Road Segments Research Articles

A Procedure to Characterize Wood Pile Inventories at Roadside

Tracking roadside wood inventories is necessary for wood procurement. However, this operation is increasingly problematic due to the costs associated with reaching remote sites, labour shortage, and the methods providing limited information on the characteristics of the logs required for transport planning. To overcome these problems, an automated procedure has been developed in Arcmap to characterize individual wood pile inventories at roadside by using GPS points of forwarders, harvester production files, and the road network shape files. An inventory of the logs in the harvest area followed by a wood pile inventory at the roadside were made to evaluate how the procedure could trace logs from machine operating trail to predicted wood pile locations. The study was done at six harvest blocks in the Saguenay-Lac-Saint-Jean region in the province of Quebec, Canada. The procedure was not able to differentiate individual wood piles and aggregated several piles into predicted unloading areas. Results indicated a similarity index of 72% to manually inventoried wood piles. The similarity index could be explained by the low percentage of inventoried unpredicted wood pile lengths (3%) and a high percentage of overpredicted wood pile lengths (27%). The positive allocation rate could not be assessed at the level of the individual piles. On the other hand, the procedure properly allocated 96% of the logs to unloading areas. With the level of precision obtained, the developed procedure could be beneficial for managing the transportation of wood at the level of the road segment since it provides all the dendrometric data of the logs available for transport without requiring human intervention in the forest.

Annual soil erosion characteristics of unpaved roads in the Loess Plateau hilly and gully region, China

The problem of unpaved road erosion is prominent in the Loess Plateau hilly and gully region. Unpaved roads contribute substantially to watershed sediment due to their high soil bulk density, low infiltration rates and extensive network. In this study, a field investigation was conducted on typical unpaved roads within a typical watershed in this region, focusing on assessing the damage state, annual soil loss and the factors influencing erosion in a comparatively wet year. The results showed that the soil erosion from unpaved roads was very severe, with an annual erosion intensity of 470 t hm−2, following three heavy rain events and two rainstorm events in the summer of 2022. The main unpaved roads (MUR) suffered the most severe road erosion, with 22.2 % of road segments experiencing severe erosion with classical gullies. The erosion gullies on the road had an average depth of 16.1 cm and an average width of 36.5 cm, with the widest being 146.0 cm and the deepest being 174.0 cm. The road erosion intensity was significantly related to drainage area, road area, road length and coverage. Road erosion reduced significantly when the land use in the drainage areas of the road was covered with shrub or grass, or road surface was covered with grass or gravel. Our findings offer valuable insights for road construction and erosion prevention in similar terrains.

Speed prediction and nearby road impact analysis using machine learning and ensemble of explainable AI techniques

Traffic speed prediction is an important topic in Intelligent Transport Systems (ITS). Although traffic speed prediction for real-life applications is burgeoning, the study of explaining and interpreting AI-based speed prediction is still in its initial stage. In this paper, we applied multiple advanced regression techniques, such as XGBoost and CatBoost optimized gradient boosting, Random Forest, and LASSO to predict traffic speed more accurately in the subsequent time windows. The experiment with prediction methods was conducted using the traffic speed data of the Seoul metropolitan road network. Each road segment represented as a node in the network is associated with neighboring roads within a configurable range. We picked heavily congested nodes as prediction targets. Then, we evaluated nearby road influences to determine critical contributions to the situation of the target nodes. We interpreted the model’s output and extracted the topmost influential neighboring nodes by using an ensemble of explainable artificial intelligence (XAI) techniques such as feature importance assessment using the GINI entropy function, Recursive Feature Elimination, Shapely Additive Explanation, and a method of measuring the impact of masked nodes. We validated the XAI interpretations through traffic flow simulation by tuning the topmost influential nearby roads’ speed and observing the effect on the roads’ traffic congestion relief correspondingly. We also proved our solution through local explanation techniques such as Local Interpretable Model-Agnostic Explanations. Our methods are applicable to any transport network and open the door to new strategies for controlling the specific nearby roads for effective congestion relief.

Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images

Road extraction techniques based on remote sensing image have significantly advanced. Currently, fully supervised road segmentation neural networks based on remote sensing images require a significant number of densely labeled road samples, limiting their applicability in large-scale scenarios. Consequently, semi-supervised methods that utilize fewer labeled data have gained increasing attention. However, the imbalance between a small quantity of labeled data and a large volume of unlabeled data leads to local detail errors and overall cognitive mistakes in semi-supervised road extraction. To address this challenge, this paper proposes a novel consistency self-training semi-supervised method (CSSnet), which effectively learns from a limited number of labeled data samples and a large amount of unlabeled data. This method integrates self-training semi-supervised segmentation with semi-supervised classification. The semi-supervised segmentation component relies on an enhanced generative adversarial network for semantic segmentation, which significantly reduces local detail errors. The semi-supervised classification component relies on an upgraded mean-teacher network to handle overall cognitive errors. Our method exhibits excellent performance with a modest amount of labeled data. This study was validated on three separate road datasets comprising high-resolution remote sensing satellite images and UAV photographs. Experimental findings showed that our method consistently outperformed state-of-the-art semi-supervised methods and several classic fully supervised methods.

Healthy urban blue space design: Exploring the associations of blue space quality with recreational running and cycling using crowdsourced data

Urban blue space offers substantial health benefits by encouraging population physical activity. Despite much evidence on the nature-health nexus, the relationship between blue space and recreational exercises remains under-studied, limiting the realisation of health benefits in blue space design. Using crowdsourced data, including volunteered geographic information and street view image data, this study investigates the associations of blue space quality with recreational running and cycling in Rotterdam, the Netherlands. Results show that recreational exercise levels on street segments vary based on the blue space type and design. Compared to inland canals and rivers, small-scale recreational waterbodies are more conducive to running but not cycling, while both activities tend to cluster around the Nieuwe Maas River. Interestingly, the Water View Index shows a general negative association with both activities after adjusting for the blue space type. Besides the waterbody characteristics, eye-level environmental factors, including higher Green View Index, lower building density, more diverse land use, greater connected street network and fewer traffic elements, are associated with more running and cycling exercises. Results for visual complexity and neighbourhood population composition are mixed depending on the exercise type. These findings are further translated into spatial design patterns for developing exercise-supportive and health-promoting blue spaces.

Combining deep learning methods and rule-based systems for automatic parking space detection

This paper presents an Automatic Parking Space Detection (APSD) algorithm designed to reduce traffic in cities while offering an information system of available parking zones. The main aim of such a system lies in its ability to identify parking spaces in a distributed manner, achieved by installing multiple APSD systems across a fleet of vehicles. This fleet, during its regular operations, communicates the availability of parking spaces to a centralized information system. Our methodology employs a rule-based system that seamlessly integrates a variety of neural networks for different specific tasks. These tasks include depth estimation, road segmentation, and vehicle detection. This approach would fall into a modular category instead of an end-to-end solution, using the Málaga Urban Dataset in the experiments. We present a preliminary experiment for parameter settings and an ablation study to quantify each subsystem contribution to the results. The proposed system achieves a parking space detection F1 score of 0.726.

Pemantauan Kualitas Udara Ambien pada Pekerjaan Preservasi Jalan

Heavy traffic, especially from large vehicles passing through the road, can potentially cause congestion and increase air pollution. Ambient air quality can significantly decrease due to vehicle emissions, impacting public health and environmental quality. This study focuses on monitoring ambient air quality during road preservation work conducted along the route of Jalan Bts. Prov. Sumsel – Tempino – Pal. Sepuluh – Lingkar Timur – Sp. Sijenjang – Pel. Talang Duku in Muaro Jambi Regency.objective of this research is to analyze the ambient air quality at th road preservation work sites. The research method applied is a survey method with the selection of three locations along the road segment: 1. AMP Tempino, located at coordinates LS 01°47’01.23’’ BT 103°28’27.56’’; 2. STA 11+050 located at coordinates LS 01°45’51’’ BT 103°33’23’’; and 3. Ruas Simpang Paal. X, located at coordinates LS 01°40’12’’ BT 103°36’1’’. This research is descriptive in nature, focusing on the observation and analysis of ambient air quality according to the SNI 7119-3:2017 standard for each test parameter. The results show that at the AMP Tempino site, Particulate dust is 46,50 µg/Nm³,Sulfur Dioxide is 99,88 µg/Nm³, and Carbon Monoxide is 6563,05 µg/Nm³. The highest Nitrgen Dioxide level of 47,88 µg/Nm³ was recorded at STA 11+050, and the highest level of Photochemical Oxidants as Ozone at 41,76 µg/Nm³ was found at Ruas Simpang Paal. X. All test parameters are below the standard quality limits. Overall, Carbon Monoxide has the highest concentration across all locations, while Nitrogen Dioxide often shows the lowest values

