scholarly journals Let Trajectories Speak Out the Traffic Bottlenecks

2022 ◽  
Vol 13 (1) ◽  
pp. 1-21
Author(s):  
Hui Luo ◽  
Zhifeng Bao ◽  
Gao Cong ◽  
J. Shane Culpepper ◽  
Nguyen Lu Dang Khoa
Keyword(s):  
Traffic Volume ◽  
Trajectory Data ◽  
Trade Offs ◽  
Bottleneck Identification ◽  
Spread Model ◽  
Efficiency And Effectiveness ◽  
Historical Trajectory ◽  
Algorithmic Approaches ◽  
Compare And Contrast ◽  
Road Segments

Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures realtime traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as trajectory-driven traffic bottleneck identification : Given a road network R , a trajectory database T , find a representative set of seed edges of size K of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic diffusion probability between two connected segments and the residual ratio of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm BF , with an approximation ratio of 1-1/ e . To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm SG . Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods.

A prediction model of vessel trajectory based on generative adversarial network

2021 ◽  
pp. 1-11
Author(s):  
Senjie Wang ◽  
Zhengwei He
Keyword(s):  
Safety Management ◽  
Route Planning ◽  
Local Area ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Generative Adversarial Network ◽  
Ship Traffic ◽  
Adversarial Network ◽  
Historical Trajectory ◽  
Vessel Motion

Abstract Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

Research on Ship Classification Based on Trajectory Features

2017 ◽  
Vol 71 (1) ◽  
pp. 100-116 ◽  
Author(s):  
Kai Sheng ◽  
Zhong Liu ◽  
Dechao Zhou ◽  
Ailin He ◽  
Chengxu Feng
Keyword(s):  
Pattern Mining ◽  
Extraction Methods ◽  
Behaviour Pattern ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Trajectory Features ◽  
Ship Classification ◽  
Historical Trajectory

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

scholarly journals Optimal Staffing for Vessel Traffic Service Operators: A Case Study of Yeosu VTS

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8004
Author(s):  
Sang-Lok Yoo ◽  
Kwang-Il Kim
Keyword(s):  
Statistical Data ◽  
Optimal Number ◽  
Traffic Volume ◽  
Near Miss ◽  
Trajectory Data ◽  
Staffing Level ◽  
Microscopic Approach ◽  
Marine Accidents ◽  
Traffic Organization

Vessel traffic volume and vessel traffic service (VTS) operator workloads are increasing with the expansion of global maritime trade, contributing to marine accidents by causing difficulties in providing timely services. Therefore, it is essential to have sufficient VTS operators considering the vessel traffic volume and near-miss cases. However, no quantitative method for determining the optimal number of workstations, which is necessary for calculating the VTS operator staffing level, has yet been proposed. This paper proposes a new, microscopic approach for calculating the number of workstations from vessel trajectories and voice recording communication data between VTS operators and navigators. The vessel trajectory data are preprocessed to interpolate different intervals. The proposed method consists of three modules: Information services, navigational assistance services, and traffic organization service. The developed model was applied to the Yeosu VTS in Korea. Another workstation should be added to the current workstation based on the proposed method. The results showed that even without annual statistical data, a reasonable VTS operator staffing level could be calculated. The proposed approach helps prevent vessel accidents by providing timely services even if the vessel traffic is congested if VTS operators are deployed to a sufficient number of workstations.

Taxi Global Positioning System Data in Urban Road Network: A Methodology to Identify Key Road Clusters Based on Travel Speed–Traffic Volume Correlation

2021 ◽  
pp. 036119812110366
Author(s):  
Yi Li ◽  
Weifeng Li ◽  
Qing Yu ◽  
Han Yang
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Travel Speed ◽  
Detection Algorithm ◽  
Traffic Volume ◽  
Urban Traffic ◽  
Mitigation Strategies ◽  
Operating Characteristics ◽  
The Road ◽  
Road Segments

Urban traffic congestion is one of the urban diseases that needs to be solved urgently. Research has already found that a few road segments can significantly influence the overall operation of the road network. Traditional congestion mitigation strategies mainly focus on the topological structure and the transport performance of each single key road segment. However, the propagation characteristics of congestion indicate that the interaction between road segments and the correlation between travel speed and traffic volume should also be considered. The definition is proposed for “key road cluster” as a group of road segments with strong correlation and spatial compactness. A methodology is proposed to identify key road clusters in the network and understand the operating characteristics of key road clusters. Considering the correlation between travel speed and traffic volume, a unidirectional-weighted correlation network is constructed. The community detection algorithm is applied to partition road segments into key road clusters. Three indexes are used to evaluate and describe the characteristic of these road clusters, including sensitivity, importance, and IS. A case study is carried out using taxi GPS data of Shanghai, China, from May 1 to 17, 2019. A total of 44 key road clusters are identified in the road network. According to their spatial distribution patterns, these key road clusters can be classified into three types—along with network skeletons, around transportation hubs, and near bridges. The methodology unveils the mechanism of congestion formation and propagation, which can offer significant support for traffic management.

scholarly journals Improved Parallel Legalization Schemes for Standard Cell Placement with Obstacles

