Expressways In Shanghai Research Articles

Field-monitoring and numerical investigation of a super-large-diameter shield tunnel undercrossing existing metro tunnels

The Beiheng Expressway in Shanghai was built using a super-large shield with a diameter of 15 m, and its construction impact on adjacent underground structures cannot be overlooked. This newly built tunnel undercrossed Shanghai Metro Line 18 with a minimum distance of 7.86 m. Noticeable additional movements induced by the crossing can be observed in both lines according to in-situ data, initially manifested as slight settlement followed by atypically pronounced heave. The maximum uplifts of the up and down lines reach 7.23 and 5.09 mm, respectively. Subsequently, a numerical study was conducted on this crossing case, and its effectiveness was verified using in-situ data. The results revealed- the soil-structure interactions during the crossing period. The displacement curves in the longitudinal direction of the metro tunnels exhibited asymmetry owing to the oblique crossing, with greater deformation occurring in the region that was crossed earlier than in the region crossed later. Significant heave was also observed for the newly built segmental linings and surrounding ground, indicating pronounced floating induced by large-diameter excavation, which in turn caused the existing metro lines to move upward simultaneously.

Do Expressway Interchanges Increase Crash Injury Severities? Insights Using Temporal Instability and Unobserved Heterogeneity

Expressway crashes in interchange areas are a critical concern in China, posing significant economic and social challenges. Utilizing three years of crash data from the Beijing–Shanghai Expressway, this study investigates the transferability and heterogeneity of crash characteristics between interchange and noninterchange areas, as well as the temporal shifts in factors influencing injury severity levels. The research employs four series of random parameters logit models to estimate three potential crash injury severity outcomes of severe injury, minor injury, and no injury (based on the most severely injured individual in each crash) and to identify key determinants, encompassing driver, vehicle, roadway, environmental, temporal, traffic, and crash attributes. Likelihood ratio tests and out-of-sample predictions are utilized to assess the temporal stability and transferability of crash area characteristics. Additionally, the marginal effects of various determinants are calculated to understand their influence across different year periods and crash types. Key variables such as overspeed, single-vehicle, AADT (annual average daily traffic volume), Ls min , and other crash type indicators are identified as significant random parameters, demonstrating heterogeneity in means and variances. Notable distinctions are observed between interchange and noninterchange crashes, indicating nontransferability, with most significant indicators revealing temporal instabilities. Furthermore, factors such as multivehicle involvement, buses, and nighttime conditions are identified as risk indicators, notably increasing the likelihood of severe injuries. These insights are invaluable for expressway designers and decision-makers, aiding in understanding the contributing mechanisms of various elements. This study suggests that stricter enforcement measures are crucial to prevent random lane changes, particularly at interchange entrances and exits. Additionally, effective management strategies and enforcement countermeasures should be implemented to mitigate crash injury outcomes in both interchange and noninterchange areas.

Assessing transportation network redundancy by integrating route diversity and spare capacity

In this paper, we develop a new transportation network redundancy measure by integrating the route diversity and spare capacity dimensions, which have been demonstrated as two complementary dimensions of redundancy and assessed separately in the literature. Instead of modelling redundancy as two separate measures with significantly different units and scales, an integrative measure is beneficial for comprehensive comparison and selection of redundancy enhancement schemes. Specifically, we represent the transportation network by adding a virtual link directly connecting each origin-destination (O-D) pair. The virtual link choice probability is affected by both the route diversity of the O-D pair and the spare capacity of these routes, which is used in the proposed measure. Numerical examples in the realistic Winnipeg network are conducted. The effectiveness of the proposed measure is further verified by applying it to identify the critical links in the Shanghai Expressway Network, where a severe traffic accident happened in 2016.

