Dynamic Route Planning Research Articles

Analysis of Quality of Service in VANET

VANET as Inter-vehicle communication is becoming a promising area of research, standardization, and growth with the ever-growing number of vehicles equipped with computer technology and wireless communication tools. Due to its erratic portability and the interrupted availability of a network, VANET is relatively incomprehensible for a strong end to end communication, In Vehicular Network the main advantage is that it communicates by using nodes. Because of its simple and basic method of communication, MANET is used most often in the military, just like data sharing between different computers. VANET and some modifications are identical to MANET. VANET encompass the mobile nodes which are indicated as (MN), The vehicle networking system has drawn significant interest from academia and industry since its establishment. Its technology is used for military purposes by several foreign manufacturing and government sectors, it also enables a wide range of applications, including collision prevention, security, blind crossing, dynamic route planning, traffic condition monitoring on a real-time basis, etc but there are certain loopholes or drawbacks like frequent network drop, high mobility which affected the overall QoS, In this paper, there are brief analyses on Quality of Service which include certain Routing Protocol like DIVERT, FBAODV, DSR and also to decrease the congestion control, there is a comparative study of the different protocol which evaluates in increasing the throughput and network efficiency.

Which quality is a route? A methodology for assessing route quality using spatio‐temporal metrics

AbstractIn the context of intelligent transport systems, dynamic route planning is an eagerly researched topic. While the research community during the last decade has focused on performance and scalability of dynamic route planning algorithms, the question concerning route qualities has been widely neglected. The current work contributes to this question by proposing a methodology to assess the quality of arbitrary routes based on seven spatio‐temporal route quality metrics (five spatial and two temporal ones). The methodology is evaluated by calculating quality metrics for 45 routes being derived from three dynamic route planning systems for three selected origin–destination pairs during one day. For objectively assessing route qualities, the approach matches the route planning results to a reference digital road network and uses reference travel times for calculating quality metrics. A spatio‐temporal cross‐evaluation illustrates different quality aspects in the context of both quality dimensions and demonstrates the usefulness of the proposed approach.

Route Planning with Dynamic Information from the EPLOS System

The paper presents the problem of distribution route planning with dynamic information about sudden customers' needs. Particular attention was paid to dynamic vehicle route planning and its influence on the distance covered by a distribution vehicle. In the article, authors assume that the quick information about customers’ sudden needs is transferred from the EPLOS tool data base. Authors analyze the available literature on transport route optimization and propose a solution to the problem of distribution among customers with sudden needs. In order to present the impact of quick information influence on the distribution route minimization, a simulation model of the vehicle routing problem was generated in the FlexSim environment.

Crowdsensing-Based Personalized Dynamic Route Planning for Smart Vehicles

Current route planning systems report to the driver routes based on expected travel time and distance. However, these systems do not provide individualized routing options. With the current routing systems lacking the provision of individualized routing choices, a routing framework which provides a personalized route option not solely based on time and distance would be a step up. With the expanding sensing and computing capabilities in both vehicles and smart devices along with the promising low-latency of 5G networks, a real-time personalized route planner is achievable. In this article, a route planning framework that utilizes the in-vehicle and smartphone sensors to build a crowdsensed database on road surface quality and the driver's personalized skillfulness in different driving environments is proposed. Such databases are leveraged to provide drivers with routing options based on their personal preferences. This framework is tested and validated through a case study of a real driving scenario in Kingston, Ontario to show the framework capabilities compared to conventional route planning.

Application of an Improved Ant Colony Algorithm in Coastal Tourism Route Optimization

Zhang, W., 2019. Application of an improved ant colony algorithm in coastal tourism route optimization. In: Li, L.; Wan, X., and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 84–87. Coconut Creek (Florida), ISSN 0749-0208.The ant colony algorithm has strong robustness, good adaptability, is easy to combine with other algorithms, and has applications in data mining and cluster analysis. Through the study of a basic ant colony algorithm and coastal travel route planning, two improvements to the ant colony algorithm have put forward, which include the adventurous algorithm and provide a new route-seeking method. The main purpose of this research was to make the ant colony algorithm meet the problem of route planning. The experimental results show that the improved ant colony algorithm achieves good results both in optimal path solution and in dynamic coastal travel route planning. The dynamic planning of the route can effectively realize the load balancing of tourist attractions and, at the same time, guide tourists to current small tourist attractions. It reflects the humanization and intelligence of coastal tourism route planning.

