Route Guidance System Research Articles

Controlling Detour Vehicle Volume for Stable Congestion Mitigation in Route Guidance Systems

Controlling Detour Vehicle Volume for Stable Congestion Mitigation in Route Guidance Systems

A Flow-Based Centralized Route Guidance System for Traffic Congestion Mitigation

A Flow-Based Centralized Route Guidance System for Traffic Congestion Mitigation

Enhancing transportation accessibility: evaluating the impact of route guidance systems and connected autonomous vehicles

Enhancing accessibility in transportation systems is an escalating interest among researchers, fueled by the rising issues with traffic congestion and technological innovations. Accessibility significantly contributes to improving the quality of life, thereby necessitating an examination of how route guidance systems impact it. This study explores the influence of system optimum (SO) and user equilibrium (UE) route guidance systems on accessibility, particularly in the context of connected autonomous vehicles (CAVs) embedded in the network. We employ a hybrid assignment model that enables the concurrent allocation of SO and UE assignments, along with the Gravity model to compute accessibility. The Sioux Falls network, previously leveraged in prior research, was chosen for numerical simulations to permit insightful comparisons. Our study scrutinizes three scenarios: the first scenario examines the repercussions of route guidance systems on human-driven vehicles, while the second and third assess their effects on CAVs. The difference between the second and third scenario is in the way of increasing the capacity in the assignment. Our findings reveal that at lower penetration rates of route guidance systems, accessibility initially dips, and then ascends. When all human-driven users adopt route guidance systems, accessibility increases by 12.75% compared to the initial state (without any intervention). Remarkably, should all users employ autonomous vehicles, accessibility would surge by 220% compared to the initial state (without any intervention). These insights highlight the importance of integrating CAVs and route guidance systems into transportation planning to enhance accessibility and improve the quality of life.

An Adaptive Route Guidance Model Considering the Effect of Traffic Signals Based on Deep Reinforcement Learning

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers’ route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network.

Research on a community life route guidance system based on virtual reality technology

Virtual reality technology holds significant potential for applications in the field of urban visualization, although it is primarily utilized for showcasing architectural forms or aiding in planning and design processes. To enhance the integration of urban visualization technology into the everyday lives of ordinary residents and expand the user base beyond planners, this paper introduces the Community Life Route Guidance System, leveraging the presentation of economic livelihood to afford residents interactive experiences within authentic living scenarios. By incorporating and aligning the location and information of Points of Interest (POIs) within the urban model of a single community and its surrounding neighborhoods, it generates, calculates, and filters 'life routes' comprising four categories of commercial nodes. The objective is to serve new residents in the community by providing life guidance or to offer prospective homebuyers a reference point for the convenience of community commercial facilities. The specific technical pathway encompasses: constructing an environmental model, processing and associating POI data, life path identification based on the Dijkstra algorithm, and path visualization with user interaction.

Map API-Based Evacuation Route Guidance System for Floods

Recently, human casualties and property damage caused by natural disasters have increased worldwide. Among these natural disasters, flood damage is affected by season. Depending on the concentration of precipitation in the summer, heavy rainfall can occur, thus resulting in typhoons, floods, and increased damage. To prevent such damages, the appropriate measures and research are being conducted in response to disasters. When a flash flood occurs, safe evacuation can be realized after detecting the situation and using announcements or laser indicators. However, these route guidance systems are typically used in fire or indoor environments, thus rendering them difficult to access outdoors. Therefore, we herein propose an evacuation route guidance system based on a map API that recognizes flood occurrences in forest areas, recreational forests, and parks. It calculates the route based on the map API and delivers the evacuation route to the nearest shelter to the user; and if there is a second problem on the moving evacuation route and it is difficult to proceed, the user’s current location is identified and the route to the next nearest shelter is provided. This will help you to evacuate safely.

Real-Time Indoor Positioning and Route Guidance System by using Beacons

Real-Time Indoor Positioning and Route Guidance System by using Beacons

Real-time indoor positioning and route guidance system by using beacons

Real-time indoor positioning and route guidance system by using beacons

Demonstration of the Effect of Evacuation Systems in Buildings

In the event of a fire in a building, casualties are likely to occur due to various causes. The most common causes of fatalities and injuries due to building fires are related to the inability to use evacuation routes smoothly. The casualties caused by fires could be greatly reduced if a systematic evacuation system is prepared by considering the characteristics of the target building. Accordingly, an appropriate evacuation route can be established based on the location of the fire. Therefore, in this study, an evacuation system that reflects the characteristics of the building was established to minimize casualties in the event of a building fire. Further, to facilitate quick and safe evacuation, an appropriate evacuation route was provided to the occupants in the event of a building fire. The usability of the proposed evacuation system and evacuation route guidance system was verified through empirical experiments involving occupants of various age groups and real buildings.