Assessing the impacts of connected-and-autonomous vehicle management strategy on the environmental sustainability of urban expressway system

Urban expressway network has become essential for over 30% of road trips in urban areas, making them significant in shaping environmental sustainability. Recently connected-and-autonomous vehicle (CAV) technology is expected to significantly transform the urban expressway systems, but little attention has been attributed to quantitatively assessing the impacts of managing CAV in mixed traffic flow on urban expressway environmental sustainability. To address this research gap, this study proposes a comprehensive simulation-optimization framework. The simulation component involves multiclass cell transmission model that simulates and evaluates the traffic flow of mixed human-driven vehicles and CAVs on the expressway, whereas the optimization component proposes a novel management strategy that jointly considers the dedicated lanes and speed management. An analytical tool is developed and validated in North-South Elevated Expressway in Shanghai, China, and further investigated the sensitivity of environmental impacts to vehicle headway, travel demand, and CAV market penetration rate. The results indicate that use of the proposed tool can reduce the total fuel consumption and exhaust emissions compared to baseline scenario. Overall, the insights from this study are valuable for policymakers of transportation in future cities, who develop management strategies on urban expressways that enhance the potential impact of CAVs on environmental sustainability.

Safety risk assessment for autonomous vehicle road testing

Objective Road testing can accelerate the development and validation of autonomous vehicles (AVs). AV road testing can come with high safety risks, particularly in a complex road traffic environment, due to the immaturity of AV technology. A priori safety risk assessments of the road traffic environment before AV road testing are of great importance, allow the quantifying of risk levels in different road scenarios, and provide guidelines for AV road testing in low to high-risk environments. Methods This study proposes a framework, namely Safety Risk Assessment for AV road testing (SRAAV), based on the probability and severity of five categories of potential AV accidents. Four groups of influencing factors are considered comprehensively in assessing AV safety risk, and their impacts are quantified using impact coefficients derived from a Bayesian network and empirical AV road testing data. The safety risk is assessed on a road section level, based on which an overall risk level is defined for a corridor and a region. Afterwards, the quantified safety risk is classified into four levels according to expert experience and knowledge, through a questionnaire survey. Results Applications of the proposed SRAAV framework are conducted for urban roads in Shanghai, and expressways in Shanghai and Gothenburg. The assessment results are validated using disengagement data from AV road testing. The results show that the SRAAV framework and its models could estimate the safety risk levels of road traffic environments for AV road testing in a sound way and have the flexibility for further extensions to be made. Conclusions The framework and assessment results can provide technical support for determining where and when to grant permission for public roads to be used for AV road testing, and how to choose public roads from a low to a high risk level, guaranteeing the safety of AV public road testing.

A Novel Approach for Modeling and Evaluating Road Operational Resilience Based on Pressure-State-Response Theory and Dynamic Bayesian Networks

Urban roads face significant challenges from the unpredictable and destructive characteristics of natural or man-made disasters, emphasizing the importance of modeling and evaluating their resilience for emergency management. Resilience is the ability to recover from disruptions and is influenced by factors such as human behavior, road conditions, and the environment. However, current approaches to measuring resilience primarily focus on the functional attributes of road facilities, neglecting the vital feedback effects that occur during disasters. This study aims to model and evaluate road resilience under dynamic and uncertain emergency event scenarios. A new definition of road operational resilience is proposed based on the pressure-state-response theory, and the interaction mechanism between multidimensional factors and the stage characteristics of resilience is analyzed. A method for measuring road operational resilience using Dynamic Bayesian Networks (DBN) is proposed, and a hierarchical DBN structure is constructed based on domain knowledge to describe the influence relationship between resilience elements. The Best Worst method (BWM) and Dempster–Shafer evidence theory are used to determine the resilience status of network nodes in DBN parameter learning. A road operational resilience cube is constructed to visually integrate multidimensional and dynamic road resilience measurement results obtained from DBNs. The method proposed in this paper is applied to measure the operational resilience of roads during emergencies on the Shanghai expressway, achieving a 92.19% accuracy rate in predicting resilient nodes. Sensitivity analysis identifies scattered objects, casualties, and the availability of rescue resources as key factors affecting the rapidity of response disposal in road operations. These findings help managers better understand road resilience during emergencies and make informed decisions.

