Branch Road Research Articles

Measuring mobility resilience with network-based simulations of flow dynamics under extreme events

Extreme events disrupt routine mobility patterns and affect citizens’ daily lives. Current disaster resilience measurements often rely on static proxies or topological analyses, failing to capture the dynamic interactions between functional organization and transportation systems, as well as critical regime shifts within the resilience process. Using big data sourced from mobile-devices, this study delineates the evolving nature of various response flows during disasters with different intensities, depicts curves illustrating shifts in maintained traffic flow proportion, maps mobility resilience heterogeneity, and examines the spatial ramifications of flow redistribution. Our findings revealed that even minor incidents prompt city-wide flow reorganization. Clusters of low resilience with gradients are caused by mobility structures rather than distance from the disaster. Arterial roads uphold connectivity for long-distance journeys, whereas branch roads facilitate shorter trips but remain vulnerable to traffic surges. Based on these findings, strategies for preparedness and emergency planning under road failure conditions are evidence-based.

Spatial Distribution Characteristics of Fugitive Road Dust Emissions from a Transportation Hub City (Jinan) in China and Their Impact on the Atmosphere in 2020

Road silt loading (sL) directly affects the fugitive road dust (FRD) emission factor, which is an important parameter in the study of FRD emissions. In this study, an improved collection method combined with the AP−42 method was newly developed to estimate the sL of asphalt roads in Jinan, China. The characteristics of sL in Jinan followed the order National highway (NH) > Branch road (BR) > Provincial highway (PH) > Country highway (CH) > Minor arterial (MiA) > Major arterial (MaA) > Urban expressway (UE) with 3.9 ± 2.5, 3.9 ± 1.9, 3.8 ± 2.8, 3.8 ± 0.9, 2.1 ± 1.4, 1.7 ± 1.2, and 1.4 ± 1.2 g/m2, respectively. The size orders of PM2.5 and PM10 emission factors are consistent with total suspended particulate (TSP). The characteristics of the TSP emission factor of FRD followed the order NH > PH > CH > Expressway (EW) > MiA > BR > MaA > UE with 27.3, 23.4, 19.4, 13.7, 7.7, 7.4, 6.2, and 3.0 g/VKT (vehicle kilometers traveled), respectively. The annual emissions of TSP, PM10, and PM2.5 from FRD in Jinan in 2020 were about 985.2, 209.8, and 57.8 kt, respectively. Laiwu, Jiyang, and Licheng districts show the top three TSP emissions of FRD; the sum of their emissions accounts for 44.7% of the TSP emissions from FRD in Jinan. TSP emissions from municipal roads and administrative roads accounted for about 29.2% and 70.8% of the total emissions in Jinan, respectively, of which emissions from MiA accounted for the largest proportion of TSP emissions from municipal roads, contributing about 37.9%, while TSP emissions from NH made the largest contribution to TSP emissions from administrative roads, with a contribution of about 35.8%. Based on Monte Carlo simulation results using Crystal Ball, the uncertainty range of the emission inventory of FRD in Jinan ranged from −79.9 to 151.8%. In 2020, about 985,200 tons of road particulate matter in Jinan City entered the atmosphere, having an adverse effect on air quality and human health.

Research on Optimization Strategy of Commercial Street Spatial Vitality Based on Pedestrian Trajectories

Commercial pedestrian streets significantly enhance urban life. Previous research often depends on limited survey data or subjective evaluations, which fail to fully capture the impact of street elements on spatial vitality under diverse spatiotemporal conditions. This study, conducted in Beijing’s Sanlitun commercial district, analyzes pedestrian data from six branch roads collected via WiFi detection to determine how various spatial elements influence the vibrancy of commercial pedestrian streets. The results show a positive correlation between street width, ranging from 5.88 to 10.83 m, and vitality. Furthermore, the influence of different street elements on vitality varies notably. During the daytime, street width is the most effective element for enhancing vitality, with a maximum standardized coefficient of 1.717. However, at night, the associations between advertising area, store type, and vitality diminish. The street recording the highest nighttime vitality peaked at a pedestrian count of 91.7831. The contribution of street width to enhancing nighttime vitality is 2.49 times greater than at noon on non-working days, 2.01 times more than on working days, and 2.92 times higher than the impact of tables and chairs. Therefore, street design should prioritize street width, ground floor interface permeability, number of tables and chairs, store density, advertising area, and store type.