The Energy-Efficient Dynamic Route Planning for Electric Vehicles

Aiming to provide an approach for finding energy-efficient routes in dynamic and stochastic transportation networks for electric vehicles, this paper addresses the route planning problem in dynamic transportation network where the link travel times are assumed to be random variables to minimize total energy consumption and travel time. The changeable signals are introduced to establish state-space-time network to describe the realistic dynamic traffic network and also used to adjust the travel time according to the signal information (signal cycle, green time, and red time). By adjusting the travel time, the electric vehicle can achieve a nonstop driving mode during the traveling. Further, the nonstop driving mode could avoid frequent acceleration and deceleration at the signal intersections so as to reduce the energy consumption. Therefore, the dynamically adjusted travel time can save the energy and eliminate the waiting time. A multiobjective 0-1 integer programming model is formulated to find the optimal routes. Two methods are presented to transform the multiobjective optimization problem into a single objective problem. To verify the validity of the model, a specific simulation is conducted on a test network. The results indicate that the shortest travel time and the energy consumption of the planning route can be significantly reduced, demonstrating the effectiveness of the proposed approaches.

Accurate Detection of Road Network Anomaly by Understanding Crowd's Driving Strategies from Human Mobility

There are thousands of road closures and changed traffic rules that impact vehicle routing every day. Detecting the road closures and traffic rule changes is essential for dynamic route planning and navigation serving. In this article, we propose a driving-behavior modeling-based method for accurately and effectively detecting the road anomalies. In the first step, we detect the areas of anomalies by using the deviation between drivers’ actual and expected behaviors. To discover the cause of anomalies, we explore the drivers’ short-term destination and find the crucial link pairs in anomalous areas through a novel optimized link entanglement search algorithm, namely, the Select Link Entanglements (SELES) algorithm. Finally, we analyze the crowd's driving patterns to explain the road network anomalies further. Experiments on a very large GPS dataset demonstrate that the proposed approach outperforms the existing methods in terms of both accuracy and effectiveness.

Robust optimization of dynamic route planning in same‐day delivery networks with one‐time observation of new demand

AbstractLocal delivery networks expect drivers to make deliveries to and/or pickups from customers using the shortest routes in order to minimize costs, delivery time, and environmental impact. However, in real‐world applications, it is often the case that not all customers are known when planning the initial delivery route. Instead, additional customers become known while the driver is making deliveries or pickups. Before serving the new demand requests, the vehicle will return to the depot for restocking. In other words, there exists a precedence relation in the delivery route to visit the depot before delivering new orders. The uncertainty in new customer locations can lead to expensive rerouting of the tour, as drivers revisit previous neighborhoods to serve the new customers. We address this issue by constructing the delivery route with the knowledge that additional customers will appear, using historical demand patterns to guide our predictions for the uncertainty. We model this network delivery problem as a precedence‐constrained asymmetric traveling salesman problem using mixed‐integer optimization. Experimental results show that the proposed robust optimization approach provides an effective delivery route under the uncertainty of customer demands.

Smart Building: A Low Cost Indoor Positioning and Intelligent Path Finding

Despite the rapid improvement in mobile devices, overall gradual growth in the ubiquitous computing field, the wide applicability, more usefulness of location based services in general and indoor navigation. The Global Positioning System (GPS) has undergone tremendous improvement since the 1900s and it, indeed is considered one of the most successful navigation systems known to date. However, it is still inefficient for sufficiently accurate positioning in both indoor environments and environments with many tall buildings such as skyscrapers since such buildings block or interfere with its signal transmissions. In particular, building a sufficiently accurate, efficient and relatively cheap indoor navigation system in a GPS-free environment is still a challenging task with a lot of tradeoffs and constraints to put into consideration. In this paper, a simple yet robust, low-cost, context-aware user-interactive, user-friendly hybrid of fingerprinting and dead reckoning indoor navigation system suitable for both the visually and the physically disabled as well that takes advantage of the results yielded by sensor fusion is proposed. The presented system is also designed to allow for efficient evacuation of users in cases of emergences. The prototype is made majorly of the following parts; user tracking, optimal, context-aware and dynamic route calculation and planning and dynamic route representation with an upper bound of 2m and an average of 0.8-1.3m accuracy. All that is required from the user is a smart phone without installation of extra hardware.

