Traffic Routing Research Articles

Low-Carbon Mixed Traffic Route Recommendation for Community Residents Based on Multilayer Complex Traffic Network

With the proposed “carbon peaking” and “carbon neutral” goals in China, the transportation sector, as the second largest consumer of oil and a major producer of greenhouse gases, is a critical area for energy efficiency and emission reduction actions. However, few studies have focused on the effective combination of solving residents' commuting challenges and low-carbon travel. In this paper, by extracting real traffic flow data from taxi and bike-sharing trajectory data, a multilayer complex traffic network is formed to realize an interactive visual exploration of urban traffic patterns. Based on this network a low-carbon travel route recommendation is implemented using a modified genetic algorithm to reduce personal carbon emission and travel costs. Meanwhile, the trip chain level carbon emission estimation method is defined for city streets and recommended routes. With the integration of the above algorithms, a visual analytics system is designed and implemented to support the joint exploration of urban traffic patterns and the street carbon emission distribution, low-carbon mixed traffic route recommendations for inter-community commuting, and optimization of low-carbon recommended routes by adjusting bike stations. Take the taxi and bike-sharing trajectory data in Xiamen, China as an example, an evaluation analysis of the system shows that the method is effective in reducing commuting costs for community residents while reducing personal travel carbon emission.

Qualitative transformations of street-seized ecstasy over a decade: A case study in Rio de Janeiro (Brazil).

The illegal drug market is constantly evolving, with new drugs being created and existing ones being modified. Adulterants are often added to the mix, and the primary substance may be secretly replaced by a new one. Once-known tablets can now be vastly different from what they are sold as, all due to the pursuit of profit and evasion of current drug regulations. These alterations in drug composition pose a threat to society, as their effects are still not well understood. Therefore, it is crucial for police intelligence and public health development to obtain the chemical profiles of illicit drugs. This study presents the chemical fingerprinting of ecstasy tablets seized in the state of Rio de Janeiro (Brazil) between 2012 and 2021. The tablet samples were weighed, extracted, diluted with methanol, and acidified before analysis using gas chromatography high-resolution mass spectrometry and attenuated total reflection Fourier transform infrared spectroscopy. The major constituents found were MDMA and clobenzorex, with fewer occurrences of MDA, MDEA, and 2C-B. The results also indicate that the occurrence of mega-events in the study location impacted the chemical fingerprints of ecstasy. A total of 27 combinations of cutting agents, including caffeine, ephedrine, and anesthetics, were identified. Samples composed of clobenzorex were observed throughout the evaluated period in areas near highways, suggesting that this product is mainly used by truck drivers. These findings can help police intelligence units anticipate the behavior of the illicit market during major events, identify traffic routes, and support public health initiatives.

TITAN: Combining a bidirectional forwarding graph and GCN to detect saturation attack targeted at SDN.

The decoupling of control and forwarding layers brings Software-Defined Networking (SDN) the network programmability and global control capability, but it also poses SDN security risks. The adversaries can use the forwarding and control decoupling character of SDN to forge legitimate traffic, launching saturation attacks targeted at SDN switches. These attacks can cause the overflow of switch flow tables, thus making the switch cannot forward benign network traffic. How to effectively detect saturation attack is a research hotspot. There are only a few graph-based saturation attack detection methods. Meanwhile, the current graph generation methods may take useless or misleading information to the attack detection, thus decreasing the attack detection accuracy. To solve the above problems, this paper proposes TITAN, a bidirecTional forwardIng graph-based saturaTion Attack detectioN method. TITAN defines flow forwarding rules and topology information, and designs flow statistical features. Based on these definitions, TITAN generates nodes of the bi-forwarding graph based on the flow statistics features and edges of the bi-forwarding graph based on the network traffic routing paths. In this way, each traffic flow in the network is transformed into a bi-directional forwarding graph. Then TITAN feeds the above bidirectional forwarding graph into a Graph Convolutional Network (GCN) to detect whether the flow is a saturation attack flow. The experimental results show that TITAN can effectively detect saturation attacks in SDNs with a detection accuracy of more than 97%.

