Traffic Routing Research Articles

Maritime Transport Network in Korea: Spatial-Temporal Density and Path Planning

The increase in maritime traffic and vessel size has strengthened the need for economical and safe maritime transportation networks. Currently, ship path planning is based on past experience and shortest route usage. However, the increasing complexity of the marine environment and the development of autonomous ships require automatic shortest path generation based on maritime traffic networks. This paper proposes an efficient shortest path planning method using Dijkstra’s algorithm based on a maritime traffic network dataset created by extracting maritime traffic routes through a spatial-temporal density analysis of large-scale AIS data and Delaunay triangulation. Additionally, the depth information of all digital charts in Korea was set as a safety contour to support safe path planning. The proposed network-based shortest path planning method was compared with the path planning and sailing distance of a training ship, and compliance with maritime laws was verified. The results demonstrate the practicality and safety of the proposed method, which can enable the establishment of a safe and efficient maritime transportation network along with the development of autonomous ships.

Data-driven evacuation and rescue traffic optimization with rescue contraflow control

In response to local sudden disasters, e.g., high-rise office or residential building fire disasters, road occupation can cause conflicts, and traffic directions may be opposite between evacuation vehicles and rescue vehicles; moreover, lane contraflow can be adopted to meet these surge traffic demands. However, lane contraflow that provides more roads for rescue vehicles reduces the traffic supply in the evacuation direction. It is unclear how to control the number of contraflow roads used by rescue vehicles to coordinate evacuation and rescue traffic operations. Here, we adjust the critical rescue traffic volume of reversing the normal road traffic direction to control rescue contraflow. Additionally, we propose a multiobjective mixed integer linear programming formulation for evacuation and rescue traffic optimization. Additionally, considering that the upper limit of the critical rescue traffic volume is unknown and that the proposed formulation includes multiple objectives and multi-priority vehicle classes, a three-stage solving algorithm is developed. Next, a large-scale evacuation and rescue traffic optimization result dataset is obtained for the Nguyen–Dupuis road network, and the impact of different rescue contraflow control plans on evacuation and rescue traffic is studied based on data-driven statistical analysis. The results show that by adjusting the optimal rescue traffic route, the critical rescue traffic volume for reversing the normal road traffic direction can reduce the interference of rescue traffic to evacuation traffic operation performance without reducing rescue traffic operation performance, and can be used to coordinate evacuation and rescue traffic operation under rescue contraflow.

Machine learning-enhanced aircraft landing scheduling under uncertainties

Aircraft delays lead to safety concerns and financial losses, which can propagate for several hours during extreme scenarios. Developing an efficient landing scheduling method is one of the effective approaches to reducing flight delays and safety concerns. Existing scheduling practices are mostly done by air traffic controllers (ATC) with heuristic rules. This paper proposes a novel machine learning-enhanced methodology for aircraft landing scheduling. Data-driven machine learning (ML) models are proposed to enhance automation and safety. ML enhancement is adopted for both prediction and optimization. First, the flight arrival delay scenarios are analyzed to identify the delay-related factors, where strong multimodal distributions and arrival flight time duration clusters are observed. A multi-stage conditional ML predictor is proposed for improved prediction performance of separation time conditioned on flight events. Next, we propose incorporating the ML predictions as safety constraints of the time-constrained traveling salesman problem formulation. The scheduling problem is then solved with mixed-integer linear programming (MILP). Additionally, uncertainties between successive flights from historical flight recordings and model predictions are included to ensure reliability. We demonstrate the real-world applicability of our method using the flight track and event data from the Sherlock database of the Atlanta Air Route Traffic Control Center (ARTCC ZTL). The case studies provide evidence that the proposed method is capable of reducing the total landing time by an average of 17.2% across three case studies, when compared to the First-Come-First-Served (FCFS) rule. Unlike the deterministic heuristic FCFS rule, the proposed methodology also considers the uncertainties between aircraft and ensures confidence in the scheduling. Finally, several concluding remarks and future research directions are given. The code used can be retrieved from Link.

