Rerouting System Research Articles

Spatial digital twin framework for overheight vehicle warning and re-routing system

Overhead road obstacles present a significant logistical hazard to the heavy vehicle industry. Traditional overheight vehicle warning systems such as passive warning systems (PWS) and active warning systems (AWS) have not adequately reduced the frequency and impact of overheight incidents, encouraging transportation agencies to employ intelligent transport system (ITS) strategies using state-of-the-art advanced technologies. This research takes an innovative approach in developing an immersive user-focused experience, harnessing multi-disciplinary methods and tools to engineer a spatial digital twin prototype for a novel Internet-of-Things (IoT)-based active warning alert and re-routing system (AWARS). LiDAR and 3D GIS were used to model the complex road environment, tailored to the strict fiscal objectives sought by economically mindful organisations. Tree crowns were extracted from near-Infrared aerial imagery and digital elevation models, supplying the dimensions necessary for 3D tree modelling. IoT connectivity was configured using a real-time analytics approach to deliver alerts and re-routing options. The World Traffic Service with live and predictive traffic data was used for the routing application programming interface (API). A standard-configuration common rigid truck (CRT) was inserted into the 3D road environment model to simulate overheight collisions and to ascertain the effect of re-routing on estimated time of arrival (ETA). Longer ETA durations were observed for routes computed by the digital twin. Theoretically, enhanced situational awareness and subsequent reduction of risk likelihood suggests an optimised response to industry demands, despite extended travel times, cultivating a favorable impact on the supply chain through enhanced safety management.

Framework for Assessing the Sustainability Impacts of Truck Routing Strategies

The impact of freight on the transportation system is accentuated by the fact that trucks consume a greater roadway capacity than other vehicles and therefore cause more significant problems including traffic congestion, traffic delays, crashes, and pavement damage. Evaluating the actual repercussions of truck traffic becomes paramount in locales where roadway expansion is unfeasible. Trucks are vital to the economy, providing essential services to commerce and industry, and yet it is crucial that their operation does not contribute to the deterioration of infrastructural quality or compromise public safety. Currently, we lack methodologies in practice for the real-time management of traffic, specifically for truck routing, to minimize travel times and prevent delays due to non-recurrent congestion, such as traffic incidents. Accordingly, this study aimed to devise a truck routing strategy utilizing a traffic micro-simulation model (VISSIM) and to assess its effects on reducing travel delays. This involved the development of real-time truck re-routing simulation models that take into account non-recurrent congestion and the resulting travel delays and fuel consumption. The VISSIM model was applied to the I-75 corridor in Marion County, Florida, focusing on non-recurrent congestion effects on travel delays and fuel consumption. The initial findings suggest that the implementation of a dynamic truck re-routing system can significantly alleviate traffic congestion, resulting in a marked decrease in travel delays and fuel usage, demonstrating the potential for such strategies to enhance the overall efficiency of the transportation system.

Collaborative Vehicle Rerouting System With Dynamic Vehicle Selection

Collaborative Vehicle Rerouting System With Dynamic Vehicle Selection

CHRT: Clustering-based hybrid re-routing system for traffic congestion avoidance

Re-routing system has become an important technology to improve traffic efficiency. The traditional re-routing schemes do not consider the dynamic characteristics of urban traffic, making the planned routes unable to cope with the changing traffic conditions. Based on real-time traffic information, it is challenging to dynamically re-route connected vehicles to alleviate traffic congestion. Moreover, how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing system. To deal with these challenges, this paper proposes CHRT, a clustering-based hybrid re-routing system for traffic congestion avoidance. CHRT develops a multi-layer hybrid architecture. The central server accesses the global view of traffic, and the distributed part is composed of vehicles divided into clusters to reduce latency and communication overhead. Then, a clustering-based priority mechanism is proposed, which sets priorities for clusters based on real-time traffic information to avoid secondary congestion. Furthermore, to plan the optimal routes for vehicles while alleviating global traffic congestion, this paper presents a multi-metric re-routing algorithm. Through extensive simulations based on the SUMO traffic simulator, CHRT reduces vehicle traveling time, fuel consumption, and CO2 emissions compared to other systems. In addition, CHRT globally alleviates traffic congestion and improves traffic efficiency.

