Urban Expressway Research Articles

The Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learning

Rapid global economic development, population growth, and increased motorization have resulted in significant issues in urban traffic safety. This study explores the intrinsic connections between road environments and driving safety by integrating multiple visual landscape elements. High-resolution remote sensing and street-view images were used as primary data sources to obtain the visual landscape features of an urban expressway. Deep learning semantic segmentation was employed to calculate visual landscape features, and a trend surface fitting model of road landscape features and driver fatigue was established based on experimental data from 30 drivers who completed driving tasks in random order. There were significant spatial variations in the visual landscape of the expressway from the city center to the urban periphery. Heart rate values fluctuated within a range of 0.2% with every 10% change in driving speed and landscape complexity. Specifically, as landscape complexity changed between 5.28 and 8.30, the heart rate fluctuated between 91 and 96. This suggests that a higher degree of landscape richness effectively mitigates increases in driver fatigue and exerts a positive impact on traffic safety. This study provides a reference for quantitative assessment research that combines urban road landscape features and traffic safety using multiple data sources. It may guide the implementation of traffic safety measures during road planning and construction.

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.

High-Accuracy, High-Efficiency, and Comfortable Car-Following Strategy Based on TD3 for Wide-to-Narrow Road Sections

To address traffic congestion in urban expressways during the transition from wide to narrow sections, this study proposed a car-following strategy based on deep reinforcement learning. Firstly, a car-following strategy was developed based on a twin-delayed deep deterministic policy gradient (TD3) algorithm, and a multi-objective constrained reward function was designed by comprehensively considering safety, traffic efficiency, and ride comfort. Secondly, 214 car-following periods and 13 platoon-following periods were selected from the natural driving database for the strategies training and testing. Finally, the effectiveness of the proposed strategy was verified through simulation experiments of car-following and platoon-following. The results showed that compared to human-driven vehicles (HDV), the TD3 and deep deterministic policy gradient (DDPG)-based strategies enhanced traffic efficiency by over 29% and ride comfort by more than 60%. Furthermore, compared to DDPG, the relative errors between the following distance and desired safety distance using TD3 could be reduced by 1.28% and 1.37% in simulation experiments of car-following and platoon-following, respectively. This study provides a new approach to alleviate traffic congestion for wide-to-narrow road sections in urban expressways.

An extended non-lane-discipline-based continuum model through driver behaviors for analyzing multi-traffic flows

Non-lane-discipline environment has an essential role in driver behavior. In this environment, drivers change their driving behavior through the traffic flow, causing perturbations and complexity. To evaluate the effect of driver behavior under non-lane-based discipline on the traffic flow, the present study investigated the physical and dynamic characteristics of drivers on an urban highway in Iran for each vehicle class based on the lateral separation distance (LSD) parameter and strength influence factor. Then, an extended non-lane-discipline-based continuum model was proposed for different traffic conditions. Further, the proposed model was simulated using the adjusted upwind scheme for lane analysis. The field data results indicated that non-lane-based discipline has an important role in changing car drivers’ driving behaviors from typical and sluggish to aggressive behavior compared with lane-based discipline. Moreover, under maximum LSD, due to an increase in the lateral distance, car drivers became aggressive. In addition, there was a high stability region in traffic flow due to the positive effect of driver behavior, under non-lane-discipline environment, on the critical region of density where there was an increase in the traffic flow. However, in the free flow similar to jam density, the effect of the behavior on traffic flow was insignificant. Furthermore, driver behavior under non-lane-based heterogeneous traffic conditions could improve traffic flow. For the case of the car’s traffic flow regarding aggressive behavior under non-lane-based discipline, the capacity of lane reached 1.18–1.45 times higher in cars than that in sluggish behavior under lane-based discipline, respectively. Accordingly, the capacity of the lane was observed to be 1.50–3.41 times higher than that of trucks as the truck proportions decreased from 80% to 0% due to different LSD parameters and strength influence factors. Thus, the extended continuum model can appropriately predict traffic flow considering driver behavior under non-lane-based discipline.