Consequences analysis of the LPG tank truck traffic accident: A case study of the Wenling explosion accident

Liquefied Petroleum Gas (LPG) is a vital energy source as well as a chemical feedstock, known for its flammable and explosive property. The demand for LPG has increased in recent years, leading to the frequent occurrence of leaks and explosion accidents, which pose a serious threat to public safety. This paper investigates the process of the consequences evolution of leakage of LPG and explosion accidents, using the Wenling, Zhejiang, China, LPG tanker explosion accident as an example. The main explosive processes that occurred after the leak, i.e., Boiling Liquid Expanding Vapor Explosion (BLEVE) and Vapor Cloud Explosion (VCE), were reconstructed through the collection and organization of data from the incident. The EFFECTS software was used for the simulation of container rupture, vapor dispersion and explosion pressure. The simulation results were consistent with the actual on-site effects. Multi-energy TNO method was used to measure the impact of VCE on overpressure hazards. This resulted in a blast energy of 3.1 × 105 MJ and a staff wound radius of 324 m. Based on the site's terrain and traffic route layout, measures were proposed to install protective walls around the turnaround area. Effective measures were taken to reduce the range and intensity of the explosion and thus to mitigate the consequences of the accident. This measure reduced the damage radius of the accident by 25.6%. This work will serve as a reference for investigation, analysis, scenario development and recommendations to prevent accidents.

Adaptive traffic aware clustering and routing model for wireless sensor networks

<span>Designing effective clustering algorithms plays a very important role in improving network lifetime target coverage (NLTC) in heterogeneous wireless sensor networks (HWSNs). However, the current clustering mechanism failed to address hotspot problem resulting in poor coverage and network lifetime performance. Recently, unequal clustering has been used for addressing hotspot problem in HWSNs. However, when performing inter-cluster routing, load balancing is not efficient and the energy degradation is high, which affects the overall network performance. This work introduces an effective model namely adaptive traffic aware clustering and routing (ATACR) model to address hotspot and load balancing issues. The ATACR introduces a novel unequal clustering and ideal distribution of load in different cluster levels. The ATACR model improves network lifetime, reduces control channel overhead, and attains better throughput in comparison with existing routing models.</span>

WIRELESS TRAFFIC AND ROUTING ENHANCEMENT USING EMPEROR PENGUIN OPTIMIZER GUIDED BY CONDITIONAL GENERATIVE ADVERSARIAL NETS

The escalating demand for efficient wireless communication systems has prompted researchers to explore innovative solutions to optimize traffic flow and routing. The existing wireless communication infrastructure faces challenges such as congestion, latency, and suboptimal routing, impeding the seamless transmission of data. Traditional optimization approaches fall short in adapting to dynamic network conditions, necessitating the exploration of advanced methodologies. Despite recent advancements in optimization techniques, a notable research gap exists in the integration of bio-inspired algorithms like the Emperor Penguin Optimizer with machine learning models such as Conditional Generative Adversarial Nets for the purpose of wireless traffic and routing enhancement. Bridging this gap is crucial for achieving adaptive and robust wireless communication systems. This study addresses the challenges posed by the dynamic nature of wireless networks, aiming to enhance their performance through the synergistic application of the Emperor Penguin Optimizer (EPO) and Conditional Generative Adversarial Nets (CGANs). This research leverages the inherent strengths of the EPO, inspired by the collective foraging behavior of emperor penguins, to dynamically optimize the wireless network parameters. Concurrently, CGAN are employed to intelligently learn and adapt routing strategies based on real-time network conditions. The symbiotic integration of these two methodologies creates a powerful framework for adaptive wireless traffic and routing. The results indicate a significant improvement in traffic flow, reduced latency, and optimized routing paths in comparison to conventional methods. The EPO-CGAN framework demonstrates adaptability to varying network conditions, showcasing its potential to revolutionize wireless communication systems.

