Road Segments Research Articles

DCN-Deeplabv3+: A novel road segmentation algorithm based on improved Deeplabv3+

DCN-Deeplabv3+: A novel road segmentation algorithm based on improved Deeplabv3+

Road network free flow speed estimation using microscopic simulation and point-to-point travel times

Roads in mesoscopic traffic simulation models are often parametrized by a free speed attribute, which describes the speed at which a vehicle can traverse a road in the absence of congestion. Unlike microscopic models, mesoscopic models do not typically simulate traffic lights, individual driver behavior or acceleration and deceleration of vehicles. In consequence, the free speed parameter of each road must account for all the aforementioned effects. Modeling free speed accurately is important, as it determines the travel time, which in turn affects the decision-making of agents in the simulation.This paper introduces an approach combining microscopic simulation with machine learning models to estimate road segment free speeds. Using the microscopic simulator SUMO, we generate training data for a model search employing Bayesian optimization. Subsequently, these models undergo fine-tuning via gradient-based optimization using real-world point-to-point travel times. A significant advantage is the adaptability of this approach to diverse road networks, including those from OpenStreetMap, and the ability to incorporate routing data from various online providers independently.Our evaluation illustrates a notable decrease in prediction error between 30% and 60% compared to baseline models that assume a uniform free speed reduction for all urban roads. The fine-tuned models are able to generalize well to unseen regions and are therefore applicable to case studies where data for new or altered roads is unavailable.

Evaluation of Parameters Service Standard Minimum (SPM) Based on Perceptions of Road Users National Segment Kayee Lon - Meulaboh

In an effort to address community needs and assist the acceleration of regional economic development, road construction has become a government priority. The problem in road services is that the Minimum Service Standards (SPM) have not been met based on the functional indicators of road sections, road structure and road safety. Road SPM refers to the provision of roads to meet the needs of the community in terms of the physical size of the road in accordance with certain technical criteria. The government, as the operator of the road, is required to carry out the legal mandate as a form of service to the community using the road. The purpose of this study was to determine the service quality of the Kayee Lon - Meulaboh National Road Segment based on the level of importance and performance and to find out which variables are the top priority for improving performance. To perform this study, 135 randomly chosen respondents were given questionnaires on a Likert scale, and the data was processed using the IPA (Importance Performance Analysis) method. The service quality of the Kayee Lon–Meulaboh National Road Segment with Functional Indicators of Road Sections is at the “Important” level with a mean of 3.924 and at the “Moderate” level with a mean of 3.400 based on performance level. Structural indicators of road sections are at the “Important” level in terms of importance, with a mean of 4.189, and at the “Moderate” level in terms of performance, with a mean of 2.995. The Road Safety Indicator is at the “Important” level based on importance, with a mean of 3.924, and at the “Not Good” level based on performance, with a mean of 2.171. Lane width is the factor whose performance has to be enhanced as a top priority for Road Section Functional Indicators. Pavement surface drainage and crack width are two variables that need to perform better in relation to the Road Section Structural Indicators’ top priority. Variables that need to be improved in terms of performance as a top priority for Road Safety Indicators are street lighting, zebra crossing and crossing facilities.

Intelligent road segmentation and obstacle detection for autonomous railway vehicle

For autonomous railway vehicle with complex crisscrossed tracks, it is a huge challenge to intelligently detect the trespassers lying in the possible track regions where the train will move along. In order to solve the issue that the existing object detection algorithms detect all obstacles in traffic scene images, a novel strategy YOLOSEG is proposed for intelligent road segmentation and obstacle detection of railway trespasser. Unet is firstly trained to intelligently segment the railway track region where the train is likely to move on, and then the generated region mask is introduced into object detection network for recognizing obstacle within the mask area. The real video of the obstacle emerging in front of the train is difficult to record, therefore the traffic scenes taken from drivers’ perspectives are randomly combined with various obstacles to create the synthetic training dataset which covers various railway traffic scenarios and lighting conditions, and at the same time the label file is automatically generated. Furthermore, a random brightness strategy is proposed for dataset enhancement. By the performance evaluation comparison of FLOPs, Top-1 Accuracy, and mAP@0.5/%, abundant trespasser detection experiments based on synthetic dataset and real images verify the accuracy and effectiveness of the proposed method.