A neural network approach for traffic prediction and routing with missing data imputation for intelligent transportation system

A robust traffic rerouting system is important in traffic management, alongside an accurate traffic simulation model. However, missing data continues to be a problem as it will inevitably cause errors in predicting the congestion levels, resulting in a less efficient rerouting. The lack of a realistic traffic simulation also serves to hamper the development of a better traffic management system. As such, this paper aims to address both problems by proposing three solutions: (i) a traffic simulation that would model a live-traffic, (ii) a pheromone-based, neural network traffic prediction and rerouting system, and (iii) a missing data handling method utilising weighted historical data method named Weighted Missing Data Imputation (WEMDI). The traffic simulation model was benchmarked using Google Maps rerouting system. WEMDI was tested by comparing the performance of the rerouting system with and without WEMDI’s integration for various levels of missing data. The results showed that the traffic simulation model displayed a high correlation to that of Google Maps, and the WEMDI-integrated system displayed 38% to 44% improvement in the related traffic factors, when compared to a situation with no rerouting system in place, and up to 19.39% increase in performance compared to the base rerouting system for missing data levels of 50%. The WEMDI system also displayed robustness in routing other locations, displaying a similarly high performance.

IoT enabled Environmental Air Pollution Monitoring and Rerouting system using Machine learning algorithms

Massive Internet of Things (IoT) is foretold to introduce surfeit of applications for a fully connected world. The increasingly degrading quality of air in several regions of the biosphere can be a reliable impact of human difficulties of increasing globalization and urbanization. Air pollution levels in many cities exceed legal and World Health Organization (WHO) limits for particulate matter and gaseous pollutants which can be found in concentrations that are hazardous to health. Regular acquaintance to high pollution intensities results in increase of humans affected from respiratory disorders such as asthma, chronic obstructive lung disease and increased mortality. In most of the cities, there is no chance for peoples to apprehend the levels of pollution they are undergoing in their day-to-day lives which is necessary to reduce their risks from poor air quality conditions. The quality of air can be measured by using a parameter named Air quality index. The Key pollutants to calculate Air quality index are particulate matter (PM2.5 and PM10), Ozone (O3), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2) and Carbon Monoxide (CO). This work deliberates the implementation of cloud based IoT system for air quality monitoring in which the sensors are used to calculate CO, PM2.5 and PM10, O3, SO2 and NOx pollution level with environmental condition like temperature and humidity. The obtained information can be updated in cloud platform using Lora nodes and Lora Gateway. The web-based application developed has a Google map API where the pollution status can be frequently updated. With the collected time series samples, the prediction analysis was done for PM with neural network Multi-Layer perceptron and support vector machine regression (SVMR) learning algorithm. This can helps a person to travel to any other places by automatically rerouting conditions in a pollution free environment.

Agent Based Modeling for Flood Inundation Mapping and Rerouting

Abstract Natural disasters like earthquakes and floods can have a serious impact on road networks, which are critical to supply chain infrastructure and to provide connectivity. These extreme events can result in isolating people in the affected area from hospitals and emergency response. This paper presents an agent-based model for understanding flood propagation and developing inundation mapping. The results from the mapping are used to identify the roads prone to floods based on elevation data and flood simulation. A simulation environment was set up in SUMO, and the costs associated with the traffic disruption are evaluated. This paper discusses the integration of various techniques for a comprehensive flood prediction and rerouting system.

An improved pheromone-based vehicle rerouting system to reduce traffic congestion

The growing number of vehicles necessitates the implementation of effective vehicle rerouting systems. Designing an effective vehicle rerouting system is challenging due to the dynamic nature of vehicular network. In this paper, a Proactive Travel-time based Pheromone Rerouting (PTPR) system is proposed. First, PTPR system predicts future congestion level using travel time and vehicle density information. Then, vehicles are distributed to multiple paths to balance the traffic load. Different from the existing pheromone-based rerouting systems, each ant (vehicle) in PTPR system can deposit its pheromone on multiple road segments away, instead of its direct adjacent road segment, based on its route. This new pheromone model aims to improve the performance of PTPR system. In addition, a localized dynamic k-shortest path (LDkSP) algorithm is proposed to reduce computational effort of PTPR system. Experiments were conducted on two different areas (i.e. suburban and urban) using Simulation of Urban Mobility (SUMO). Results show that the proposed PTPR system outperforms the existing rerouting system by reducing mean travel time, fuel consumption, and increasing number of arrive vehicles by 8.2%, 2%, and 15.1% respectively in Woodlands (suburban) and 28.7%, 17.2%, and 29.5% respectively in Novena (urban). The computation time used to reroute each vehicle is also reduced by 68.3% and 92.1% in suburban and urban area respectively using the proposed LDkSP. Finally, experiments over various usage rates and estimation errors showed that the proposed PTPR system is robust to usage rates ranging from 80% to 100% and is able to function properly with estimation error of up to 20%.

