Urban Road Research Articles

Assessing the impact of urban road transport development on haze pollution in the Yangtze River Delta region

The aim of this paper is to explore whether and how urban road transport (URT) development affects haze pollution. One of the innovations of this paper is that URT development is measured by road accessibility with novel digital elevation model datasets, which have been used by few scholars. The endogenous problem caused by revere causality issue in the relationship between URT development and haze pollution is also considered. Based on the panel data of prefecture-level cities of Yangtze River Delta (YRD) region in China from 2011 to 2018, this paper uses long-lagged values of URT development as the instrumental variable, employing the two-stage least squares (2SLS) method. The study shows that URT development leads to an increase of haze pollution. Moreover, mechanism tests based on moderating and mediating models support the finding that decreasing haze pollution resulted from better connection effects, while rising agglomeration effects tend to bring about increasing haze pollution, and the latter effect is larger in magnitude than the former. Current URT development may have long-term negative consequences for livability of YRD cities, and urban decision makers should reconsider the effectiveness of the current road transport investment and construction.

A Numerical Simulation-Based Adaptation of the Pedestrian-Level Wind Environment in Village Streets: A Case Study on the Chuan Dao Area of the Hanjiang River in Southern Shaanxi

Village streets are indispensable spaces for people to perform outdoor activities, and they also directly affect the outdoor wind environment in villages. At present, people are paying more attention to the wind environment comfort of urban residential areas and urban commercial streets, but there is a lack of attention and research on the wind environment comfort of village and town streets. By summarizing the field research and meteorological data of Lefeng Village, we propose the outdoor wind environment evaluation requirements applicable to the Hanjiang River’s Chuan Dao area in the winter and summer seasons. We found that more than 80% of the outdoor wind environment in the summer is less than 1 m/s. Based on the numerical simulation method of computational fluid dynamics, and on the basis of the characteristics of the streets and lanes in the Hanjiang River’s Chuan Dao area, we found that the wind environment is poor in the winter and summer seasons; regarding streets and lanes, we propose three appropriate values, namely building density, building height, and street width. It is suggested that it is appropriate for the building density of the area to be less than 36%, the height of the building to be less than 15 m, and the width of the street to be 6–11 m when the street is open to traffic and 3–6 m when only pedestrians are passing through the area.

Sources Analysis and Health Risk Assessment of Heavy Metals in Street Dust from Urban Core of Zhengzhou, China

Fifty-one street dust samples were systematically collected from the urban core of Zhengzhou, China, and analyzed for potentially toxic metals. The concentrations of vanadium (V), manganese (Mn), copper (Cu), zinc (Zn), arsenic (As), lead (Pb), and nickel (Ni) in the samples surpassed the background values of the local soil, indicating a notable potential for contamination. Spatially, the traffic area was the most polluted with a total heavy metal concentration of Cu, Zn, As, Pb, and Ni, while the pollution levels were lower in the culture and education area and commercial area with total concentrations of V and Mn. Seasonal variations were discerned in the concentrations of heavy metals, with V, Cu, Zn, and As exhibiting heightened levels during the fall and winter, while Mn, Ni, and Pb reached peaks in the spring season. Zn exhibited the highest mean geo-accumulation index (Igeo) value at 2.247, followed by Cu at 2.019, Pb at 0.961, As at 0.590, Ni at 0.126, Mn at −0.178, and V at −0.359. The potential ecological risk index (RI) in the traffic-intensive area markedly exceeded other functional areas. Health risk assessments showed that children were more vulnerable to heavy metal exposure than adults, particularly through the ingestion pathway. Correlation analysis, principal component analysis (PCA), and cluster analysis (CA) were applied in conjunction with the spatial–temporal concentration patterns across various functional areas to ascertain the plausible sources of heavy metal pollutants. The results indicated that heavy metals in the urban street dust of Zhengzhou were multifaceted, stemming from natural processes and diverse anthropogenic activities such as coal burning, industrial emissions, traffic, and construction operations.