Analyzing Transportation Equity in the D.C. Metropolitan Area Using Large-Scale Agent-Based Modeling and Simulation

Policymakers face the challenge of providing equitable infrastructure services to all city dwellers with limited budgets. Further, equity analysis based on the socioeconomic characteristics of travelers is not palatable for some. This paper proposes a novel framework for analyzing equity in terms of access to opportunity rather than equity of outcomes. We showcase the use of a large-scale, high-fidelity agent-based, activity-based transportation model to analyze equity in the Washington DC metropolitan region. Travel time between each traffic analysis zone and its nearest activity area is used as a measure of access-to-opportunities, thereby carrying out equity analysis without using the socioeconomic characteristics of the traveler. The results of this analysis are visualized in a GIS heatmap. Decision-makers can visually identify regions needing attention and focus their funding and efforts on improving infrastructure services in the specified regions. Decision makers may elect to increase the capacity of road segments or develop a new employment activity center in the area of interest. The paper contributes to the literature by analyzing equity of opportunities without considering individuals’ socioeconomic characteristics, applying a higher fidelity activity-based model to a large metropolitan area, and documenting a framework and the results of large-scale equity analysis of the Washington DC metropolitan region.

Crime-Avoiding Routing Navigation

Extensive prior work has provided methods for the optimization of routing based on the criteria of travel time and/or the cost of travel and/or the distance traveled. A typical method of routing involves building a graph comprised of street segments, assigning a normalized weighted value to each segment, and then applying the weighted-shorted path algorithm to the graph to find the best route. Some users desire that the routing suggestion include consideration pertaining to the reduction of risk of encountering violent crime. For example, a user desires a leisurely walk via a safe route from her hotel in an unknown city. Here, we present a method to quantify such user preferences and the risks of encountering crime and to augment the standard routing methods by assigning weights to safety considerations. The proposed method’s advantages, in comparison to other crimeavoidance routing algorithms, include weighting crime types with respect to their potential detrimental value to the user, with temporal qualification and quantification of crime and its statistical aggregation at the geographic resolution down to a city block.

Changes in the provision of ecosystem services based on sidewalk tree-composition scenarios

Cities have been experiencing environmental and social issues due to urban expansion processes, be them planned or not. However, several environmental solutions have been proposed and sidewalk tree planting stands out among them. The aim of the current research is to assess the process to estimate ecosystem services under different sidewalk tree composition scenarios in Irati and Curitiba cities, Paraná State. In order to do so, 10 street segments from neighborhoods sampled in city-wide inventories were selected. Information about sidewalk length, curb ramps’ width, and number of utility poles was collected for each street segment. The number of trees to be fit in each segment and sampled neighborhood was determined based on safety recommendations for these urban structures. Tree dimensions (Diameter at Breast Height - DBH, total height and crown diameter) were projected for the 55th year post-planting, based on using minimum dimensions of seedlings from small, medium and large-sized species. It was done to estimate ecosystem services in i-Tree Eco software. Significant differences among floristic composition scenarios (p<0.05) were observed for all ecosystem services in both cities. Compositions featuring large-sized species have show higher ability to provide ecosystem services, as well as higher monetary valuation, despite fitting a smaller number of trees in the assessed street segments. The herein formulated scenarios have shown the potential to increase the number of planted trees and to select compositions capable of maximizing ecosystem services’ provision and trees’ monetary value.

Deep Convolutional Neural Network for Indoor Regional Crowd Flow Prediction

Crowd flow prediction plays a vital role in modern city management and public safety prewarning. However, the existing approaches related to this topic mostly focus on single sites or road segments, and indoor regional crowd flow prediction has yet to receive sufficient academic attention. Therefore, this paper proposes a novel prediction model, named the spatial–temporal attention-based crowd flow prediction network (STA-CFPNet), to forecast the indoor regional crowd flow volume. The model has four branches of temporal closeness, periodicity, tendency and external factors. Each branch of this model takes a convolutional neural network (CNN) as its principal component, which computes spatial correlations from near to distant areas by stacking multiple CNN layers. By incorporating the output of the four branches into the model’s fusion layer, it is possible to utilize ensemble learning to mine the temporal dependence implicit within the data. In order to improve both the convergence speed and prediction performance of the model, a building block based on spatial–temporal attention mechanisms was designed. Furthermore, a fully convolutional structure was applied to the external factors branch to provide globally shared external factors contexts for the research area. The empirical study demonstrates that STA-CFPNet outperforms other well-known crowd flow prediction methods in processing the experimental datasets.