Strong and flame-retardant wood-based triboelectric nanogenerators toward self-powered building fire protection

In the new era of industry 4.0, improving the energy efficiency and safety is of vital importance for the construction of intelligent building. Here, we report a strong and flame-retardant wood-based triboelectric nanogenerator (FW-TENG) for self-powered building fire protection. Based on a three-step method involving delignification, infiltration with bentonite nanosheets, followed by hot-pressing, the mechanical strength and flame retardancy can be greatly improved. Compared to the natural wood, the flame-retardant wood exhibits a 7.7-fold enhancement in strength, 32% reduction in peak heat release rate, as well as self-extinguishing property. The resulting FW-TENG is further used to develop the self-powered intelligent wireless fire monitoring and escape route guidance system for fire rescue and evacuation. This work extends the application area of self-powered systems to building fire protection, and may greatly promote the development of intelligent buildings and smart cities.

Linking the cognitive load induced by route instruction types and building configuration during indoor route guidance, a usability study in VR

Every route instruction type (e.g. map, symbol, photo) induces a specific cognitive load. However, when these types are used at different decision points in a building, the building configuration of these points also influences the induced cognitive load. Therefore, the process of route guidance results in an interaction between the instruction type and the decision point, which determines the induced cognitive load. One way of reducing cognitive load during route guidance is by using adaptive systems that show specific route instruction types at specific decision points. Therefore, in this VR experiment, the usability of such an adaptive indoor route guidance system is tested by tracking the wayfinding and gaze behavior of the users. First, the difference in wayfinding and gaze behavior between all route instruction types is compared. Next, the building configuration at the decision points is quantified through the architectural theory of space syntax, and the correlation with the wayfinding and gaze behavior is determined. Our findings indicate that adapting the route instruction type does make a difference for the user.

An Advanced Intelligent Route Guidance System in Urban Traffic Network with Mixed Autonomous Vehicles and Human-Driven Vehicles

An Advanced Intelligent Route Guidance System in Urban Traffic Network with Mixed Autonomous Vehicles and Human-Driven Vehicles

An Advanced Intelligent Route Guidance System in Urban Traffic Network with Mixed Autonomous Vehicles and Human-Driven Vehicles

An Advanced Intelligent Route Guidance System in Urban Traffic Network with Mixed Autonomous Vehicles and Human-Driven Vehicles

Delay Factors Modelling for Real-Time Traffic Information Systems

The paper is presenting an overview of the factors that lead to critical delays which may occur in multi-modal realtime traffic and travel information systems (MRTTI) employing cellular data network communications. The analysis is taking into account MRTTI systems that use mobile devices to detect position and speed of a vehicle and receive traffic information furthermore used to adjust a previously defined route from origin to destination points. Information transmission delays are also investigated for route guidance systems used in critical applications, such as emergency vehicles management. The identification of the delay factors and some models regarding the delays are presented along with measurements on a real system.

Using Hybrid Clustering to Approximate Fastest Paths on Urban Networks

Estimating fastest paths on large networks is a crucial problem for dynamic route guidance systems. The present paper proposes a statistical approach for approximating fastest paths on urban networks. The traffic data used for conducting the statistical analysis is generated using a macroscopic traffic simulation software developed by us. The traffic data consists of the input flows, the arc states or the number of cars in the arcs and the paths joining the various origins and the destinations of the network. To find out the relationship between the input flows, arc states and the fastest paths of the network, we subject the traffic data to hybrid clustering. The hybrid clustering uses two methods namely k-means and Ward’s hierarchical agglomerative clustering. The strength of the relationship among the traffic variables was measured using canonical correlation analysis. The results of hybrid clustering are decision rules that provide fastest paths as a function of arc states and input flows. These decision rules are stored in a database for performing predictive route guidance. Whenever a driver arrives at the entry point of the network, the current arc states and input flows are matched against the database parameters. If agreement is found, then the database provides the fastest path to the driver using the corresponding decision rule. In case of disagreement, the database recommends the driver to choose the shortest path as the fastest path in order to reach the destination.

