scholarly journals Incident Duration Modeling Using Flexible Parametric Hazard-Based Models

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ruimin Li ◽  
Pan Shang
Keyword(s):  
Failure Time ◽  
Economic Losses ◽  
Incident Management ◽  
Accelerated Failure Time ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Duration Time ◽  
Incident Duration ◽  
Traffic Incident Management ◽  
Log Normal

Assessing and prioritizing the duration time and effects of traffic incidents on major roads present significant challenges for road network managers. This study examines the effect of numerous factors associated with various types of incidents on their duration and proposes an incident duration prediction model. Several parametric accelerated failure time hazard-based models were examined, including Weibull, log-logistic, log-normal, and generalized gamma, as well as all models with gamma heterogeneity and flexible parametric hazard-based models with freedom ranging from one to ten, by analyzing a traffic incident dataset obtained from the Incident Reporting and Dispatching System in Beijing in 2008. Results show that different factors significantly affect different incident time phases, whose best distributions were diverse. Given the best hazard-based models of each incident time phase, the prediction result can be reasonable for most incidents. The results of this study can aid traffic incident management agencies not only in implementing strategies that would reduce incident duration, and thus reduce congestion, secondary incidents, and the associated human and economic losses, but also in effectively predicting incident duration time.

Traffic Incident Duration Estimation Based on a Dual-Learning Bayesian Network Model

2018 ◽  
Vol 2672 (45) ◽  
pp. 196-209 ◽  
Author(s):  
Haozhe Cong ◽  
Cong Chen ◽  
Pei-Sung Lin ◽  
Guohui Zhang ◽  
John Milton ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Expert Knowledge ◽  
Transportation Systems ◽  
Practical Significance ◽  
Incident Management ◽  
Highway Traffic ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Incident Duration ◽  
Traffic Incident Management

Highway traffic incidents induce a significant loss of life, economy, and productivity through injuries and fatalities, extended travel time and delay, and excessive energy consumption and air pollution. Traffic emergency management during incident conditions is the core element of active traffic management, and it is of practical significance to accurately understand the duration time distribution for typical traffic incident types and the factors that influence incident duration. This study proposes a dual-learning Bayesian network (BN) model to estimate traffic incident duration and to examine the influence of heterogeneous factors on the length of duration based on expert knowledge of traffic incident management and highway incident data collected in Zhejiang Province, China. Fifteen variables related to three aspects of traffic incidents, including incident information, incident consequences, and rescue resources, were included in the analysis. The trained BN model achieves favorable performance in several areas, including classification accuracy, the receiver operating characteristic (ROC) curve, and the area under curve (AUC) value. A classification matrix, and significant variables and their heterogeneous influences are identified accordingly. The research findings from this study provide beneficial reference to the understanding of decision-making in traffic incident response and process, active traffic incident management, and intelligent transportation systems.

A Study on Freeway Traffic Incident Duration and Improvement Countermeasures

2013 ◽  
Vol 411-414 ◽  
pp. 2752-2757
Author(s):  
Jing Ru Gao
Keyword(s):  
Incident Management ◽  
Key Factors ◽  
Traffic Incidents ◽  
Freeway Traffic ◽  
Emergency Rescue ◽  
Traffic Incident ◽  
Incident Duration ◽  
Clearing Time ◽  
Discovery Time ◽  
Traffic Incident Management

Reducing the duration of freeway traffic incident plays a significant role in improving the efficiency of freeway transportation. The duration of freeway traffic incidents is composed of four stages: the discovery time, response time, clearing time and the recovery time. Through analyzing the key factors influencing different duration stations, this article studies how factors including incident severity, aid resource allocation and emergency rescue preplan influence the duration, and pointedly propose countermeasures to reduce freeway traffic incidents duration. The conclusion of this article provides reference for improving the efficiency in freeway traffic incident management. Key words: traffic incident; duration; factor analysis; improve countermeasures

Highway Traffic Incident Detection and Optimal Sensor Placement Layout

2013 ◽  
Vol 650 ◽  
pp. 460-464 ◽  
Author(s):  
Lan Bai ◽  
Qi Sheng Wu ◽  
Mei Yang ◽  
Lan Xin Wei ◽  
Bo Li ◽  
...  
Keyword(s):  
Road Traffic ◽  
Detection Algorithm ◽  
Simulation Software ◽  
Incident Management ◽  
Incident Detection ◽  
Highway Traffic ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Before And After ◽  
Traffic Incident Management

Traffic incident detection is critical to the core of the traffic incident management process. In order to study the highway traffic incident detection algorithm and the layout spacing of the fixed detector, under the assumptions of the linear traffic flow, to detect traffic incidents as the goal, using TransModeler traffic simulation software to simulate the highway traffic conditions from Xian to Hanzhong, getting the changes in the macroscopic traffic parameters before and after the traffic incident, and analysis of the data, finally puts forward the optimal layout of spacing of basic road traffic incident detection.