Evolution of Urban Street Greening Structure Based on Time Series Street View Data: A Case Study of the Central Urban Area of Shanghai

Evolution of Urban Street Greening Structure Based on Time Series Street View Data: A Case Study of the Central Urban Area of Shanghai

Border Noir: Guilt, Shame and Uncanny Sexuation in Ride the Pink Horse

Although Orson Welles’s 1958 Touch of Evil is the most critically and commercially recognised film within the border noir submode, this essay instead examines Robert Montgomery’s often overlooked 1947 film noir Ride the Pink Horse because of its close attention to the borderland setting. The article utilises a psychoanalytic understanding of affect and a border-influenced reverse anthropology to examine how the deterritorialised effect of the border zone and the uncanny atmosphere of the carnival environment in the film distresses the traditional noir antihero. Specifically examined is the relationship between two major manifestations of anxiety, two major affects: guilt and shame. This attention illustrates how the relocating of the noir universe out of the confines of the conventional dark urban streets to the liminal border zone uncannily transforms the guilt usually associated with the noir protagonist into a type of shame and a stronger encounter with anxiety as, appropriately, a borderless enigma. While guilt usually provides a distance from this enigma for the conventional noir antihero, shame produces a flight into being, as illustrated in the film’s narrative details. Ultimately, Montgomery’s cocksure character (Lucky Gagin), through his encounter with the deterritorialisation of the uncanny border zone, is transported from the conventional noir hero’s masculine position (psychoanalytically conceived) into an abject position that aligns more precisely with the feminine discursive position.

Traffic Speed Prediction Based on Dynamic Spatial-Temporal Attention Graph Convolutional Network

Abstract The prediction of traffic speed represents a crucial branch within the broader field of traffic forecasting. Its objective is to forecast the average speed or speed distribution on road segments or within a network for a designated future time period . The prediction results can provide scientific decision support for traffic management departments to effectively control and guide traffic flow. However, given the limitations of urban road network topology and its dynamic temporal shifts, traffic prediction has consistently been deemed a perpetual scientific challenge. To simultaneously encapsulate both spatial and temporal dependencies, we introduce an innovative Dynamic Spatio-Temporal Attention Network (DSTANet). This model integrates spatio-temporal attention with graph convolutional networks and temporal convolution to enhance its analytical capabilities. Specifically, the graph convolutional network is leveraged to learn the intricate topology for capturing spatial relations, while temporal convolution is employed to ascertain the dynamic fluctuations of traffic data, thereby acquiring temporal dependencies. Subsequently, we implement our model to forecast traffic patterns within the framework of road networks. Experimental results demonstrate that our model effectively captures the spatio-temporal dependencies among roadway junctions derived from traffic data, outperforming state-of-the-art benchmarks on real-world traffic datasets in terms of predictive accuracy.

Evaluating Collaborative Emission Reduction Efforts in Urban Road Passenger Transportation: insights from the City Scale

Road passenger transportation contributes significantly to carbon dioxide (CO2) and pollutant emissions, posing challenges for urban sustainability, particularly in rapidly urbanizing cities such as Fuzhou, which was recognized as a global sustainable development city for 2023. This study addresses the research gap by integrating road green factors, which refer to the ecological attributes of roads, such as green space, vegetation, and tree coverage that contribute to improving air quality and reducing pollution, into emission reduction strategies for city-scale road passenger transportation. This novel approach has not been extensively explored in the existing literature. The purpose of this study was to identify a rational pathway for emissions reduction by analyzing the economic, demographic, transportation infrastructure, road green space, and policy factor impacts on emissions. Employing a system dynamics (SD) model, this research evaluates the emissions from road passenger transportation in Fuzhou from 2010 to 2030 under various scenarios, including a baseline scenario (BS), controlled private cars travel demand (CPD), promotion of new energy vehicles (PVE), improvement in energy efficiency (IEE), increase in road capacity (IRC), and improvement in road green level (IRG). The principal results indicate that by optimizing the traffic structure and integrating other emission reduction measures, peak CO2 emissions can be achieved by 2024 under the three scenarios or by 2023 under the four scenarios. Furthermore, significant reductions in pollutants of 84.62% and 86.07% are projected, respectively. The study concluded that the economic benefits of CO2 emissions reductions consistently exceeded those of carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NOx) reductions, highlighting the importance of a holistic approach to emissions reduction strategies. Based on these findings, policy recommendations were proposed to guide the formulation of effective road passenger transportation policies aimed at achieving sustainable urban development.