Risk prediction for cut-ins using multi-driver simulation data and machine learning algorithms: A comparison among decision tree, GBDT and LSTM

The cut-ins (one kind of lane-changing behaviors) have result in severe safety issues, especially at the entrances and exits of urban expressways. Risk prediction and characteristics analysis of cut-ins are part of the essential research for advanced in-vehicle technologies which can reduce crash occurrences. This paper makes some efforts on these purposes. In this paper, twenty-four participants were recruited to conduct the experiments of multi-driver simulation for risky driving data collection. The surrogate measures, Time Exposure Time-to-Collision (TET) and Time Integrated Time-to-collision (TIT) were employed to quantify the risk of cut-ins, then k-means clustering was applied for risk classification of 3 levels. Multiple candidate variables of two kinds were extracted including 10 behavioral variables and 7 driver trait variables. Based on these variables, three prediction models including decision tree (DT), gradient boosting decision tree (GBDT) and long short-term memory (LSTM) are used for predicting the risks of cut-ins. Results from data validity verification show that the data collected from multi-driver simulation experiments is valid compared with real-world data. From results of risk prediction models, the LSTM, with an overall accuracy of 87%, outperforms the GBDT (80.67%) and DT (76.9%). Despite this, this paper also concludes the merits of the DT over the GBDT and LSTM in variable explanation and the results of DT suggest that controlling the proper lane-changing gap and short duration of cut-ins can help reduce risks of cut-ins.

A Cooperative Lane-Changing Strategy for Weaving Sections of Urban Expressway under the Connected Autonomous Vehicle Environment

To alleviate the lane-changing conflicts between weaving vehicles and enhance the traffic efficiency in the weaving section of urban expressway under the connected autonomous vehicle (CAV) environment, a cooperative lane-changing strategy for CAVs is proposed. The strategy consists of an upper layer of decision making, which determines the lane-changing sequences of weaving vehicles based on their lane-changing advantages quantified by a set of utility functions, and a lower layer of control, which generates detailed instructions of speed adjustments and lane-changing manoeuvres for weaving vehicles. To verify the effectiveness of the proposed strategy under different traffic demand settings, a numerical simulation, including a base case and a control case, is conducted. Then, to further verify the effectiveness of the proposed strategy for the mixed traffic state and compare its performance with the existing CAV lane-changing method, benchmark and comparison tests with six different market penetration rates (MPRs) of CAVs are carried out under the congested demand setting. In addition, the delay improvement ratio, inverse time-to-collision, and ratio of large deceleration time are selected as performance indicators to investigate the effect of the proposed strategy on enhancing the operational efficiency, traffic safety, and passenger’s comfort within the weaving section. According to the simulation results, the overall efficiency, safety and comfort in the weaving section under the CAV environment, are all improved, when the proposed strategy is applied to weaving vehicles. The proposed strategy is also superior to the existing CAV lane-changing method on maintaining traffic efficiency and safety. Therefore, the proposed cooperative lane-changing strategy, based on CAV technologies, shows good potential in solving the problem of lane-changing conflicts within the weaving section and facilitating the traffic management and traffic control of urban expressway.

Electricity and hydrogen production by cogeneration system applied in a fuel station in Brazil: Energy analysis of a combined SOFC and ethanol steam reforming model