Investigation and susceptibility assessment of regional geological hazards along the Karakoram highway, northeast margin of Pamir Plateau

The Karakoram Highway (KKH) is a major road traffic route connecting China to South Asia and the Middle East via Pakistan. Geological hazards occur often in the study area due to the influence of complex geological features, active glaciers, and a changing climate, causing major disruptions to traffic operations. As a result, a geological hazard susceptibility map was created in this study to aid in the prevention and control of highway slope disasters. Through field investigation and map interpretation, 102 geohazards, including landslides, debris flows, and collapses, were observed along the route. The logistic regression method was utilized to generate a predictive model for the susceptibility map considering topography, faults, peak ground acceleration, geology, land cover, rainfall, and drainages. The results imply that the model performs well in terms of prediction. The AUC (area under the receiver operating characteristics (ROC) curve) value is 90.6%. The risk associated with the highway was assessed using the susceptibility map. Some sections of the highway are in high-risk zones, where disasters are more perilous and endanger people and automobiles. This effort will support geohazard protection, management, and safe operation of road traffic.

Quantifying the diffusion history of Yangmingism

AbstractYangmingism, a crucial component of Chinese Confucianism, has profoundly impacted the development of Eastern civilization throughout history and gained new significance within modern contexts. This study employs social network and GIS spatial analyses to investigate the spatio-temporal evolution and patterns of Yangmingism during the Ming and Qing dynasties. Conclusions drawn include: (1) Yangmingism spread during the Ming and Qing dynasties followed five phases (rise-peak-decline-revival-trough), consistent with general cultural diffusion processes. (2) The spatial structure of regions accepting Yangmingism changed from polycentric to localized ribbon-polycentric, then to monocentric, polycentric, and ultimately, fragmented distribution. Diffusion began with a point-axis structure along traffic routes, transitioned to a core-periphery spatial structure, and returned to a point-axis structure due to reduced diffusion potential energy. (3) Yangmingism diffusion exhibited distance decay characteristics, with the most extensive diffusion in the Han cultural region, primarily located within the middle and lower reaches of the Ganjiang River basin and the Yangtze River Delta. (4) Yangmingism diffusion reflected influences from local administrative hierarchies and cultural development. During the rise-to-peak phase, Yangmingism reached areas with higher administrative and cultural levels, while during the peak-to-decline phase, it disseminated from higher to lower hierarchy regions. (5) The spatial diffusion of Yangmingism is mainly relocation diffusion, followed by expansion diffusion. In the process of relocation diffusion, Yangmingism may reverse the diffusion from the “target regions” to the “source regions” due to the influence of the radiation of the high-ranking regions and the cultural innovation of the “target regions”.

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

An Intelligent Transport System (ITS) is a wide-ranging framework that influences advanced technologies to improve efficiency of transportation networks. An ITS integrated with an IoT enhances the capabilities of transportation networks. Owing to quick development of vehicles, traffic congestion during road network is major problems. Therefore, intelligent traffic control system is required to create smart transportation through optimization of vehicle route, and so on. In this paper, Cox Regressive Fuzzified Outlier Robust Incremental Extreme Learning Machine (CRFORIELM) is developed for smart ITS. The IoT technology in IT’S is used to collect traffic data, vehicle information, Parking slot occupancy data. After data collection process, proposed CRFORIELM technique includes two major processing steps namely parking slot availability detection and traffic aware route optimization. The extreme learning machine includes three types of layers such as input, three hidden layers as well as output layer. Initial hidden layer predicts the parking slot availability using Cox regression is performed. With the parking lot availability predictions, integrate this information into a route optimization algorithm. This algorithm considers the real-time traffic conditions as well as parking availability for detecting optimal route by applying fuzzy triangular membership function in the second hidden layer. Finally, nearest route path is effectively determined to minimize travel time in intelligent transport system. Experimental analysis indicates that the CRFORIELM technique achieved 2%, 5%, 10% improvement in accuracy, sensitivity, specificity and reduced error rates, route detection time by 39%, and 36%, compared to existing methods.

