Expressways In Shanghai Research Articles

Road pothole extraction and safety evaluation by integration of point cloud and images derived from mobile mapping sensors

The automatic detection and extraction of road pothole distress is an important issue regarding healthy road structures, monitoring, and maintenance. In this paper, a new algorithm that integrates the mobile point cloud and images is proposed for the detection of road potholes. The algorithm includes three steps: 2D candidate pothole extraction from the images using a deep learning method, 3D candidate pothole extraction via a point cloud, and pothole determination by depth analysis. Because the texture features of the pothole and asphalt or concrete patches greatly differ from those of a normal road, pothole or patch distress images are used to establish a training set and train and test the deep learning system. Subsequently, the 2D candidate pothole is extracted from the images and labeled via the trained DeepLabv3+, a state-of-the-art pixel-wise classification (semantic segmentation) network. The edge of the candidate pothole in the image is then used to establish the relationship between the mobile point cloud and images. The original road point cloud around the edge of the candidate pothole is categorized into two groups, that is, interior and exterior points, according to the relationship between the point cloud and images. The exterior points are used to fit the road plane and calculate the accurate 3D shape of the candidate potholes. Finally, the interior points of a candidate pothole are used to analyze the depth distribution to determine if the candidate pothole is a pothole or patch. To verify the proposed method, two cases, including real and simulation cases, are selected. The real case is an expressway in Shanghai with a length of 26.4 km. Based on the proposed method, 77 candidate potholes are extracted by the DeepLabv3+ system; 49 potholes and 28 patches are finally filtered. The affected lanes and pothole locations are analyzed. The simulation case is selected to verify the geometric accuracy of the detected potholes. The results show that the mean accuracy of the detected potholes is ∼1.5–2.8 cm.

Expressway crash risk prediction using back propagation neural network: A brief investigation on safety resilience

This study presents the work in predicting crash risk on expressways with consideration of both the impact of safety critical events and traffic conditions. The traffic resilience theory is introduced to learn safety problems from the standpoint of 1) considering safety critical events, such as traffic violations, as the safety disturbances, and 2) considering safety resilience as the ability of the traffic, greatly associated with traffic conditions, to resist critical events turning into crashes. The concept of safety resilience was illustrated qualitatively through simulation experiments. Aimsun microsimulation software was used to simulate traffic conditions with safety critical events (vehicle violations, in this paper) involved based on the geometric design of the G15 Expressway in Shanghai. Based on data from the simulation experiment, a two-staged model was developed which classifies crash risk status into three types including no-risk, low-risk and high-risk status. Modeling approach that relies on the back propagation neural network method was applied. The performance of the model in prediction was validated through the Receiver Operating Characteristic (ROC) curve test. Results indicated that the model performed well in predicting crash risks in the simulated environment. After training the model, an extra simulation experiment involving six additional tests was conducted. Results show that the traffic resilience theory may work in explaining the relationship between traffic conditions, safety critical events and crash risk, which are the key elements in road safety field. The introduction of safety resilience may inspire further exploration on this topic in both research and practice. Meanwhile, the model can be used to predict and monitor risks on expressways in a potentially more precise way.

Relationship between Lane Width and Safety along Urban Expressways in Shanghai

AbstractSafety issues along urban expressways have gained increasing attention for their direct impact on traffic operation, especially after a substantial amount of reconstruction work that altere...

Using ALPR data to understand the vehicle use behaviour under TDM measures

In Shanghai, China, two transportation demand management (TDM) measures, auctions of Shanghai vehicle licence plates and a narrow-time-based travel restriction policy, have been implemented to control the vehicle ownership and the use of vehicles registered outside Shanghai (VROS). To investigate the impact of these two TDM measures on VROS, vehicle use behaviour is analysed with automatic licence plate recognition (ALPR) data. A two-step k-means clustering algorithm is proposed to classify VROS' use behaviour from ALPR raw data. Moreover, the spatiotemporal patterns of each type of VROS and the structure of total transportation demand are analysed. The results show that VROS in Shanghai expressway network can be classified into five types. Type 1 vehicles are used for commuting (COM), and type 2 vehicles are used with high intensity during both on workdays and non-workdays (HHI). COM and HHI are mainly used by local Shanghai residents who cannot obtain a local licence plate under the auction policy. These two types of VROS only account for 3.6% of total vehicles but generate 14.2% of total traffic demand. If the users of COM and HHI transferred to public transit, the traffic congestion of expressway network would be greatly alleviated.