Aircraft re-routing optimization and performance assessment under uncertainty

The need for aircraft re-routing arises when there is disruption in the system, such as when an airport is closed due to extreme weather. In this paper, we investigate a simulation-based approach to optimize the aircraft re-routing process, by considering multiple sources of uncertainty. The proposed approach has four main components: system simulation, uncertainty representation, aircraft re-routing algorithm, and system performance assessment. Several sources of uncertainty are accounted for in this approach, related to incoming aircraft, space availability in neighboring airports, radar performance, and communication delays. An aircraft re-routing optimization model is formulated to make periodic re-routing decisions with the objective of minimizing the overall distance travelled by all the aircraft, subject to the system resources. We analyze the performance of this aircraft re-routing system using system failure time as the metric. Since the simulation time is limited, right-censored data arises with respect to system failure time. A novel methodology is developed to compute the lower bound of system failure time in the presence of right-censored data, and to analyze the sensitivity of the system performance metric to the uncertain variables relating to the aircraft, radars, nearby airports, and communication system. Since the simulation is time-consuming, we build a Support Vector Regression (SVR) surrogate model to efficiently construct the system failure time distribution.

Increased Traffic Flow Through Node-Based Bottleneck Prediction and V2X Communication

Transport delays due to traffic jams are manifested in many urban areas worldwide. To make road traffic networks more efficient, intelligent transport services are currently being developed and deployed. In order to mitigate (or even avoid) congestion, vehicle-to-vehicle and vehicle-to-infrastructure communications provide a means for cooperation and intelligent route management in transport networks. This paper introduces the novel predictive congestion minimization in combination with an A * -based router (PCMA * ) algorithm, which provides a comprehensive framework for detection, prediction, and avoidance of traffic congestion. It assumes utilization of vehicle-to-X communication for transmission of contemporary vehicle data such as route source and destination or current position, as well as for provision of the routing advice for vehicles. PCMA * further contains a component for intelligent selection of vehicles to be rerouted in case of a congestion, as well as an A * -based routing algorithm, taking into consideration the current road conditions and predicted future congestion. We prove the performance by dynamic microscopic traffic simulations in a real-world and an artificial road network scenario. Due to the well-performing prediction, the results reveal substantial advantages in terms of time and fuel consumption compared not only with situations with no active rerouting system but also with simple rerouting algorithms and more sophisticated approaches from literature.

Secure Vehicular Traffic Re-routing System using SCMS in Connected Cars

Secure Vehicular Traffic Re-routing System using SCMS in Connected Cars

DIVERT: A Distributed Vehicular Traffic Re-Routing System for Congestion Avoidance

Centralized solutions for vehicular traffic re-routing to alleviate congestion suffer from two intrinsic problems: scalability, as the central server has to perform intensive computation and communication with the vehicles in real-time; and privacy, as the drivers have to share their location as well as the origins and destinations of their trips with the server. This article proposes DIVERT, a distributed vehicular re-routing system for congestion avoidance. DIVERT offloads a large part of the re-routing computation at the vehicles, and thus, the re-routing process becomes practical in real-time. To take collaborative re-routing decisions, the vehicles exchange messages over vehicular ad hoc networks. DIVERT is a hybrid system because it still uses a server and Internet communication to determine an accurate global view of the traffic. In addition, DIVERT balances the user privacy with the re-routing effectiveness. The simulation results demonstrate that, compared with a centralized system, the proposed hybrid system increases the user privacy by 92 percent on average. In terms of average travel time, DIVERT's performance is slightly less than that of the centralized system, but it still achieves substantial gains compared to the no re-routing case. In addition, DIVERT reduces the CPU and network load on the server by 99.99 and 95 percent, respectively.

Next Road Rerouting: A Multiagent System for Mitigating Unexpected Urban Traffic Congestion

During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.