scholarly journals Logical Structure of Actions to Reduce the Impacts of Traffic Incidents

2021 ◽  
Vol 11 (4) ◽  
pp. 5909-5927
Author(s):  
Marina Leite De Barros Baltar ◽  
Victor Hugo Souza De Abreu ◽  
Andrea Souza Santos
Keyword(s):  
Sustainable Development ◽  
Intelligent Transportation System ◽  
Logical Structure ◽  
Incident Management ◽  
Urban Context ◽  
Traffic Incidents ◽  
The Sustainable Development ◽  
Traffic Incident ◽  
Important Concern ◽  
Traffic Incident Management

Traffic incidents (such as broken-down vehicles, accidents, flat tires and other) constitute an important concern in the urban context, impacting the sustainable development. Thus, currently, the proposition of efficient traffic incident management systems has been encouraged to re-establish road safety and restore the network's traffic capacity. Thus, this paper aims to investigate the main impacts of traffic incidents and elaborate a logical structure of actions that should be employed to improve their management. The results show that many impacts can be identified in the three spheres of sustainable development and improvement actions must accelerate responses to emergencies, invest in Intelligent Transportation System (ITS), develop urban planning with a focus on more roads secure and enforce existing laws and regulations.

scholarly journals TRAFFIC INCIDENT MANAGEMENT IN THE PRESENCE OF HAZARDS

2016 ◽  
Vol III-4/W1 ◽  
pp. 45-49
Author(s):  
Z. Wang ◽  
S. Zlatanova ◽  
John Steenbruggen
Keyword(s):  
First Responders ◽  
Incident Management ◽  
Traffic Incidents ◽  
The Road ◽  
Traffic Incident ◽  
Road Users ◽  
Navigation Support ◽  
The Status ◽  
Traffic Incident Management ◽  
Geo Database

Traffic incidents can result in different kinds of hazards (e.g., plumes) that influence the status of road networks, therefore there is a great need for incident management in the presence of the hazards. When incidents occur, the created hazards not only affect the normal road users (make them detour or blocked), but also influence the movement of first responders. Traffic managers, who are responsible for maintaining the road safety and traffic stability, should carry out quick and effective measures to manage the incidents. In this paper, we present four issues to help people better understand the situations that could occur in the management of incidents with hazards: 1). Evacuation in the presence of hazards; 2). 3D incident management; 3). Navigation support for first responders; 4). Navigation support for road users. To address these issues, we propose a solution which combines agent technology, geo-database, hazard simulation, and traffic simulation. Further research would be needed to investigate the potentials of the proposed solution in real applications.

scholarly journals Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Weiwei Zhu ◽  
Jinglin Wu ◽  
Ting Fu ◽  
Junhua Wang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Incident Management ◽  
Intelligent Transport System ◽  
Content Type ◽  
Traffic Incident ◽  
Flow Parameters ◽  
Incident Duration ◽  
Traffic Incident Management ◽  
Duration Prediction

Purpose Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps. Design/methodology/approach This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing. Findings Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction. Research limitations/implications The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations. Practical implications The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications. Originality/value This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

scholarly journals TRAFFIC INCIDENT MANAGEMENT IN THE PRESENCE OF HAZARDS

2016 ◽  
Vol IV-4/W1 ◽  
pp. 45-49
Author(s):  
Z. Wang ◽  
S. Zlatanova ◽  
J. Steenbruggen
Keyword(s):  
First Responders ◽  
Incident Management ◽  
Traffic Incidents ◽  
The Road ◽  
Traffic Incident ◽  
Road Users ◽  
Navigation Support ◽  
The Status ◽  
Traffic Incident Management ◽  
Geo Database

Traffic incidents can result in different kinds of hazards (e.g., plumes) that influence the status of road networks, therefore there is a great need for incident management in the presence of the hazards. When incidents occur, the created hazards not only affect the normal road users (make them detour or blocked), but also influence the movement of first responders. Traffic managers, who are responsible for maintaining the road safety and traffic stability, should carry out quick and effective measures to manage the incidents. In this paper, we present four issues to help people better understand the situations that could occur in the management of incidents with hazards: 1). Evacuation in the presence of hazards; 2). 3D incident management; 3). Navigation support for first responders; 4). Navigation support for road users. To address these issues, we propose a solution which combines agent technology, geo-database, hazard simulation, and traffic simulation. Further research would be needed to investigate the potentials of the proposed solution in real applications.