Expressway Rear-End Conflict Pattern Classification and Modeling

The study of expressway rear-end conflicts is of great significance to analyze driving behaviors and improve traffic safety. However, research on the classification and modeling of conflict patterns is still lacking. This study aimed to explore conflict patterns and their relationship with influencing factors. The conflict data used in this study was extracted from a trajectory data set that collected 3 h of data during a morning peak hour on an expressway in Shanghai, China. An improved k-means algorithm, which can automatically obtain the optimal number of clusters, was used to classify the conflict events into six conflict patterns. The conflict patterns were interpreted from five aspects: risk level, speed of risk-changing, risk-avoidance response, risk-avoidance attitude, and risk-avoidance action. Furthermore, a multivariate Poisson-lognormal (MVPLN) model considering spatial–temporal correlation was applied. The relationship between the independent variables and the number of each conflict pattern within the spatial–temporal unit was obtained. The root mean square error of the MVPLN model was 0.81. Compared with univariate Poisson model, univariate negative binomial model, and univariate Poisson-lognormal model, the MVPLN model improved by 73.8%, 81.3%, and 29.6% in accuracy respectively. The results of this study can classify expressway rear-end conflict patterns and obtain the number of each conflict pattern within spatial–temporal units using available traffic data.

Research on Demand Price Elasticity Based on Expressway ETC Data: A Case Study of Shanghai, China

The research on price elasticity of demand, especially in the field of transportation, has high theoretical and application value. Based on the perspective of price elasticity of demand, the study presents the impact of adjusting expressway rates on the traffic flow of cars with seven seats or less. The data are from the measured data of the Shanghai expressway Electronic Toll Collection (ETC) from 2019 to 2020. In order to eliminate the impact of the surge of ETC users in 2019 on the results, Empirical Mode Decomposition (EMD) is used to optimize the data. The research shows that the price elasticity of demand will increase with the increase in charge amount (distance).

Evaluating the Renewal Degree for Expressway Regeneration Projects Based on a Model Integrating the Fuzzy Delphi Method, the Fuzzy AHP Method, and the TOPSIS Method

As the volume and scale of urban expressways continue to increase, renewal remains a concern for urban development. The renewal and decision-making of an urban expressway need to be endowed with new concepts to adapt to the rapid development of cities. Nevertheless, in addition to considering road factors such as facility conditions, driving conditions, and environmental protection, the existing evaluation system lacks comprehensive consideration of factors that improve resilience and adapt to future urban development, and it lacks a quantifiable general update evaluation system. Thus, the establishment of a comprehensive renewal indicator system and a mixed evaluation framework is a challenge. This study proposes an evaluation framework of expressway renewal indicators that integrates the three dimensions of macro, meso, and micro based on the fuzzy Delphi method, the fuzzy AHP method, and the TOPSIS method. A q-rung orthopair fuzzy linguistic set was used to handle expert uncertainty information in the process of conducting fuzzy evaluations. The indicators were refined into general and quantifiable evaluation indicators to improve their versatility. Moreover, the renewal value of expressways was measured and calculated using the TOPSIS method, and four renewal intervals were divided according to the calculation results. As a result, 28 renewal indicators were screened out, and the five factors with the greatest impact on renewal were the demand for transport development, the renewal of facility and service functions, the upgrading of institutional resilience, structural renewal, and economic development. The model was applied to eight expressways in Shanghai to calculate the renewal degree value and divide the renewal status. The model could identify the renewal needs of each road to guide the renewal decision. This study proposes an evaluation model to measure urban expressway renewal studies and provides a reference for urban renewal in the area of sustainable development

Temporal stability of factors affecting injury severity in rear-end and non-rear-end crashes: A random parameter approach with heterogeneity in means and variances