Overdue waiting of wagons on railway branch tracks: problems and solutions

The purpose of the study: to prevent wagons from being waited aimlessly by freight owners (Railway customers) on the branch roads of freight cars in rail transport, and to improve wagon turnover indicators Method of study: on the basis of statistical data, comparisons were made on the example of regional railway nodes of the railway in the case of the fact that cars remain standing due to the fault of Railway customers on Branch roads for more than a specified period. The history of the occurrence of the coefficient of the amount of the current fine was studied. it was studied to what extent the statement of the indicators of the amount of fines on the main myory cells was correctly interpreted on the secondary myory cells, and analyzed by comparing the definition of fines for leaving wagons waiting for myory abroad. The main result from the research. The amount of the fine levied for the time of waiting for railway-owned wagons without a goal is obtained as a result of increasing their effectiveness in relation to Railway customers, the wagons on the branch roads received a traffic jam. The number of re-releases of wagons decreases, Efficient use of shunting locomotives and freight fronting over-unloading machines. The station leads to a mutadillation of the operating mode, in a word, by increasing the turnover of wagons, an improvement in the indicators of the effective use of wagons is achieved.

Data mining and spatio-temporal characteristics of urban road traffic emissions: A case study in Shijiazhuang, China

Accurate estimation of traffic emissions and analysis of spatio-temporal distribution on urban roads play a crucial role in the development of low-carbon transportation system. Traditionally, a region’s emission characteristics have been studied using numerous emission models with GPS-based spatio-temporal data. Due to the heavy data processing needs of GPS-based data, emission characteristics for a large region have been studied by dividing the region into a limited number of smaller areas or units. Additionally, GPS data are based on a few vehicles in the traffic which does not fully reflect road conditions. This paper proposed an approach that can be used to study and calculate the spatio-temporal emission pattern of a region at a roadway section level by using Baidu’s online traffic data and COPERT model. The proposed method can be used to estimate road-level emission patterns while avoiding the impact of redundant data in large datasets, making the dataset more reliable, applicable, and scalable. The proposed approach has been demonstrated through a study of spatio-temporal emission patterns in the Qiaoxi district within city of Shijiazhuang, China. Online data crawling technology was used to obtain data on urban road traffic speed and driving distance. The linear reference technology was used to construct a two-layer road network model to conduct the coupling and matching of traffic data with the road network data. The COPERT model was implemented to calculate the average traffic emissions on each road in the road network, and a traffic emission intensity index was proposed to quantify the CO, VOC, NOx and CO2 emissions on urban roads in the study area. The analysis results show that the traffic emission intensity of the expressway, trunk road, secondary road, and branch road is high during the morning peak (7 AM-9 AM) and evening peak (5 PM—7 PM). The sections with higher traffic emission intensity are mainly concentrated on the main roads and secondary roads such as Jiefang South Street, Shitong Road and Xinhua Road. Nearly one-third of 2nd Ring and 3rd Ring roads also have relatively high emission intensity. The research results provide new ideas for estimating traffic emissions in urban road networks and analyzing the spatio-temporal distribution of traffic emissions. The research results can also provide a decision-making basis for traffic management departments to formulate energy-saving and emission-reduction measures and promote the development of urban green and low-carbon transportation.