Distribution and Risk Assessment of VOCs in Road Dust Across Different Urban Areas

ABSTRACT Volatile organic compounds (VOCs) are significant pollutants in urban road dust, but their distribution and associated health risks remain unclear. VOC distribution in road dust from six distinct urban areas in Beijing Daxing District, China was systematically investigated. Eighteen VOCs were detected, with the highest load in commercial areas (7.73 μg/g), followed by industrial parks (6.38 μg/g), residential areas (3.35 μg/g), leisure areas (2.79 μg/g), traffic areas (2.01 μg/g) and urban-rural fringe (1.85 μg/g), much lower than in air and water phases. Typical VOCs in road dust include methyl tertiary butyl ether (MTBE) (0.82–1.24 μg/g), benzene series compounds (0.02–4.19 μg/g) and volatile halogenated alkanes (0.002-0.095 μg/g). Incomplete combustion of road vehicle fuels is the key source of VOCs. Particular attention was given to VOCs in typical sites within the urban-rural fringe, known for their unique pollution characteristics. The VOCs loads were ranked as follows: Gas Stations (2.61–2.62 μg/g) > Intersection (1.41 μg/g) > Main Road (1.34 μg/g) > University Gate (1.27 μg/g). Increased vehicle activity and frequent stalling contribute to higher VOCs accumulation in road dust. Health risk assessment indicates there is no appreciable non-carcinogenic risk associated with exposure to road dust VOCs. Further research should consider VOCs in the broader environmental context, including air and water.

An Evaluation of Backfill Material Applicability through Deformation Measurements of Long-Span Buried Corrugated Steel Structures

Corrugated steel plates are increasingly being applied to various types of structures, such as eco-corridors, noise reduction tunnels, and urban elevated roads. However, applications for spans exceeding 25 m are lacking, as they require clear standards for calculating the elastic moduli of backfill materials and reinforced sections. This study evaluated the appropriateness of mechanical assumptions applied in the design, by measuring displacements at each stage and comparing the finite element analysis results for a concrete-reinforced corrugated steel plate arch underground structure with a width of 27.5 m and length of 467 m. Based on the measurement results, deformations ranging from 19.8% to 63.7% of allowable deflection were observed when applying an elastic modulus of 60 MPa to solidified soil backfill, which is twice the maximum standard value applied to general soil, thereby confirming the suitability for the serviceability of underground structures.

Evaluation of thermal effects on urban road spatial structure: A case study of Xuzhou, China

Urban heat islands (UHI) are important environmental issue in cities where urban spatial structure has been proven to play an important role in alleviating UHI effects. The relationship between land surface temperature and urban spatial structures has been explored, providing strong support for their cooling effects. Urban roads are the skeleton of urban spatial structures, with obvious spatial structure characteristics; however, research on the relationship between roads and the thermal environment has been mostly focused at the micro and meso level, lacking exploration at the macro spatial structure scale. Xuzhou—a typical average-sized city in China—was selected as the research object and the road system as the carrier. The thermal environmental effects of road elements such as their structural attributes, geometric attributes and unique construction attributes were quantitatively analyzed using geographically weighted regression analysis. The results revealed that 1) the contribution of roads in the study area to the UHI effect is relatively stable; therefore, this area should become an important cooling space to decompose UHI patch connectivity and thus decrease the UHI effect. 2) the self-organizing structural characteristics of urban roads affect their thermal environments where in the straightness of the road structure and road thermal environment showed a clear overall negative correlation And 3) the length and width of the road segments had negative and positive effects on the thermal environment, respectively. The green coverage of the roads has a global negative effect on the thermal environment, but shows obvious spatial non-stationarity. Therefore, green measures must be implemented in different regions. The results here provide a quantitative basis for urban road system planning and urban form management and control that incorporates thermal environment improvements, as well as a reference for the study of urban thermal environments under different spatial forms and planning control systems in other countries and regions.