Rear-end crashes have become a serious global issue, with increasing injuries and fatalities accounting for massive property loss. The purpose of this study is to investigate the variation in the influence of factors affecting injury severity in rear-end and non-rear-end crashes and the change in impact degree over time. Using the three-year crash data of the Beijing–Shanghai Expressway from 2017 to 2019, the heterogeneity and temporal stability of contributing factors affecting rear-end and non-rear-end crashes were investigated through a group of random parameter logit models with unobserved heterogeneity in means and variances. Then, the temporal stability and transferability of the models were evaluated using likelihood ratio tests. Moreover, the marginal effects were calculated to explore the temporal stability and potential heterogeneity of the contributing variables from year to year. Using four possible injury severity outcomes, namely, fatal injury, severe injury, minor injury, and no injury, a wide variety of possible factors significantly affecting injury severity outcomes including environmental, temporal, spatial, traffic, speed, geometric, and sight distance characteristics were analyzed. Considerable differences were observed in the rear-end and non-rear-end crashes, and the contributing factors indicated statistically significant temporal instability in both crashes over the three-year period. This study can be of value in promoting highway safety aimed at rear-end and non-rear-end crashes and developing suitable safety countermeasures.

A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model

Timely and accurate traffic prediction information is essential for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). Because of the characteristics of nonlinearity, nonstationarity, and randomness, short-term traffic flow prediction could be still a challenging task. In this study, a hybrid short-term traffic flow multistep prediction method is proposed by combining the variational mode decomposition (VMD) algorithm and long short-term memory (LSTM) model. Firstly, the VMD algorithm is employed to decompose the original traffic flow data into a series of intrinsic mode function (IMF) components. Secondly, different LSTM models are established to predict different IMF components. For each prediction model, one-step to three-step predictions are carried out. Finally, the component prediction results are aggregated to obtain the final traffic flow multistep prediction values. The prediction performance of the proposed hybrid model is investigated using inductive loop data measured from the north-south viaduct expressway in Shanghai. The experiment results show that (1) VMD algorithm could effectively avoid the problems of endpoint effects and modal aliasing, and the decomposition effect is better than empirical mode decomposition algorithm and wavelet decomposition algorithm; (2) among all the involved methods, the proposed hybrid model is more effective and robust in extracting the trend information, which has the best multistep prediction performance.

Graph Convolutional Network-based Model for Incident-related Congestion Prediction: A Case Study of Shanghai Expressways

Traffic congestion has become a significant obstacle to the development of mega cities in China. Although local governments have used many resources in constructing road infrastructure, it is still insufficient for the increasing traffic demands. As a first step toward optimizing real-time traffic control, this study uses Shanghai Expressways as a case study to predict incident-related congestions. Our study proposes a graph convolutional network-based model to identify correlations in multi-dimensional sensor-detected data, while simultaneously taking into account environmental, spatiotemporal, and network features in predicting traffic conditions immediately after a traffic incident. The average accuracy, average AUC, and average F-1 score of the predictive model are 92.78%, 95.98%, and 88.78%, respectively, on small-scale ground-truth data. Furthermore, we improve the predictive model’s performance using semi-supervised learning by including more unlabeled data instances. As a result, the accuracy, AUC, and F-1 score of the model increase by 2.69%, 1.25%, and 4.72%, respectively. The findings of this article have important implications that can be used to improve the management and development of Expressways in Shanghai, as well as other metropolitan areas in China.

Jacking precision control of pipe roof and large box culvert below urban expressway-a case study of a large underpass in Shanghai

Pipe roof-culvert box method is an effective way for shallow buried tunnel passing beneath important facilities. In soft ground, the culvert can be jacked under the pre-reinforcement of pipe roof. This paper presents an investigation on the precision analysis and control of a large- scale pipe roof jacking (the scale of this structure is 21.648m*8.148m) of an underpass, which passes beneath the Mid-Ring expressway in Shanghai. The conclusions drawn from this paper show that: (1) In this project, more than 70 percent of the deflection values of jacking machines and pipes are controlled within 20mm. (2) During the process of pipe jacking, the deflections of pipes tend to lag behind the machines’ about 3~10m. 68 percent of the rangeability of pipes’ deflection are 50%~100% of the corresponding machines’ deflection. (3) Due to Stiffness effect of pipeline, posture of pipe is difficulty to be rectified during the stages of pipe starting and receiving. A compelling rectification will result in overlarge deviation and increase the difficulty of subsequent operation. (4) For two adjacent pipes, the posture of latter pipe is positively related by the stiffness of prior pipe. The Pearson correlation coefficient of horizontal pipes and vertical pipes can be 0.46 and 0.68 respectively. (5) Considering the interspace between top pipes and box culvert, the control criterion of vertical deviation of box culvert is recommended as 5cm.