An Energy-Efficient Dynamic Route Optimization Algorithm for Connected and Automated Vehicles Using Velocity-Space-Time Networks

This paper introduces three types of status information based on the current environmental perception and the information computing power of connected and automated vehicles (CAVs) and proposes an energy-efficient dynamic route planning algorithm without starting and stopping for the CAVs. The information types include real-time road traffic flow, traffic light and vehicle driving status (driving velocity and direction). The algorithm aims to minimize both time and energy consumption for the route. For this study, the feasibility of this energy-efficient route planning algorithm was first theoretically deduced. After introducing traffic light and driving energy consumption elements, a velocity-space-time three-dimensional network (VSTN) model was mathematically formulated to describe a dynamic route in addition to the design of the shortest route searching algorithm in VSTN. Two experimental road networks were then constructed to test and compare the energy efficiency. The first test scenario is a line road with two road conditions and various signal cycles and initial phases. The experiment shows that the cumulative energy consumption of the energy-efficient route under various scenarios is no greater than the normal route, ranging from 5.18% to 16.4%. The second test scenario involves a network located in the Jing'an district of Shanghai and is intended to verify the route searching function and energy efficiency. Compared to the traditional driving method, the new route reduces energy consumption by 10.36%. This study demonstrates that the energy efficiency does not correlate with the signal cycle or initial phase, and it appears to be random. The new algorithm proposed in this paper can be applied to dynamic route planning by CAVs and the goal of saving energy can be achieved in various road networks.

A dynamic rescue route planning method based on 3D network in mine water inrush hazard

Water inrush in mine is one of the most common hazards in the mining industry. To solve the problem of rescue route planning during a mine water inrush hazard, a dynamic rescue route planning method based on a 3 D network is proposed. First, the basic elements in the 3 D network model of the mine roadway are abstractly described, furthermore, the weights of edges, the topological connectivity of the elements, and the data structure of the network are defined. Then, the Dijkstra algorithm and the Breadth-first search algorithm are combined to implement the best rescue search route and return route planning based on the 3 D network model. Finally, the dynamic rescue route planning is simulated in 3 D virtual spatial scenario of a real mine. The factors such as topological connectivity of the elements, real-time water level, and slope of the mine roadways are considered in the process of rescue route planning, as the rescue route is adjusted dynamically according to the actual situations. This method proposed can provide powerful decision support for mine water inrush hazard rescue.

Shared Subway Shuttle Bus Route Planning Based on Transport Data Analytics

The development requirements of shared buses are extremely urgent to alleviate urban traffic congestions by improving road resource utilization and to provide a neotype transportation mode with good user experiences. The key to shared bus implementation lies in accurately predicting travel requirements and planning dynamic routes. However, the sparseness and the high volatility of shared bus data bring a great resistance to accurate prediction of travel requirements. Based on the consideration of user experiences, optimization objectives of shared bus route planning are significantly different from traditional public transportation and shared bus route planning is far more challenging than online car-hailing services due to the relatively high number of passengers. In this paper, we put forward a two-stage approach (SubBus), which is composed of travel requirement prediction and dynamic routes planning, based on various crowdsourced shared bus data to generate dynamic routes for shared buses in the “last mile” scene. First, we analyze the resident travel behaviors to obtain five predictive features, such as flow, time, week, location, and bus, and utilize them to predict travel requirements accurately based on a machine learning model. Second, we design a dynamic programming algorithm to generate dynamic, optimal routes with fixed destinations for multiple operating buses utilizing prediction results based on operating characteristics of shared buses. Extensive experiments are performed on real crowdsourced shared subway shuttle bus data and demonstrate that SubBus outperforms other methods on dynamic route planning for the “last mile” scene. Note to Practitioners —This paper is inspired by the problem of shared subway shuttle bus dynamic route planning for the “last mile” scene, and it is also applicable to other scenes, including commuting scenes, urban transportation hub scenes, and destination scenes of the tourist market. Shared bus operation routes at such scenes are usually aimed at trips with fixed destinations. Existing approaches to planning routes are generally designed for traditional transportation, such as traditional buses and taxis. In this paper, we propose a novel two-stage dynamic route planning approach (SubBus) based on the operation characteristics of shared subway shuttle buses. We perform a resident travel behavior analysis to improve the accuracy of travel requirement prediction. After that, we combine the prediction results and station properties to gain shared bus optimal routes. We then display how to apply SubBus to optimize shared bus operation status based on crowdsourced shared subway shuttle bus data generated by Panda Bus Company. We keep a continuous collaboration with the company to optimize the approach details and experimental effects, which demonstrate that our approach can generate effective routes for shared subway shuttle buses to optimize operation status on the “last mile” issue.