Evaluation of Traffic Congestion in an Urban Roads: A Review

Abstract: Urban traffic congestion significantly impacts economic productivity, environmental health, and residents' quality of life. This study provides a comprehensive evaluation of traffic congestion in urban areas, focusing on recent innovations in traffic management, urban planning, public transportation, and technological advancements. Intelligent Transportation Systems (ITS), incorporating real-time data and adaptive signal controls, have proven effective in reducing delays and improving traffic flow. The study underscores the importance of mixed-use urban planning in minimizing travel distances and traffic volumes. Expanding public transportation networks is highlighted as a critical strategy for alleviating congestion, with evidence indicating that cities with extensive transit systems experience lower congestion levels. Additionally, the environmental and health implications of traffic congestion are considered, linking high congestion levels to increased pollution and negative health outcomes. . Road construction as a policy intervention to reduce congestion is debated, with the understanding that road widening may sometimes increase traffic. Evaluating the technologies contributing to congestion is crucial for devising effective solutions. Urban transport is central to economic activity and the well-being of urban residents. The rising population and car ownership rates, coupled with unchanged natural land areas, exacerbate traffic issues, leading to accidents and increased carbon dioxide emissions. The article explores the complexities of urban road traffic, addressing transport infrastructure, organization, and the high share of car traffic. It discusses theoretical and practical aspects of traffic flow and design, as well as the role of driver behavior in traffic jams.

Generative design of walkable urban cool spots using a novel heuristic GAN×GAN approach

Enhancing pedestrian-level outdoor thermal comfort (OTC) and walkability in urban environments is vital for mitigating thermal risks induced by global warming. This study introduces walkable urban cool spots (WUCS), a novel concept which incorporates pedestrian walkability into the design of thermally comfortable urban street blocks. The methodology combines OTC simulation, pedestrian walkability analyses, and surrogate models based on integrated generative adversarial networks (GAN×GAN)-enhanced genetic generative design (GGD) approach. And maximize the design metrics of WUCS through the multi objective optimization algorithm in GGD. The method is tested in To Kwa Wan, an urban renewal area in Hong Kong. Results demonstrate that it can efficiently identify high-performing WUCS solutions, with proportion of WUCS areas increasing from 53.3 % to 74 %, and the number of walkable urban cool spots increasing by 51 %. The average universal thermal climate index (UTCI) in high walkability areas decreasing from 31.7 °C to 30.5 °C, and total average UTCI during extreme heat days dropping from 31.73 °C to 31.22 °C. This research thus contributes an efficient design tool for enhancing both pedestrian-level OTC and walkability in urban environments.

Analyzing Riders’ Behavioral Adaptation to Driving Patterns of Advanced Autonomous Vehicles: A Virtual Reality Simulation Study

The necessity of human supervision and intervention during autonomous driving has long been a topic of controversial discussion. From a developer’s perspective, it is expected that users will readily adapt to well-calibrated autonomous driving systems (ADS) due to their superior performance in dynamic driving tasks (DDT) compared to conventional human-driven vehicles. However, when passengers experience an autonomous vehicle (AV), there may be an adjustment period during which they modify their behavior to accommodate the driving patterns of the ADS. Additionally, some passengers might not adapt to autonomous driving at all, highlighting potential limitations in the current ADS development strategy. This work studies the dynamics of human-automation interaction and introduces an “objective method”, which employs a Virtual Reality (VR)-enabled simulation approach for in-depth behavioral analysis concerning riders’ behavioral adaptation to autonomous driving. Specifically, we examined how participants interacted with and intervened in Level 4 ADS operating under conservative, moderate, and aggressive driving patterns in a fully autonomous environment. A realistic urban road network was recreated in VR, integrated with traffic microsimulation to generate various driving scenarios. Twenty-seven participants completed driving tasks across different AV modes, with their intervention behaviors analyzed in relation to traffic conditions and AV aggressiveness. Key findings include: (1) Participants showed higher intention to intervene but lower actual intervention rates under aggressive AV modes compared to moderate and conservative modes, suggesting quicker adaptation to more challenging driving scenarios. (2) Interventions generally proved unnecessary and sometimes detrimental to overall traffic performance in a full-AV environment. (3) Aggressive AV modes significantly improved traffic efficiency, with a 40% increase in average travel speed and a 53% reduction in waiting time. However, human interventions posed the greatest challenge to achieving optimal traffic conditions. This research provides insights into the complex dynamics of human-AV interaction and adaptation, offering valuable implications for AV interface design, implementation strategies, and public acceptance of autonomous driving technologies.

