Urban Road System Research Articles

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.

Road Design and Traffic Detection Methods for Autonomous Driving Scenarios

With the rapid promotion of autonomous driving technology, it is extremely important to scientifically anticipate the related technologies and analyze their possible impact on urban road systems. The accuracy of detection and localization of traffic elements of autonomous driving is closely related to the ability of autonomous driving devices to make control decisions and the safety of autonomous driving. The study designs a new high-speed road driving scheme based on autonomous driving by analyzing the challenges related to urban traffic that may be brought about by unmanned driving. On the basis of the faster R-CNN algorithm, the context information around the target is introduced to locate and detect small-scale traffic signs. A new pedestrian detection model is designed, which is based on the feature pyramid network and introduces the SE module to highlight the features of the visible part of the pedestrian and reduce the missed detection rate caused by inter-class occlusion. The improved traffic sign detection framework improves the detection accuracy by 18.91% compared to the original faster R-CNN, while the enhanced pedestrian inspection method improves the detection accuracy by 14.00%. For both traffic sign detection and pedestrian detection accuracy and speed are improved compared to the original method.

Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model

The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health, scarcity of clean water, rise in air pollution, and exacerbated traffic congestion resulting in significant delays in vehicular transportation. To address the intricate nature of transportation issues, many researchers and planners have analyzed the complexities of urban and regional road systems using transportation models by employing transportation indices such as road length, network density, accessibility, and connectivity metrics. This study addresses the complexities of predicting road network density for small and medium-sized Indian cities that come under the Integrated Development of Small and Medium Towns (IDSMT) project at a national level. A hybrid framework based on Kernel Ridge Regression (KRR) and the CityGAN model is introduced to predict network density using spatial indicators of human settlements. The major goal of this study is to generate hyper-realistic urban patterns of small and medium-sized Indian cities using an unsupervised CityGAN model and to study the causal relationship between human settlement indices (HSIs) and transportation index (network density) using supervised KRR for the real cities. The synthetic urban universes mimic Indian urban patterns and evaluating their landscape structures through the settlement indices can aid in comprehending urban landscape, thereby enhancing sustainable urban planning. We analyzed 503 real cities to find the actual relationship between the urban settlements and their road density. The nonlinear KRR model may help urban planners in deriving the network density for GAN-generated futuristic urban patterns through the settlement indicators. The proposed hybrid process, termed as RidgeGAN model, can gauge the sustainability of urban sprawl tied to infrastructure and transportation systems in sprawling cities. Analysis results clearly demonstrate the utility of RidgeGAN in predicting network density for different kinds of human settlements, particularly for small and medium Indian cities. By predicting future urban patterns, this study can help in the creation of more livable and sustainable areas, particularly by improving transportation infrastructure in developing cities.

Comprehensive strength evaluation system of commercial centres based on multi-source data: a case of Hefei central city

Urban commercial centres are the most concentrated areas of economic activity. Understanding the spatial distribution pattern and comprehensive strength of urban commercial centres is important to guide the reasonable graded allocation of urban commercial space, spatial structure optimization and sustainable development of the commercial economy. Herein, mobile phone signalling data are used to identify the functional connection between recreational and residential places and local spatial autocorrelation analysis is used to identify the 24 commercial centres in the central city of Hefei. The comprehensive strength evaluation system of commercial centres is constructed from their basic conditions and customer consumption behaviours, and their comprehensive strength indices are accordingly measured and graded. The spatial distribution characteristics of commercial centres at all levels are analysed, and optimisation suggestions are made for the whole area and region. The following conclusions are drawn from the results of this study. (1) Compared to traditional single-perspective evaluations, the developed comprehensive strength evaluation system considers supply and demand perspectives for commercial centres, providing a more holistic and accurate portrayal of the strengths of various centres within a region. (2) The current commercial centres are characterised by ‘large dispersion and small concentration’ in spatial and hierarchical distribution. (3) The commercial centres in Hefei have formed a relatively complete ‘first, second, and third level’ commercial centre system, with the first level as the core and relying on the urban road system to form a network spatial connection. (4) Most of the commercial centres are concentrated in the First Ring Road, Swan Lake in the Government Affairs District and Binhu Century Town Estate, while most areas north of the Second Ring Road and Binhu New District still lack large-scale commercial centres. This study provides a technical reference for analysing urban commercial spatial structure patterns and provides decision support for optimising the spatial layout of urban commercial service functions.