Quantifying Bus Accessibility and Mobility for Urban Branches: A Reliability Modeling Approach

In order to quantitatively assess the realization degree of urban branch road function, an evaluation method based on reliability theory was established. Firstly, the main characteristics of urban branch road functions, that is, emphasizing accessibility while maintaining mobility, were analyzed. Based on common road network patterns, the branch road network between adjacent higher-level roads was selected as the research object. Public transportation, which is more representative of accessibility, was considered in the evaluation method. Then, a reliability model for branch road function evaluation was proposed, in which walking distance and travel time by bus were taken as the main indicators. Combining Dijkstra algorithm and Monte Carlo simulation, the model was solved. The feasibility of the method was verified through a case study of two branch road units. Then, influences of road network density, connectivity, and bus route layout on the reliability of branch road unit functions were clarified by sensitivity analysis. Results showed that the reliability of the branch unit exhibited a trend of initially increasing and then decreasing with the growth of road network density and connectivity, which indicated that excessively high road network density may hinder the performance of the branch road unit. Adjusting the layout of bus routes within branch road units and expanding the service area of bus routes can enhance reliability. Finally, some suggestions for optimizing branch road unit reliability were provided.

Nonlinear impact analysis of built environment on urban road traffic safety risk

With the rapid development of economy, the increasing number of motor vehicles and the total road mileage, which leads to the increasingly prominent traffic safety problems. In order to explore the quantitative relationship between the built environment and the risk of urban road traffic safety, this paper reconstructs the built environment system based on the ‘5D' element model of the built environment combined with the factors influencing traffic safety risks, and describes the built environment from multiple aspects such as density, diversity &traffic design etc, and then build the gradient lift decision tree model to explore the importance and dependency of variables. The empirical analysis selects a district in Chongqing as the research unit, and the results show that: the RMSE the model was 0.0036, the MAPE was 1.9%, and the determination coefficient R2 was 0.84. GBDT algorithm results shows: the cumulative importance of population density, road facilities, intersection density, secondary road and branch road density, average intersection distance, land use mix, and economic density reaches 77.87%. Some variables show obvious nonlinearity and threshold effect.

Perception about Less Physical Activity and its Effect among Service Holders in Dhaka City of Bangladesh

Introduction: Physical activity has been traditionally defined as any bodily movement produced by contraction of skeletal muscle that substantially increases energy expenditure, although the intensity and duration can vary substantially. Lack of physical activity is known to be one of the most important risk factors for leading to morbidity and mortality around the world leading to an estimated 3.2 million deaths globally. Objectives: To assess perception about less physical activity and its effect among service holders in Dhaka city of Bangladesh. Methods: The study was conducted in Civil Aviation Authority of Bangladesh, Kurmitola; Janata Bank Limited, Satmasjid Road Branch; Rayerbazar Government Primary School, Rayerbazar and six gyms (training centers) of Dhaka, Bangladesh. A total of 15 government workers and 15 key informants purposefully (trainers of physical activities) selected for in-depth interview. Socio-demographic data were entered into SPSS version 21. Thematic area were identified and analyzed based on the themes. Result: Total 30 study participants (15 Government office worker and 15 Key Informant) were interviewed. All participants have good perception of physical activities. 7 of workers are moderately physically active (fulfill globally recommended physical activity rate) with 150 minutes of physical activity performance per week. The rest 3 workers have insufficient physical activities which is less than 150 minutes of physical activity per week. Factor like laziness, overloaded by office work, lack of self-confidence in doing physical activity, lack of awareness about physical activity, place factor, poor time management, lack of communication and economic status of workers were found as behaviors or factors affecting doing of physical activities among government office workers. Conclusion and recommendations: There is high number of physical activity insufficiency among workers and there are large numbers of factors that affect physical activity doing of workers. Therefore, the government should work on these factors to make worker healthy and productive.

The Function of Urban Green Space in Avoiding Disasters in Central Shanghai, China

Urban green space is an essential green infrastructure and spatial guarantee for sustainable development in Shanghai, China. Additionally, its spatial layout’s rationality directly influences the city’s disaster prevention and reduction effect. To use the buffer analysis method, this study selected the ArcGIS 9.0 software platform for making a quantitative analysis of disaster-proof green space service capacity in downtown Shanghai based on relevant construction standards and specifications. The results showed the following: (1) the distribution pattern of disaster-proof green space in downtown Shanghai is less homogeneously distributed in the central area but more homogeneously distributed in the edge area; (2) the service area of all disaster-proof green spaces in the central urban area of Shanghai accounted for 85.07% of the total area, with a service population of 12,516,600. Less than 15% of the area was still uncovered, meaning 2,196,700 people were unserved. These results suggest that the buffer radius of disaster-avoidance green space reaches 400 m. It covers the largest area and serves the largest population, accounting for more than 50% of the central urban area. The mismatch between urban green space distribution and population distribution patterns affected the service effect difference of green space for disaster avoidance. Therefore, new urban green spaces, stadiums, school playgrounds, and other open spaces could be added to enhance the (emergency) urban disaster avoidance ability of central urban areas; (3) the whole central urban area should form an urban mesh disaster avoidance system by combining points, lines, and surfaces with emergency and temporary shelters as first-level disaster avoidance points, fixed and central shelters as second-level disaster avoidance points, urban branch roads and secondary roads as disaster avoidance channels, and expressways and main urban roads as disaster-relief channels.

Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting

Recent works demonstrate that capturing correlations between road network nodes is crucial to improving traffic flow forecasting accuracy. In general, there are spatial, temporal, and joint spatial-temporal correlations between two nodes, whose strength is related to spatial and temporal position factors. For example, traffic congestion that occurs at a traffic hub has a wider and stronger impact than that at a branch road. Moreover, the above impacts can vary with temporal position. Although spatial-temporal graph convolution networks have become a popular paradigm for modeling those correlations, there are still three problems with existing models: (i) failing to effectively model joint spatial-temporal correlations; (ii) ignoring spatial and temporal position factors when modeling the aforementioned correlations; and (iii) failing to capture distinct spatial-temporal patterns of each node. To cope with the above issues, this paper proposes a novel Spatial-Temporal Position-aware Graph Convolution Network (STPGCN) for traffic flow forecasting. Specifically, a trainable embedding module is constructed to represent the spatial and temporal positions of the nodes. Subsequently, a spatial-temporal position-aware relation inference module is proposed to adaptively infer the correlation weights of the three important spatial-temporal relations. Based on this, the generated spatial-temporal relations are integrated into a graph convolution layer for aggregating and updating node features. Finally, we design a spatial-temporal position-aware gated activation unit in the graph convolution, to capture the node-specific pattern features under the guidance of position embedding. Extensive experiments on six real-world datasets demonstrate the superiority of our model in terms of prediction performance and computational efficiency.

Material stock analysis of urban road from nighttime light data based on a bottom-up approach

In recent years, there has been an increasing focus on the dynamics of material stock, that is, the basis of material flow in the entire ecosystem. With the gradual improvement of the global road network encryption project, the uncontrolled extraction, processing, and transportation of raw materials impose serious resource concerns and environmental pressure. Quantifying material stocks enable governments to formulate scientific policies because socio-economic metabolism, including resource allocation, use, and waste recovery, can be systematically assessed. In this study, OpenStreetMap road network data were used to extract the urban road skeleton, and nighttime light images were divided by watershed to construct regression equations based on geographical location attributes. Resultantly, a generic road material stock estimation model was developed and applied to Kunming. We concluded that (1) the top three stocks are stone chips, macadam, and grit (total weight is 380 million tons), (2) the proportion of asphalt, mineral powder, lime, and fly ash is correspondingly similar, and (3) the unit area stock decreases as the road grade declines; therefore, the branch road has the lowest unit stock.

Vector Road Map Updating from High-Resolution Remote-Sensing Images with the Guidance of Road Intersection Change Detection and Directed Road Tracing

Updating vector road maps from current remote-sensing images provides fundamental data for applications, such as smart transportation and autonomous driving. Updating historical road vector maps involves verifying unchanged roads, extracting newly built roads, and removing disappeared roads. Prior work extracted roads from a current remote-sensing image to build a new road vector map, yielding inaccurate results and redundant processing procedures. In this paper, we argue that changes in roads are closely related to changes in road intersections. Hence, a novel changed road-intersection-guided vector road map updating framework (VecRoadUpd) is proposed to update road vector maps with high efficiency and accuracy. Road-intersection changes include the detection of newly built or disappeared road junctions and the discovery of road branch changes at each road junction. A CNN-based intersection-detection network (CINet) is adopted to extract road intersections from a current image and an old road vector map to discover newly built or disappeared road junctions. A road branch detection network (RoadBranchNet) is used to detect the direction of road branches for each road junction to find road branch changes. Based on the discovery of direction-changed road branches, the VecRoadUpd framework extracts newly built roads and removes disappeared roads through directed road tracing, thus, updating the whole road vector map. Extensive experiments conducted on the public MUNO21 dataset demonstrate that the proposed VecRoadUpd framework exceeds the comparative methods by 11.01% in pixel-level Qual-improvement and 13.85% in graph-level F1-score.