STUDY OF ROAD PERFORMANCE IN AHMAD YANI DUE TO MARKET ACTIVITIES IN PALANGKA RAYA CITY

Road performance is a quantitative measurement that describes certain conditions that occur on a roadsegment. Generally, in assessing road performance, it can be seen from capacity, degree of saturation (DS).Road performance is usually influenced by several things such as side Side Barrier and so on. Indonesia HighwayCapacity Manual, explains that side barriers are an impact on traffic performance and activities on the side ofthe road segment, such as pedestrians (weight = 0.5), public vehicles or other vehicles stopping (weight = 1.0),vehicles exiting or entering the side of the road (weight = 0.7), slow vehicles (weight = 0.4). The number of sidebarriers that affect the road segment results in the emergence of delays which result in decreased roadperformance. The results of the study show that the performance of the road is saturated (DS>1) due to the sideobstacle that occur on A. Yani, street the market area of Palangka Raya City

Road segment and driving schedule effects in real-time operation on electric vehicle performance and operating cost analysis

Electric two-wheelers (E2W) are getting attraction as personal commuting vehicles owing to their key features like zero tailpipe emissions and lower energy consumption. However, the driving range, maximum speed and load-carrying capacity are the major hurdles associated with the E2W penetration rate in modern mobility. Likewise, these performance characteristics are varying based on the powertrain size, driving and atmospheric conditions. To address these challenges, The present study is focused on evaluating the operating regions of E2W powertrain components under three major road segments including rural, highway and urban at different driving schedules such as morning, afternoon and evening. The speed, acceleration and time-related driving parameters for the selected driving routes and trip timings are calculated to study their influence on the performance of E2W along with ambient conditions. Further, this research work is extended to compare the operating cost and CO2 emission of E2W with spark-ignition two-wheeler (SI-2W) based on real-time driving conditions. The energy consumed per km for E2W is 47.10, 40.53 and 41.63 Wh/km for morning, afternoon and evening driving trips. Similarly, the battery state of charge is recorded about 14%, 30% and 27% respectively at the end of the trips. Also, the results revealed that higher energy is consumed on the morning driving schedule due to higher average speeds with unfavourable ambient conditions. On the other way around, the CO2 emission, operating cost per trip and 100 km of E2W are 6.4, 6.5 and 2.4 times lesser than SI-2W.

Modelling Mixed Traffic Time Headway Distribution Induced by Bus Rapid Transit (BRT) in Cape Town, South Africa

Background: The safety and capacity of roadways are immensely influenced by time headways, a major traffic flow characteristic. Time headways are frequently utilized in various aspects of traffic and transportation engineering studies, including capacity analyses, studies of safety, modelling of lane-changing and car-following behaviour, and level of service assessment. Time headway, measured in seconds, refers to the distance between two subsequent passing cars moving over a single spot on the road. This paper reports the statistical modelling of the time headway distribution of a mixed traffic flow scenario caused by BRT via statistical headway distribution models. The paper posits that the introduction of BRT dedicated lane and its adopted design configuration induces anomalous traffic flows on its adjoining lanes with attendant time headway differentials, irrespective of the type of adopted design configuration for the corridor. The objectives of this study were to fit the BRT-induced time headway distribution to probability distribution models and determine the most appropriate distribution model that fits the data derived using the goodness of fit tests. Consequently, this contribution, therefore, fills the gap in research with respect to determining the precise model that fits the time headway distribution of mixed traffic flow scenarios induced by BRT. Methods: The time headway distribution data collected at four designated road segments on a multilane provincial route R27 caused by BRT dedicated lane in Cape Town, South Africa, were fitted to five probability distribution models viz, Lognormal, Inverse Gaussian, Log-logistic, Generalized Extreme Value (GEV), and Burr using MATLAB software. The fitted headway data were with respect to the traffic flows on the adjoining lanes to BRT or traffic flow ‘without BRT’. Precisely, the empirical data were collected over a three-month period using an Automatic Traffic Counter (ATC). Using ModelRisk software, the goodness of fit of the probability models was assessed by the Akaike Information Criterion (AIC), the Schwartz or Bayesian Information Criterion (SIC or BIC), the Hannan Quinn Information Criterion (HQIC), and the loglikelihood (LLH) model performance criteria respectively. The distribution model with the lowest AIC criterion and largest loglikelihood (LLH) values describe the model that best fits the headway data across the four sites. Results: Results showed that the models fitted the headway data well at each site by visual inspection of the attendant Probability Density Function (PDF) and the Probability plots (P-P). However, the Burr distribution provided the overall best fit based on the AIC and LLH values at 95% confidence and 0.05 significance levels across the four sites. At sites 02, 03, and 04, it ranked first with the lowest AIC values of 4025.99, 2595.56, 3815.36, and corresponding largest LLH values of -2008.98, -1293.76, and -1903.66, respectively, while the lognormal distribution performed best at site 01, with AIC value of 4445.14 and LLH value of -2220.57, closely followed by the Burr distribution with AIC and LLH values of 4453.05 and -2222.51, respectively. The P-values, which ranged between 0.65 and 0.80, showed the likelihood of the occurrence of the data sets under the null hypothesis. Hence the null hypothesis was accepted. Conclusion: The study concluded that the introduction of BRT dedicated lanes affects the adjoining lanes’ mixed traffic time headways’ distribution, and the headways are continuously distributed, hence fitting continuous probability distributions.