Effect of Takeover Request Time and Warning Modality on Trust in L3 Automated Driving.

This study investigated the effects of four takeover request (TOR) times and seven warning modalities on performance and trust in automated driving on a mildly congested urban road scenario, as well as the relationship between takeover performance and trust. Takeover is crucial in L3 automated driving, where human-machine codriving is employed. Establishing trust in takeover scenarios among drivers can enhance the acceptance of autonomous vehicles, thereby promoting their widespread adoption. Using a driving simulator, data from 28 participants, including collision counts, takeover time (ToT), electrodermal activity (EDA) data, and self-reported trust scores, were collected and analyzed primarily using Generalized Linear Mixed Models (GLMM). Collisions during the takeover undermined participants' trust in the autonomous driving system. As TOR time increased, participants' trust improved, and the longer TOR time did not lead to participant confusion. There was no significant relationship between warning modality and trust. Furthermore, the combination of three warning modalities did not exhibit a notable advantage over the combination of two modalities. The study examined the effects of TOR time and warning modality on trust, as well as preliminarily explored the potential association between takeover performance, including collisions and ToT, and trust in autonomous driving takeovers. Researchers and designers of automotive interactions were given referenceable TOR time and warning modality by this study, which extended the autonomous driving takeover scenarios. These findings contributed to boosting drivers' confidence in transferring control to the automated system.

Information density threshold of urban road traffic signs based on visual comfort

The content and density of traffic signs directly affect the operation of urban road traffic and drivers. To overcome the limitations of quantitative research on the density threshold of traffic signs on urban roads, a real vehicle experiment was conducted to record the psychological characteristics of drivers. Four psychological parameters of drivers—pupil area, fixation intensity, heart rate change rate, and heart rate variability—were explored. Subsequently, principal component analysis was used to present a new index, S, divided into 5 grade scales, to represent the driving visual comfort level. The information entropy theory was applied to quantify the amount of information on road traffic signs that are included in driving tests, and a regression relationship between the traffic sign information and comfort index S was established. The visual psychological load thresholds for different comfort levels were −2.289≤S < −1.526 for very comfortable, 1.526≤S < −0.763 for relatively comfortable, −0.763≤S ≤ 0.763 for comfortable, 0.763<S ≤ 1.526 for uncomfortable, and 1.526<S ≤ 2.289 for very uncomfortable. To maintain the visual comfort of drivers, the information density of traffic signs should be less than 0.373 bits/m and the maximum information density of traffic signs should not exceed 0.507 bits/m. This conclusion provides a reference for the rational layout of traffic signs on urban roads.

Assessment of air pollution removal by urban trees based on the i-Tree Eco Model: The case of Tehran, Iran.

