Streaming Route Assignment for Connected Autonomous Vehicles (Systems Paper)

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.

Efficient fastest path computation on a large scale road network is an essential problem in logistics distribution, especially based on real-time traffic information. In this paper, we present an adaptive fastest path algorithm capable of efficiently accounting for the network with real-time traffic data. (1) Logistics vehicle routing between two cities usually choose the road network through the largest roads. According to this traveler character and the natural hierarchy present in road network, we classify the roads into largest roads and smaller ones. (2) Based on the largest roads and smaller roads, we can partition the road network into areas. (3) Finally, we propose an efficient A* algorithm that uses the road network partition to limit the search space and considers the real-time traffic information. Through an extensive experimental evaluation on real road network we show that our algorithm provides desirable routes, and that it is significantly fast and effective.

Real-time traffic management systems with integrated proactive decision support capabilities are expected to operate with ( a) limited prediction accuracy ( b) decision-making latency, and ( c) partial coverage of the managed area. Such deficiencies are difficult to avoid in most real-world traffic network management applications, and there is a need to quantify the effect of these deficiencies on the performance of traffic network management systems. This paper studies the effectiveness of a proactive traffic management system. Various levels of prediction accuracy of the traffic network state, decision-making latency, and partial area coverage are considered. A traffic management system that emulates real-time operations is developed. The system adopts a closed-loop rolling horizon framework, which integrates network state estimation and prediction modules as well as decision support capabilities. A set of simulation experiments considers a hypothetical highway network. The results show that the effectiveness of the traffic management system can be affected by the deficiencies. However, the impact could be smaller if these deficiencies are kept under certain levels.

Due to the propagation of traffic congestion from upstream to downstream links and the uncertainty of path choice behaviours, travel times between different links, particularly adjacent links, are highly correlated in a typical period from day to day. To improve the estimation accuracy of both the mean and covariance of link travel times, a novel measurement is proposed to optimize the locations of multi-type traffic sensors for link travel time estimation. Multi-source data from different types of traffic sensors can be integrated to better estimate link travel time in an entire road network. In practice, the allocation of multi-type traffic sensors is constrained by the total financial budget and should be optimized in accordance with measurement errors and the cost ratio. Numerical examples of synthetic and real road networks are conducted to demonstrate the applications and merits of the proposed multi-type sensor location model with covariance effects.

In recent years, there is an extreme increase in road vehicle usage which in turn a challenge to manage the traffic system. The current traffic system is not based on the vehicle density level and a pre-established time is distributed to the traffic lights for every lane crossing which had an issue like traffic congestion & wastage of time and this condition turns out to be worse in the peak hours, and it also increases the emission of CO2 in the environment. In this paper, real-time dynamic traffic management system based on density level of each junction with the help of approximate geo-density information is developed. The density level on each junction is then updated to the real-time database. Then, this real-time data is computed with an exclusive priority-based algorithm where the crossing with high density is prioritized over the other (emergency vehicles are given the maximum priority) while maintaining the congestion around the junction under control. The prototype of GPS based real-time traffic management system is implemented and tested.KeywordsGPSPriority based algorithmTraffic system

Network Voronoi Diagram on uncertain objects for nearest neighbor queries

On road networks, the clustering of moving objects is important for traffic monitoring and routes recommendation. The existing schemes find out density route by considering the number of vehicles in a road segment. Since they don’t consider the features of each road segment such as width, length, and directions in a road network, the results are not correct in some real road networks. To overcome such problems, we propose a clustering method for congested routes discovering from the trajectories of moving objects on road networks. The proposed scheme can be divided into three steps. First, it divides each road network into segments with different width, length, and directions. Second, the congested road segments are detected through analyzing the trajectories of moving objects on the road network. The saturation degree of each road segment and the average moving speed of vehicles in a road segment are computed to detect the congested road segments. Finally, we compute the final congested routes by using a clustering scheme. The experimental results showed that the proposed scheme can efficiently discover the congested routes in different directions of the roads.

Fusion of engineering insights and emerging trends: Intelligent urban traffic management system

The problem of identifying the shortest path along a road network is a fundamental problem in network analysis, ranging from route guidance in a navigation system to solving spatial allocation problems. Since this type of problem is solved so frequently, it is important to craft an approach that is as efficient as possible. Based upon past research, it is generally accepted that several efficient implementations of the Dijkstra algorithm are the fastest at optimally solving the ‘one‐to‐one’ shortest path problem (Cherkassky et al.1996). We show that the most efficient state‐of‐the‐art implementations of Dijkstra can be improved by taking advantage of network properties associated with GIS‐sourced data. The results of this paper, derived from tests of different algorithmic approaches on real road networks, will be extremely valuable for application developers and researchers in the GIS community.

