Application of Accident Prediction Models for Computation of Accident Risk on Transportation Networks

2002 ◽  
Vol 1784 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Dominique Lord
Keyword(s):  
Traffic Flow ◽  
Prediction Models ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Primary Objective ◽  
Individual Risk ◽  
Original Research ◽  
Accident Risk ◽  
Accident Prediction

Accident risk has been applied extensively in transportation safety analysis. Risk is often used to describe the level of safety in transportation systems by incorporating a measure of exposure, such as traffic flow or kilometers driven. The most commonly applied definition of accident risk states that risk is a linear function of accidents and traffic flow. This definition, however, creates problems for transportation systems that are characterized by a nonlinear relationship between these variables. The primary objective of the original research was to illustrate the application of accident prediction models (APMs) to estimate accident risk on transportation networks. (APMs are useful tools for establishing the proper relationship between accidents and traffic flow.) The secondary objective was to describe important issues and limitations surrounding the application of APMs for this purpose. To accomplish these objectives, APMs were applied to a computerized transportation network with the help of EMME/2. The accident risk was computed with the traffic flow output of the computer program. The results were dramatic and unexpected: in essence, the individual risk of being involved in a collision decreases as traffic flow increases. The current and most common model form of APMs explains this outcome. The application of these results may have significant effects on transportation policy and intelligent transportation system strategies.

scholarly journals Spatiotemporal Traffic Flow Prediction with KNN and LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xianglong Luo ◽  
Danyang Li ◽  
Yu Yang ◽  
Shengrui Zhang
Keyword(s):  
Support Vector Regression ◽  
Traffic Flow ◽  
Prediction Models ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals Hub-Periphery Hierarchy in Bus Transportation Networks: Gini Coefficients and the Seoul Bus System

2020 ◽  
Vol 12 (18) ◽  
pp. 7297 ◽  
Author(s):  
Chansoo Kim ◽  
Segun Goh ◽  
Myeong Seon Choi ◽  
Keumsook Lee ◽  
M. Y. Choi
Keyword(s):  
Gini Coefficient ◽  
City Planning ◽  
Transportation Networks ◽  
Transportation Network ◽  
Growth Dynamics ◽  
Transportation Systems ◽  
The Gini Coefficient ◽  
Bus Stops ◽  
Bus System ◽  
Bus Transportation

Bus transportation networks are characteristically different from other mass transportation systems such as airline or subway networks, and thus the usual approach may not work properly. In this paper, to analyze the bus transportation network, we employ the Gini coefficient, which measures the disparity of weights of bus stops. Applied to the Seoul bus system specifically, the Gini coefficient allows us to classify nodes in the bus network into two distinct types: hub and peripheral nodes. We elucidate the structural properties of the two types in the years 2011 and 2013, and probe the evolution of each type over the two years. It is revealed that the hub type evolves according to the controlled growth process while the peripheral one, displaying a number of new constructions as well as sudden closings of bus stops, is not described by growth dynamics. The Gini coefficient thus provides a key mathematical criterion of decomposing the transportation network into a growing one and the other. It would also help policymakers to deal with the complexity of urban mobility and make more sustainable city planning.

scholarly journals Dynamic measures for transportation networks

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242875
Author(s):  
Oriol Lordan ◽  
Jose M. Sallan
Keyword(s):  
Transportation Networks ◽  
Low Cost ◽  
Transportation Network ◽  
Waiting Times ◽  
Shortest Paths ◽  
Dynamic Network ◽  
Transportation Systems ◽  
Accurate Information ◽  
Network Analyses ◽  
Dynamic Representation

Most complex network analyses of transportation systems use simplified static representations obtained from existing connections in a time horizon. In static representations, travel times, waiting times and compatibility of schedules are neglected, thus losing relevant information. To obtain a more accurate description of transportation networks, we use a dynamic representation that considers synced paths and that includes waiting times to compute shortest paths. We use the shortest paths to define dynamic network, node and edge measures to analyse the topology of transportation networks, comparable with measures obtained from static representations. We illustrate the application of these measures with a toy model and a real transportation network built from schedules of a low-cost carrier. Results show remarkable differences between measures of static and dynamic representations, demonstrating the limitations of the static representation to obtain accurate information of transportation networks.

