BOSSO. Numerical results indicate that compared with the real-field case, the average travel time and Atkinson index are reduced respectively by at most 13.48% and 23.49% for optimized non-coordinated signal plans, and respectively by at most 25.58% and 2.83% for optimized coordinated ones. It is further validated that under variable traffic volumes, the non-coordinated signal plan can well improve both traffic efficiency and equity, and the coordinated one is capable to improve traffic efficiency at a larger degree but sacrifice traffic equity. Moreover, the balance analyses show the existence of competing relationship between bi-objectives, and BOSSO is confirmed to outperform NSGA-II, BOTR and BOEGO in searching the better Pareto optimal signal plans under the same budged simulations. In conclusion, BOSSO is promising to address bi-objective optimization problems characterized by costly evaluation, high dimensions and stochastic noises. [ABSTRACT FROM AUTHOR]

Efficiency and equity have always been the two points of focus of transport projects. Compared with efficiency, equity is easily overlooked in the evaluation of transport projects. Many studies emphasize that defining and operationalizing costs and benefits and the distributive principle are critical parts in the assessment of transportation equity. However, the scope and time frame of the assessment target are also critical. In this paper, we took China’s fastest urbanizing city, Shenzhen, as a case study to assess transport equity by comparing accessibility among groups. First, the public transport system was divided into bus and subway, and the residents were divided into two groups: urban village and nonurban village. Second, we adopted an enhanced potential opportunity model to measure residents’ bus and subway accessibility and summarized them as transit opportunity. Third, we used the Dagum Gini coefficient decomposition and kernel density estimation method to explore the fair distribution of transit opportunity among groups and districts from 2011 to 2020. Decade-long changes in disparity and distribution of transit opportunity gave us a clear picture. On the one hand, the development of Shenzhen public transport system had a positive effect. All populations are benefiting, and their accessibility is increasing. On the other hand, it also had a negative effect to exacerbate inequality between populations. For the absolute value of the opportunity, Shenzhen’s urban village populations do have fewer transportation opportunities than nonurban villages, and this gap between them will be wider more and more. The public transport system is more inclined to improve the population with high initial opportunity and make them higher. The results illustrated the importance of examining transportation equity over an extended period and could provide information on urban development strategies. [ABSTRACT FROM AUTHOR]

Vehicular travel time distributions (TTDs) are of great importance for traffic management and control, and various probability distributions have been used for TTD prediction in previous studies. However, it is difficult to determine a generalized probability distribution of vehicular travel times on urban roads that is applicable to all traffic conditions in real situations. To solve this problem, this paper develops a machine learning-based generative model, named the travel time distribution prediction-generative adversarial network (TTDP-GAN) model, that uses license plate recognition data for TTD prediction. The TTDP-GAN model generates samples of predicted travel time to account for its probability distribution, and these samples are not based on any assumed distribution. In addition, the TTDP-GAN model considers the spatial and temporal correlations of the TTD predictions by applying the multi-head spatial and temporal self-attentions, structural similarity index measure (SSIM), and long short-term memory (LSTM) neural networks. The performance of the TTDP-GAN model is demonstrated in a case study of an urban road network in a medium-sized city in China. The results show that the TTDP-GAN model outperforms several state-of-art machine learning models (e.g., an LSTM neural network model, a GAN model, a Wasserstein GAN model, and an LSTM-GAN model) in the measurement of Jensen–Shannon (JS) divergence and in terms of mean, standard deviation, skewness, and kurtosis. In addition, the TTDP-GAN model with the SSIM has 21.43% better predictive accuracy for JS divergence than the TTDP-GAN model without SSIM. These results demonstrate that the adoption of SSIM is efficient in capturing the probability distribution for TTD prediction. A sensitivity analysis is also carried out to showcase the performance of the TTDP-GAN model in applications. • The TTDP-GAN can predict TTD without a pre-determined probability distribution. • Spatial and temporal correlation is considered by SAs, SSIM, and LSTM. • The performance of the TTDP-GAN is evaluated on a road network in a city in China. [ABSTRACT FROM AUTHOR]