The order k Voronoi diagram (OkVD) is an effective geometric construction to partition the geographical space into a set of Voronoi regions such that all locations within a Voronoi region share the same k nearest points of interest (POIs). Despite the broad applications of OkVD in various geographical analysis, few efficient algorithms have been proposed to construct OkVD in real road networks. This study proposes a novel algorithm consisting of two stages. In the first stage, a new one-to-all k shortest path finding procedure is proposed to efficiently determine the shortest paths to k nearest POIs for each node. In the second stage, a new recursive procedure is introduced to effectively divide boundary links within different Voronoi regions using the hierarchical tessellation property of the OkVD. To demonstrate the applicability of the proposed OkVD construction algorithm, a case study of place-based accessibility evaluation is carried out. Computational experiments are also conducted on five real road networks with different sizes, and results show that the proposed OkVD algorithm performed significantly better than state-of-the-art algorithms.

The goal of the project is to construct a density-based traffic signal management system that can automatically modify the timing of the signal based on the volume of traffic at the crossing. Traffic signal regulation is becoming a major issue that causes congestion and has a major role in the delay of emergency vehicles because of the rise in automobiles on the road these days. The majority of urban areas in the world have serious traffic problems, thus it's time to switch from more human or fixed modes to automated systems with decision-making skills. The fixed-based traffic signalling system of today may become ineffective if only one lane is used at a time. In this work, Arduino-based intelligent traffic control system framework helps to reduce this issue.

Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions.

In this paper, we study the problem of searching k-discriminative paths on road networks. Given a source node src and a destination node dest on a road network, we aim to search k paths between src and dest, where these k paths satisfy the multi-objective goal including the minimization of the path overlapping and the minimization of the path length. Specifically, the requirement of minimizing the overlapping among paths, which is a NP-hard issue, is highly demanded in applications of disaster rescue management such as the evacuation plan. In this paper, we consider the deployment of k-discriminative paths for various applications, including queries for emergency-purpose applications, queries of multi-objective Pareto front for pre-schedule transportation plan, and queries with multiple sources and destinations for the regional evacuation. Due to its NP-hard nature, the heuristic strategy based on the ant colony optimization is devised in this work. As validated by our experimental studies on real road networks, the proposed algorithm can achieve the discovery of k-discriminative paths efficiently and effectively, showing its prominent advantages to be a practicable service for evacuation-related applications.

Path queries such as finding the shortest path in travel time from my hotel to the are heavily used in many applications of road networks. Currently, simple statistic aggregates such as the average travel time between two vertices are often used to answer path queries. However, such simple aggregates often cannot capture the uncertainty inherent in traffic. In this paper, we study how to take traffic uncertainty into account in answering path queries in road networks. To capture the uncertainty in traffic such as the travel time between two vertices, the weight of an edge is modeled as a random variable and is approximated by a set of samples. We propose three novel types of probabilistic path queries using basic probability principles: (1) a probabilistic path query like what are the paths from my hotel to the airport whose travel time is at most 30 minutes with a probability of at least 90%?; (2) a weight-threshold top-k path query like what are the top-3 paths from my hotel to the airport with the highest probabilities to take at most 30 minutes?; and (3) a probability-threshold top-k path query like what are the top-3 shortest paths from my hotel to the airport whose travel time is guaranteed by a probability of at least 90%? To evaluate probabilistic path queries efficiently, we develop three efficient probability calculation methods: an exact algorithm, a constant factor approximation method and a sampling based approach. Moreover, we devise the P* algorithm, a best-first search method based on a novel hierarchical partition tree index and three effective heuristic evaluation functions. An extensive empirical study using real road networks and synthetic data sets shows the effectiveness of the proposed path queries and the efficiency of the query evaluation methods.

On the basis of analyzing the present urban traffic management systems in China, this paper presents a hierarchical intelligent control and coordination conceptual architecture of urban traffic control system and urban traffic guidance system, which are two dominant and efficient traffic management systems. The technique expects to be much easier for deployment. In the primary coordination of fixed timing control for different time of the day and outdoor guidance system, the paper suggests the signal timing parameters should be regulated in a week interval according to statistical intersection delays, and the guidance information should be explicit traffic condition estimation with fuzzy matching from congestion measurement of links and intersections. Further more, the higher coordination mechanism of adaptive signal control and in-vehicle route guidance is also put forward, a system optimum equilibrium flow model is described with minimizing the total delay of the road networks, getting optimal traffic volume on a link, then calculating the signal timing parameters and optimal route so to make full use of road network resources and realize efficient transportation.