Urban Road Network Research Articles

Graph convolutional networks for street network analysis with a case study of urban polycentricity in Chinese cities

Graph theory effectively explains urban structures via street–street connectivity. However, systematic comparisons of street structures across cities remain challenging. This study employs graph convolutional networks (GCNs) to analyze street network structures. A two-branch GCN was used as the backbone to extract comparable features among street networks. The proposed approach was used to examine the structures of different urban road networks in a case study of polycentricity prediction across 298 Chinese cities. The model transformed approximately 4.5-million street segments into natural streets to create urban street graphs, which were subsequently analyzed to extract local and global embeddings. The extracted embeddings – with a portion labeled with a known urban polycentricity score – were used to predict the score for each city through a single-layer perceptron (SLP) model. Our results show consistency between the predicted polycentricity scores based on the derived street embeddings and those based on the population. Thus, the proposed GCN-based method can effectively predict the complexity and interconnection of street networks in different cities. This innovative integration of GCNs into urban studies demonstrates that deep learning techniques can analyze and comprehend the intricate patterns of street networks on a large scale.

Research on Chinese Fire Station Optimal Location Model Based on Fire Risk Statistics: Case Study in Shanghai

With the rapid development of urbanization in China, the gap between urban and rural areas is decreasing. The traditional approach of constructing fire stations based on urban built-up areas is no longer suitable for the needs of modern fire rescue. Therefore, a comprehensive fire station location model is proposed based on fire risk assessment. This method divides the protected area units based on the urban road network. By evaluating different regions based on spatial position, land attributes, population density, floor area ratio, and fire incident indicators, the fire rescue risk levels and categorize regions into four risk levels are assessed. Corresponding response times were determined, and an objective model was developed to maximize the coverage area for fire response. The Baidu API was utilized to accurately calculate driving distances and times, and Gurobi optimization software was used to solve the model. Taking Shanghai as an example, the fire station location and layout from two perspectives—re-planning based on overall station placement and re-planning based on existing stations—were analyzed. The results suggest that constructing around 150 fire stations in Shanghai would effectively meet the fire rescue needs, which aligns with the actual situation in Shanghai and demonstrates the strong applicability of this model. This approach enables the meeting of new demand for fire station construction due to the significant increase in the coverage area while effectively utilizing firefighting resources.

A traffic state prediction method based on spatial–temporal data mining of floating car data by using autoformer architecture

AbstractFloating car data (FCD), characterized by wide spatiotemporal coverage, low collection cost, and immunity to adverse weather conditions, are one of the key approaches for intelligent transportation systems to obtain real‐time urban road network traffic information. The research aims to utilize GPS data from taxis in Shanghai and vector geographic information data of the road network, with urban expressways as the research focus. Based on the different driving characteristics of expressways and the vehicles on the ramps below, a clustering analysis is employed to determine all floating vehicles traveling on the target road. Furthermore, an adaptive buffer zone consistent with the road orientation is established based on road vector geographic data. This allows for the extraction of FCD within segmented areas, and the average vehicle speed for that road segment is obtained through weighted calculations. This method fully exploits the natural characteristics of taxis in urban areas with a wide spatiotemporal distribution. The data effectiveness and coverage reach 90.2% and 85.7%, respectively, significantly surpassing the traditional grid‐based extraction method for FCD. Additionally, to capture the long‐term spatiotemporal dependencies of road network traffic states, a spatial–temporal autoformer (STAF) network based on spatial–temporal sequence autocorrelation is employed for traffic state prediction. The results indicate that the STAF method demonstrates good performance in medium‐ and long‐term prediction. We believe that the proposed FCD mining method in this paper provides a new approach for efficiently extracting large‐scale road network traffic states and conducting medium‐ to long‐term predictions.