ESTABLISHMENT OF A UNIFORM ROUTE GUIDANCE SYSTEM IN UKRAINE TAKING INTO ACCOUNT THE WORLD’S BEST PRACTICES TO IMPROVE ROAD SAFETY

Route guidance tools have a significant impact on road safety on public roads, as they carry important information on route guidance: provide drivers with information on the location on a specified road, regularly confirm the planned direction of movement, timely inform road users about the change of specified route direction and indicate the distance to the final object. This is very important information for the driver and it is difficult to overestimate it in road safety. Timely informing of drivers on the route direction will allow them carrying out adjustments in advance and feel confident on the road. Ensuring the completeness of route guidance information prevents emergency braking of vehicles in the area of traffic junctions and violation of the maneuvering rules to make a turn / turnaround in the right direction, and such maneuvers lead to road accidents. The establishment of a uniform route guidance system will reduce the road accident level and the cost of transportation due to excessive mileage of vehicles in the case of wrong route and help the drivers freely and easily get to their destination. The main need for information is felt by the drivers of vehicles whose actions directly impact the road safety. This, in turn, determines the basic principles and requirements for the composition, placement and choice of information tools. During the trip, the drivers need the information that would allow them navigating freely on the roads when passing the selected route that will relieve the stress of drivers and reduce the probability of road accidents, as well as increase the capacity of roads. It helps the drivers to adjust the previously selected route taking into account the real traffic conditions during the trip thereby minimizing the time consumption. To navigate the roads during movement, the drivers need the information about streets, objects and directions. Such information is provided by information signs, with which the road network must be equipped in sufficient quantity. The information provision should differ depending on the settlements, public roads and traffic conditions. A comprehensive solution of the issue of quality information provision to the road users can be achieved only by establishing a uniform route guidance system for road users which will take into account the needs for information of different categories of road users.

An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments.

Study on portable haptic guide device with omnidirectional driving gear

In the past, white canes, guide dogs, and guide helpers have served to assist visually impaired people when walking outdoors. However, these assistance methods have various limitations for extended and suitable usage. Therefore, our laboratory herein proposes a route guidance device that signals commands to the end-user by giving a sense of force to the thumb. The guide device is driven on the common plane with two degrees of freedom by using an XY stage with omnidirectional driving gear. The current prototype presents challenges for its practical implementation as a smooth, safe and reliable route guidance system. In this study, we conducted experiments aiming to optimize the combination of the update position of the intermediate target point and the presentation cycle in blindfolded clear-eyed people.

Route Guidance System for the Road Network-A Review

A comprehensive and satisfying route- guidance system is considered valuable for citizens and tourists worldwide. An effective route-guidance system is essential because of the sophisticated city formation and transportation system in India. For better utilization of route-guidance systems. It needs to be easy to use and informational but to be more sensible to give optimal route options in terms of low cost and low time in various situations (Such as climate conditions, traffic jam, road structure). This paper aims to comprehend the research trend in the route guidance system and easily observe the various technique of route guidance. Different impressive findings have come out of this study, supporting current and future researchers to evaluate and set their research roadmap. This paper also explains the existing approaches for route guidance systems in simple language, which will be fruitful for researchers. Furthermore, this paper also envisions the future of RGs, which may open up new research directions in this domain.

Cloud-Assisted Mobile Crowd Sensing for Route and Congestion Monitoring

Accurate and reliable real-time urban traffic management can benefit urban citizens’ daily life by reducing stress, travel time and carbon footprint. The provision of reliable and accurate traffic information has however proven to be a major challenge in intelligent transportation systems. Citizens carrying smartphones can be exploited as an important provider of traffic information and the mobile crowd sensing paradigm can be used as a solution to this challenge. In this paper, an urban traffic monitoring system which exploits the power of participatory sensing and cloud messaging is proposed. Crowd intelligence that is used to estimate traffic congestion levels, arrival times, and average road speed is harvested from crowd sensed data. Traffic congestion control at route level is implemented with a route guidance system. Proactive warnings or recommendations to drivers in the vicinity of or on the route to reported events are provided. The drivers can also report short-term traffic events and physical road conditions for road monitoring. Real-world experiments are conducted with a prototype implementation and the results demonstrates both system feasibility and traffic estimation accuracy.