HEAVY METAL POLLUTION FROM TOURISM IN THE ALTAI MOUNTAINS

The paper presents information about soil pollution with heavy metals from tourism in the Altai Mountains of Kazakhstan and the Altai Mountains of Mongolia. During the pandemic, traveling to the Altai Mountains became a trend became trend for domestic tourism in both countries. Bad infrastructure is here. Therefore, tourist vehicles have damaged the surface of the soil by driving on dirt roads, building new branch roads, approaching snow-capped peaks, and driving through swampy areas. The authors hypothesized that the soil is likely to be contaminated with heavy metals, mostly due to heavy vehicles with large motors. As a result, compared to clean soil, the lead levels increased by 12.5–15.8 mg/kg, nickel for 16.1–33.7 mg/kg, zinc for 15.6–17.1 mg/kg and the amount of heavy metals increased by 2–4 times. The results of the experiment confirmed the presence of heavy metal contamination in disturbed soil. But this pollution has not exceeded the standard level. Because contaminated soils are washed away by snow and rain.

Failure Mechanism of the Yizhuxiang Collapse under the Joint Effect of Freeze–Thaw and Mining

At 10:05 a.m. on 4 February 2022, the perilous rock mass Yizhuxiang in Leizu Town, Yuan’an County, Hubei Province, China collapsed on a large scale. The Yizhuxiang collapse was about 35 m in length, 52 m in height, and 29 m in maximum thickness. The volume of the collapse was 5.32 × 104 m3. It threatened the transport of the national trunk road, destroyed the branch road and some cement mixing station workshops, and caused serious economic losses. The rock mass exposed in the collapse was dolomite with developed joint fractures. Under the effects of mining and unloading, fractures occurred in the rock mass; the top of the rock mass was bent and deformed towards the free face; and tension cracks were formed on the rear edge. The safety ore pillar directly below the collapsed body was compressed and deformed under the action of gravity, thus accelerating the internal deformation of the rock mass and leading to the formation of multiple dominant joints inside the rock mass. In the winter of 2021, the weather was extreme, with heavy snowfall and low temperatures. Under the effects of freeze–thaw, the strength of the rock mass declined and the tension cracks further expanded so that the rock mass experienced an accelerated deformation and finally collapsed. Causes of the collapse include mining activity beneath the collapsed mass and heightened extreme weather. The cause of the landslide disaster in this area is, however, freeze–thaw, which deserves the attention of scholars and the vigilance of the local government.

The Amount of Tourist Waste in the Altai Mountains. Mongolia and Kazakhstan Cases

The article presents information about the result of the experiment that determined the amount of tourist waste in the Altai Mountains of Kazakhstan and the Altai Mountains of Mongolia. During the pandemic, traveling to the Altai Mountains became a trend for domestic tourism in both countries. Due to the low development of the infrastructure in this region, tourist vehicles drive on unpaved roads, build new branch roads, cross clay swamps to approach high mountains, damage the soil cover, and dump garbage into nature. The authors conducted a special experiment on the main tourist routes in the Altai Mountains and determined the amount of waste per tourist. Based

Study on the Relationship between Urban Street-Greenery Rate and Land Surface Temperature Considering Local Climate Zone.