Low-latency Virtual Network function Scheduling Algorithm Based on Deep Reinforcement Learning

This paper addresses the problem of mapping, scheduling, and routing of virtual network functions (VNF) on a service function chain (SFC) that is sensitive to latency in a virtual network. A scheduling algorithm for VNF is proposed, which aims to minimize the SFC rejection rate while taking into account VNF mapping, scheduling, and traffic routing during the scheduling process. To achieve this goal, a Markov decision process (MDP)-based VNF scheduling model is established that guarantees SFC resource requirements are met. The model uses the D3QN (Dueling Double DQN) algorithm based on composite rules to select the SFC at each scheduling time point, and selects virtual nodes and routes using a routing optimization algorithm to minimize the SFC rejection rate. We compare our algorithm with the single rule, DQN and genetic algorithm, and the simulation results show that the proposed algorithm can reduce the rejection rate of SFC by approximately 8% compared to genetic algorithms.

Role of Earliest Arrival Flow in an Evacuation Network

Traffic route guidance, destination optimization, and optimal route choice are some of the approaches to accelerate the evacuation process of transit-based evacuation problems. Their effectiveness depends upon the evacuee arrival patterns at the pickup locations and their assignment in an appropriate way to the resources in the network. The arrival patterns of the evacuees at different pickup locations might be a constant or random variable, deterministic or stochastic, time-dependent, or flow-dependent, or can be categorized either way. In this work, we are focused on the collections of evacuees at the pickup locations from the danger zone in the earliest arrival flow patterns, which can be further assigned to the transit vehicles in the integrated evacuation network by treating such pickup locations as the sources for the subsequent process to minimize the overall network clearance time from the danger zone to the safety.

Investigating the Impact of Topology and Physical Impairments on the Capacity of an Optical Backbone Network

Optical backbone networks constitute the fundamental infrastructure employed today by network operators to deliver services to users. As network capacity is a key factor influencing optical network performance, it is important to understand how topological and physical properties impact its behavior and to have the capability to estimate its value. In this context, we propose here a method to evaluate the network capacity that relies on the optical reach to account for physical layer aspects in conjunction with constrained routing techniques for traffic routing. As this type of routing can lead to traffic blocking, particularly due to the limitation on the number of wavelengths per fiber, we also propose a fiber assignment algorithm designed to deal with this problem. We apply this method to a set of randomly generated networks using a modified Waxman model, and for a network with 60 nodes, in a scenario without blocking, we obtain capacities of about 2.5 Pbit/s for a symbol rate of 64 Gbaud and about 5 Pbit/s for a symbol rate of 128 Gbaud. Remarkably, this duplication in the total network capacity is achieved by an increase in the total fiber length of only about 51%.

RF based Automatic Traffic Route Clearance System for Emergency Vehicle

Traditional time-based traffic management systems are often inadequate for addressing the complexities of modern urban traffic, resulting in delays for emergency service vehicles such as ambulances and fire services. This project proposes a wireless communication-based approach to alleviate ambulance congestion in urban areas, presenting an innovative traffic clearance system that is easily deployable and cost-effective. Keywords— Arduino, ESP 32 Microcontroller, Solar panel ,GPS,RFID Sensor.

Reliability Optimization of Traffic Routing in Train Communication Networks Based on Faulty Link Diagnosis

With the increasing adoption of industrial Ethernet in train communication network (TCN), the limitations of insufficient reliability and incompatibility between different Ethernet technologies have become increasingly apparent. To address these challenges, time‐sensitive networking (TSN) has emerged as a solution, enabling multi‐network convergence and enhancing data transmission determinism and reliability through techniques such as frame replication and elimination for reliability (FRER). However, the utilization of these policies will adversely affect schedulability due to increased network load. To address the issue of link failures in TCN topologies, a routing policy based on faulty link diagnosis is proposed. The proposed policy utilizes an improved max‐min ant system (IMMAS) for diagnosing and resolving faulty links. Furthermore, a routing optimization algorithm is introduced to enhance transmission reliability by planning alternative paths for traffic. To evaluate the performance of the proposed routing algorithm, multiple sets of test cases are conducted separately under different failure scenarios to compare them with other routing policies. The results demonstrate the superior reliability of the proposed routing policy over alternative approaches. Additionally, the traffic utilizing the proposed routing policy show improved schedulability following integer linear programming (ILP) scheduling. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Enhancing Traffic Routing Inside a Network through IoT Technology & Network Clustering by Selecting Smart Leader Nodes

. IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.