Graffiti Writers Choose Locations That Optimize Exposure

We investigate the spatial decision-making of graffiti writers when selecting target locations, examining how street segment characteristics influence exposure. Extending the rational choice perspective to expressive offending, we collected data through Systematic Social Observation in Ghent’s city center in November 2017, covering 12,655 graffiti instances across 2,233 street segments. The SSO involved counting graffiti items and public facilities likely to attract crowds, and measuring street network centrality. Conditional logit models assessed the impact of these characteristics on graffiti location choices. Results show that street network centrality and the presence of bars, night shops, residential units, and schools significantly increased graffiti writers’ preferences. Our findings suggest that graffiti writers aim to maximize exposure, highlighting the rational choice perspective’s relevance for expressive offending.

Clustering Urban Roads Using Local Binary Patterns to Enhance the Accuracy of Traffic Flow Prediction

Many studies employ a dynamic statistical approach to model the relationship between roads for predicting traffic flow. Statistical relationships among roads pertain to the associations between road segments in nearby areas. Roads with similar traffic patterns usually define the relationship. Previous studies have shown that adjacent roads demonstrated a similar traffic pattern on the same day and time interval. Studying similar patterns between roads in surrounding areas provides information about the traffic state among roads in a cluster. Furthermore, the results of this finding may be considered to increase the performance of prediction of traffic conditions. In general, road segment roads are correlated with other road segments. They are connected upstream, downstream, or both; they are connected upstream, downstream, or both. To address this, we propose a Local Binary Pattern (LBP) to explore road connections by clustering congestion features between the target roads and their neighboring roads. The outcomes show that the roads with high connectivity obtained from the clustering process were utilized to predict traffic conditions using the SVM approach. The David-Bouldin Index of LBP with k-means shows the lowest compared with PCA k-means and k-means only. By evaluating the clustering result using the Davies-Bouldin Index and assessing the prediction results with SVM, we discovered that incorporating LBP with K-Means improved outcomes in identifying highly connected roads using K-Means.

Design of a Location-Based Case-Control Study of Built Environment Risk Factors for Pedestrian Fatalities in the U.S.

The location-based case-control design is a useful approach for studies where the exposures of interest are aspects of the environment around the location of a health event such as a pedestrian fatality. In this design locations are the unit of analysis and an enumerated cohort of locations are followed through time for the health events of interest and a case-control study of locations is nested within the cohort. Locations where events occurred (case-locations) are compared to matched locations where these events did not occur (control-locations). We describe the application of this design to the issue of pedestrian fatalities using a cohort of 9,612,698 intersections, 17,737,728 road segments, and 222,318 entrance/exit ramp segments that existed in 2017 across all 384 U.S. Metropolitan Statistical Areas. This cohort of locations was followed up from Jan 1, 2017 to Dec 31, 2018 for pedestrian fatalities using the National Highway Traffic Safety Administration Fatality Analysis Reporting System. In total, 10,587 fatalities were identified as having occurred on cohort locations and 21,174 matched control locations were selected using incidence density sampling. Geographic information systems, spatially linked administrative data sets and virtual neighborhood audits via Google Street View are underway to characterize study locations.