A model is presented that hydrogen and electricity are cogenerated by a compact system, regarding operational integration of units of SOFC and Ethanol reformer. This is a model of a simultaneously production of electricity and hydrogen to be applied and allocated strategically in fuel stations, considering the Brazilian conditions that such fuel stations follow marketing and logistics premises that allow to be regularity provided with ethanol biofuel as well are usually near to the consumers. The strategic is the use of the wide network of fuel stations that generally has relevant locations such as urban centers and highways for electricity and hydrogen supply.The objective refers to direct green-hydrogen usage after the processing of ethanol steam reforming, mitigating the usual technical and economics difficult and limitations related to storage and transportation of high volumes of hydrogen that alternatively could be accomplished with ethanol and water, using the proposed model. Concurrently, the same fuel stations would be a green-hydrogen provider, contributing to the reduction of pollution by using ethanol as a biofuel already extremely applied over all the Brazilian territory, attempting to the demands needed. The main novelty of this model refers to combination of three characteristics: (i) high heat produced by the SOFC that could be provided as the energy required for the (ii) endothermic Ethanol reforming, and this fuel is already used throughout (iii) Brazilian territory in fuel stations, which startup to offering also hydrogen and electricity as new products.The full study of this model consists in two papers, that in this first work is presented the thermodynamic analysis, aiming to identify the potential of the energy integration specially related to the SOFC and Reformer functionalities, as a technical feasibility validation. The conclusion of the theoretical results regarding mathematical modelling demonstrates this feasibility, indicating the existence of a minimum energy parity required between SOFC and Ethanol reformer.

Research on Energy Management of Hydrogen Fuel Cell Bus Based on Deep Reinforcement Learning Considering Velocity Control

In the vehicle-to-everything scenario, the fuel cell bus can accurately obtain the surrounding traffic information, and quickly optimize the energy management problem while controlling its own safe and efficient driving. This paper proposes an energy management strategy (EMS) that considers speed control based on deep reinforcement learning (DRL) in complex traffic scenarios. Using SUMO simulation software (Version 1.15.0), a two-lane urban expressway is designed as a traffic scenario, and a hydrogen fuel cell bus speed control and energy management system is designed through the soft actor–critic (SAC) algorithm to effectively reduce the equivalent hydrogen consumption and fuel cell output power fluctuation while ensuring the safe, efficient and smooth driving of the vehicle. Compared with the SUMO–IDM car-following model, the average speed of vehicles is kept the same, and the average acceleration and acceleration change value decrease by 10.22% and 11.57% respectively. Compared with deep deterministic policy gradient (DDPG), the average speed is increased by 1.18%, and the average acceleration and acceleration change value are decreased by 4.82% and 5.31% respectively. In terms of energy management, the hydrogen consumption of SAC–OPT-based energy management strategy reaches 95.52% of that of the DP algorithm, and the fluctuation range is reduced by 32.65%. Compared with SAC strategy, the fluctuation amplitude is reduced by 15.29%, which effectively improves the durability of fuel cells.

Selecting ecologically appropriate scales to assess landscape ecological risk in megacity Beijing, China

Assessing the landscape ecological risk (LER) will facilitate optimal planning of land-use patterns and mitigating regional ecological risks, especially in megacities under rapid urbanization. And selecting the appropriate spatial analysis scales is the prerequisite for accurate assessment of the LER. In this study, we demonstrate the spatio-temporal dynamics of LER by determining the appropriate spatial scales in Beijing, China from 2000 to 2020. The results indicate that a granularity of 50 m and a magnitude of 5 km are optimal for regional LER assessment. The study area had predominantly medium risks, with medium, high and higher risks accounting for approximately 70% that revealed LER was at high level. Fortunately, the high and higher risks decreased by 14.64% while low and lower risks increased by 16.62% over the past two decades, which indicated LER was on a general downward trend. The LER pattern varies from east to west, with high and higher risks clustered in the northeast and southeast of the city center, and gradually dispersing elsewhere. In contrast, low and lower risks were predominantly in the city center and western regions, expanding outward from the city center. The LER was low in built-up land and cropland in the central city, but relatively high in areas away from it. The urban fringe areas dominated by woodland and grassland exhibited a high LER. The regional LER differentiation is spatially correlated with urban expressways and significant changes in LER were predominantly observed in areas adjacent to urban expressways. Specifically, the expansion of low and lower risks occurred outward along the ring roads of Beijing, and a typical case was that the lower risks were primarily enclosed by the fifth ring road in 2000 and gradually expanded towards the areas near the sixth ring road by 2020. Moreover, the LER manifests obvious spatial agglomeration influenced by human disturbance, with apparent high-high and low-low agglomerations. It is suggested to implement landscape optimization by enhancing the landscape diversity and connectivity to effectively mitigate ecological risks. These findings will provide scientific reference for researches aimed at mitigating ecological risks and fostering high-quality sustainable development in metropolitan areas.