Merging behavior is inevitable at on-ramp bottlenecks and is a significant factor in triggering traffic breakdown. In modeling merging behaviors, the gap acceptance theory is generally used. Gap acceptance theory holds that when a gap is larger than the critical gap, the vehicle will merge into the mainline. In this study, however, analyses not only focus on the accepted gaps, but also take the rejected gaps into account, and the impact on merging behavior with multi-rejected (more than once rejecting behavior) gaps was investigated; it shows that the multi-rejected gaps have a great influence on the estimation of critical gap and merging prediction. Two empirical trajectory data sets were collected and analyzed: one at Yan’an Expressway in Shanghai, China, and the other at Highway 101 in Los Angeles, USA. The study made three main contributions. First, it gives the quantitative measurement of the rejected gap which is also a detailed description of non-merging event and investigated the characteristics of the multi-rejected gaps; second, taking the multi-rejected gaps into consideration, it further expanded the concept of the “critical gap” which can be a statistic one and the distribution function of merging probability with respect to such gaps was analyzed by means of survival analysis. This way could make the full use of multi-rejected gaps and accepted gaps and reduce the sample bias, thus estimating the critical gap accurately; finally, considering multi-rejected gaps, it created logistic regression models to predict merging behavior. These models were tested using field data, and satisfactory performances were obtained. [ABSTRACT FROM AUTHOR]

Exploring travel time distribution and variability patterns is essential for reliable route choices and sophisticated traffic management and control. State-of-the-art studies tend to treat different types of roads equally, which fails to provide more detailed analysis of travel time characteristics for each specific road type. In this study, based on a vast amount of probe vehicle data, 200 links inside the Third Ring Road of Beijing, China, were investigated. Four types of roads were covered including urban expressways, auxiliary roads of urban expressways, major roads, and secondary roads. The day-of-week distributions of unit distance travel time were first analyzed. Kolmogorov-Smirnov test, Anderson-Darling test, and chi-squared test were employed to test the goodness-of-fit of different distributions and the results showed lognormal distribution was best-fitted for different time periods and road types compared with normal, gamma, and Weibull distribution. In addition, four reliability measures, that is, unit distance travel time, coefficient of variation, buffer time index, and punctuality rate, were used to explore the day-of-week travel time variability patterns. The results indicated that urban expressways, auxiliary roads of urban expressways, and major roads have regular and distinct morning and afternoon peaks on weekdays. It is noteworthy that in daytime the travel times on auxiliary roads of urban expressways and major roads share similar variability patterns and appear relatively stable and reliable, while urban expressways have most reliable travel times at night. The results of analysis help enable a better understanding of the volatile travel time characteristics of each road type in urban network. [ABSTRACT FROM AUTHOR]

Road safety has become a major public issue in China. This study collected a total of 396 road traffic accident cases that occurred in 28 provinces from 1985 to 2014 in China. The type of vehicles involved in the accidents includes cargo vehicles (126), passenger vehicles (253), dangerous chemicals transport vehicles (128), and cars (9). A new systems approach that integrates the causal categories framework based on the human factor analysis and classification system (HFACS) and the contributory factor interactions model (CFIM) was applied to conduct analysis of road traffic accidents to determine whether systems approaches should be used during road accident analysis efforts. The analysis of results leads to the following conclusions. (i) According to the causal categories framework based on HFACS, the frequency of “unsafe behaviours” is highest at the category level; the frequency of “violations” is the highest at the subcategory level. “Overloading/overcrowding,” “speeding,” “failed to provide supervision,” and “fatigue driving” should receive attention at the special indicator level. (ii) The new systems-based method that integrated the HFACS and the CFIM, which highlights the interactions between all levels of the causal categories, is a suitable method to analyse road traffic accidents. (iii) All latent categories, including “outside factors,” “organizational influences,” “unsafe supervision,” and “preconditions for unsafe behaviours,” can affect “unsafe behaviours”; “outside factors,” “organizational influences,” “unsafe supervision,” and “preconditions for unsafe behaviours” can influence each other. These interactions have not been quantitatively examined in previous studies. The findings of this study also demonstrate that the OR is a suitable technique to quantitatively examine the interactions among contributory factors. [ABSTRACT FROM AUTHOR]

The rapid increase in private car use in large metropolitan areas has led to irrational travel mode splits and severe traffic problems. Traffic demand management (TDM) is an effective policy to achieve a more sustainable development of traffic systems. This study analyzes the relationships between TDM policy, mode split, and travel mode choice using Stackelberg game theory. Then, using 0-1 programming, it establishes a combination of TDM policy instruments that can achieve a more sustainable mode split in a city and provides a case study in China. The method presented in this research has strong theoretical implications for TDM policymakers. [ABSTRACT FROM AUTHOR]