The planning and construction of a large underpass crossing urban expressway in Shanghai: An exemplary solution to the traffic congestions at dead end roads

To alleviate the increasing pressure from traffic chaos and congestion at peak hours in urban areas, more and more city expressways are constructed to facilitate the transportation. However, due to lacking of integrated traffic planning, massive arterial roads are separated as so called “dead end roads” by the expressways, leading to the inconvenience of commuting, even a sever to the transportation of the local network. In order to solve the problem, this paper investigated the traffic characteristics of dead end roads and the effect of underground crossing project, then proposed an innovative method of construction of large underpass in Shanghai soft ground. The experiences learnt from this project can provide useful references for solutions of dead end roads as follows: (1) The existing of dead end roads has direct influences on the traffic congestions of local roadwork, it’s necessary to take measures to solve the transportation problems induced by it. (2) Underground solution is effective for tackling the problems induced by dead end roads. The commuting time is shortened by 70% and more than 34% of the surficial traffic flow of auxiliary lanes are diverted into the underpass. (3) The underpass can improve traffic conditions of road intersection and adjacent arterial roads. Turning traffic flows at the intersection between Tianlin Road and Mid-Ring Road has a dramatic decline from 65% to 81% and the traffic saturation of adjacent arterial roads has a reduction of about 10%. (4) For the advantages of little impact on traffic and environment, the Roof-box jacking method with earth pressure balanced cutter head is recommended for shallow buried undercrossing project in urban intensive areas.

Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study

Although car-following behavior is the core component of microscopic traffic simulation, intelligent transportation systems, and advanced driver assistance systems, the adequacy of the existing car-following models for Chinese drivers has not been investigated with real-world data yet. To address this gap, five representative car-following models were calibrated and evaluated for Shanghai drivers, using 2100 urban-expressway car-following periods extracted from the 161,055 km of driving data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The models were calibrated for each of the 42 subject drivers, and their capabilities of predicting the drivers’ car-following behavior were evaluated.The results show that the intelligent driver model (IDM) has good transferability to model traffic situations not presented in calibration, and it performs best among the evaluated models. Compared to the Wiedemann 99 model used by VISSIM®, the IDM is easier to calibrate and demonstrates a better and more stable performance. These advantages justify its suitability for microscopic traffic simulation tools in Shanghai and likely in other regions of China. Additionally, considerable behavioral differences among different drivers were found, which demonstrates a need for archetypes of a variety of drivers to build a traffic mix in simulation. By comparing calibrated and observed values of the IDM parameters, this study found that (1) interpretable calibrated model parameters are linked with corresponding observable parameters in real world, but they are not necessarily numerically equivalent; and (2) parameters that can be measured in reality also need to be calibrated if better trajectory reproducing capability are to be achieved.

Key Assumption to Evaluate the Mechanical Performance of Widened Voided-slab Bridge Due to Foundation Settlement

In concrete bridge widening projects, it is a common practice to provide a monolithic connection between the existing and new decks by casting an in situ concrete stitching slab. As a result, the differential foundation settlement between the two decks has a considerable effect on the concrete stitching slab and the widened bridge. The widened bridge noted as multi-girder bridge is a simple-supported, precast, voided-slab bridge, located on the Beijing–Shanghai Expressway in east of China. When using a grillage Finite Element Model (FEM) to model the mechanical behavior of the multi-girder bridge after it has been widened, the general approach to account for the effect of differential foundation settlement is to load a calculated forced vertical displacement to the bearing of each precast slab of the new bridge. However, there is no suitable mathematical model to be used for calculating the forced displacement for all new slab beams, which requires the ability to describe the transverse distribution mode of these forced displacement under a given differential foundation settlement. Under this background, an experimental study and finite element analysis are conducted to find this suitable mathematical model in this paper. Based on a comprehensive research results, a quadratic curve model was considered to be a suitable mathematical model to meet the design need of such type of widened multi-girder bridge, and its quadratic equation was also derived in the paper. This finding is the key assumption for accurately analyzing the mechanical behavior of a widened multi-girder bridge, with a grillage finite element model.

Modeling and Predicting Stochastic Merging Behaviors at Freeway On-Ramp Bottlenecks

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.

The impact of vehicle moving violations and freeway traffic flow on crash risk: An application of plugin development for microsimulation.