A study on dynamic simulation and improvement strategies of flood resilience for urban road system

As one of the most important and directly affected infrastructure by flood disasters in urban areas, the road system plays a crucial role in maintaining the order of human production and life, as well as providing a foundation for the implementation of emergency response work in disaster scenarios. The existing flood resilience quantification methods based on maximum inundation depths of each individual road unit neglects the influences on flood resilience caused by the connectivity of road system. Therefore, this paper focuses on the road system, simulates the flood resilience of the road system in multiple disaster scenarios based on the functionality curve of road system under rainfall-inundation processes, then quantifies the improvement effect of Low Impact Development (LID) measures on flood resilience. A case study is conducted in Yunmen community, Jingdezhen city in China. The results indicate that: (1) The flood resilience of the road system decreases with the increase of rainfall intensity under different scenarios, and the magnitude of the decrease reduces. (2) The road system exhibits strong adaptability during the early stages of rainfall; The duration of functionality loss increases with increasing of rainfall intensity in different scenarios. (3) Implementation of LID measures can significantly improve system robustness by enhancing the reliability of the roads around critical nodes, thereby effectively enhancing flood resilience. The conclusions can provide a theoretical basis for decision-makers to develop urban flood mitigation engineering plans and resilience strategies tailored to local conditions.

Influence of Terrain Factors on Urban Pluvial Flooding Characteristics: A Case Study of a Small Watershed in Guangzhou, China

Urban roads in China, particularly low-lying areas such as underpasses, tunnels, and culverts, are highly vulnerable to the dangers of urban pluvial flooding. We used spatial interpolation methods and limited measured data to assign elevation values to the road surface. The road network was divided into tiny squares, enabling us to calculate each square’s elevation, slope, and curvature. Statistical analysis was then employed to evaluate the impact of terrain on flood characteristics in urban road systems. Our analysis reveals a strong spatial correspondence between the distribution of flood-prone points and the curvature parameters of the terrain. The spatial coincidence rate can reach 100% when an appropriate sampling scale is chosen. The presence of depressions is necessary but insufficient for forming flood-prone points. In lowland/gentle slope (LL/GS) areas with higher drainage pressure, we observe a significant negative correlation between flood-prone points and terrain curvature (Spearman’s r = 0.205, p < 0.01). However, in highland/steep slope (HL/SS) areas, we find no significant correlation between them. Notably, terrain matters, but effective drainage is more influential in flood-prone areas. The maximum flood depth (MFD), submerged area, and ponding volume during urban pluvial flooding are constrained by depression topography, while the characteristics of the upstream catchment area also play a role in determining the MFD and flood peak lag time(FPLT). Larger upstream catchment areas and longer flow paths normally result in greater MFD and longer emergency response times/FPLT. Additionally, a higher flow path gradient will directly contribute to an increased flood risk (greater MFD and shorter FPLT). These findings have important implications for flood risk identification and the development of effective flood mitigation strategies.

Evolution of the periphery of a self-organized road network

Urban roads are geographical planar networks, making their growth patterns constrained over the available physical space. In this work, we quantify the center (core)-periphery structure of an urban road system based on the betweenness centrality of the nodes, and then measure their spread over the evolution of the urban zone. Using corrected and georeferenced historical maps of Manila, Philippines, we observe temporal invariance of the betweenness centrality distributions, along with the preponderance of comparable-betweenness node connections, both of which signify the preservation of the planarity of the network. Despite the geographical expansion of the road network, we observe topologically peripheral nodes in the geographical center for all the map periods considered, signifying the presence and growth of dendritic dead-ends which may ultimately affect the traffic flow. The shortest paths among the topological periphery shows an evolution towards a homogenized, grid-like structure for the entire network. Understanding peripheral connections, not only for planar networks but also for other complex networked entities, may hint at the efficiency of transport within the system, as these peripheral nodes represent the least “important” sites in the network.

Оцінка пропускної спроможності міської дорожньої мережі з урахуванням пропозиції паркування

In order to improve the estimation of network capacity for a given urban road system, it is assumed that the impact of destination parking management strategies should be considered in the network capacity estimation model. This study presents an extended road network capacity model that takes into account both the number of parking spaces and parking rates in each traffic zone. The network capacity model is formulated as a two-level programming problem with maximizing the total trip generation at the upper level and the combined trip distribution and traffic assignment (CTDTA) problem at the lower level. In order to reasonably characterize the impact of parking pricing and parking delays due to congestion, the CTDTA model involves two classes of travel demand. An efficient and practical algorithm for solving the two-level network capacity model is presented. Numerical experiments show the advantages of the proposed model and demonstrate the impact of parking supply and pricing on the results of road network capacity assessment.