Dynamic route planning in the Baltic Sea Region – A cost-benefit analysis based on AIS data

Increased international trade, together with a growing concern about global emissions, raises demands to make transportation at sea more efficient. Most ways to reduce emissions will increase sea transportation costs. Dynamic route planning at sea is a concept that allows simultaneous emissions reduction and savings for ship owners. With route plans and coordination centres, ships may take shorter routes and adjust to more efficient speeds, instead of anchoring and then awaiting for berth. Using data from ships’ Automatic Identification System (AIS) transmitters, we measure the sailed distance, as well as the number and the anchored time for cargo (general cargo, bulk, container and RoRo) and tanker ships on a representative day in the Baltic Sea Region. Benefits and costs are estimated using unit prices for emissions and other costs. We find that the benefits of dynamic route planning outweigh its costs; the most decisive factors are (i) by how much distances are reduced and (ii) the unit values of emissions. For each percentage point distances that can be reduced, benefits are estimated at €102 million per year. Adding benefits of adjusted arrival times and subtracting estimated costs, net gains are found in the range of €84–98 million per year. The availability of AIS data provides new opportunities to quantify the traffic at sea and evaluate proposals to make sea transportation more efficient. The methodology can, apparently, be applied to other areas with dense sea traffic.

FGWSO‐TAR: Fractional glowworm swarm optimization for traffic aware routing in urban VANET

SummaryIn mobile distributed applications, such as traffic alert dissemination, dynamic route planning, file sharing, and so on, vehicular ad hoc network (VANET) has emerged as a feasible solution in recent years. However, the performance of the VANET depends on the routing protocol in accord with the delay and throughput requirements. Many of the routing protocols have been extensively studied in the literature. Although there are exemptions, they escalate research challenges in traffic aware routing (TAR) protocol of VANET. This paper introduces the fractional glowworm swarm optimization (FGWSO) for the TAR protocol of VANET in an urban scenario that can identify the optimal path for the vehicle with less traffic density and delay time. The proposed FGWSO searches the optimal routing path based on the fitness function formulated in this paper. Fractional glowworm swarm optimization is the combination of the GWSO and fractional theory. Moreover, exponential weighted moving average is utilized to predict the traffic density and the speed of the vehicle, which is utilized as the major constraints in the fitness function of the optimization algorithm to find the optimal traffic aware path. Simulation of FGWSO shows the significant improvement with a minimal end‐to‐end delay of 6.6395 seconds and distance of 17.3962 m, respectively, in comparison with the other existing routing approaches. The simulation also validates the optimality of the proposed TAR protocol.

Scalable space-time trajectory cube for path-finding: A study using big taxi trajectory data

Route planning is an important daily activity and has been intensively studied owing to their broad applications. Extracting the driving experience of taxi drivers to learn about the best routes and to support dynamic route planning can greatly help both end users and governments to ease traffic problems. Travel frequency representing the popularity of different road segments plays an important role in experience-based path-finding models and route computation. However, global frequency used in previous studies does not take into account the dynamic space-time characteristics of origins and destinations and the detailed travel frequency in different directions on the same road segment. This paper presents the space-time trajectory cube as a framework for dividing and organizing the trajectory space in terms of three dimensions (origin, destination, and time). After that, space-time trajectory cube computation and origin-destination constrained experience extraction methods are proposed to extract the fine-grained experience of taxi drivers based on a dataset of real taxi trajectories. Finally, space-time constrained graph was generated by merging drivers’ experience with the road network to compute optimal routes. The framework and methods were implemented using a taxi trajectory dataset from Shenzhen, China. The results show that the proposed methods effectively extracted the driving experience of the taxi drivers and the entailed trade-off between route length and travel time for routes with high trajectory coverage. They also indicate that road segment global frequency is not appropriate for representing driving experience in route planning models. These results are important for future research on route planning or path finding methods and their applications in navigation systems.