Technologies ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Panagiotis Oikonomou ◽  
Antonios Dadaliaris ◽  
Kostas Kolomvatsos ◽  
Thanasis Loukopoulos ◽  
Athanasios Kakarountas ◽  
...  
Keyword(s):  
Target Function ◽  
Problem Formulation ◽  
Search Space ◽  
Standard Cell ◽  
Chip Area ◽  
Time Performance ◽  
Trade Offs ◽  
Cell Placement ◽  
Placement Algorithm ◽  
Algorithmic Approaches

In standard cell placement, a circuit is given consisting of cells with a standard height, (different widths) and the problem is to place the cells in the standard rows of a chip area so that no overlaps occur and some target function is optimized. The process is usually split into at least two phases. In a first pass, a global placement algorithm distributes the cells across the circuit area, while in the second step, a legalization algorithm aligns the cells to the standard rows of the power grid and alleviates any overlaps. While a few legalization schemes have been proposed in the past for the basic problem formulation, few obstacle-aware extensions exist. Furthermore, they usually provide extreme trade-offs between time performance and optimization efficiency. In this paper, we focus on the legalization step, in the presence of pre-allocated modules acting as obstacles. We extend two known algorithmic approaches, namely Tetris and Abacus, so that they become obstacle-aware. Furthermore, we propose a parallelization scheme to tackle the computational complexity. The experiments illustrate that the proposed parallelization method achieves a good scalability, while it also efficiently prunes the search space resulting in a superlinear speedup. Furthermore, this time performance comes at only a small cost (sometimes even improvement) concerning the typical optimization metrics.

scholarly journals Biobjective Emergency Logistics Scheduling Model Based on Uncertain Traffic Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Youqiang Sun ◽  
Yeqing Ren ◽  
Xingjuan Cai
Keyword(s):  
Cloud Model ◽  
Traffic Volume ◽  
One Dimension ◽  
Trajectory Data ◽  
Inference Procedure ◽  
Traffic Conditions ◽  
Mutation Operators ◽  
Scheduling Model ◽  
Emergency Logistics ◽  
Proposed Model

Emergency logistics scheduling appears more and more important in modern society because of frequent occurrence of unpredictable disasters. Most of the existing studies consider a certain emergency logistics scheduling model, and most of them are based on an ideal scenario. Considering the uncertain traffic condition and the real road condition, a biobjective emergency logistics scheduling model is proposed, which includes two objectives: transportation time and transportation cost. The uncertainty of the proposed model is reflected in two aspects: the occurrence time of emergencies and the traffic volume predicted by the cloud model. The numerical characteristics of traffic information are abstracted from the spatial-temporal trajectory data by the reverse cloud model, and the inference procedure of the one-dimension cloud model further predicts the uncertain traffic volume using the numerical characteristics. In addition, the crossover and mutation operators of multiobjective evolutionary algorithms are modified to solve the model. The experimental results show that the inference procedure of one-dimension cloud model can accurately predict the traffic volume at the departure time; and the proposed model is more reasonable than the existing scheduling models; at the same time, the improved NSGA-II can also provide superior schemes in different departure times and traffic conditions for decision makers.

Citywide Traffic Volume Estimation Using Trajectory Data

2017 ◽  
Vol 29 (2) ◽  
pp. 272-285 ◽  
Author(s):  
Xianyuan Zhan ◽  
Yu Zheng ◽  
Xiuwen Yi ◽  
Satish V. Ukkusuri
Keyword(s):  
Traffic Volume ◽  
Volume Estimation ◽  
Trajectory Data ◽  
Traffic Volume Estimation

Valuing a Logging Externality: Loss of the Water Purification Service of Temperate Coastal Rainforests

2017 ◽  
Vol 03 (02) ◽  
pp. 1650032 ◽  
Author(s):  
Duncan Knowler ◽  
Ashley Page ◽  
Andrew Cooper ◽  
H. Andres Araujo
Keyword(s):  
Ecosystem Services ◽  
Forest Management ◽  
Water Purification ◽  
Environmental Influence ◽  
Treatment Plant ◽  
Water System ◽  
Timber Harvesting ◽  
Traffic Volume ◽  
Multiple Use ◽  
Trade Offs

In many biodiversity rich watersheds, there is a lack of understanding concerning the trade-offs between timber harvesting and maintaining the watershed’s other ecosystem services, where losses of these services can occur as an externality from timber harvesting. As a result, the potential benefit from an appropriate mix of activities in multiple-use watersheds frequently remains unrealized. Our study provides insight into such trade-offs by estimating the value of a loss in a forest’s water purification/filtration service due to sedimentation caused by logging (the externality). More specifically, we develop a model to quantify the economic impact of increased sedimentation from forest roads on the quality of raw water withdrawn by a municipal water utility. Our approach is novel in several ways. First, we recognize the complex response of the water treatment plant to elevated sedimentation (turbidity) by considering a stochastic environmental influence on water system performance; to accommodate this complexity, we estimate the number of times turbidity exceeds an acceptable threshold by using a count data estimation procedure. Second, we generate alternative time series for turbidity that vary according to assumptions about forest management (logging versus no logging), traffic volume (road use intensity) and aggregate road length. We find that reductions in the economic value of the water purification/filtration service is more sensitive to traffic volume than other considerations but only when the road use is High, as the welfare effect in other cases is modest. Our analysis will be helpful to forest planners who must consider the trade-offs in forest management when timber harvesting can have harmful impacts on important ecosystem services, such as water purification/filtration.

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