Application of Electromagnetic Wave Computerized Tomography in Metro Tunnel Detection in Karst Area

When tunnelling in karst areas, a series of geological hazards caused by karst are often encountered, resulting in huge economic losses as well as human casualties. In order to ensure the safety of tunnel construction, risk assessment and early warning of karst-related geological hazards, it is necessary to use geophysical methods to find out the location, size, shape and filling of karst, and to provide necessary reference information for traffic route planning and tunnel construction planning. Based on theoretical model research, the construction plan of electromagnetic wave CT method was designed for data acquisition in an underground tunnel detection project in a city of Jiangsu province based on existing geological data, and the inverse interpretation of the measured data was carried out in combination with borehole core data to study the feasibility, effectiveness and accuracy of the electromagnetic wave CT method for karst detection in urban underground space.

A Comprehensive Review on Different Aspects on Current Scenario of Analysis of Box Culvert

Abstract: Box culverts serve as essential structure for drainage relief, water passage and traffic route under roads. They have been constructed in various shapes and sizes, accommodating different waterway and traffic needs. This review paper provides an in-depth analysis of various research papers associated with the topic of box culvert and its various advancements. Through a thorough literature review and analysis of previous works in this field, this study has identified conclusive outcomes that form the basis of our research objectives for checking the box culvert with different skew angles along with different S/H ratios for 2 cell box culvert and aim to provide technical insights and recommendations for future research in this area.

Regional characteristics of iron manufacturing techniques and the related issue of the supply system on Han’s southern Frontiers: A metallurgical study of iron implements from Chenzhou, Hunan

Southern Hunan is known to be one of the few iron manufacturing regions in the Han period. The study of iron implements from this region should yield important evidence concerning the regional iron-making techniques used and the local iron industry. We provide a detailed account of the technological aspects of iron implements yielded from Chenzhou, a major node on the traffic routes entering the broader southern frontiers. Our analysis focuses on the metallographic features and chemical compositions of inclusions in the examined objects. The results reveal that cast iron and related annealing or decarburization techniques were commonly used. Most implements were made using solid-state decarburization of cast iron, along with malleable cast iron and fined iron. This technological profile is similar to other case studies in the literature, but also highlights regional characteristics such as the relatively sparse presence of fined iron. Based on this observation, we discuss various factors that could have contributed to this situation. In a broader context, the analysis of this collection can contribute to the further investigation of the regional characteristics of the Han period iron industry.

Numerical study of the vibrational absorption considering the periodic foundation effect in a small structure

The growth of urban areas has led buildings to come closer and closer to road traffic routes, exposing civil buildings to traffic vibrations, causing discomfort for users and damage to structures. Although there are numerical studies on traffic vibrations, effective vibrational isolation techniques are little explored in this research. A promising approach is the use of periodic structures. Although used in several areas of engineering, periodic structures still have limited application in civil engineering, especially to isolate vibrations caused by vehicles rolling on pavement in small buildings. In this context, this research developed three numerical finite element models for a small structure, which represent residential and/or commercial buildings with a number of floors equal to or less than three, in reinforced concrete. The first model involved a periodic block-type structure foundation, while the two comparative models included periodic raft-type foundations and block-type concrete foundations. The objective was to analyze the vibrational absorption of different foundations for forced vibrations arising from road traffic in the 1-50Hz range. In order to compare them and observe how beneficial the use of periodic foundation is, in the context of the study. The methodology used consisted of creating structural models using finite elements, applying boundary conditions to couple the foundation layers and the structure, establishing continuity. The study looked for modal responses, and in the frequency domain, resulting from excitation in the foundation, using the Ansys® software (2023) for all simulations. The results revealed that periodic foundations are capable of reducing amplitude peaks at frequencies above 30 Hz, which is beneficial for attenuating vibrations in this range. With an appropriate design, adjusting the geometric and physical parameters of the materials, it is possible to obtain effective vibrational isolation for structures, minimizing vibrations arising from ground waves in buildings. This suggests that the use of periodic structures would be promising in isolating vibrations caused by traffic in small structures.