Dealing with location uncertainty for modeling network-constrained lattice data

The spatial analysis of traffic accidents has long been a useful tool for authorities to implement effective preventive measures. Initial studies were conducted at the areal level considering administrative or traffic-related units, but a more precise analysis at the street level is necessary for developing targeted interventions. In recent years, there has been a significant increase in studies conducted at the road network level, which require using new statistical techniques that are suitable for linear networks. However, modeling accident counts at the street level presents several challenges, primarily due to the need for accurate georeferenced data to correctly assign events to specific streets or road segments. Despite advancements in geocoding methods, discrepancies can still arise between the true event locations and the locations mapped by a geocoding method. In this paper, we propose a model to deal with the presence of location uncertainty and enable an analysis of accident intensity constrained to the road network. The model does not assume any specific mechanism for location uncertainty, as this reflects the most common practical scenario. By tackling this inherent problem, the proposed model aims to enhance the accuracy of accident analysis and contribute to the development of effective preventive measures for traffic safety. The model is evaluated with both a simulation study and a case study on the city of Valencia, Spain. For the latter, the proposed model reveals a greater association of road intersections with accident rates than that estimated by the standard model.

Assessing large-scale roadside tree removal using aerial imagery and crash analysis: A difference-in-differences approach

Transport agencies worldwide remove roadside trees in the name of traffic safety. In 2017, the Georgia Department of Transportation (GDOT), the agency managing state and federal highways in the US state of Georgia, began clear-cutting trees on all transport agency property statewide, amounting to thousands of hectares of cleared land. As justification, GDOT cited fatal traffic crashes with trees.In this paper, we quantified the extent of tree removal within a 60 m buffer along the five major interstates in Georgia, using aerial imagery from the United States Department of Agriculture (USDA) Farm Service Agency’s National Agricultural Imagery Project (NAIP). We used supervised image classification and change detection on aerial imagery between 2015 and 2021. We also collected pre- and post-treatment crash data and conducted a difference-in-differences analysis on sampled road segments to isolate the effect of tree removal on crash rates.Results showed that 28.4 sq km of previously forested land in the interstate corridors were cleared of trees, 25% of the total forested land in the corridors. Tree crash fatalities did not decrease.Findings provide insight into approaches to roadside safety like tree removal while helping assess impacts and plan for next steps. The lack of evidence for reduced tree crash fatalities indicate that transport agencies should consider measures other than large-scale tree removal to prevent fatal crashes with trees.

Identifying private pumping wells in a land subsidence area in Taiwan using deep learning technology and street view images

Study regionThe Choushui River Fan, Taiwan. Study focusGroundwater overdraft has led to not only groundwater depletion but also environmental disasters, such as subsidence and seawater intrusion in the Choushui River Alluvial Fan, Taiwan. The influence of land subsidence is gradually shifting from the coast to the center of the fan and threatening Taiwan high-speed rail. However, it remains a great challenge to manage and model the groundwater aquifer due to numerous unregulated wells. This study maps and locates private wells using deep learning technologies. We trained and validated convolutional-based deep learning neural networks (DNNs), using street view images. We applied the DNNs to a land subsidence area along the Taiwan high-speed rail, termed the Golden Corridor in Taiwan. The results showed that DNNs can recognize pumping wells with at least 90% accuracy. The testing cases showed their capability to recall all the pumping wells in three road segments along the Golden Corridor. Finally, we spatially estimated potential pumping of a subsidence area using the fine-trained DNNs. New hydrological insights for the regionGiven the prevalence of unknown private pumping in the Choushui River Fan, our image data-driven computer vision approach not only eases labor-intensive private well investigations but also advances hydrologic understanding for groundwater modeling. We enhance comprehension of unknown sinks and provide their spatial distribution to improve groundwater modeling.