Statistical estimation of noise induced hearing loss among the drivers in one of the most polluted cities of India

In the present study, an attempt has been made to assess the impact of vehicular noise upon the 3-wheeler tempo drivers and to know whether there is any relationship between hearing loss and cumulative noise exposure. For this purpose, 3-wheeler tempo drivers (Exposed group) and non-commercial light motor vehicle car drivers (Unexposed group) were chosen as study subjects. Three traffic routes were selected to assess the noise level during waiting and running time in the exposed and unexposed groups. Among all three routes, the highest mean noise level (Leq) was observed on the Chowk to Dubagga route for waiting and en-route noise measurement. It was measured as 84.13 dB(A) and 86.36 dB(A) for waiting and en-route periods of 7.68 ± 3.46 and 31.05 ± 6.6 min, respectively. Cumulative noise exposure was found to be significantly different (p < 0.001) in all age groups of exposed and unexposed drivers. Audiometric tests have been performed over both exposed and unexposed groups. The regression analysis has been done keeping hearing loss among tempo drivers as the dependent variable and age (years) and Energy (Pa2 Hrs) as the independent variable using three different criteria of hearing loss definitions, i.e., World Health Organization, National Institute for Occupational Safety and Health (NIOSH), Occupational Safety and Health Administration criteria. Among these three criteria, the NIOSH criterion of hearing loss best explained the independent variables. It could explain the total variation in dependent variable by independent variable quite well, i.e., 68.1%. The finding showed a linear relationship between cumulative noise exposures (Pa2 Hrs) and the exposed group's hearing loss (dB), i.e., hearing loss increases with increasing noise dose. Based on the findings, two model equations were developed to identify the safe and unsafe noise levels with exposure time.

Două planuri necunoscute ale Craiovei din a doua jumătate a secolului al XVIII-lea – jumătatea secolului al XIX-lea

In this study, we publish two plans of Craiova, discovered at the Military-Historical Archive of Moscow: a plan made during the occupation of Wallachia in 1771–1774 and another made either during the occupation after the 1848 revolution (1848–1851) or during the first year of the Crimean War (August 1853 – April 1854). Both plans support the idea that the town underwent organic development, which took into account the local landforms and the way man intervened. Its location in the Jiu Valley played a significant role in the emergence and evolution of this centre, as the course of the river was accompanied by an important road, Craiova being at the intersection with other major traffic routes of this region of the country. The valleys bordering the urban core had the role of delimiting the original inhabited area and the position of the “Bănia” area, with the church of St. Dumitru, as a strategic area and centre of power, on the edge of the coast overlooking the valley towards the west, is evident. Separately, across another small valley, the heart of the town developed, the market, the area with the best economic outlook, not coincidentally located at the crossroads of the main east-west and south-north axes. The medieval town evolved around these two cores, but development and population growth meant the valleys bordering it were overtaken by the 18th century. How the streets converged in the town underlines the idea of gradual growth, and the concentration of roads around the focal point that was the town market marked the layout of Craiova. Human intervention up to the 1830s and 1840s took place not only through the building of dwelling houses, shops and workshops but also through the construction of major landmarks represented by churches and monasteries, around which the neighbourhood (mahala) gravitated. Once the process of modernization began, the anthropic footprint became even more important through the extensive building actions initiated after the Organic Regulation, some of which are visible in the second plan studied. Unfortunately, we could not identify the plan’s authors among the Russian army surveyors of those periods. The two cartographic documents contribute to a better knowledge of Craiova in the second half of the 18th century and the first half of the following century, adding to the already known plans (the Austrian plan of 1790 and the Romanian one of 1845).