An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing

The rapid adoption of electric vehicles (EVs) has led to dramatic increase in charging demands that poses great challenges for efficient charging infrastructure rollout and operation. It is crucial to accurately assess charging demand in urban areas to optimize the siting and sizing of charging infrastructure. This paper proposes a novel urban charging load forecasting model for private passenger EVs based on massive operating data of EVs in Beijing. First, the characteristics of travel patterns for private passenger EVs, urban road network, functional area distribution and charging infrastructure distribution within the entire Beijing area are identified. Then a charging load forecasting model that can simultaneously simulate trip chains for EVs is constructed by considering the occupancy states of public charging piles and the interactions among different EVs. Finally, the effectiveness of the proposed charging load forecasting model is verified based on comprehensive test data. Our findings imply that the number of EVs at recharge and the charging power can be reliably predicted with the accuracy of over 84.73 % and 81.92 %, respectively. It provides the foundation for optimal charging infrastructure planning and charging scheduling.

Choosing Routes in Urban Areas that are Robust Against Minor Nonrecurring Traffic Incidents

The paper looks at certain vehicle-level re-routing issues within urban road networks, and related network-level traffic management issues. These arise mostly when the traffic along a route of significance is hindered, slowed down, or even blocked because of some – possibly minor – unexpected, nonrecurring traffic incident at a sensitive road location, or road section. Considerations for planning routes in urban areas – routes that are in some sense robust against such incidents – are presented herein. Also, the on–the–spot detection of traffic queues by an ego-vehicle – relying on data streams from on-board visual line-of-sight (LoS) exteroceptive sensors watching, scanning and monitoring the ego-vehicle's road environment, and by some on-board dedicated real-time detection systems processing and analyzing the incoming data streams – is touched upon. However, this traffic congestion avoidance and mitigating approach – effectuated either by individual autonomous vehicles, or by human drivers – presumes availability of alternative routes, which is not the case for a good portion of the route considered. A route planning approach that could be used for routes with such critical sections is proposed and motivated through an example of an urban route of significance.

Democratizing traffic control in smart cities

To improve the performance of systems, optimization has been the prevailing approach in the past. However, the approach faces challenges when multiple goals shall be simultaneously achieved. For illustration, we study a multi-agent system, where agents have a plurality of different, and mutually inconsistent goals. We then allow agents in the system to vote on which traffic signal controllers, which were trained on different goals using deep reinforcement learning, would control the intersection. Taking decisions based on suitable voting procedures turns out to lead to favorable solutions, which perform highly for several goals rather than optimally for one goal and poorly for others. This opens up new opportunities for the management or even self-governance of complex systems that require the consideration and achievement of multiple goals, such as many systems involving humans. Here, we present results for traffic flows in urban street networks, which suggest that “democratizing traffic” would be a promising alternative to centralized control of traffic flows.

Understanding route choice behaviors' impact on traffic throughput in a dynamic transportation network

Route planning is one of the most important and attractive topics in complex networks, geographical information science (GIS) and logistics. Travelers' route choice behaviors may affect the actual throughput of a transportation network. This study aims to analyze the influence of travelers' route choice behaviors on network traffic throughput. Two kinds of route choice behaviors, called continuous optimization behavior and hybrid optimization behavior, are defined. Based on this, two traffic scenarios are constructed to describe these behaviors and examine their effect on the network's actual traffic throughput. The application of our approach to urban street networks, Barabási-Albert scale-free network and Erdös-Rényi random network demonstrates that if all travelers choose optimal paths constraint with time from the start, network throughput is insensitive to the continuous optimization behavior but depends on the relationship between time and distance in the routing algorithm. Besides, network throughput is sensitive to the hybrid optimization behavior that the more travelers choose time-dependent optimal paths, the higher the throughput. The results indicate that the choice of initial paths and the time dependence of routing algorithms can directly affect the network's actual throughput which is enhanced >50 % when the time dependence parameter is >0.2 or the proportion of time-dependent shortest paths in initial paths is >0.2. Our findings provide supports for exploring efficient routing algorithms to enhance the throughput of urban street networks and other kinds of networks.