Deep tree neural network for multiple‐time‐step prediction of short‐term speed and confidence estimation

To solve the multiple-time-step prediction of traffic speed and confidence for segment types of expressway, a deep tree neural network (DTNN) with multitask learning is proposed. DTNN contains a classification network, regression networks and a confidence network. These sub backbone networks accomplish the tasks of distinguishing segment types, fitting the speed of segments and the confidence estimation on the predicted speed, respectively. Through multitask learning, the sub networks in DTNN share feature representation and complement each other. To further improve the accuracy of speed prediction on congestion, the mean absolute percentage error loss function (MAPE-loss) is applied in DTNN. It makes the learning and extracted features biased to low speed samples. The traffic speed dataset of the Shanghai Expressway is used to test the DTNN and 12 comparison methods. Results show that the proposed DTNN with MAPE-loss can efficiently improve the predictive accuracy of low-speed samples over the other methods. The trained DTNN also gave highly accurate low speed prediction on the dataset of Suzhou expressway. In addition, the smallest reduction in the R-squared value from the training stage to the testing stage illustrates the best generalization of DTNN model.

Dual-attention network with multitask learning for multistep short-term speed prediction on expressways

In this study, a dual-attention network (DAN) with multitask learning is proposed to solve the short-term prediction problems of traffic speed. The proposed DAN includes a road-type attention module (RAM), which performs accurate short-term speed prediction using road-type attention scores, a low-speed attention module (LAM), which is trained on weighted samples and fits low speed, and a decision support module, which outputs either RAM or LAM by estimating the level of the predicted speed. DAN can improve the transfer in the feature and speed prediction task layers by learning-associated and time-dependent tasks. The Shanghai expressway dataset is used to test and compare the proposed method and 15 other techniques. The results show that DAN with a multitask loss function obtains the smallest mean squared error (MSE) and mean absolute percentage error (MAPE) in most cases. LAM efficiently improves the predictive accuracy of low-speed samples, whereas RAM performs better in terms of the overall error reduction. DAN achieves the largest R-squared of 0.93 with a small reduction in R-squared by 0.12% from the training data to the test data, thereby illustrating its excellent generalization. DAN outperforms the other models by at least 13.5% in terms of the MSE and by 5.07% in terms of the MAPE on different road types. Adding LAM effectively improves the MAPE by at least 21.4% over RAM without increasing the error of the other speed levels. In terms of the MSE, RAM outperforms DAN by 12.6% in the best case. This study proved that the short-term speed prediction based on DAN has the ability to improve the accuracy on low-speed level and the generalization on different road types.

Short-Term Travel Speed Prediction for Urban Expressways: Hybrid Convolutional Neural Network Models

Deep learning models for short-term travel speed prediction on urban expressways, such as the convolutional neural network (CNN), still present several limitations in multiscale spatiotemporal feature extraction. Hence, in this paper, three hybrid CNN models are proposed to improve the basic CNN model with regard to three target aspects for short-term (i.e., 5 min) travel speed prediction on urban expressways. More specifically, long short-term memory (LSTM), AutoEncoder (AE), and Inception module are incorporated into the basic CNN model to capture multiscale spatiotemporal features of travel speed data effectively and improve the accuracy and robustness of the basic CNN model. Based on loop detector data collected on the Yan’an expressway in Shanghai, the proposed hybrid CNN models are trained and tuned. To validate the improvements on the target aspects, a comprehensive comparison is conducted using a classical statistical model (i.e., autoregressive integrated moving average), a typical shallow neural network model (i.e., artificial neural network), and two basic deep learning models (i.e., recurrent neural network and CNN). Results show that the prediction accuracies of all the proposed hybrid CNN models exceed 96% and the mean absolute errors are less than 2.5 km/h, which are superior to other models. In terms of target improving aspects, two new metrics were introduced, and the proposed models, especially the AE–CNN model, showed better robustness under various input data structures and traffic states. The LSTM–CNN model outperformed the other models in learning time-series features, and the Inception–CNN model is superior in reproducing the dynamics of traffic congestion patterns on urban expressways.