Study on the Application of One Way Traffic Organization for Narrow High Frequency Road Sections—Take Yutuo Road in Lhasa as an Example

By analyzing the characteristics of single traffic and the current utilization of branch roads in urban road system, this paper takes narrow high-frequency road sections as the optimization object of single traffic organization and branch roads as the auxiliary optimization object to reduce road load. The TTI method is used to calculate the index when the road is congested, and the saturation of the road section is calculated. At the same time, considering the impact of intersection delay on one-way traffic and path selection, the traffic signal timing scheme at the intersection is calculated.

Top-to-down segment process based urban road extraction from high-resolution remote sensing image

Aiming at improving the quality and efficiency of urban road extraction from High-Resolution Remote Sensing Image (HRRSI), this paper focuses on the top-to-down modeling framework which is constructed from network level to pixel level, and further proposes the segment process based road extraction algorithm. Firstly, segment process is defined to model centerlines of road. Based on that, network structure model is constructed according to the network-topology feature of urban road system. Secondly, the local geometry models characterizing road area and non-road area are constructed by marking network structure (characterized by segment process) with rectangles and buffers, respectively. Then according to the mixed spectral feature of pixels covered by road area and non-road area in HRRSI, mixed spectral measurement constraining model is constructed by using Gaussian mixture model and Kullback-Leibler divergence. Finally, Reversible Jump Markov Chain Monte Carlo (RJMCMC) simulation algorithm including birth, death and transformation transfer operations is designed to solve the parameters of road extraction model constructed by combining the above models. Different HRRSIs (resolution: <1 m) are tested and different comparing algorithms are experimented to illustrate advantages of the proposed algorithm. By analyzing and comparing the testing results qualitatively and quantitatively, it proves that the proposed algorithm can extract the urban road efficiently, especially in completeness and connectivity.

Simulation and Analysis of Influencing Factors of Pavement Thermal Environments in Guangzhou

Pavements are closely related to the livelihoods of urban residents as an important part of the urban road system. Based on the early field measurements of our team, we found that plant spacing, pavement orientation, and pavement material influence the pavement thermal environment. Our objective was to quantitatively evaluate the influence of various factors on pavements in Guangzhou along West Zhonghuan Road, Guangzhou University. Based on our team’s previous research, environment-met (ENVI-MET) numerical simulation software was used to simulate pavements under different road orientations, underlying surface materials, and plant distances. Quantitative analysis of the effects of different factor combinations on the temperature, humidity, wind speed, physiological equivalent temperature (PET), and radiant temperature (Tmrt) was performed. The results show that among the various factors affecting the thermal environment of pavement, street tree spacing was found to have the greatest effect on the thermal environment of pavement. The effect of direction is negligible; however, it has a significant effect on the wind speed. There was no significant difference in the air temperature and relative humidity between concrete and asphalt. The average air temperature of red floor tile was slightly lower than that of asphalt and concrete. Reducing the spacing of street trees can effectively reduce the penetration of direct solar radiation, physiological equivalent temperature (PET), and temperature (Tmrt) and improve pedestrian walking comfort. The results can serve as a reference for pavement design in Guangzhou and improve the wellbeing of citizens and promote environmental sustainability.

A Route Choice Model for Road Network Users in Mountainous Cities considering Vulnerability

Urban resilience has become one of the core arguments of sustainable urban development. The urban road system should also respond effectively to various changes or impacts and reduce the uncertainty and vulnerability of road network caused by emergencies. A route choice model considering the vulnerability was proposed. The model analyzed the traveler’ behavior in complex road network. The model is validated by the road network in Chongqing city. First, the vulnerability of road network in mountainous cities is defined, and the failure situation and state classification of road network considering vulnerability are analyzed. The results show that travelers with different sensitive states have obvious different route choice behavior in vulnerable networks. Since nonsensitive users do not reserve travel time in the early days, they are more inclined to respond to emergencies quickly and then choose fast detour paths. However, sensitive users have set off earlier than normal time and have psychological expectations of emergencies. They have more time to maintain the existing path. Finally, a calculation example is used to test that a certain section of the road network fails randomly, and network users can effectively choose the individual optimal route under different failure states.