Transportation Development in Indochina

1996 ◽  
Vol 1563 (1) ◽  
pp. 41-47 ◽  
Author(s):  
Yuichiro Motomura
Keyword(s):  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Mekong River ◽  
Viet Nam ◽  
The West ◽  
The Past ◽  
To Come ◽  
Transportation Development ◽  
First Time

For the first time in history the three countries in Indochina—Cambodia, Laos, and Viet Nam—have started a massive effort to upgrade their transportation systems, particularly those linking each one to the others. Despite the fact that the great Mekong River runs through all three countries, natural barriers formed by the massive Annamite Mountains, which extend from the Himalayas, effectively divide the peninsula, preventing both the Chinese civilization from the east and the Indian civilization from the west from crossing the barrier. Such seclusion suited the region's socialist regimes well in the 1970s and 1980s. Since the 1990s, however, circumstances have induced these three countries to adopt more market-oriented and outward-looking policies, which created interest in expanding and strengthening the region's transportation network. In addition to the drawing up of plans for domestic transportation networks, frequent international conferences have been convened to seek cooperation among the Indochinese countries and from abroad. Many projects have been identified, and some are being implemented. The extreme neglect under which the transportation network has operated during the past two decades has made such efforts daunting. The task of upgrading transportation infrastructure in Indochina will be a priority for some time to come.

scholarly journals FR-MTL: Traffic Flow Prediction Using Fused Ridge Denoising and Multi-Task Learning

2020 ◽  
Vol 24 (7) ◽  
pp. 829-836
Author(s):  
Di Yang ◽  
◽  
Ningjia Qiu ◽  
Peng Wang ◽  
Huamin Yang
Keyword(s):  
Traffic Flow ◽  
Prediction Models ◽  
Road Traffic ◽  
Transportation Systems ◽  
Natural Order ◽  
Traffic Data ◽  
Traffic Flow Prediction ◽  
Task Learning ◽  
Flow Prediction ◽  
Highly Correlated

Traffic flow prediction is one of the fundamental components in Intelligent Transportation Systems (ITS). Many traffic flow prediction models have been developed, but with limitation of noise sensitivity, which will result in poor generalization. Fused Lasso, also known as total variation denoising, penalizes L1-norm on the model coefficients and pairwise differences between neighboring coefficients, has been widely used to analyze highly correlated features with a natural order, as is the case with traffic flow. It denoises data by encouraging both sparsity of coefficients and their differences, and estimates the coefficients of highly correlated variables to be equal to each other. However, for traffic data, the same coefficients will lead to overexpression of features, and losing the trend of time series of traffic flow. In this work, we propose a Fused Ridge multi-task learning (FR-MTL) model for multi-road traffic flow prediction. It introduces Fused Ridge for traffic data denoising, imposes penalty on L2-norm of the coefficients and their differences. The penalty of L2-norm proportionally shrinks coefficients, and generates smooth coefficient vectors with non-sparsity. It has both capability of trend preservation and denoising. In addition, we jointly consider multi-task learning (MTL) for training shared spatiotemporal information among traffic roads. The experiments on real traffic data show the advantages of the proposed model over other four regularized baseline models, and on traffic data with Gaussian noise and missing data, the FR-MTL model demonstrates potential and promising capability with satisfying accuracy and effectiveness.

scholarly journals A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Huiming Duan ◽  
Xinping Xiao
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Prediction Models ◽  
Transportation Systems ◽  
Flow State ◽  
Matrix Perturbation ◽  
Grey Prediction ◽  
Flow Data ◽  
Short Term ◽  
Proposed Model

Short-term traffic flow prediction is an important theoretical basis for intelligent transportation systems, and traffic flow data contain abundant multimode features and exhibit characteristic spatiotemporal correlations and dynamics. To predict the traffic flow state, it is necessary to design a model that can adapt to changing traffic flow characteristics. Thus, a dynamic tensor rolling nonhomogeneous discrete grey model (DTRNDGM) is proposed. This model achieves rolling prediction by introducing a cycle truncation accumulated generating operation; furthermore, the proposed model is unbiased, and it can perfectly fit nonhomogeneous exponential sequences. In addition, based on the multimode characteristics of traffic flow data tensors and the relationship between the cycle truncation accumulated generating operation and matrix perturbation to determine the cycle of dynamic prediction, the proposed model compensates for the periodic verification of the RSDGM and SGM grey prediction models. Finally, traffic flow data from the main route of Shaoshan Road, Changsha, Hunan, China, are used as an example. The experimental results show that the simulation and prediction results of DTRNDGM are good.

scholarly journals Evaluation of Microsimulation Models for Roadway Segments with Different Functional Classifications in Northern Iran