An Optimal Road Network Extraction Methodology for an Autonomous Driving-Based Demand-Responsive Transit Service Considering Operational Design Domains

In addition to addressing the labor shortage due to an aging population, the transition to autonomous vehicle (AV)-based mobility services offers enhanced efficiency and operational flexibility for public transportation. However, much of the existing focus has been on improving AV safety without fully considering road conditions and real-world service demand. This study contributes to the literature by proposing a comprehensive framework for efficiently integrating AV-based mobility services at the network level, addressing these gaps. The framework analyzes and optimizes service networks by incorporating actual demand patterns, quantifying road segment difficulty from an AV perspective, and developing an optimization model based on these factors. The framework begins by quantifying the operational difficulty of road segments through an evaluation of Operational Design Domains (ODDs), providing a precise measure of AV suitability under varying road conditions. It then introduces a quantitative metric to assess operational feasibility, considering factors such as the service margin, costs, and safety risks. Using these metrics alongside Genetic Algorithms (GAs), the framework identifies an optimal service network that balances safety, efficiency, and profitability. By analyzing real-world data from different mobility services, such as taxis, Demand-Responsive Transport (DRT), and Special Transportation Services (STSs), this study highlights the need for service-specific strategies to optimize AV deployment. The findings show that optimal networks vary with demand patterns and road difficulty, demonstrating the importance of tailored network designs. This research provides a scalable, data-driven approach for integrating AV services into public transportation systems and lays the foundation for further improvements by incorporating dynamic factors and broader urban contexts.

Large scale multi-GPU based parallel traffic simulation for accelerated traffic assignment and propagation

Traffic simulation is a critical tool for congestion analysis, travel time estimation, and route optimization in urban planning, benefiting navigation apps, transportation network companies, and state agencies. Traditionally, traffic micro-simulation frameworks are based on road segments and can only support a limited number of main roads. Efficient traffic simulation on a regional scale remains a significant challenge due to the complexity of urban mobility and the large scale of spatiotemporal data. This paper introduces a Large Scale Multi-GPU Parallel Computing based Regional Scale Traffic Simulation Framework (LPSim), which leverages graphical processing unit (GPU) parallel computing to address these challenges. LPSim utilizes a multi-GPU architecture to simulate extensive and dynamic traffic networks with high fidelity and reduced computation time. Using the parallel processing capabilities of GPUs, LPSim can perform tens of millions of individual vehicle dynamics simulations simultaneously, significantly outperforming traditional CPU-based approaches. The framework is designed to be scalable and can easily accommodate the increasing complexity of traffic simulations. We present the theory behind GPU-based traffic simulation, the architecture of single- and multi-GPU based simulations, and the graph partition strategies that enhance computation resource load balance. Our experimental results demonstrate the effectiveness of LPSim in simulating large-scale traffic scenarios. LPSim is capable of completing simulations of 2.82 million trips in just 6.28 min on a single GPU machine equipped with 5120 CUDA cores (Tesla V100-SXM2). Furthermore, utilizing a Google Cloud instance with two NVIDIA V100 GPUs, which collectively offer 10240 CUDA cores, LPSim successfully simulates 9.01 million trips within 21.16 min. We further tested our simulator with the same demand on dual NVIDIA A100-PCIE-40GB GPUs, which finished the simulation in 0.0398 h, approximately 113 times faster than the same simulation scenario running on an Intel(R) Xeon(R) Gold 6326 CPU @ 2.90 GHz, which takes 4.49 h to complete. This performance not only demonstrates its speed and scalability advantages over traditional simulation techniques but also highlights LPSim’s unique position as the first traffic simulation framework that is scalable for both single- and multiple-GPU configurations. Consequently, LPSim provides an invaluable tool for individuals and extensive research teams alike, enabling the acquisition of large-scale traffic simulation results in a time-efficient manner. LPSim code is available at: https://github.com/Xuan-1998/LPSim

Optimizing an express delivery mode based on high-speed railway and crowd-couriers

The rapid development of e-commerce has led to various challenges in the express delivery industry. To provide efficient and economical delivery services, this study introduces a joint transportation system for parcel delivery utilizing high-speed railway (HSR) and crowd-couriers. In this system, long-haul transportation between cities is conducted by HSR, which enables multiple transshipments in different operation lines. The pre-haul parcel pickup and end-haul delivery services within the city are performed by crowd-couriers, thus achieving door-to-door transportation. A mathematical model is developed to jointly optimize the freight allocation problem in the rail segment and the crowd-courier routing problem in the road segment. The freight allocation problem is formulated based on the concept of transportation plans. The transportation plans with and without transshipments are found by a depth-first search-based algorithm in the rail segment. A customized heuristic algorithm based on the adaptive large neighborhood search with multiple operators is designed to find a near-optimal solution. A set of numerical experiments demonstrates the speed and accuracy with which the proposed method solves different-sized versions of problem. A sensitivity analysis based on a real HSR network is also conducted to provide more reliable managerial insights for operators.