Complex network response to node loss is a central question in different fields of science ranging from physics, sociology, biology to ecology. Previous studies considered binary networks where the weight of the links is not accounted for. However, in real-world networks the weights of connections can be widely different. Here, we analyzed the response of real-world road traffic complex network of Beijing, the most prosperous city in China. We produced nodes removal attack simulations using classic binary node features and we introduced weighted ranks for node importance. We measured the network functioning during nodes removal with three different parameters: the size of the largest connected cluster (LCC), the binary network efficiency (Bin EFF) and the weighted network efficiency (Weg EFF). We find that removing nodes according to weighted rank, i.e. considering the weight of the links as a number of taxi flows along the roads, produced in general the highest damage in the system. Our results show that: (i) in order to model Beijing road complex networks response to nodes (intersections) failure, it is necessary to consider the weight of the links; (ii) to discover the best attack strategy, it is important to use nodes rank accounting links weight. [ABSTRACT FROM AUTHOR]

Station inflow control (SIC) is an important and effective method for reducing recurrent congestion during peak hours in the Beijing, Shanghai, and Guangzhou subway systems. This work proposes a practical and efficient method for establishing a static SIC scheme in normal weekdays for large-scale subway networks. First, a traffic assignment model without capacity constraint is utilized to determine passenger flow distributions on the network. An internal relationship between station inflows and section flows is then constructed. Second, capacity bottlenecks are identified by considering the transport capacity of each section. Then, a feedback-based bottleneck elimination strategy is established to search target control stations and determine their control time and control strength. To validate the effectiveness of the proposed approach, a decision support system coded in the C# programming language was developed, and the Beijing subway was used as a case study. The results indicate that the proposed method and tool are capable of practical applications, and the generated SIC plan has better performance over the existing SIC plan. This study provides a practical and useful method for operation agencies to construct SIC schemes in the subway system. [ABSTRACT FROM AUTHOR]

From the point of view on four aspects which contain transportation investment, infrastructure construction, freight turnover and transportation of human resources, this paper using the spatial econometric methods on the spillover effect of 31 Provinces Panel Data of traffic infrastructure on regional economic growth in China from 2013to2015. The empirical analysis are as follows, (1) The comprehensive variable traffic infrastructure has significant spatial spillover effect on regional economic growth; (2) The opening degree, capital stock and the level of scientific research also has significant positive effects on regional economic growth; (3) Transport infrastructure on regional economic growth spatial contribution reached 37%; (4) The rate of city effect on regional economic growth is not significant; (5) The transportation infrastructure significantly affect the growth of the regional economy of the Central part and Western part in China but not so significant in eastern regions. [ABSTRACT FROM AUTHOR]

In this study, an inexact fuzzy bi-level programming model was developed for regional energy and traffic system management under uncertainty in Urumqi city, China. The energy system and traffic system are important subsystems of regional areas such as cities. The coordinated management of regional subsystems is a difficult problem for regional management. A bi-level programming model is an appropriate and simple method to describe the coordinated management of regional subsystems. The energy and traffic structure adjustment, clean power generation and pollutant emission–reduction targets are designed to support the construction of an environmentally sustainable city in China. Methods of interval parameter programming and bi-level programming were incorporated into the developed model to tackle uncertainties and reflect the features in the system. The environmental impacts of energy and traffic activities and policies were analysed. The results are valuable for supporting the management or justification of the existing energy and traffic policies and schemes under uncertainty. [ABSTRACT FROM PUBLISHER]

Measures of traffic organization and traffic management in school zones, key protection areas for traffic safety, have been increasingly discussed by government administrations at all levels and researchers as well. Juveniles' trips occur frequently near school areas, especially in primary and secondary school zones. The sensitive topic of these vulnerable road users encountering traffic accidents tends to draw the whole society's attention. In this regard, the paper analyzed different traffic characteristics of 20 primary school zones in Beijing. With the starting point of traffic safety in the area, the paper divided these school zones by type, which was then verified preliminarily reliable and proper, by means of field measurement and driving simulation test, with speed characteristics as the major basis for judgment. By doing this, the authors look forward to providing a theoretical basis for carrying out measures of traffic organization and traffic management. [ABSTRACT FROM AUTHOR]