Simultaneous optimization of evacuation route and departure time based on link-congestion mitigation

Link-level congestion, such as link spillback and long intersection queues, should be avoided during emergency evacuation. The reason is that these local traffic incidents can cause traffic safety risks and hinder evacuation tasks. To determine reliable routes and departure times for the whole evacuation, we establish the link-congestion mitigation-based dynamic evacuation route planning (LCM-DERP) model. The distinct difference with the typical DERP model lies in the objective composition. The system cost objective in our model consists of not only total evacuation time but also external congestion cost. The penalization for link spillback and long intersection queues is used to represent external congestion cost. An improved cell transmission structure, composed of tail cell and head cell and approach cell, is proposed to simulate dynamic traffic flow. Specifically, tail cell and head cell can detect the information of link spillback and long intersection queue separately. This function enables the representation of external congestion cost expressed by multiplying link-level congestion vehicles with penalty parameter. A method of successive average, including a calculation of the local path marginal cost, is used to solve the model. We applied the LCM-DERP model on a road network around Olympic Stadium in Nanjing, China, to test its effectiveness in the aspect of link-congestion control. Compared with the typical DERP model, our method can improve system cost, especially in the high demand range, wherein the reduced external congestion cost is larger than this reduced system cost.

IMPROVING QUALITY OF SERVICE THROUGH ROAD SIDE BACK-BONE NETWORK IN VANET

The vehicular ad hoc Networks (VANETs) are expected to support a large spectrum of traffic alert, dynamic route planning, file sharing, safety and infotainment applications to improve traffic management. User satisfaction plus in time delivery of real-time messages is the most significant quality evaluation criterion for vehicular applications. High mobility and rapidly changing topologies always lead to intermittent quality of services, higher delay and packet dropping issues in network. To improve the quality of services for multi-hop and dynamic environment, different types of solutions have been proposed. The article introduces multi-protocol label switching based on roadside backbone network to provide widespread, scalable, high-speed, robust quality of services and improve network efficiency. The simulation results showed that proposed model improves data transmission and routing performance in terms of data delivery, throughput, end-to-end delay and achieve adequate utilization of resources.

Cost Benefit Analysis of Dynamic Route Planning at Sea

Route optimization through dynamic route planning, where ships can shorten their routes taking other vessels movements into account through shared information, has a potential to make transportation at sea more efficient. Fuel and emissions can be saved through green steaming without increased cargo transit times, and without reducing safety. This study estimates the potential net benefits to society in major areas in the Baltic Sea and the North Sea. It is found plausible that routes can be shortened by one percent on average, which would reduce costs to society by 80 million euros per year, of which 35 percent are reduced fuel costs and 65 percent are reduced emission costs. Alternative unit values of emissions give an interval of 55 to 113 million euros in benefits. The project's costs are estimated at 15 million euros per year. With a growing world trade, the potential for more efficient and environmentally friendly sea transportation through dynamic route planning may be substantial.

Incorporating Bidirectional Heuristic Search and Improved ACO in Route Planning

A multi-objective multi-node dynamic route planning system for a vehicle is presented in this paper. In this system, a bidirectional heuristic search algorithm is designed to perform path planning between two nodes in a topological map created by OpenStreetMap for urban scenarios. And then an improved multi-objective Ant Colony Optimization (ACO) algorithm considering the timeliness of goal nodes is proposed to generate the node sequence. Experimental results validated the proposed approach.

VHSSA Model for Predicting Short-Term Traffic Flow of Urban Road

AbstractA short-term traffic prediction model VHSSA (prediction model based on vertical and horizontal sequence similarity algorithm) is proposed for accurate traffic prediction and dynamic route planning. The model is based on the similarity of vertical and horizontal sequences and analyzes historical traffic time series data and cyclic similarity characteristics of traffic volume in urban roads. The model can overcome the deficiency of the traditional model VSSA (prediction model based on vertical sequence similarity algorithm), which focuses only on vertical cyclic sequence similarity. Complete data are transformed into basic sequences that reflect basic characteristics and fluctuant sequences as well as variation characteristics by utilizing the wavelet transformation function. This transformation can achieve both basic and complete sequence prediction. For complete sequence prediction, the paper corrected the fluctuant sequence based on confidence interval and overlapped it with the basic sequence. V...