Relationship exploration between the street-greenery rate (SGR) of different street types and land surface temperature (LST) is of great significance for realizing regional sustainable development goals. Given the lack of consideration of the local climate zone concept (LCZ), Chongqing's Inner Ring region was selected as a case to assess the relationship between SGR and LST. Firstly, the LST was retrieved based on Landsat 8 imagery, which was calibrated by the atmospheric correction method; next, the street-greenery rates of different streets were calculated based on the semantic segmentation method; finally, street types were classified in detail by introducing LCZ, and the relationship between SGR and LST was investigated. The results showed that: (1) The LST spatial distribution pattern was closely related to human activity, with the high-temperature zones mainly concentrated in the core commercial areas, dense residential areas, and industrial cluster areas; (2) The average SGR values of expressways, main trunk roads, secondary trunk roads, and branch roads were 21.70%, 22.40%, 24.60%, and 26.70%, respectively. The level of SGR will decrease when the street width increases; (3) There is a negative correlation between the SGR and the LST in most streets. Among them, the LST of secondary trunk roads in low-rise and low-density built-up areas with a south-north orientation had a strong negative correlation with the SGR. Moreover, the wider the street, the higher the cooling efficiency of plants. Specifically, the LST of streets in low-rise and low-density built-up areas with south-north orientation may decrease by 1°C when the street-greenery rate is increased by 3.57%.

Modeling spatiotemporal interactions in single-vehicle crash severity by road types

Introduction: Spatiotemporal correlations have been widely recognized in single-vehicle (SV) crash severity analysis. However, the interactions between them are rarely explored. The current research proposed a spatiotemporal interaction logit (STI-logit) model to regression SV crash severity using observations in Shandong, China. Method: Two representative regression patterns-mixture component and Gaussian conditional autoregression (CAR)-were employed separately to characterize the spatiotemporal interactions. Two existing statistical techniques-spatiotemporal logit and random parameters logit-were also calibrated and compared with the proposed approach with the aim of highlighting the best one. In addition, three road types-arterial road, secondary road, and branch road-were modeled separately to clarify the variable influence of contributors on crash severity. Results: The calibration results indicate that the STI-logit model outperforms other crash models, highlighting that comprehensively accommodating spatiotemporal correlations and their interactions is a recommended crash modeling approach. Additionally, the STI-logit using mixture component fits crash observations better than that using Gaussian CAR and this finding remains stable across road types, suggesting that simultaneously accommodating stable and unstable spatiotemporal risk patterns can further strengthen model fit. According to the significance of risk factors, there is a significant positive correlation between distracted diving, drunk driving, motorcycle, dark (without street lighting), and collision with fixed object and serious SV crashes. Truck and collision with pedestrian significantly mitigate the likelihood of serious SV crashes. Interestingly, the coefficient of roadside hard barrier is significant and positive in branch road model, but it is not significant in arterial road model and secondary road model. Practical Applications: These findings provide a superior modeling framework and various significant contributors, which are beneficial for mitigating the risk of serious crashes.

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.

Improvement of service technology on railway branch roads

The article highlights the issues of effective use of shunting locomotives by introducing shunting winches on branch lines of railway transport, as well as improving the technology of effective service for freight forwarders. For this, the article envisages achieving it by optimizing the sequence of execution of several technological processes. Including: the time it takes for one wagon to be occupied for entering and exiting the branch tracks, the number of wagons entering and exiting the branch tracks during the time interval in question, as well as revising the locomotive occupancy times depending on the wagon, freeing the shunting locomotive from additional inefficient work issues of more efficient use of shunting locomotives by introducing shunting winches are scientifically based.

Research on Influencing Factors of Urban Road Traffic Casualties through Support Vector Machine

Urban road traffic safety has always been vital in transportation research. This paper analyzed the factors influencing the degree of traffic accident casualties on Jinan Jingshi Road and its branch roads, taking them as the study area for urban road traffic safety problems. Additionally, it used the application of Particle Swarm Optimization (PSO), a Support Vector Machine (SVM) model, and a recursive feature elimination (RFE) to rank the contribution degree of the influencing factors. The results showed that driving on rainy days has a high probability of casualties, while the type of collision was a minimum influence factor. Additionally, on rainy days, cars were accident-prone road vehicles, and 8:00–12:00 and 18:00–22:00 were accident-prone periods. Based on the results, preventive measures were further put forward regarding the driver, road drainage capacity, policy management, and autopilot technology. This study aimed to guide urban traffic safety planning and provide a basis for developing traffic safety measures.