Mean-Field Multiagent Reinforcement Learning: A Decentralized Network Approach

One of the challenges for multiagent reinforcement learning (MARL) is designing efficient learning algorithms for a large system in which each agent has only limited or partial information of the entire system. Whereas exciting progress has been made to analyze decentralized MARL with the network of agents for social networks and team video games, little is known theoretically for decentralized MARL with the network of states for modeling self-driving vehicles, ride-sharing, and data and traffic routing. This paper proposes a framework of localized training and decentralized execution to study MARL with the network of states. Localized training means that agents only need to collect local information in their neighboring states during the training phase; decentralized execution implies that agents can execute afterward the learned decentralized policies, which depend only on agents’ current states. The theoretical analysis consists of three key components: the first is the reformulation of the MARL system as a networked Markov decision process with teams of agents, enabling updating the associated team Q-function in a localized fashion; the second is the Bellman equation for the value function and the appropriate Q-function on the probability measure space; and the third is the exponential decay property of the team Q-function, facilitating its approximation with efficient sample efficiency and controllable error. The theoretical analysis paves the way for a new algorithm LTDE-Neural-AC, in which the actor–critic approach with overparameterized neural networks is proposed. The convergence and sample complexity are established and shown to be scalable with respect to the sizes of both agents and states. To the best of our knowledge, this is the first neural network–based MARL algorithm with network structure and provable convergence guarantee. Funding: X. Wei is partially supported by NSFC no. 12201343. R. Xu is partially supported by the NSF CAREER award DMS-2339240.

Impact of Static Urban Traffic Flow-Based Traffic Weighted Multi-Maps Routing Strategies on Pollutant Emissions

Addressing urban traffic congestion is a pressing issue requiring efficient solutions that need to be analyzed regarding travel time and pollutant emissions. The traffic weighted multi-maps (TWM) method has been proposed as an efficient mechanism for congestion mitigation that enables differential traffic routing and path diversity by strategically distributing different network views (maps) to the drivers. Previous works have focused on TWM generation by creating optimal edge weights, but the complexity exponentially increases with the network size and traffic group diversity. This work describes how congestion and emissions can be addressed using TWM maps based on the k-shortest paths for the traffic flows (instead of individuals) that are optimally assigned and distributed to the components of the traffic flow. The map allocation strategies optimal TWM (OTV), optimal TWM per path flow with linear constraints (LCTV), and its variant unconstrained optimal TWM per path flow (UCTV) are described. They use maps generated from the k-shortest paths of the traffic flows (kSP-TWM). The heuristic solution obtained is compared with the theoretical static traffic assignment estimation baseline with different configurations, regarding congestion reduction, total travel time enhancement, and pollutant emissions. Experiments are developed using a synthetic traffic grid network scenario with a mesoscopic simulation. They show that the solution provided is adequate for its proximity to the theoretical equilibrium solutions and can generate minimum emissions patterns. The presented solution opens new possibilities for further congestion and pollutant management studies and seamless integration with existing traffic management frameworks.

Advanced Data Science Applications in Vehicles: A Comprehensive Review

Purpose: This research paper aims to explore the transformative impact of data science applications within the automotive industry, with a focus on the evolution of vehicles into intelligent, connected, and autonomous entities. Through an extensive literature review and analysis of recent developments, the paper seeks to provide a comprehensive understanding of the current state and future prospects of data science in vehicles.&#x0D; Methodology: Advancements in sensor technologies, such as LiDAR, HD maps, radars, location tracking and cameras, are discussed, highlighting their role in data collection for applications like advanced driver assistance systems (ADAS) and autonomous driving. Relevant citations are provided. This subsection covers the collection and transmission of real-time data through telematics systems, showcasing their importance for predictive maintenance, fleet management, and personalized insurance programs. Citations support the presented information. The deployment of edge computing for real-time data processing in vehicles is discussed, emphasizing its significance for safety-critical applications like collision avoidance. Citations are provided to support the information. This section explores the application of machine learning algorithms to predict vehicle failures, optimize fuel efficiency, and analyze driver behavior. The importance of leveraging historical data to create accurate models is highlighted with supporting citations.&#x0D; Findings: The pivotal role of data science in enabling autonomous vehicles to navigate complex environments is discussed, emphasizing the use of machine learning models for real-time decision-making. Citations support the presented information. This subsection explores the integration of data science in traffic management systems, covering dynamic traffic signal control, congestion prediction, and route optimization. Citations support the findings related to traffic management applications. The paper discusses challenges associated with widespread implementation, including data privacy concerns, cybersecurity risks, and the need for standardized communication protocols. Additionally, potential future directions are outlined, such as the integration of blockchain for secure data sharing and the development of advanced human-machine interfaces.&#x0D; Unique Contribution to Theory, Practice and Policy: This research paper provides a well-rounded contribution by seamlessly integrating theoretical concepts, practical applications, and policy considerations in the realm of advanced data science applications in vehicles.