Research on Urban Street Network Structure Based on Spatial Syntax and POI Data

The rapid development of cities has led to increasingly problems in the road network structure of urban streets. Combining emerging big data technology with traditional street network analysis methods has become a new way to tackle it. Guilin is a famous international tourist city, and the “Two Rivers and Four Lakes” scenic area is an iconic symbol of Guilin’s scenery. Its streets connect various tourist spots. This study focused on the street’s layout of the “Two Rivers and Four Lakes” scenic area, and used a combination of spatial syntax and POI big data to analyse their spatial structure. The research results indicated that: (1) there was a positive correlation between the global integration value of the street and the POI value; (2) by combining functional density indicators with global integration analysis, streets that significantly deviate from the overall trend can be identified, and classified according to their characteristics to reveal the reasons for their contradictions; (3) we needed to propose three plans for optimizing the proportion of high street, enhancing street functions, and “improving street space” for different types of streets to ultimately realize the purpose of sustainable development of streets and cities.

Nonlinear and Synergistic Effects of Built Environment Indicators on Street Vitality: A Case Study of Humid and Hot Urban Cities

Street vitality has become an important indicator for evaluating the attractiveness and potential for the sustainable development of urban neighborhoods. However, research on this topic may overestimate or underestimate the effects of different influencing factors, as most studies overlook the prevalent nonlinear and synergistic effects. This study takes the central urban districts of humid–hot cities in developing countries as an example, utilizing readily available big data sources such as Baidu Heat Map data, Baidu Map data, Baidu Building data, urban road network data, and Amap’s Point of Interest (POI) data to construct a Gradient-Boosting Decision Tree (GBDT) model. This model reveals the nonlinear and synergistic effects of different built environment factors on street vitality. The study finds that (1) construction intensity plays a crucial role in the early stages of urban street development (with a contribution value of 0.71), and as the city matures, the role of diversity gradually becomes apparent (with the contribution value increasing from 0.03 to 0.08); (2) the built environment factors have nonlinear impacts on street vitality; for example, POI density has different thresholds in the three cities (300, 200, and 500); (3) there are significant synergistic effects between different dimensions and indicators of the built environment, such as when the POI density is high and integration exceeds 1.5, a positive synergistic effect is notable, whereas a negative synergistic effect occurs when POI is low. This article further discusses the practical implications of the research findings, providing nuanced and targeted policy suggestions for humid–hot cities at different stages of development.

Cooperative traffic signal control through a counterfactual multi-agent deep actor critic approach

Signal control has been effective to alleviate urban traffic congestion. Massive related works about signal timing optimization have been proposed and led to many signal control methods and systems. In recent years, reinforcement learning (RL) algorithms have attracted the increasing attention of researchers in the area of signal control optimization, since they can learn the optimal timing policy themselves by analyzing changing patterns between the traffic condition and signal timing plans. Multi-intersection traffic signal control problems face more challenges than classical single intersection problems such as the dimensionality issue as the joint action space of multiple agents grows exponentially with the number of intersections. The existing state-of-art multi-agent reinforcement learning algorithms developed for other areas may not suit well with traffic signal control given the complex spatial–temporal nature of traffic flows. In this paper, we propose a novel multi-agent counterfactual actor–critic with scheduler (MACS) framework for multiple interactions. In this method, decentralized actors control the traffic signals and the centralized critic combines recurrent policies with feed-forward critics. Additionally, a scheduler module that exchanges information among agents helps the individual agent better understand the entire environment. The proposed method was evaluated using simulation experiments based on a real-world urban street network in Shenzhen, China. Results showed that the proposed method outperforms the classic model-based method and several existing RL-based methods according to different measures such as queue length, average travel time delay, and throughput.