Traffic flow simulation study under large gathering activities based on VISSIM

Based on the above background, this paper starts from the theory of traffic flow and the macro-basic diagram, and simulates and researches the traffic congestion problems generated in the period of large-scale gathering activities during festivals. First of all, this paper summarizes the time correlation and spatial correlation of traffic flow, analyses the judgement method and rating index of traffic congestion, and completes the macroscopic basic map; after defining the simulation parameters of each road segment and signal intersection in the road network, it is implemented by using the VISSIM simulation software; finally, it carries out the simulation analysis with the example of the New Year's Eve firework display at the Chaotianmen Gate in Chongqing, and calculates and analyses the traffic flow state of the road network with output data, and then compares the output data with that of the road network. Finally, we use the output data to calculate and analyses the traffic flow status of the road network, compare it with the traffic flow data under the smooth period, judge the congestion characteristics, draw the macroscopic basic map of the road network under each period, simulate the changes of the macroscopic basic map, analyses the impact of traffic congestion on the traffic flow of the road network, calculate the degree of improvement of the traffic status of the network, and put forward the reasonable optimization scheme to provide a decision-making basis for the evacuation of the traffic under the festival and large-scale gathering activities.

An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs

Unexpected traffic incidents cause safety concerns and intense traffic congestion on crowded urban road networks. Vehicular ad-hoc network (VANET)-aided Intelligent Transport Systems (ITS) aim to mitigate these risks through timely dissemination of alert messages. However, conventional Radio frequency (RF) mobile ad-hoc routing protocols are ill-suited for dynamic VANET environments due to high mutual interference, packet collisions, high end-to-end delay from frequent route discoveries, and periodic beaconing requirements. Fortunately, the quickly emerging Visible Light Communications (VLC) provide complementary short-range connectivity with high bandwidth and low interference. This paper proposes an efficient adaptive routing protocol for emergency messages in dense VANET scenarios leveraging a hybrid RF/VLC system. When an incident or congestion happens, the source vehicle disseminates the information to the incoming vehicles as quickly as possible using a combination of VLC and RF communication networks. Multi-hop relays extend the connectivity if the direct links are blocked. The coverage area is partitioned into zones based on road segments, intersections, and traffic flows. The Road Side Units (RSU)s are intelligently assigned to zones and they analyze the historical traffic data to characterize each zone and decide a response strategy. We also propose a congestion detection scheme that utilizes traffic simulations to forecast the clearance times under different response strategies. The highest-scoring strategy is selected based on the predicted impacts on travel time, emissions, and driver stress levels. The proposed algorithm adaptively uses the selected strategy to proactively alleviate the predicted congestion through optimized routing and control. Overall, the protocol maximizes safety and efficiency during emergencies by leveraging the hybrid RF/VLC, incorporating real-time congestion forecasting and dynamic rerouting into the response strategies.

Geo-climates and street developments shape urban tree characteristics: A street-view inventory analysis of over 200,000 trees of 11 metropolises in China

Street trees play an important role in the city, but large-scale, multi-city inventory data are very limited to date, which can help to define geo-climatic and social development influences on urban forest characteristics. In this paper we speculate that at national level, geocliamtes and street development shape the different street tree characteristics, and large scale street View images (SVIs)-measurements favor the identification of factors responsible for the street tree variations in China. By collecting urban trees from 11 metropolises through SVIs method, an inventory of urban trees in China, including 201,942 trees at 9807 sites, was obtained from a latitude gradient from tropical 18oN to cold-temperate 45oN. Individual tree size-related growth status, tree-shrub-herb-related vertical structure, tree species identity, and street condition and street development (total 20 social development parameters) in the inventory is recorded. We analyzed trends and factors influencing street trees characteristics through latitudinal variation, distribution, linear regression, redundancy (RDA) ordination, and inter-city comparisons. The results showed that 1) with latitude increased, DBH and CPS linearly decreased, together with more highly dense forests (>100 trees/100 m street segment) observed. Latitude independence was in TH and forest vertical structural complexity. 2) All tree size data were in the log-normal distribution pattern when the two-parameter model was used and was best fitted by the Johnson distribution pattern when the >2-parameter model was used. 3) Tree growth status showed strong latitude dependency (R2 > 0.4, p < 0.05), with latitude increase accompanied by a higher percentage of trees with poor growth status (diebacks, dead trees, etc.). 4) The top abundant trees were Populus spp., Cinnamomum camphora, Salix spp., Platanus acerifolia, Ficus macrocarpa (5.5 %–14.6 %), and the arbor-shrub-herb three-layer structured forests took 52.3 % of total sites. With latitude rise, increasing abundance of Populus spp., Salix spp., elm, and pine but decreasing abundance of the unrecognizable tree groups were found (p < 0.05). 5) We also constructed a street tree comprehensive index based on their potential for providing services to citizen from the inventory data and found it was negatively related to latitudes. RDA ordination showed that geo-climatic conditions (49 %–61.5 %) and social developments (21.4 %–52.7 %) were almost equally responsible for tree size, growth status, and vertical structural variations, while road width (lane number of the street) was the most potent predictor (coefficient > 2.0 %, p < 0.01) for these variations. Our study can benefit the national-level management of urban forests and inventory-based various ecological service precise evaluation.