Assessing movement-specific resilience of a signalized road network under lane-level cascading failure

Accurately assessing the resilience of the road network is crucial for responding to emergencies and enhancing public safety. Signal control plays a significant role in managing traffic flow. However, its impact is often overlooked in resilience assessments, where traffic flow and signal control are usually considered separately. A Movement-Specific Resilience (MSR) assessment model is proposed to integrate signal timing into resilience analysis. To accurately represent traffic flow paths under phase control, a dual graph is used to depict the topological network, allowing the assessment of relationships among all movements at an intersection. Based on this, a cascading failure model is developed to analyze the impact of signal control on traffic flow reassignment, reflecting how signal timing influences traffic flow propagation after failures. The method is validated using data collected from a sub-road network in Xi’an city. Results reveal the cumulative resilience of single lanes is not equivalent to the resilience of road segments. The MSR is higher when the network’s failure degree is low and decreases as the failure level increases. Furthermore, road saturation is inversely related to MSR, while MSR is proportional to capacity. MSR remains unaffected by failures and oversaturation when capacity exceeds a certain threshold. These insights could be a theoretical foundation for bolstering resilience via signal control adjustments.

Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and Evaluation

Background: The rise in work zone crashes due to distracted and aggressive driving calls for improved safety measures. While Truck-Mounted Attenuators (TMAs) have helped reduce crash severity, the increasing number of crashes involving TMAs shows the need for improved warning systems. Methods: This study proposes an AI-enabled vision system to automatically alert drivers on collision courses with TMAs, addressing the limitations of manual alert systems. The system uses multi-task learning (MTL) to detect and classify vehicles, estimate distance zones (danger, warning, and safe), and perform lane and road segmentation. MTL improves efficiency and accuracy, making it ideal for devices with limited resources. Using a Generalized Efficient Layer Aggregation Network (GELAN) backbone, the system enhances stability and performance. Additionally, an alert module triggers alarms based on speed, acceleration, and time to collision. Results: The model achieves a recall of 90.5%, an mAP of 0.792 for vehicle detection, an mIOU of 0.948 for road segmentation, an accuracy of 81.5% for lane segmentation, and 83.8% accuracy for distance classification. Conclusions: The results show the system accurately detects vehicles, classifies distances, and provides real-time alerts, reducing TMA collision risks and enhancing work zone safety.

Disaster vulnerability in road networks: a data-driven approach through analyzing network topology and movement activity

The rise in natural disasters and climate-induced events, such as wildfires, hurricanes, and flooding, has significantly affected urban life. These events can disrupt daily activity and flows of individuals and goods on road and transit networks. To enhance urban resilience against disasters, it’s crucial to study and understand road network vulnerability, utilizing data-driven insights to inform planning and preparedness efforts. The aim of this paper is to develop a data-driven exploratory approach to assess vulnerability in road networks in response to a disruption. To accomplish this, we compare the centrality of road segments before, during, and after disaster, considering the network topological structure and movement activity as it is observed through large tracking data of cellphone traces on the network. The novelty of our approach lies in inferring the impact from movement data, instead of manually removing links from the network. The results obtained from this study suggest that incorporating movement data into the assessment of network functionality provides a more realistic estimation of the road network vulnerability in response to a disruption, compared to solely using network topology.