Reliability Assessment of Integrated Power and Road System for Decarbonizing Heavy-Duty Vehicles

With the continual expansion of urban road networks and global commitments to net zero, electric vehicles (EVs) have been considered to be the most viable solution to decarbonize the transportation sector. In recent years, the electric road system (ERS) has been introduced and piloted in a few countries and regions to decarbonize heavy-duty vehicles. However, little research has been carried out on its reliability. This paper fills the gap and investigates the reliability of electric truck power supply systems for electric road (ETPSS–ER), which considers both the power system and truck traffic networks. First, a brief introduction of electric roads illustrates the working principle of EV charging on roads. Then, an optimized electric truck (ET) travel pattern model is built, based on which the corresponding ET charging load demand, including both static charging and dynamic charging, is conducted. Then, based on the new ET travel pattern model, a daily travel-pattern-driven Monte Carlo simulation-based reliability assessment method for ETPSS–ER system is presented. Case studies based on the IEEE RBTS system shows that ETs driving on ERS systems can meet the daily travel demands. The case studies also examine the impacts of increasing number of ETs, extra wind power, and battery energy storage systems (BESS) on the reliability of ERS power systems.

Variability in road surface temperature in urban road network – A case study making use of mobile measurements

Abstract This work presents the results of the research on the thermal state of the road surface measured by means of a mobile road condition sensor. A 15 km route circumnavigating the city centre and used by urban traffic was taken as the research area. Sixteen test runs were performed under summer and winter conditions. An analysis of the locations at which the rate of change of surface temperature had extreme values between consecutive test runs (which lasted around 25 min) was carried out. Based on that, critical sections of the test route were identified, and thus points characterised by readings deviating significantly from the mean values. Based on the thermal mapping methodology, an analysis of the rate of surface cooling was carried out. It was shown to be characterised by temporal (as well as spatial) non-uniformity. The rate of change of surface and air temperatures was calculated as a function of time, which was equal to −2.22 and −1.97°C/h, respectively. During the analysis, it came to light that hourly intervals represent the optimal frequency for thermal monitoring of a road network. Exceptions to the above are the aforementioned sections selected on the basis of the mobile measurements, in which permanent monitoring of road conditions via ESS stationary systems are recommended.

Gated Fusion Adaptive Graph Neural Network for Urban Road Traffic Flow Prediction

Accurate prediction of traffic flow plays an important role in maintaining traffic order and traffic safety, which is a key task in the application of intelligent transportation systems (ITS). However, the urban road network has complex dynamic spatial correlation and nonlinear temporal correlation, and achieving accurate traffic flow prediction is a highly challenging task. Traditional methods use sensors deployed on roads to construct the spatial structure of the road network and capture spatial information by graph convolution. However, they ignore that the spatial correlation between nodes is dynamically changing, and using a fixed adjacency matrix cannot reflect the real road spatial structure. To overcome these limitations, this paper proposes a new spatial-temporal deep learning model: gated fusion adaptive graph neural network (GFAGNN). GFAGNN first extracts long-term dependencies on raw data through stacking expansion causal convolution, Then the spatial features of the dynamics are learned by adaptive graph attention network and adaptive graph convolutional network respectively, Finally the fused information is passed through a lightweight channel attention to extract temporal features. The experimental results on two public data sets show that our model can effectively capture the spatiotemporal correlation in traffic flow prediction. Compared with GWNET-conv model on METR-LA dataset, the three indexes in the 60-minute task prediction improved by 2.27%,2.06% and 2.13%, respectively.

An Architecture and Review of Intelligence Based Traffic Control System for Smart Cities

City traffic congestion can be reduced with the help of adaptable traffic signal control system. The technique improves the efficiency of traffic operations on urban road networks by quickly adjusting the timing of signal values to account for seasonal variations and brief turns in traffic demand. This study looks into how adaptive signal control systems have evolved over time, their technical features, the state of adaptive control research today, and Control solutions for diverse traffic flows composed of linked and autonomous vehicles. This paper finally came to the conclusion that the ability of smart cities to generate vast volumes of information, Artificial Intelligence (AI) approaches that have recently been developed are of interest because they have the power to transform unstructured data into meaningful information to support decision-making (For instance, using current traffic information to adjust traffic lights based on actual traffic circumstances). It will demand a lot of processing power and is not easy to construct these AI applications. Unique computer hardware/technologies are required since some smart city applications require quick responses. In order to achieve the greatest energy savings and QoS, it focuses on the deployment of virtual machines in software-defined data centers. Review of the accuracy vs. latency trade-off for deep learning-based service decisions regarding offloading while providing the best QoS at the edge using compression techniques. During the past, computationally demanding tasks have been handled by cloud computing infrastructures. A promising computer infrastructure is already available and thanks to the new edge computing advancement, which is capable of meeting the needs of tomorrow's smart cities.