Hyperpath Searching Algorithm considering Delay at Intersection and Its Application in CVIS for Vehicle Navigation

The periodic change of intersection signals in urban road systems is the leading cause of uncertain delays. Therefore, aiming to minimize the travel time on the road segments and the expected delay at the intersections, a hyperpath search method based on intersection signal timing is proposed. The expanded network representation is used to capture the different turning delays at intersections. According to the intersection signal timing, the maximum waiting time of each turn and the turning movement ratio is obtained. A “recommendation priority passable phase” guidance strategy is proposed for the cooperative vehicle-infrastructure system (CVIS) based on the optimal hyperpath. The simulation experiments show that the vehicles can shift the driving route in the hyperpath set according to the guidance strategy, which effectively reduces the actual delay at intersections, and further realize an optimized distribution of traffic flows in the road network.

Typology, network features and damage response in worldwide urban road systems.

We survey the network properties and response to damage sustained of road networks of cities worldwide, using OpenStreetMap (OSM) data. We find that our primary damage response variable , which is the average shortest time needed to reach all nodes in a road network (which stand in for locations within a metropolitan area) from an initial node (which stands in for the location of a center for disaster relief operations), is strongly linearly–correlated with pd, the fraction of the road network segments damaged. We find that this result, previously reported for a city’s road network as opposed to grid and scale-free idealizations, is widely present across the road networks we have examined regardless of location. Furthermore, we identify three families of road networks according to their damage response, forming a typology by which we can classify city road networks. Using this typology, we identify the family of road networks which may be of most concern from a humanitarian standpoint. We also find that, of the properties of the road networks we examined, the average shortest path length, 〈lmin〉 and the average node degree, 〈k〉, proxies for city road network size and complexity respectively, are very significantly–correlated with damage susceptibility. In addition to forming a damage response typology by which city road networks could be classified, we consider five cities in detail, looking at risks and previous disaster events. Our results offer a generalizable framework in evaluating the feasibility of coursing relief efforts within disaster–affected areas using land–based transportation methods. They also provide, albeit in retrospect, a glimpse of the time difficulties which occurred, and the stakes of life involved in the humanitarian crisis which developed in the Kathmandu area due to the earthquakes of April and May 2015.

Merger of Network Graph Indicators to Estimate Resilience in Latin American Cities

In Latin America, cities are characterized by uncontrolled urban growth and locations in critical geographic areas. This factor affects an individual&#x2019;s ability to react efficiently to an action known as resilience to disasters caused by natural phenomena or catastrophes that involve human participation. This study proposes a method for detecting vulnerabilities in urban road systems in large, intermediate, and small cities in Latin America, so that resilience can be better supported. Depending on the structural characteristics of a city, measurement techniques can be used to combine the topological, geographic, and spatiotemporal indicators. Several measurements, such as mesh and connectivity, betweenness centrality, closeness centrality, robustness of networks with interruption attacks, location of dead ends, measurement of travel times between origin and destination, elevations and fusion of indicators with clusters on the same map, were applied using network graphs from OpenStreetMap (OSM) to estimate the resilience of road networks. The results are exposed to clusters of fused geospatial maps, which show that intersections and streets tend to be classified as vulnerable, with morphological structures of growth, resistance to degradation of the urban network, marginal urban sectors with high conglomeration, and mobility. The results highlight the importance of applying resilient practices in the region and generating urban management options to strengthen the response capacity of cities in Latin America.

Developing Transportation Livability-Related Indicators for Green Urban Road Rating System in Taiwan

Although indicators in rating systems have been initiated to measure and promote the sustainability performance of roadway projects in some developed countries, applying those indicators to other regions/countries may still be difficult. In response to the United Nations’ sustainable development goals, local road agencies in Taiwan urgently need to establish systematic and quantifiable sustainable roadway strategies. As part of the project to develop a green urban road rating system in Taiwan, this study aims to develop transportation livability-related indicators (TLIs) and identify critical barriers to TLI application in Taiwan’s urban road system. To this end, the research employed an adaptive approach that integrates top-down and bottom-up approaches. The top-down approach included the comprehensive literature review and panel discussion to derive four TLIs and 21 corresponding requirements, and nine potential barriers to hold the indicator adoption. Four TLIs are pedestrian facilities, universal design, multimodal transportation, and utility facilities. The bottom-up approach used the Analytic Hierarchy Process (AHP) to assign weights to proposed indicators/requirements. Four critical barriers were also investigated through the Weighted Sum Model (WSM) method, namely unfavorable in-situ conditions, lack of stakeholders’ coordination, unsupported government policy and regulation, and limited budget and schedule. The findings can be beneficial to engineers and decisionmakers to enhance the livability standard of urban streets. The framework proposed in this research can be applied to other roadway characteristics aspects in different regions/countries.