This paper presents the use of the Aimsun microsimulation program to simulate vehicle violating behaviors and observe their impact on road traffic crash risk. Plugins for violations of speeding, slow driving, and abrupt stopping were developed using Aimsun’s API and SDK module. A safety analysis plugin for investigating probability of rear-end collisions was developed, and a method for analyzing collision risk is proposed. A Fuzzy C-mean Clustering algorithm was developed to identify high risk states in different road segments over time. Results of a simulation experiment based on the G15 Expressway in Shanghai showed that abrupt stopping had the greatest impact on increasing collision risk, and the impact of violations increased with traffic volume. The methodology allows for the evaluation and monitoring of risks, alerting of road hazards, and identification of hotspots, and could be applied to the operations of existing facilities or planning of future ones.

Modeling the Various Merging Behaviors at Expressway On-Ramp Bottlenecks Using Support Vector Machine Models

Abstract Merging behaviors on the acceleration lane are viewed as a key trigger in expressway breakdown and potentially increase driving risk. The main goal of this article is to make a high-precision prediction on four kinds of merging behaviors at expressway on-ramp bottlenecks. Based on the videos of traffic flow at two on-ramp bottlenecks at Yan’an Expressway in Shanghai, 403 empirical samples are collected by extracting trajectories from merging vehicles, as well as each adjacent one using trajectory processing software. A learning-based support vector machine (SVM) approach is adopted to predict the various merging behaviors. Considering four merging behaviors have different degrees of merging risk and effect on the mainline traffic flow, three SVM models are established. To overcome the potential over-fitting problem, variance analysis is used to extract the key variables in each model. The results show that the SVM models perform well. The highest prediction accuracy in the binary-classification models reaches up to nearly 90% followed by 80.10% in the multi-classification model. Besides, the results of SVM models are compared with several frequently used models, including discrete choice model, Bayesian network and classification and regression tree. It turns out that SVM achieves the best prediction results. The proposed method can be used for traffic simulation and real-time driver assistant system on future automated and connected vehicles.

Traffic Multiresolution Modeling and Consistency Analysis of Urban Expressway Based on Asynchronous Integration Strategy

The paper studies multiresolution traffic flow simulation model of urban expressway. Firstly, compared with two-level hybrid model, three-level multiresolution hybrid model has been chosen. Then, multiresolution simulation framework and integration strategies are introduced. Thirdly, the paper proposes an urban expressway multiresolution traffic simulation model by asynchronous integration strategy based on Set Theory, which includes three submodels: macromodel, mesomodel, and micromodel. After that, the applicable conditions and derivation process of the three submodels are discussed in detail. In addition, in order to simulate and evaluate the multiresolution model, “simple simulation scenario” of North-South Elevated Expressway in Shanghai has been established. The simulation results showed the following.(1)Volume-density relationships of three submodels are unanimous with detector data.(2)When traffic density is high, macromodel has a high precision and smaller error and the dispersion of results is smaller. Compared with macromodel, simulation accuracies of micromodel and mesomodel are lower but errors are bigger.(3)Multiresolution model can simulate characteristics of traffic flow, capture traffic wave, and keep the consistency of traffic state transition. Finally, the results showed that the novel multiresolution model can have higher simulation accuracy and it is feasible and effective in the real traffic simulation scenario.

Real‐time crash prediction on urban expressways: identification of key variables and a hybrid support vector machine model

The traffic safety on expressways is crucial for the efficient operation of the expressway system, and there is a close relationship between traffic states and crashes on expressways, and the occurrence of crashes may be influenced by the interaction of different combinations of traffic states upstream and downstream of the crash location. Based on the crash data and the corresponding traffic flow detector data collected on expressways in Shanghai, this study proposes a hybrid model combining a support vector machine (SVM) model with a k-means clustering algorithm to predict the likelihood of crashes. The random forest (RF) model is employed to select the important and significant variables for model construction from the data of the traffic flow 5-10 min before the crash occurred. Then, the cross-validation and transferability of different models (SVM model without variable selection, SVM model with variable selection, and hybrid SVM model with variable selection) are determined using 577 crashes and 5794 matched non-crash events. The results show that the crash prediction model along with the four most important variables selected using the RF model can obtain a satisfactory prediction performance for crashes. With the combination of the clustering algorithm and SVM model, the accuracy of the crash prediction model can be as high as 78.0%. Moreover, the results of the transferability of the three different models imply that the variable selection and clustering algorithm both have an advantage for crash prediction.