Which route is better for hazmat transport? An optimised routing index for urban networks

The safety of vehicles and citizens on urban road networks is a major subject, especially if these vehicles carry hazardous materials (hazmats). The paper focuses on developing an optimised index to identify the safest routes between any origin–destination pair within urban networks. The considered parameters include population, traffic safety, volume–capacity ratio, emergency response time, type of hazmat and sensitive and vulnerable places. Notably, this study is the first to incorporate the last two parameters and the combination of all parameters in route optimisation. The research formulates a hazardous material vehicle routing problem and proposes an algorithm to solve it. The approach is applied to a case study in Mashhad city, presenting the safest routes for transporting three types of hazmats. The findings indicate that the reduction in average risk score is comparable with the increase in average travel time; however, gain of the safest routes is lower than loss of the longer distance. The proposed method is practical and applicable to diverse urban networks, aiding in the routing and planning of any hazmat-type transportation.

Framework to Identify Vehicle Platoons under Heterogeneous Traffic Conditions on Urban Roads

Vehicle platoon studies are essential for understanding and managing traffic on urban arterial roads. The identification of vehicle platoons on urban roads has drawn more attention in recent years. Researchers have been exploring various methods and algorithms to detect and classify platoons, as well as investigating the benefits and implications of their presence on road capacity, safety, fuel consumption, and environmental pollution. The present study formulated a three-step strategy to identify vehicle platoons in the urban road network under heterogeneous traffic conditions. The proposed three steps are recognizing vehicle interaction, the estimation of critical headway, and vehicle platoon identification. Traffic data were collected for 13 h in a six-lane divided urban arterial road using an infrared sensor. A Python program was developed to recognize vehicle platoons. The results revealed that out of a total of 42,500 vehicles observed, 74% of vehicles were in vehicle platoons. The characteristics of the identified vehicle platoons were studied, thus focusing on key aspects such as platoon size, intra-platoon headway, platoon stream speed, and vehicle composition in the platoon. The results revealed a linear relationship between the percentage of vehicles in the platoon and traffic volume. The findings of the study will be beneficial in examining platoon-based data aggregation, the utilization of road capacity, and traffic flow optimization.

Urban street network design and transport-related greenhouse gas emissions around the world

This study estimates the relationships between street network characteristics and transport-sector CO2 emissions across every urban area in the world and investigates whether they are the same across development levels and urban design paradigms. The prior literature has estimated relationships between street network design and transport emissions – including greenhouse gases implicated in climate change – primarily through case studies focusing on certain world regions or relatively small samples of cities, complicating generalizability and applicability for evidence-informed practice. Our worldwide study finds that straighter, more-connected, and less-overbuilt street networks are associated with lower transport emissions, all else equal. Importantly, these relationships vary across development levels and design paradigms – yet most prior literature reports findings from urban areas that are outliers by global standards. Planners need a better empirical base for evidence-informed practice in under-studied regions, particularly the rapidly urbanizing Global South.

Development of a multicriteria matrix for decision making in the management of flexible pavements in an urban road network. Case study the city of Itagüí – Colombia

Decision-making for interventions in urban road networks is often carried out reactively, addressing roads in the worst condition first, which reduces the length of intervention in the entire urban road network under an assigned budget. The objective of this work was to establish a methodology to prioritize the intervention of asphalt pavements on urban roads through a multi-criteria matrix that allows optimizing the management of pavements in the network. The criteria were previously adjusted and validated by road infrastructure experts using the Likert scale. Subsequently a large group of experts completed the multi-criteria matrix to qualify and weigh each of the criteria and intervention alternatives considered to address each of the established problems; the goal is to preserve the urban road network in good condition. With the support of Geographic Information Systems (GIS) tools, each criterion was represented by a vector shapefile format, each of them was transformed into raster format and reclassified to assign a specific score. The attributes of the raster format used information from the road network of the city of Itagüí (Colombia). The final result was the weighted overlay of maps that allowed establishing the prioritization of the roads to intervene according to the score obtained in each road segment, which allows the administrator to make efficient decisions in an urban network.