2021 ◽  
Vol 6 (3) ◽  
pp. 46
Author(s):  
Amir Masoud Rahimi ◽  
Maxim A. Dulebenets ◽  
Arash Mazaheri
Keyword(s):  
Intelligent Transportation Systems ◽  
Network Performance ◽  
Traffic Simulation ◽  
Transportation Networks ◽  
Transportation Network ◽  
Travel Speed ◽  
Transportation Systems ◽  
Mitigation Strategies ◽  
Northern Iran ◽  
Microsimulation Models

Industrialization, urban development, and population growth in the last decades caused a significant increase in congestion of transportation networks across the world. Increasing congestion of transportation networks and limitations of the traditional methods in analyzing and evaluating the congestion mitigation strategies led many transportation professionals to the use of traffic simulation techniques. Nowadays, traffic simulation is heavily used in a variety of applications, including the design of transportation facilities, traffic flow management, and intelligent transportation systems. The literature review, conducted as a part of this study, shows that many different traffic simulation packages with various features have been developed to date. The present study specifically focuses on a comprehensive comparative analysis of the advanced interactive microscopic simulator for urban and non-urban networks (AIMSUN) and SimTraffic microsimulation models, which have been widely used in the literature and practice. The evaluation of microsimulation models is performed for the four roadway sections with different functional classifications, which are located in the northern part of Iran. The SimTraffic and AIMSUN microsimulation models are compared in terms of the major transportation network performance indicators. The results from the conducted analysis indicate that AIMSUN returned smaller errors for the vehicle flow, travel speed, and total travel distance. On the other hand, SimTraffic provided more accurate values of the travel time. Both microsimulation models were able to effectively identify traffic bottlenecks. Findings from this study will be useful for the researchers and practitioners, who heavily rely on microsimulation models in transportation planning.

Protecting public health and global freight transportation systems during an influenza pandemic

2008 ◽  
Vol 3 (2) ◽  
pp. 99-107 ◽  
Author(s):  
Thomas C. Luke, MD ◽  
Jean-Paul Rodrigue, PhD
Keyword(s):  
Public Health ◽  
Pandemic Influenza ◽  
Transportation Networks ◽  
Transportation Network ◽  
Influenza Pandemic ◽  
Transportation Systems ◽  
Prevention Measures ◽  
Transportation Industry ◽  
H5n1 Influenza ◽  
Health Support

The H5N1 influenza threat is resulting in global preparations for the next influenza pandemic. Pandemic influenza planners are prioritizing scarce vaccine, antivirals, and public health support for different segments of society. The freight, bulk goods, and energy transportation network comprise the maritime, rail, air, and trucking industries. It relies on small numbers of specialized workers who cannot be rapidly replaced if lost due to death, illness, or voluntary absenteeism. Because transportation networks link economies, provide critical infrastructures with working material, and supply citizens with necessary commodities, disrupted transportation systems can lead to cascading failures in social and economic systems. However, some pandemic influenza plans have assigned transportation workers a low priority for public health support, vaccine, and antivirals. The science of Transportation Geography demonstrates that transportation networks and workers are concentrated at, or funnel through, a small number of chokepoints and corridors. Chokepoints should be used to rapidly and efficiently vaccinate and prophylax the transportation worker cohort and to implement transmission prevention measures and thereby protect the ability to move goods. Nations, states, the transportation industry and unions, businesses, and other stakeholders must plan, resource, and exercise, and then conduct a transportation health assurance and security campaign for an influenza pandemic.

Macrolevel Accident Prediction Models for Evaluating Safety of Urban Transportation Systems

2003 ◽  
Vol 1840 (1) ◽  
pp. 87-95 ◽  
Author(s):  
Alireza Hadayeghi ◽  
Amer S. Shalaby ◽  
Bhagwant Persaud
Keyword(s):  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Transportation Systems ◽  
Linear Modeling ◽  
Traffic Demand ◽  
Accident Prediction ◽  
Estimated Parameters ◽  
Binomial Regression ◽  
Intersection Density

A series of macrolevel prediction models that would estimate the number of accidents in planning zones in the city of Toronto, Ontario, Canada, as a function of zonal characteristics were developed. A generalized linear modeling approach was used in which negative binomial regression models were developed separately for total accidents and for severe (fatal and nonfatal injury) accidents as a function of socio-economic and demographic, traffic demand, and network data variables. The variables that had significant effects on accident occurrence were the number of households, the number of major road kilometers, the number of vehicle kilometers traveled, intersection density, posted speed, and volume-capacity ratio. The geographic weighted regression approach was used to test spatial variations in the estimated parameters from zone to zone. Mixed results were obtained from that analysis.

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