Optimization of Signal Timing for Urban Expressway Exit Ramp Connecting Intersection.

To alleviate the traffic problems of congestion and queue overflow on a mainline at the intersection of an urban expressway exit ramp articulation during peak hours, a bi-level programming optimization model of signal timing is proposed. The lower-level optimization objective is to maximize the capacity of the expressway exit ramp that articulates with the entrance road, while the upper-level optimization objective is to minimize the average vehicle delay and the number of stops per vehicle, taking into account the queue length in the direction of the ramp and other directions. The particle swarm optimization algorithm is selected to solve the proposed model, applied to a real case, and is validated using MATLAB and VISSIM simulation platforms. The simulation results show that the average vehicle delay and the number of stops per vehicle in the exit ramp on the expressway are reduced by 22.09% and 18.60%, while those in the intersection area are reduced by 20.96% and 17.19%, respectively. The conclusion indicates that the signal timing scheme obtained by this method can effectively improve the traffic efficiency at the intersection of the exit ramp on the expressway and alleviate the problem of congestion and the overflow of the exit ramp back to the mainline.

Blockage and Permeability of Permeable Asphalt Pavement Materials Based on the CAVF Method

The permeable asphalt concrete pac-13a is designed based on the CAVF method. By measuring the porosity, connected porosity, and pavement water permeability coefficient of the test piece, it has better water permeability performance than pac-13b, designed by a traditional empirical method. The vertical permeability coefficient tester is used to carry out the water permeability test under the action of four kinds of different blocking substances. The three stages of blocking the permeable pavement are reproduced. The permeability coefficient and the residual rate of the permeability coefficient were calculated by modifying Darcy’s law. The regression model of the permeability coefficient and blocking times of four kinds of plugging materials was established. The permeability of the test pieces was restored to varying degrees by high-pressure water gun injection. The test results showed that pac-13a had good water permeability and more connected porosity. It could maintain a certain permeability even if it were blocked; the blockage of urban expressways and arterial roads has the least impact on the permeability of PAC.

Traffic forecasting for city highways of the radial direction Evgeny V. Parsaev, Irina A. Teterina

The article presents a method for determining the predicted traffic intensity on city highways with a radial direction. The main stages of the technique are presented in the form of an algorithm with step-by-step implementation. The main problems associated with determining the predicted intensity on urban highways of the road network are noted. The results of the implementation of the proposed methodology are presented, on the example of the projected road network of one of the micro districts of the city of Omsk. Graphically presented are the results of measurements of changes in the daily traffic intensity of both directions on the projected section of the road network. The assumption about the influence of the period of continuous survey of traffic intensity on the occurrence of an error in the obtained numerical values was experimentally confirmed. The time interval for measuring traffic intensity is recommended to obtain the most correct research results. Keywords: forecasting, traffic flows, road network, traffic intensity, micro district, city highway

Microsimulation Modelling and Scenario Analysis of a Congested Abu Dhabi Highway

Today’s roadways are subject to traffic congestion, the deterioration of surface-assets (often due to the overreliance on private vehicle traffic), increasing vehicle-operation and fuel costs, and pollutant emissions. In Abu Dhabi, private car traffic forms the major share on urban highways, as the infrastructure was built to a high quality and the public transport network needs expansion, resulting in traffic congestion on major highways. These issues are arguably addressable by appropriate decisions at the planning stage. Microsimulation modeling of driving behavior in Abu Dhabi is presented for empirical assessment of traffic management scenarios. This paper presents a technique for developing, calibrating, validating, and the scenario analysis of a detailed VISSIM-based microsimulation model of a 3.5 km section of a 5-lane divided highway in Abu Dhabi. Traffic-count data collected from two sources, i.e., the local transport department (year 2007) and municipality (2007 and 2015–2016) were used. Gaps in traffic-counts between ramps and the highway mainline were noted, which is a common occurrence in real-world data situations. A composite dataset for a representative week in 2015 was constructed, and the model was calibrated and validated with a 15% (<100 vehicles per hour) margin of error. Scenario analysis of a potential public bus transport service operating at 15 min headway and 40% capacity was assessed against the base case, for a 2015–2020 projected period. The results showed a significant capacity enhancement and improvement in the traffic flow. A reduction in the variation between vehicle travel times was observed for the bus-based scenario, as less bottlenecking and congestion were noted for automobiles in the mainline segments. The developed model could be used for further scenario analyses, to find optimized traffic management strategies over the highway’s lifecycle, whereas it could also be used for similar evaluations of other major roads in Abu Dhabi post-calibration.