Research and Application of Concrete Winter Construction Techniques on Cast-in-Place Beams

In this study, according to the climate characteristics of the micro-frozen area, the winter construction of cast-in-place beams is based on heat preservation, supplemented by electric tracing band (ETB) in weak areas, and verified by the model experiment. According to the situation of the construction site, winter construction techniques are formulated and optimized. The winter construction techniques are widely used in the reconstruction and expansion of Beijing to Shanghai expressway, Xintai to Ningyang expressway, Xintai to Taierzhuang expressway and other projects. No diseases in winter construction occurred in all application projects. The techniques are cost-effective and environmentally friendly.

Short-Term travel speed prediction for urban expressways using convolutional neural network and tensor decomposition

Prediction is an important part of the traffic management system (TMS) which supports route planning, dynamic traffic control, and information provision. We developed a multi-dimensional learning machine for predicting the traffic speed. Proposed methodology considered both historical experience and near past observation of traffic data by combining a convolutional neural network (CNN) with tensor decomposition (TD) predictor. TD based method is treated as an effective traffic predictor considering temporal-spatial neighbourhood data. However, limited by learning mechanism, such a method cannot extract historical traffic pattern from large datasets. Our main contribution is converting the traffic prediction into a tensor imputation problem, which considers the historical pattern information from CNN by constructing input tensor. Besides, multiple low-rank choosing and weighted optimization are introduced to improve the accuracy of TD-based prediction. We validated our methodology using empirical detector data of urban expressway in Shanghai. Compared with single algorithms, our method has smaller absolute and relative error.

Crash prediction based on traffic platoon characteristics using floating car trajectory data and the machine learning approach

Predicting crash propensity helps study safety on urban expressways in order to implement countermeasures and make improvements. It also helps identify and prevent crashes before they happen. However, collecting real-time wide-coverage traffic information for crash prediction has been challenging. More importantly, previous studies have failed to consider the characteristics of the traffic platoon (vehicle group) that the crash vehicle belongs to before the crash occurs. This paper aims to model crash propensity based on traffic platoon characteristics collected by the floating car method, which provides a time-efficient and reliable solution to collecting traffic information. Crash and floating car data are collected from the Middle Ring Expressway in Shanghai, China. Both the binary logistic model and the support vector machine are applied. A data preparation method, involving crash data filtering, floating car data filtering and data matching on the road network, is introduced for the safety analysis purpose. Results suggest that the traffic platoon information collected from floating cars accompanied works reasonably in predicting crashes on expressways. The support vector machine, with an overall accuracy of 85%, outperformed the binary logistic model which had an overall accuracy of 60%. Results further suggest the application of floating car technologies and the support vector machine in real-time crash prediction. Despite this, the study also concludes the merits of the binary logistic model over the support vector machine model in explaining the impact of different factors that contribute to crash occurrences.

Estimation of total fatigue life for in-service asphalt mixture based on accelerated pavement testing and four-point bending beam fatigue tests

The purpose of this study was to estimate the total fatigue life for in-service asphalt mixture fatigue with in situ accelerated pavement testing (APT) and laboratory four-point bending beam tests. On a selected expressway in Shanghai, China, a series of full-scale APT tests were conducted. During the APT tests, a portable seismic property analyzer was used to monitor the pavement deterioration through modulus reduction. An equivalent factor between the APT loading and equivalent single axle loads (ESALs) was estimated. Additionally, asphalt concrete slabs were cut from the APT test sections and then transferred to the laboratory for four-point bending beam fatigue tests. A new fatigue equation was proposed for the in-service asphalt mixtures. Furthermore, a shift factor between the laboratory fatigue life and field ESAL was recommended. Finally, the field total fatigue life of the in-service asphalt mixture was estimated based on all the work in this study.