Research on Traffic Flow Forecasting Based on Dynamic Spatial-Temporal Transformer

Accurate traffic flow forecasting is crucial for urban traffic control and route planning. Aiming at the difficulty in capturing dynamic spatio-temporal complexity of traffic flow, a dynamic spatio-temporal transformer (DST-Trans) model capable of modeling dynamic correlation of traffic flow is proposed, which consists of gated temporal convolutional network (GTCN), graph convolutional network (GCN), and spatio-temporal transformer (ST-TF). GTCN and GCN are utilized to capture the temporal and spatial characteristics of traffic flow, respectively. ST-TF includes a temporal transformer using temporal gated convolution and temporal multi-head self-attention to capture short-long term temporal features, and spatial transformer using spatial gated graph convolution and spatial multi-head self-attention to capture local-global dynamic spatial features. In addition, to take full advantage of the dynamic and static associations of road networks, multi-graph models of road relationship graph, similarity graph, and adaptive dynamic graph with SGGC are constructed. Experimental results show that the DST-Trans model in this paper shows good prediction performance in short-term (15 min), medium-term (30 min), and long-term (60 min) prediction, outperforming existing state-of-the-art models by up to approximately 7%.

Using various geotechnical techniques for challenging conditions at the West Link project

AbstractThe city of Gothenburg in western Sweden is currently experiencing the construction of one of the biggest infrastructure projects in the country – The West Link (Västlänken). The West Link is an 8 km double‐track railway project, of which 6 km will be through underground tunnels with two new stations, Haga and Korsvägen, and construction of a bigger Central station. The construction of the project began in May 2018 and the finalization is expected in 2026. The project is split into five different sections which were awarded and are being executed individually. The widely varied geological profile of the area consisting of weak‐to‐very weak clay and silty‐sandy‐gravelly moraine along with challenging local conditions like existing waterways, traffic routes, and buildings requires a range of soil improvements and shoring system solutions suiting the local conditions. Some techniques executed by Keller include dry deep soil mixing (DDSM) to stabilize the quick clay to facilitate excavation, jet grouting for water sealing, excavation support in addition to soil improvement purposes, uplift piles over a flowing river, drilled steel piles at the bottom of excavation pit as foundation support, drilled steel pile wall, and sheet piling. Apart from technical challenges, the West Link has strong focus on adopting sustainable solutions like methods to reduce disposal of wet masses from jet grouting. A team of experts of design and execution from different parts of the world is working together to make it a success story.

Graph-based ship traffic partitioning for intelligent maritime surveillance in complex port waters

Maritime Situational Awareness (MSA) is a critical component of intelligent maritime traffic surveillance. However, it becomes increasingly challenging to gain MSA accurately given the growing complexity of ship traffic patterns due to multi-ship interactions possibly involving classical manned ships and emerging autonomous ships. This study proposes a new traffic partitioning methodology to realise the optimal maritime traffic partition in complex waters. The methodology combines conflict criticality and spatial distance to generate conflict-connected and spatially compact traffic clusters, thereby improving the interpretability of traffic patterns and supporting ship anti-collision risk management. First, a composite similarity measure is designed using a probabilistic conflict detection approach and a newly formulated maritime traffic route network learned through maritime knowledge mining. Then, an extended graph-based clustering framework is used to produce balanced traffic clusters with high intra-connections but low inter-connections. The proposed methodology is thoroughly demonstrated and tested using Automatic Identification System (AIS) trajectory data in the Ningbo-Zhoushan Port. The experimental results show that the proposed methodology 1) has effective performance in decomposing the traffic complexity, 2) can assist in identifying high-risk/density traffic clusters, and 3) is sufficiently generic to handle various traffic scenarios in complex geographical waters. Therefore, this study makes significant contributions to intelligent maritime surveillance and provides a theoretical foundation for promoting maritime anti-collision risk management for the future mixed traffic of both manned and autonomous ships.