Congestion and Pollutant Emission Analysis of Urban Road Networks Based on Floating Vehicle Data

Global warming caused by greenhouse gas (GHG) is receiving increasingly attention from all over the world, and urban transportation is a significant source of greenhouse gas and pollutant emission. However, the research on traffic state of urban road networks (URNs) based on sparse floating vehicle data (FVD) is insufficient. Therefore, we mainly utilize big data techniques to explore the congestion and pollutant emission of URN with FVD. Firstly, the location of vehicles is identified and matched with the URN. We then grid the FVD and city maps to more accurately identify areas of congestion and emission in later section. Following this, we use the congestion index and K-means clustering algorithm to evaluate the traffic state over time, pollutant emission is calculated based on emission calculation standards and carbon emission is estimated by using the fuel consumption-speed model. The results indicate that congestion and emission are very severe during peak hours (e.g., 8:00 a.m.), particularly in some transportation hub areas, such as high-speed rail stations. During off-peak hours (e.g., 11:00 p.m.), congestion and emission are relatively lower. The negative correlation between congestion index and emission is also revealed. This study provides some practical approaches to more accurately estimate the overall urban traffic state by using sparse traffic data, and may offer support to urban traffic managers in managing traffic congestion and pollutant emissions.

ЗАКОНОМЕРНОСТИ ПОКАЗАТЕЛЯ ТРАНСПОРТНОГО ЗАТОРА НА НЕКОТОРЫХ ПЕРЕСЕЧЕНИЯХ УЛИЧНО-ДОРОЖНОЙ СЕТИ

The average speed and density of road transport are used as indicators of the congestion situation, but do not allow tracking the evolution (stages of formatting, progressing, and vanishing) of traffic congestion. The authors proposed and justified a quantitative indicator of traffic congestion, which allows in an automated mode to identify the congestion situation on the urban road network using hardware and software video recording systems. The purpose of this study is due to the need to study the quantitative characteristics of the proposed indicator at characteristic intersections of urban roads, which will allow us to develop scientifically based recommendations for predicting congestion situations, substantiating, and making optimal decisions on measures to promptly eliminate traffic congestion. The object of study is the traffic flow at three types of intersections of the Perm city road network, equipped with a photo and video recording software and hardware complex. The subject of the study is the regularities of the evolution of the listed deterministic indicators of traffic flows, which can be used for operational forecasting of the formation, development, and elimination of traffic congestion. The theoretical and methodological approach is based on the methods of mathematical statistics used to process the results of observations of traffic flows at different types of intersections using a «sliding window», calculating the average daily value and standard deviation. The initial data were obtained with the help of hardware and software complexes for fixing violations of traffic rules installed on the street and road network of the Perm city. As a result of the study, the rational parameters of the «sliding window» were determined, ensuring the structuring of the traffic congestion indicator; the facts of the congestion situations formation were revealed; the features of the congestion evolution and the presence of problematic traffic directions for which it is advisable to change the traffic light regulation mode were determined. The theoretical and practical significance of the work consists in checking the operability of the proposed indicator and criterion of traffic congestion, which is of practical interest from the point of view of predicting anomalies in the movement of vehicles on the road network, adjusting the operating modes of traffic lights, etc. It is also possible to use the proposed traffic congestion indicator to assess the effectiveness of traffic light regulation on the Perm city road network. The direction of further research is to study the patterns of traffic congestion at intersections of the urban road network, of various types that are not included in this study.