scholarly journals Automated Generation of Traffic Incident Response Plan Based on Case-Based Reasoning and Bayesian Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yongfeng Ma ◽  
Wenbo Zhang ◽  
Jian Lu ◽  
Li Yuan
Keyword(s):  
Bayesian Theory ◽  
Incident Management ◽  
Case Based Reasoning ◽  
Incident Response ◽  
Traffic Incidents ◽  
Automated Generation ◽  
Traffic Incident ◽  
Response Plan ◽  
Traffic Incident Management ◽  
Case Based

Traffic incident response plan, specifying response agencies and their responsibilities, can guide responders to take actions effectively and timely after traffic incidents. With a reasonable and feasible traffic incident response plan, related agencies will save many losses, such as humans and wealth. In this paper, how to generate traffic incident response plan automatically and specially was solved. Firstly, a well-known and approved method, Case-Based Reasoning (CBR), was introduced. Based on CBR, a detailed case representation andR5-cycle of CBR were developed. To enhance the efficiency of case retrieval, which was an important procedure, Bayesian Theory was introduced. To measure the performance of the proposed method, 23 traffic incidents caused by traffic crashes were selected and three indicators, PrecisionP, RecallR, and IndicatorF, were used. Results showed that 20 of 23 cases could be retrieved effectively and accurately. The method is practicable and accurate to generate traffic incident response plans. The method will promote the intelligent generation and management of traffic incident response plans and also make Traffic Incident Management more scientific and effective.

scholarly journals Application of nonparametric regression in predicting traffic incident duration

Transport ◽  
2015 ◽  
Vol 33 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Shi Wang ◽  
Ruimin Li ◽  
Min Guo
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Nonparametric Regression ◽  
Prediction Algorithm ◽  
Incident Management ◽  
K Nearest Neighbor ◽  
Incident Type ◽  
Traffic Incident ◽  
Duration Time ◽  
Incident Duration

Predicting the duration time of incidents is important for effective real-time Traffic Incident Management (TIM). In the current study, the k-Nearest Neighbor (kNN) algorithm is employed as a nonparametric regression approach to develop a traffic incident duration prediction model. Incident data from 2008 on the third ring expressway mainline in Beijing are collected from the local Incident Reporting and Dispatching System. The incident sites are randomly distributed along the mainline, which is 48.3 km long and has six two-way lanes with a single-lane daily volume of more than 10000 veh. The main incident type used is sideswipe and the average incident duration time is 32.69 min. The most recent one-fourth of the incident records are selected as testing set. Vivatrat method is employed to filter anomalous data for the training set. Incident duration time is set as the dependent variable in Kruskal–Wallis test, and six attributes are identified as the main factors that affect the length of duration time, which are ‘day first shift’, ‘weekday’, ‘incident type’, ‘congestion’, ‘incident grade’ and ‘distance’. Based on the characteristics of duration time distribution, log transformation of original data is tested and proven to improve model performance. Different distance metrics and prediction algorithms are carefully investigated. Results demonstrate that the kNN model has better prediction accuracy using weighted distance metric based on decision tree and weighted prediction algorithm. The developed prediction model is further compared with other models based on the same dataset. Results show that the developed model can obtain reasonable prediction results, except for samples with extremely short or long duration. Such a prediction model can help TIM teams estimate the incident duration and implement real-time incident management strategies.

Collection, Analysis, and Reporting of Kentucky Traffic Incident Management Performance

2021 ◽  
pp. 036119812110010
Author(s):  
Xu Zhang ◽  
Reginald R. Souleyrette ◽  
Eric Green ◽  
Teng Wang ◽  
Mei Chen ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Management Strategies ◽  
Incident Management ◽  
Vehicle Crashes ◽  
Clearance Time ◽  
Emergency Responders ◽  
Traffic Incident ◽  
Choropleth Maps ◽  
Traffic Incident Management ◽  
Secondary Crashes

Traffic incidents remain all too common. They negatively affect the safety of the traveling public and emergency responders and cause significant traffic delays. Congestion associated with incidents can instigate secondary crashes, exacerbating safety risks and economic costs. Traffic incident management (TIM) provides an effective approach for managing highway incidents and reducing their occurrence and impacts. The paper discusses the establishment and methods of calculation for five TIM performance measures that are used by the Kentucky Transportation Cabinet (KYTC) to improve incident response. The measures are: roadway clearance time, incident clearance time, secondary crashes, first responder vehicle crashes, and commercial motor vehicle crashes. Ongoing tracking and analysis of these metrics aid the KYTC in its efforts to comprehensively evaluate its TIM program and make continuous improvements. As part of this effort, a fully interactive TIM dashboard was developed using the Microsoft Power BI platform. Dashboard users can apply various spatial and temporal filters to identify trends at the state, district, county, and agency level. The dashboard also supports dynamic visualizations such as time-series plots and choropleth maps. With the TIM dashboard in place, KYTC personnel, as well as staff at other transportation agencies, can identify the strengths and weaknesses of their incident management strategies and revise practices accordingly.

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