Do Smart Loading Zones help reduce traffic congestion? A causal analysis in Pittsburgh

Rising demand for ride-hailing services and e-commerce delivery intensifies competition for urban curbside spaces, leading to uncoordinated travel behavior, increased traffic congestion and social costs. One possible solution to address those issues is Smart Loading Zones (SLZs), equipped with advanced technologies to optimize curbside use. Yet, the real-world impact of SLZs on traffic flow is unclear due to a lack of real-world data and rigorous studies investigating SLZ’s causal effect on traffic speed. With granular speed data and real-world implementations of SLZs from Pittsburgh, PA, this study applies the regression discontinuity design method to rigorously examine the causal impact of SLZs on traffic speed in the downtown network. The results showed that the introduction of SLZs could enhance the traffic speed of the nearby road segments by 4.5%, while controlling for the underlying trend of speed and multiple influential factors such as time, weather, and road characteristics. In addition, SLZs with a short length could statistically improve traffic speed but those with a long length exert no significant effect. These heterogenous effects might be attributed to the weak enforcement at the time of SLZ deployment in Pittsburgh. The results confirmed the overall positive impact of SLZs on improving congestion. However, policies such as effective dimension planning and robust enforcement policies are essential to maximize the benefits of SLZs.

Functional assessment of pavements on a university campus

With the constant increase in traffic volume at the Darcy Ribeiro Campus of the University of Brasília (UnB), attention to the preservation of road infrastructure is necessary. The implementation of an Urban Pavement Management System (UPMS) emerges as a strategy. This study aims to evaluate the functional condition of the pavements at the UnB University Campus by observing distresses on their surfaces, using the Fixed Value Matrix for 5 Distress method by Zanchetta (2017). A total of 161 road segments were evaluated, and both Objective and Subjective Pavement Condition Index (PCI) values were obtained, along with the recommended intervention for each segment. The most common types of defects found in the pavements were Raveling and Fatigue Cracks. The most suggested interventions were Preventive Maintenance (23.60%) and Corrective Maintenance (22.98%). Statistical analysis led to the conclusion that there were no significant differences between the Objective and Subjective PCI data. Thematic maps were generated for the Objective PCI, Subjective PCI, and maintenance activities in order to make georeferenced spatial information available.

On delivery policies for a truck-and-drone tandem in disaster relief

This paper introduces the traveling salesman problem with a truck and a drone under incomplete information (TSP-DI). TSP-DI is motivated by the deliveries of emergency supplies under unknown road conditions in the immediate aftermath of a disastrous event. The urgency may force the immediate dispatch of relief vehicles, such that road damages blocking the truck’s planned route are detected ’on-the-fly’. The relief transport must schedule deliveries anticipating possible unplanned truck detours, enforce (planned) drone detours for early checking of key road segments, and consider the dynamic nature of road condition information. In this paper, we perform a competitive analysis of a widely used delivery policy for TSP-DI in practice – the online re-optimization policy (Reopt) – and compare it to several alternative delivery strategies. Competitive analysis examines the worst-case performance of the strategies and is particularly important in the context of disaster relief, where worst-case outcomes must be avoided. Our analysis shows that Reopt is dominated by alternative delivery policies in terms of the competitive ratio even at a medium level of damage on the road. It also underscores the importance of surveillance detours performed by the drone, even if the surveillance delays the start of the deliveries.

Urban street clusters: unraveling the associations of street characteristics on urban vibrancy dynamics in age, time, and day

Understanding urban vibrancy has been considered crucial to promoting human activities and interactions in public open spaces. Recent advancements in urban big data have facilitated the potential to understand and measure vibrancy patterns throughout cities. While streets are considered the center stage of human activity, previous studies have often overlooked their multifaceted nature and their association with urban vibrancy. In this study, we incorporate multi-source big data and combine a set of features that comprehensively describe the scale, function, and topology of street segments in two Seoul districts: Jung-gu and Gangnam-gu. Using these features, we employ a machine learning clustering technique to classify them into five distinct typologies. Then, with street-level aggregated mobile phone tracking data, we investigate whether street typology characteristics are associated with urban vibrancy with respect to age groups, time of day, and day types (weekends/weekdays). The results show varying relationships between street characteristics with age-, time- and day-vibrancy measures by the identified street typology. Further, we contrast the results of the two districts to evaluate urban vibrancy differences in organic and planned urban layouts. This study enables a more nuanced understanding of urban streets to better comprehend their impact on people’s use of street space. The derived novel insights could assist planners and designers to better pinpoint street management solutions for different age- and time-dependent needs based on the complexities in urban vibrancy dynamics.