Assessing driver behavior in work zones: A discretized duration approach to predict speeding

Higher speeds in work zones have been linked to an increased likelihood of crashes and more severe crash outcomes. To enhance safety, speed limits are often reduced in work zones, aiming to create a steady flow of traffic and safer traffic operations such as merging and flagging. However, this speed reduction can also lead to abrupt speed changes, resulting from sudden braking or acceleration, increasing the risk of crashes. This disruption in speed and flow results increases the likelihood of rear-end crashes. Ensuring driver compliance with the reduced speed limits and traffic flow operations is challenging as work zones may cause frustration and lead to more instances of speeding. Therefore, proactively predicting speeding events in work zones can be crucial for the safety of both workers and road users, as it enables the implementation of speed enforcement measures to maintain and improve driver compliance in advance. In this study, we employ the duration-based prediction framework to forecast speeding occurrences in work zones. The model is used to identify significant predictors of speeding including visibility, number of lanes, posted speed limit, segment length, coefficient of variation in speed, and travel time index. Among these variables, the number of lanes, posted speed limit, and coefficient of variation of speed are positively associated with speeding. On the other hand, visibility, segment length, and travel time index are negatively associated with speeding. Results show the model’s predictive accuracy is higher for speeding events with shorter durations between consecutive occurrences. The model predicted speeding within 61% of the actual epoch when speeding events within 5 h of one another were considered for validation. This indicates that the model is more effective for road segments and work zones where speeding occurs more frequently. The prediction framework can be a great asset for agencies to improve work zone safety in real-time by enabling them to proactively implement effective work zone enforcement measures to control speeding and to stay prepared, preventing potential hazards.

Edge-based graph neural network for ranking critical road segments in a network.

Transportation networks play a crucial role in society by enabling the smooth movement of people and goods during regular times and acting as arteries for evacuations during catastrophes and natural disasters. Identifying the critical road segments in a large and complex network is essential for planners and emergency managers to enhance the network's efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire network.

An automated procedure to determine construction year of roads in forested landscapes using a least‐cost path and a Before‐After Control‐Impact approach

AbstractProximity to roads is one of the main determinants of deforestation in the Amazon basin. Determining the construction year of roads (CYR) is critical to improve the understanding of the drivers of road construction and to enable predictions of the expansion of the road network and its consequent impact on ecosystems. While recent artificial intelligence approaches have been successfully used for road extraction, they have typically relied on high spatial‐resolution imagery, precluding their adoption for the determination of CYR for older roads. In this article, we developed a new approach to automate the process of determining CYR that relies on the approximate position of the current road network and a time‐series of the proportion of exposed soil based on the multidecadal remote sensing imagery from the Landsat program. Starting with these inputs, our methodology relies on the Least Cost Path algorithm to co‐register the road network and on a Before‐After Control‐Impact design to circumvent the inherent image‐to‐image variability in the estimated amount of exposed soil. We demonstrate this approach for a 357 000 km2 area around the Transamazon highway (BR‐230) in the Brazilian Amazon, encompassing 36 240 road segments. The reliability of this approach is assessed by comparing the estimated CYR using our approach to the observed CYR based on a time‐series of Landsat images. This exercise reveals a close correspondence between the estimated and observed CYR (). Finally, we show how these data can be used to assess the effectiveness of protected areas (PAs) in reducing the yearly rate of road construction and thus their vulnerability to future degradation. In particular, we find that integral protection PAs in this region were generally more effective in reducing the expansion of the road network when compared to sustainable use PAs.