ICARUS: Improvement of traffic Condition through an Alerting and Re-routing System

In cities, where the number of vehicles continuously increase faster than the available infrastructure to contain them, traffic congestion is a difficult issue to deal with. This problem becomes even worse in case of accidents and affects many aspects of the modern society, including economic development, accidents, CO (Carbon monoxide) emission, trip time, and health. Several solutions for Traffic Management System (TMS) have been proposed to identify congestions and re-route the vehicles afterward. To this end, they exchange messages periodically between vehicles and central server, what can cause an overhead in the communication channel. In this scenario, it is important to identify the source of the problem and inform the drivers of new routes before the congestion takes place with, considering the limitations of vehicular communication. This work introduces ICARUS, a distributed and pro-active Traffic Management System, which receives notifications about a traffic events then it can calculates new routes, and, then, notifies drivers to follow new paths pro-actively by using inter-vehicle communications. Simulation results show the effectiveness of ICARUS in calculating new routes and disseminating them to vehicles approaching a congested area. Hence, ICARUS reduces the travel time, fuel consumption, and CO emissions of vehicles in urban environments when compared to existing approaches. In addition, ICARUS reduces the broadcast storm problem and maximizes the data dissemination capabilities with short delays and low overhead.

Effect of Sea Level Rise and Groundwater Withdrawal on Seawater Intrusion in the Gulf Coast Aquifer: Implications for Agriculture

The two main factors contributing to depletion of freshwater resources are climate change and anthropological variables. This study presents statistical analyses that are local in its specifics yet global in its relevance. The decline in Gulf Coast aquifer water quality and quantity has been alarming especially with the increased demand on fresh water in neighboring non-coastal communities. This study used seawater levels, groundwater use, and well data to investigate the association of these factors on the salinity of water indicated by chloride levels. Statistical analyses were conducted pointing to the high significance of both sea water level and groundwater withdrawals to chloride concentrations. However, groundwater withdrawal had higher significance which points to the need of water management systems in order to limit groundwater use. The findings also point to the great impact of increased groundwater salinity in the Gulf Coast aquifer on agriculture and socioeconomic status of coastal communities. The high costs of desalinization point to the increased signification of water rerouting and groundwater management systems. Further investigation and actions are in dire need to manage these vulnerabilities of the coastal communities.

REMUS: A Rerouting and Multiplexing System for Grid Connectivity Across Firewalls

The Grid provides unique opportunities for high-performance computing through distributed applications that execute over multiple remote resources. Participating institutions can form a virtual organization to maximize the utilization of collective resources as well as to facilitate collaborative projects. However, there are two design aspects in distributed environments like the Grid that can easily clash: security and resource sharing. It may be that resources are secure but are not entirely conducive to resource sharing, or networks are wide open for resource sharing but sacrifice security as a result. We developed REMUS, a rerouting and multiplexing system that provides a compromise through connection rerouting and wrappers. REMUS reroutes connections using proxies, ports and protocols that are already authorized across firewalls, avoiding the need to make new openings through the firewalls. We also encapsulate applications within wrappers, transparently rerouting the connections among Grid applications without modifying their programs. In this paper, we describe REMUS and the tests we conducted across firewalls using two Grid middleware case studies: Globus Toolkit 2.4 and Nimrod/G 3.0.

Measuring the effects of internet path faults on reactive routing

Empirical evidence suggests that reactive routing systems improve resilience to Internet path failures. They detect and route around faulty paths based on measurements of path performance. This paper seeks to understand why and under what circumstances these techniques are effective.To do so, this paper correlates end-to-end active probing experiments, loss-triggered traceroutes of Internet paths, and BGP routing messages. These correlations shed light on three questions about Internet path failures: (1) Where do failures appear? (2) How long do they last? (3) How do they correlate with BGP routing instability?Data collected over 13 months from an Internet testbed of 31 topologically diverse hosts suggests that most path failures last less than fifteen minutes. Failures that appear in the network core correlate better with BGP instability than failures that appear close to end hosts. On average, most failures precede BGP messages by about four minutes, but there is often increased BGP traffic both before and after failures. Our findings suggest that reactive routing is most effective between hosts that have multiple connections to the Internet. The data set also suggests that passive observations of BGP routing messages could be used to predict about 20% of impending failures, allowing re-routing systems to react more quickly to failures.