On the Capacity of Optical Backbone Networks

Optical backbone networks, characterized by using optical fibers as a transmission medium, constitute the fundamental infrastructure employed today by network operators to deliver services to users. As network capacity is one of the key factors influencing optical network performance, it is important to comprehend its limitations and have the capability to estimate its value. In this context, we revisit the concept of capacity from various perspectives, including channel capacity, link capacity, and network capacity, thus providing an integrated view of the problem within the framework of the backbone tier. Hence, we review the fundamental concepts behind optical networks, along with the basic physical phenomena present in optical fiber transmission, and provide methodologies for estimating the different types of capacities, mainly using simple formulations. In particular, we propose a method to evaluate the network capacity that relies on the optical reach to account for physical layer aspects, in conjunction with capacitated routing techniques for traffic routing. We apply this method to three reference networks and obtain capacities ranging from tens to hundreds of terabits/s. Whenever possible, we also compare our results with published experimental data to understand how they relate.

Impact of the Sea Effect on Sudden Fog on the Western Coast of the Bohai Sea: A Case Study

The term “sea effect” generally refers to the process of air mass modification after cold air flows above a warm sea surface. Affected by the sea effect, small-scale and sudden fogs have occasionally been observed on the western coast of the Bohai Sea. A more in-depth study of this type of fog is crucial for ensuring the safety of maritime and aerial traffic routes in this region. This study investigated the formation mechanism of this specific type of fog on the morning of 17 October 2007, utilizing both meteorological stations and 255 m tower observations, combined with the results of the Weather Research and Forecasting model (WRF). It is demonstrated that Bohai Sea evaporation and the associated water vapor advection played crucial roles in the formation of fog along the west coast of the Bohai Sea. The cold return flow became more moist as it passed over the warm Bohai Sea, which was the primary contributor to triggering regional fog on the western coast. A moisture budget analysis revealed that water vapor from the Bohai Sea intruded into its western coast along an eastward trajectory, dominating the oscillations in the net moisture flux. The eastern water vapor flux significantly increased at 17:00 on the 16th (Local time, LST), reaching its peak at 21:00. Correspondingly, the fog water growth rate began to increase at 23:00 on the 16th, reaching its maximum at 03:00 on the 17th. A sensitivity experiment on evaporation further indicated that the Bohai sea effect played a decisive role in fog formation. With a tenfold reduction in evaporation from the Bohai Sea and subsequent significant weakening of water vapor advection, the simulated fog along the western coast of the Bohai Sea completely disappeared. Understanding the formation mechanism of this type of fog is beneficial for refining forecasting focal points, thereby enhancing forecast accuracy in a targeted manner.

Basement membrane diversification relies on two competitive secretory routes defined by Rab10 and Rab8 and modulated by dystrophin and the exocyst complex.

The basement membrane (BM) is an essential structural element of tissues, and its diversification participates in organ morphogenesis. However, the traffic routes associated with BM formation and the mechanistic modulations explaining its diversification are still poorly understood. Drosophila melanogaster follicular epithelium relies on a BM composed of oriented BM fibrils and a more homogenous matrix. Here, we determined the specific molecular identity and cell exit sites of BM protein secretory routes. First, we found that Rab10 and Rab8 define two parallel routes for BM protein secretion. When both routes were abolished, BM production was fully blocked; however, genetic interactions revealed that these two routes competed. Rab10 promoted lateral and planar-polarized secretion, whereas Rab8 promoted basal secretion, leading to the formation of BM fibrils and homogenous BM, respectively. We also found that the dystrophin-associated protein complex (DAPC) and Rab10 were both present in a planar-polarized tubular compartment containing BM proteins. DAPC was essential for fibril formation and sufficient to reorient secretion towards the Rab10 route. Moreover, we identified a dual function for the exocyst complex in this context. First, the Exo70 subunit directly interacted with dystrophin to limit its planar polarization. Second, the exocyst complex was also required for the Rab8 route. Altogether, these results highlight important mechanistic aspects of BM protein secretion and illustrate how BM diversity can emerge from the spatial control of distinct traffic routes.