Calibration of a micro-traffic simulation model with respect to the spatial-temporal evolution of expressway on-ramp bottlenecks

On-ramp bottlenecks are usually the direct cause of traffic breakdowns on expressways, affecting the overall traffic operation of the expressway. A high-precision simulation model is the basis of an on-ramp bottleneck active traffic management strategy design and evaluation. This paper presents a practical and effective calibration method with respect to the congestion spatial-temporal evolution and two-capacity phenomenon of bottlenecks. An integrated criterion is proposed to evaluate how well the micro-simulation model can replicate the breakdown process. The widely used micro-traffic simulation model VISSIM is selected to simulate two on-ramp bottlenecks on an expressway in Shanghai, China. Firstly, 28 parameters of car-following and lane-changing behaviors are analyzed, and nine significant parameters of the operation at on-ramp bottlenecks are selected using sensitive analysis. Then, an orthogonal experiment is conducted to seek the best parameter combination. Finally, the calibrated simulation model is validated using a dataset of other days. Moreover, the transferability of the calibrated model is tested at Wuning bottleneck. The results show that the calibrated model can not only replicate the congestion evolution process and the special early onset breakdown phenomenon, but it can also apply to other similar on-ramp bottlenecks directly. The proposed calibration procedure, the integrated criterion for precision evaluation, and the selected significant parameters are meaningful to inspire rapid and effective calibration for other simulators.

Analysis of safety factors for urban expressways considering the effect of congestion in Shanghai, China

Urban expressways are the key components of the urban traffic network. The traffic safety situation on expressways directly influences the efficiency of the whole network. A total of 48,325 crashes were recorded by Shanghai Expressway Surveillance System in a three-year period. Considering the different crash mechanisms under different congestion levels, models for the total crashes, non-congested-flow crashes and congested-flow crashes were respectively formulated based on the real-time traffic condition corresponding to each crash. Moreover, considering the potential spatial correlation among segments, the adjacent-correlated spatial and distance-correlated spatial models were formulated and compared to the traditional non-spatial-correlated model. A Bayesian approach was employed to estimate the parameters. The results showed that the congestion index, merging ratio, ramp density, and average daily traffic significantly affect the crash frequency. The safety factors in non-congested flow and congested flow are different; diverging behavior is more risky in non-congested flow, more lanes tend to increase the risk of crashes in congested flow, and horizontal curves tend to decrease the crash risk in congested flow but cause high risk in non-congested flow. In addition, the distance-correlated spatial model is found to be the best-fitting model. The results of this study suggested that dedicated safety countermeasures can be designed for different traffic situations on urban expressways.

A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data

Traffic crashes occurring on freeways/expressways are considered to relate closely to previous traffic conditions, which are time-varying. Meanwhile, most studies use volume/occupancy/speed parameters to predict the likelihood of crashes, which are invalid for roads where the traffic conditions are estimated using speed data extracted from sampled floating cars or smart phones. Therefore, a dynamic Bayesian network (DBN) model of time sequence traffic data has been proposed to investigate the relationship between crash occurrence and dynamic speed condition data. Moreover, the traffic conditions near the crash site were identified as several state combinations according to the level of congestion and included in the DBN model. Based on 551 crashes and corresponding speed information collected on expressways in Shanghai, China, DBN models were built with time series speed condition data and different state combinations. A comparative analysis of the DBN model using flow detector data and a static Bayesian network model was also conducted. The results show that, with only speed condition data and nine traffic state combinations, the DBN model can achieve a crash prediction accuracy of 76.4% with a false alarm rate of 23.7%. In addition, the results of transferability testing imply that the DBN models are applicable to other similar expressways with 67.0% crash prediction accuracy.

Automatic Incident Detection for Urban Expressways Based on Segment Traffic Flow Density

Urban expressways play an important role in urban road networks. Although automatic incident detection (AID) methods have been studied for a long time, most of the existing AID algorithms are designed for freeways and do not explicitly consider the detection of incidents near ramps with frequent in and out weaving flows. Urban expressways usually have short spaced ramps, which makes the traffic flow characterizations quite different from the ones of freeways. In addition, expressways usually have heavy traffic flow, which makes it more difficult to distinguish incidents from congestion. This article presents an AID method for urban expressways, using loop detector data. Based on the geometric conditions and detector locations, the expressway is divided into short segments. The equivalent upstream and downstream traffic flow density difference is defined and calculated using the loop detector data. A detection logic based on the pattern of the density difference fluctuation is then proposed. The performance of the proposed algorithm was tested using real incident data from a corridor of the Shanghai expressway and compared with two classic AID algorithms. The results indicate that this method performs very well and is suitable for urban expressways.