Air quality concerns have become increasingly serious in metropolises such as Tehran (Iran) in recent years. This study aims to assess the contribution of urban trees in Tehran toward mitigating air pollution and to evaluate the economic value of this ecosystem service using the i-Tree Eco model. To accomplish this objective, we utilized Tehran's original land use map, identifying five distinct land use categories: commercial and industrial, parks and urban forests, residential areas, roads and transportation, and urban services. Field data necessary for this analysis were collected from 316 designated plots, each with a radius of 11.3 m, and subsequently analyzed using the i-Tree Eco model. The locations of these plots were determined using the stratified sampling method. The results illustrate that Tehran's urban trees removed 1286.4 tons of pollutants in 2020. Specifically, the annual rates of air pollution removal were found to be 134.8 tons for CO; 299.7 tons for NO2; 270.3 tons for O3; 0.7 tons for PM2.5; 489.4 tons for PM10 (particulate matter with a diameter size between 2.5 and 10 µm); and 91.5 tons for SO2, with an associated monetary value of US$1 536 619. However, despite this significant removal capacity, the impact remains relatively small compared with the total amount of pollution emitted in 2020, accounting for only 0.17%. This is attributed to the high emissions rate and low per capita green space in the city. These findings could serve as a foundation for future research and urban planning initiatives aimed at enhancing green spaces in urban areas, thereby promoting sustainable urban development. Integr Environ Assess Manag 2024;00:1-11. © 2024 SETAC.

A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions.

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss.

Efficiency Assessment of Urban Road Networks Connecting Critical Node Pairs under Seismic Hazard

Efficiency Assessment of Urban Road Networks Connecting Critical Node Pairs under Seismic Hazard

Determining priority control factors for heavy metal management in urban road dust based on source-oriented probabilistic ecological-health risk assessment: A study in Xi'an during peak pollution season

Urban road dust (URD) is essential for transporting heavy metals (HMs), which can be a major danger to both the environment and human health. Moreover, URD has the potential to be carried into bodies of water, leading to contamination of the aquatic ecosystem. A study was conducted in Xi'an, a city in northwestern China known for high air pollution levels, during January 2024 - a period characterized by peak pollution due to frequent low wind speeds and temperature inversions. The research investigated the presence of 10 types of HMs (Cu, Zn, Cd, Cr, Pb, As, Ni, Hg, Co, and Mn) in URD. Findings revealed elevated levels of Cu, Zn, Cd, Cr, Pb, As, and Hg in URD compared to background levels. Hg showed the most significant contamination (moderate to heavy), followed by moderate contamination of Cd, and lower levels of As, Zn, and Cu. The main sources of HMs were traffic (58.2%), mixed natural and industrial (30.3%), and industrial (11.5%). The ecological risk in the area was deemed to be very high, primarily because of Hg and Cd. Based on probabilistic health risk assessments, it was determined that non-carcinogenic risks were deemed acceptable for all groups. Nevertheless, the possibility of carcinogenic risks should not be disregarded. Strategies for controlling ecological-health risks prioritize mixed natural and industrial sources, with a focus on Hg, Cd, and As in URD. The results offer a foundation for policymakers to create specific control strategies.

Urban streets profiling with coupled spatio-temporal characteristics and topological information from the biking perspective

Urban street profiling is the spatio-temporal pattern discovery of street-level urban areas, which plays a vital role in understanding urban structures and dynamics. Due to the natural topology and various geographic characteristics on the streets, it is necessary to combine multi-dimensional spatio-temporal information to understand different profiles of streets. This research aims to develop a street profiling framework according to the coupled characteristics of streets. At the start, a bidirected dual graph and a spatial weighted graph embedding method were used to solve the street representation. Then, the street profiles can be extracted by clustering embedding vectors of streets and feature importance analysis. As the case study, we employed the bike trajectories and street view images in Xiamen, China to depict the geographic attributes of streets. The results can reveal nine spatio-temporal street profiles from the biking perspective, including three spatial distribution patterns and two spatial semantic patterns. Urban streets in the study area show a significant hierarchical pattern because of locations and the spatial lags of the biking behaviors. Meanwhile, the spatio-temporal characteristics of biking behaviors are the main factors of street profiles, though the street environment attributes participate in over half the number of profile types. We further evaluated the profiling ability of the proposed framework and the importance of urban street profiles using coupled characteristics. Overall, this study explored the profiling method for coupling static and dynamic characteristics of urban streets. The profiling results also help understand street usage and experiences by bikers, which have a practical value on the human-oriented classification of streets and further urban development from a geographic view.