A dynamic self-improving ramp metering algorithm based on multi-agent deep reinforcement learning

ABSTRACT We present a novel ramp metering algorithm that incorporates multi-agent deep reinforcement learning (DRL) techniques, which utilizes monitoring data from loop detectors. Our proposed approach employed a multi-agent DRL framework to generate optimized ramp metering schedules for each ramp meter in real-time, enhancing the operational efficiency of urban freeways with less investment. To simplify the implementation and training of the algorithm, we developed a simulation platform based on SUMO microscopic traffic simulator. We conducted a series of simulation experiments, including local and coordinated ramp metering scenarios with various traffic demands profiles. The simulation results indicate that the proposed DRL-based algorithm outperforms the state-of-the-practice ramp metering methods, considering a comprehensive evaluation index encompassing mainstream speed at the bottleneck and queue length on ramp. Additionally, the method exhibits robustness, scalability, and the potential for further improvement through online learning during implementation.

Creating building-level, three-dimensional digital models of historic urban neighborhoods from Sanborn Fire Insurance maps using machine learning.

Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. They are a valuable resource for studying changes in urban environments, such as the legacy of urban highway construction and urban renewal in the 20th century. However, it is a challenge to automatically extract the building-level information effectively and efficiently from Sanborn maps because of the large number of map entities and the lack of appropriate computational methods to detect these entities. This paper contributes to a scalable workflow that utilizes machine learning to identify building footprints and associated properties on Sanborn maps. This information can be effectively applied to create 3D visualization of historic urban neighborhoods and inform urban changes. We demonstrate our methods using Sanborn maps for two neighborhoods in Columbus, Ohio, USA that were bisected by highway construction in the 1960s. Quantitative and visual analysis of the results suggest high accuracy of the extracted building-level information, with an F-1 score of 0.9 for building footprints and construction materials, and over 0.7 for building utilizations and numbers of stories. We also illustrate how to visualize pre-highway neighborhoods.

Performances and Environmental Impacts of Connected and Autonomous Vehicles for Different Mixed-Traffic Scenarios

As the transition towards connected and autonomous vehicles gradually happens, different phases with CAVs and human-driven vehicles sharing the same network will occur. This paper’s purpose is to increase the knowledge of these mixed situations, studying the impacts of an increasing number of CAVs within the vehicle fleet on road capacity, travel time savings and energy consumption, providing new insights into the debate that is still open. The methodology focused on a microsimulation-based approach on an urban motorway in the city of Rome. Some of the outcomes from simulations, run with the software PTV VissimTM 21, were used to analyse variations in general performances of the transportation system, whereas the remaining results were fed into the emission model COPERT for assessing the impacts of CAV penetration on the energy consumption of the fleet. Results show how, in congested cases, appreciable improvements can be recorded in terms of road capacity, mean speeds, and environmental impacts, while in lower-congested situations, any enhancement in traffic fluidification counteracts the environmental performances of the whole system.

Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis

Although hazardous material (HAZMAT) truck-involved crashes are uncommon compared to other types of traffic crashes, these crashes pose considerable threats to the public, property, and environment due to the unique feature of low probability with high consequences. Using ten-year (2010–2019) crash data from the Fatality Analysis Reporting System (FARS) database, this study applies cluster correspondence analysis to identify the underlying patterns and the associations between the risk factors for HAZMAT-truck-involved fatal crashes. A low-dimensional space projects the categorical variables (including the crash, road, driver, vehicle, and environmental characteristics) into different clusters based on the optimal clustering validation criterion. This study reveals that fatal HAZMAT-truck-involved crashes are highly distinguishable concerning collision types (angle and front-to-front crashes, single-vehicle crashes, and front-to-end crashes) and roadway geometric variables, such as two-way undivided roadways, curve alignments, and high-speed (65 mph or more) urban interstate highways. Driver behavior (distraction, asleep or fatigue, and physical impairment), lighting conditions (dark–lighted and dark–not lighted), and adverse weather are also interrelated. The findings from this study will help HAZMAT carriers, transportation management authorities, and policymakers develop potential targeted countermeasures for HAZMAT-truck-involved crash reduction and safety improvement.

Research on the Control Method of Urban Expressway Weaving Area

With the rapid development of urban expressway construction, there are some problems in the actual operation process, which leads to the traffic efficiency not reaching the expected level. Due to the long-term and large amount of actual data is difficult to obtain, this paper uses the method of combining actual data and simulation to explore the traffic operation law of urban expressway weaving area, analyzes the factors affecting the traffic efficiency of weaving area, and applies the variable speed limit guidance control method based on DQN algorithm, which can effectively improve the traffic efficiency of urban expressway weaving area.

Stability evaluation method of large cross-section tunnel considering modification of thickness-span ratio in mechanized operation

PurposeThis study aims to research the large cross-section tunnel stability evaluation method corrected after considering the thickness-span ratio.Design/methodology/approachFirst, taking the Liuyuan Tunnel of Huanggang-Huangmei High-Speed Railway as an example and taking deflection of the third principal stress of the surrounding rock at a vault after tunnel excavation as the criterion, the critical buried depth of the large section tunnel was determined. Then, the strength reduction method was employed to calculate the tunnel safety factor under different rock classes and thickness-span ratios, and mathematical statistics was conducted to identify the relationships of the tunnel safety factor with the thickness-span ratio and the basic quality (BQ) index of the rock for different rock classes. Finally, the influences of thickness-span ratio, groundwater, initial stress of rock and structural attitude factors were considered to obtain the corrected BQ, based on which the stability of a large cross-section tunnel with a depth of more than 100 m during mechanized operation was analyzed. This evaluation method was then applied to Liuyuan Tunnel and Cimushan No. 2 Tunnel of Chongqing Urban Expressway for verification.FindingsThis study shows that under different rock classes, the tunnel safety factor is a strict power function of the thickness-span ratio, while a linear function of the BQ to some extent. It is more suitable to use the corrected BQ as a quantitative index to evaluate tunnel stability according to the actual conditions of the site.Originality/valueThe existing industry standards do not consider the influence of buried depth and span in the evaluation of tunnel stability. The stability evaluation method of large section tunnel considering the correction of overburden span ratio proposed in this paper achieves higher accuracy for the stability evaluation of surrounding rock in a full or large-section mechanized excavation of double line high-speed railway tunnels.

An intelligent road accident reduction system using device-to-device communication

<p>Every year thousands of people are dying due to road accidents and most of the accidents are occurring in urban areas and highways. As the number of vehicles is increasing day by day, the probability of the occurrence of the accident is also increasing. A system has been introduced in this paper that can reduce road accidents by sending an alert to drivers via smartphones. For this purpose, an android-based application has been developed. To reduce the accident, every driver of the car must have the application. Firstly, the location of the vehicle will be collected from the driver’s smartphone, then the data will be sent to the server through the application. An algorithm has been also developed using Haversine Formula which calculates the distance among the vehicles. A total of five cases have been implemented according to the dataset and compared for the performance evaluation. An extensive experimental study and comparison have been performed with the other methods where a complete performance is focused that can be claimed to reduce road accidents via the device-to-device communication process.</p>