Relationship of Lane Width to Capacity for Urban Expressways

To increase the capacity of urban expressways in Shanghai, China, additional lanes were created during the past decade through reconstruction. Field measurements indicate that maximum lane width is 3.97 m and minimum width is only 2.73 m. To investigate the relationship between lane width and capacity for urban expressways, 3 months of traffic flow data were extracted and filtered from the system to manage inductive detectors on Shanghai's urban expressways. Analysis of all 440 segments of expressways in Shanghai showed that only 60 sites could reach their capacity; the characteristics of these 60 sites were further analyzed statistically. An analysis of a variance, a regression analysis, and a t-test were used to explore the relationship between lane width and capacity. The research showed that lane width had no statistically significant effect on capacity. Two causes that might account for this finding are discussed further: (a) free-flow speeds are similar for different lane widths according to the findings of a t-test that found no statistically significant differences and (b) the critical speed when capacity is reached for a Shanghai expressway is very low, only 45 km/h. Thus, drivers can deal with a narrow lane width at such a low critical speed. This finding suggests that geometric design policies for capacity purposes should provide substantial flexibility for use of narrower lane widths on urban expressways with low speed limits, although those lane widths must be subject to safety considerations.

Key variables identification and proactive assessment of real-time traffic flow accident risk on urban expressway

Based on accident data and detector data collected on two expressways in Shanghai, important variables for model construction were selected from the data of traffic flow within 5~10 min before the accident with random forest model. Then, the Bayesian network (BN) model based on the Gaussian mixture model and expected maximum algorithm was established for the analysis of real-time traffic flow state and accident risk. Meanwhile, the transferability of BN model was also assessed. The results show that BN model built with selected important variables is better than that with direct detection data, with the accident prediction accuracy rate of 82.78%. The results of the transferability show that the improved BN model is still better than the traditional model, though the accident prediction accuracy of BN model decreases.

Online evaluation of an integrated control strategy at on‐ramp bottleneck for urban expressways in Shanghai

Variable speed limits (VSLs) and ramp metering (RM) have been proved to be effective alternatives to alleviate the traffic congestions occurred on freeways in many studies. However, no research has so far evaluated the efficiency of the above strategies on urban expressways in Chinese cities, where the early onset breakdown often occurs at the bottlenecks because of different driver behaviour from those reported in overseas. Hence, this study was intended to propose an integrated control strategy of VSL and RM to dynamically postpone the early onset breakdown of on-ramp bottlenecks based on its occurring probability. The proposed strategy is capable of simultaneously optimising mainline speed and RM rate based on the modified METANET model and the ALINEA model. Meanwhile, an online simulation platform that integrates VISSIM, Visual Basic and MATLAB programs was developed for the purpose of a close-to-reality evaluation. Empirical results on urban expressways in Shanghai show that the average travel time and the speed difference were significantly reduced by 8.6 and 41.4%, respectively; the pre-queue flow at the bottleneck was increased by 14.7%, which indicates that the early onset breakdown could be efficiently postponed or prevented by the use of the proposed control strategy.

Mechanism of Early-Onset Breakdown at On-Ramp Bottlenecks on Shanghai, China, Expressways

The mechanism of early-onset breakdowns was studied at on-ramp bottlenecks on expressways in Shanghai, China. From four on-ramp breakdown events captured on video, key parameters were extracted: prequeue flow, queue-discharge flow, speed variation per minute, lane change (LC) times in the mainline lanes and the acceleration lane, LC types, and LC locations (longitudinal and lateral). A total of 1,583 LC samples were analyzed. The findings showed a great difference in LC patterns when breakdowns occurred earlier than normal (i.e., before the bottleneck reaches expected capacity). In the case of an early breakdown, most LCs were forced LCs that occurred near the downstream end of the bottleneck, which spread laterally rather quickly. In contrast, in normal breakdowns in the United States, LCs were mostly free LCs that occurred evenly along the bottleneck longitudinally but were concentrated in rightmost lanes laterally.