Software-defined network traffic routing optimization: A systematic literature review

Software-defined network traffic routing optimization: A systematic literature review

SINGLE CROSS-BORDER FREE ROUTE AIRSPACE CONCEPT IN THE ASEAN REGION

Single Cross-Border Free Route Airspace (FRA) represents the ultimate long-term idealistic routing rules allowing airspace users freely plan a route in en-route airspace, which is not immediately possible from the administrative and technological point of view. In Single Cross-Border FRA, the whole ASEAN airspace is treated as a single-large FRA where all 12 Flight Information Regions (FIRs) implement FRA, with direct routing between the entry (horizontal entry into FIR or end of Standard Instrument Departure Route, SID) and the exit (horizontal exit from FIR or beginning of Standard Arrival Route, STAR). Single Cross-Border FRA is the most fuel-efficient reference that we could ever reach in the future.All otherhypothetical routing rules fall between the Single Cross-Border FRA and those hypo-thetical routing rules in terms of fuel efficiency. In this paper, we described the potential in fuel efficiency (or benefit achievement scaling) with refer-ence to this ultimate basic fuel requirement and scaled the excess fuel with different routings. We evaluated the fuel burn with a fast-time simulation (FTS) in 12 different scenarios (3 traffic volume levels × 4 routing scenarios). As for the traffic volume scenarios, we simulated 1-day of commercial ASEAN flight schedule data of 3 traffic volume levels (100%, 150%, and 200% of pre-COVID19 traffic volume). As for four routing scenarios with increasing levels of directness: conventional Air Traffic Service route network (ATS) (R1), hypothetical Several-Small FRA route network (R2), Single Cross-Border FRA route network with Danger Areas (R3) and Single Cross-Border FRA network without Danger Areas (R4). Results showed that flight time, fuel burn, and distance travelled were less in R2, R3, and R4 than in R1. There was an average difference of 2.2% in flight time, 1.9% in fuel burn, and 2.2% in distance travelled from R1 to R4.

Hyper-heuristic algorithm for traffic flow-based vehicle routing problem with simultaneous delivery and pickup

Abstract To address the realistic problem of seriously reducing distribution efficiency and increasing distribution cost caused by road traffic congestion, this paper constructs a time-dependent speed describing vehicle travel speed and road traffic flow by simulating the change of urban traffic flow, to establish a vehicle route problem model considering traffic flow with distribution cost and customer satisfaction as optimization objectives. To solve this problem, a hyper-heuristic algorithm based on tabu search is designed in this paper, in which the underlying search operator is selected more efficiently by a high-level heuristic strategy. In addition, the correctness of the model and the effectiveness of the algorithm are verified by conducting simulation experiments on several benchmark sets. Experiment results are shown as the travel speed of the vehicle increases, the average customer satisfaction in lc1-type instances increases to 0.94. And the impact of urban traffic changes on logistics costs and customer satisfaction is further analyzed.

Characterization of road surfaces using three near-infrared diode lasers

Rapid road network expansion has heightened the importance of surface condition information for traffic accident prevention and route optimization. This article introduces a laser diode-based sensor that identifies seven surface conditions and accurately measure ice, water, and snow film thicknesses on roads. An optical module was developed to detect weak optical signals based on the characteristic absorption spectrum of the target surface. The module used three laser diodes (1310, 1440, and 1550 nm wavelengths) as light sources. Additionally, a road classification algorithm that is adaptable to foggy weather was developed using a multi-wavelength processing protocol. The sensor was subjected to numerous calibration and performance verification experiments. During thick foggy measurements, the processed spectra displayed a maximum variation of 2.372% across a 600 to 25,000 m visibility range with a relative standard deviation of only 0.328%. This demonstrated effective weakening of the effects of visibility variations. During winter field testing, the sensor classified road conditions effectively and accurately measured ice, snow, and water film thicknesses, with a correlation coefficient of 0.97444. The accuracy of the measurements was less than 0.5 mm. The sensor’s effectiveness for long-term field-based road testing has been verified.