Multi-Criteria Analysis of a People-Oriented Urban Pedestrian Road System Using an Integrated Fuzzy AHP and DEA Approach: A Case Study in Harbin, China

Increasingly, cities worldwide are striving for green travel and slow traffic, and vigorously developing people-oriented urban pedestrian traffic with sustainability has become a fixture in recent discourse. This paper comprehensively considers the sidewalk’s facilities environment and the status of pedestrian traffic flow; divides the urban pedestrian road system (UPRS) into five subsystems around the underpass, overpass, crosswalk, sidewalk, and road crosswalk; and introduces the basic structure as well as the function of each system. Then, the indicators are classified into two types of crosswalk facilities and sidewalk facilities, and a comprehensive pedestrian road indicator system with the combination of subjective and objective is established. Consequently, the integration of the fuzzy AHP and DEA-based symmetrical technique for the subjective evaluation indicator combined with pedestrian traffic characteristics is developed. A nine-step semantics scale of relative importance was used so that the symmetry of the response of pedestrian satisfaction was maintained. Fuzzy evaluation based on AHP is further modeled, and the DEA is employed to achieve an overall evaluation of the quality of service (QoS) for UPRS. The applicability of the established evaluation system is finally verified through a real case study in Harbin, China. The serviceability assessment method in this paper provides a new idea for planners to conduct sustainability evaluation for UPRS in future urban renewal development.

Research on the Traffic Flow Control of Urban Occasional Congestion Based on Catastrophe Theory

Aimed at the problem of occasional congestion control, the cusp catastrophe theory is used to establish the catastrophe model of the urban road system under occasional congestion, finding breakpoints and analyzing stability after urban road system catastrophes by constructing the energetic function; based on the catastrophe characteristics of the urban road system, the feasibility method of congestion control is discussed. The results show that the control method of traffic flow based on catastrophe characteristics of the urban road system can effectively improve the efficiency of the road system in theory. Finally, the applicability of the control method based on catastrophe characteristics is analyzed by examples under different occasional congestion situations.

Material stock quantification and environmental impact analysis of urban road systems

Road construction area expands rapidly in China which challenges the conservations of natural resources. While available studies largely focused on consumptions at project level, this study integrated material flow analysis and life cycle assessment methods to quantify the material stocks and environmental impacts of urban road system. Remote sensing images were processed to extract road network skeletons. Taking Nanjing as an example, the results show that: 1) arterial road stores the largest amount of materials (42.9 million tons) of entire road system; 2) macadam occupies nearly 80% of total material stocks; 3) increase of road class coincides with the increase of material stock per unit area and enhances the material utilization efficiency; 4) arterial road is over-emphasized and expressway is still of a small scale in Nanjing. The methodology developed in this study provides a useful tool for road planning to effectively control resource depletion in the long term.

Congestion recognition for hybrid urban road systems via digraph convolutional network

Congestion recognition is the prerequisite for traffic control and management, vehicle routing, and many other applications in intelligent transportation systems. Different types of roads with traffic facilities provide multi-source heterogeneous field traffic data, which contain the fundamental information and distinct features for congestion recognition. To exploit these traffic big data, in this paper, we propose a machine learning-based framework to tackle the congestion recognition problem. It can be divided in two parts, a digraph-based representation for hybrid urban traffic network and a Dirgraph Convolutional Neural Network (DGCN)-based learning model. At first, the representation incorporates the fundamental traffic variables with the correlation of different traffic flows, and partially decouples the global network topology from local traffic information. And then, to proceed with digraph-based samples, a new type of graph feature extraction method is introduced and the graph Fourier transform is defined accordingly. This distinguishes the proposed model from the conventional graph convolutional networks. Comprehensive experiments are conducted based on real traffic data. The results demonstrate the advantages of the proposed framework over the existing congestion recognition methods.