Role Of Robotic Process Automation in Managing Transportation Systems for Smart Cities

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

Route planning model based on multidimensional eigenvector processing in vehicular fog computing

With the rapid development of information technology, the informationization level of sport tourism has been improved in an all-around way, which makes a large number of data accumulated in the management system of road traffic. However, the traditional association rule algorithm cannot deal with huge data. To make up for the deficiency of predicting suitability from a single perspective, this paper, from the perspective of road daily travel planning, constructs a multi-dimensional characteristic model. Based on eigenvector processing as a research content, by carrying out a multi-dimensional prediction on the whole data set and considering the limitation problem of the influence of time, space, and items on daily travel routes, the invention provides a daily travel suitability road daily travel planning route prediction research method based on multiple dimensions. The invention also extracts the outstanding performance of a network model in a single variable problem based on deep interest and double characteristics. The combination of SVR and GBRT algorithm makes up for the one-sidedness of single perspective prediction. It uses the weighted fusion principle to fuse the results and establishes a multi-dimensional route suitability prediction model under this mode. Experiments verify that the prediction of multivariate dimensional data achieves the desired results. With the help of the dependence between the level and element dimension data, the future route planning trend can be judged. This algorithm compared to the ant colony algorithm and the traditional genetic algorithm increased by 15.6% and 15.1%. The system response time has been increased by more than 60%, which can effectively improve the accuracy of prediction. Therefore, in the VFC environment, the actual user needs can be improved, the planning and management of traffic routes can be guided, and the development of sport tourism systems of Taihang Mountain can be further promoted.

TRANSPORTATION ROUTE SELECTION ANALYSIS OF THE TUBAN-BOJONEGORO ROUTE DUE TO THE DAMAGE OF THE GLENDENG BRIDGE

A bridge is a structure built to connect two or more points separated by a river, valley, ravine, or highway. Damage to a bridge can cause several adverse impacts, including: traffic accidents, transportation disruptions, economic disruptions, social disruptions, environmental damage, financial losses. The traffic flow diversion through the three-way intersection before the Glendeng Bridge to the Kaliketek Bridge is intended for small vehicles while large vehicles are diverted to the Soko-Ponco highway. The 4 km traffic diversion route is quite far for the local community. Apart from the Kaliketek Bridge alternative, people can also use boat crossings as access between cities. But the boat can only be used by two wheels. Therefore this study aims to determine the effectiveness of alternatives between Kaliketek routes and boat crossings. The purpose of this research is to analyze which path is more effective for 2-wheeled riders between the Kaliketek and Ferry Boat Routes and to find out the more effective path between the two. The method used in making decisions and selecting solutions is to use the AHP method. The results of this study include: Alternative value analysis is carried out by calculating pairwise comparisons starting from alternatives, sub-criteria and criteria. In this study, the calculated value of the sub-criteria was obtained with the GM value of each sub-criteria, namely time 0.93, convenience 0.88, cost 0.00 and safety 0.00. While the Vp value is obtained by dividing the GM value by the total GM value. And from this calculation the value of Vp time is 0.51, comfort 0.49, cost 0.00 and security 0.00. Then calculate the value of the evaluation criteria for the sub-criteria and get a GM value for the Kaliketek route of 0.79 while the ferry boat route is 0.81. The GM value is used to find the VP value by dividing each GM value by the total GM value. From the calculation, the VP value for the Kaliketek route is 0.49 and for the boat crossing route is 0.51. The choice of an alternative connecting route as a result of the construction of the Simo-Glendeng Bridge for 2 wheels is a boat route with a priority vector value of 0.51 and an emphasis on the time factor with a priority vector value of 0.51.