Traffic Incident Management Performance Measures: Ranking Agencies on Roadway Clearance Time

This study develops a procedure to rank agencies based on their incident responses using roadway clearance times for crashes. This analysis is not intended to grade agencies but to assist in identifying agencies requiring more training or resources for incident management. Previous NCHRP reports discussed usage of different factors including incident severity, roadway characteristics, number of lanes involved and time of incident separately for estimating the performance. However, it does not tell us how to incorporate all the factors at the same time. Thus, this study aims to account for multiple factors to ensure fair comparisons. This study used 149,174 crashes from Iowa that occurred from 2018 to 2021. A Tobit regression model was used to find the effect of different variables on roadway clearance time. Variables that cannot be controlled directly by agencies such as crash severity, roadway type, weather conditions, lighting conditions, etc., were included in the analysis as it helps to reduce bias in the ranking procedure. Then clearance time of each crash is normalized into a base condition using the regression coefficients. The normalization makes the process more efficient as the effect of uncontrollable factors has already been mitigated. Finally, the agencies were ranked by their average normalized roadway clearance time. This ranking process allows agencies to track their performance of previous crashes, can be used in identifying low performing agencies that could use additional resources and training, and can be used to identify high performing agencies to recognize for their efforts and performance.

Methodology for Automatically Setting Camera View to Mile Marker for Traffic Incident Management

Traffic incident management (TIM) is a FHWA Every Day Counts initiative with the objective of reducing secondary crashes, improving travel reliability, and ensuring safety of responders. Agency roadside cameras play a critical role in TIM by helping dispatchers quickly identify the precise location of incidents when receiving reports from motorists with varying levels of spatial accuracy. Reconciling position reports that are often mile marker based, with cameras that operate in a Pan-Tilt-Zoom coordinate system relies on dispatchers having detailed knowledge for hundreds of cameras and perhaps some presets. During real-time incident dispatching, reducing the time it takes to identify the most relevant cameras and setting their view on the incident is an important opportunity to improve incident management dispatch times. This research develops a camera-to-mile marker mapping technique that automatically sets the camera view to a specified mile marker within the field-of-view of the camera. Over 350 traffic cameras along Indiana’s 2250 directional miles of interstate were mapped to approximately 5000 discrete locations that correspond to approximately 780 directional miles (~35% of interstate) of camera coverage. This newly developed technique will allow operators to quickly identify the nearest camera and set them to the reported location. This research also identifies segments on the interstate system with limited or no camera coverage for decision makers to prioritize future capital investments. This paper concludes with brief discussion on future research to automate the mapping using LiDAR data and to set the cameras after automatically detecting the events using connected vehicle trajectory data.

Design and analysis of traffic incident detection based on random forest

In order to avoid the noise and over fitting and further improve the limited classification performance of the real decision tree, a traffic incident detection method based on the random forest algorithm is presented. From the perspective of classification strength and correlation, three experiments are performed to investigate the potential application of random forest to traffic incident detection： comparison with a different number of decision trees; comparison with different decision trees; comparison with the neural network. The real traffic data of the 1-880 database is used in the experiments. The detection performance is evaluated by the common criteria including the detection rate, the false alarm rate, the mean time to detection, the classification rate and the area under the curve of the receiver operating characteristic （ROC）. The experimental results indicate that the model based on random forest can improve the decision rate, reduce the testing time, and obtain a higher classification rate. Meanwhile, it is competitive compared with multi-layer feed forward neural networks （MLF）.

Practical approach to determining traffic congestion propagation boundary due to traffic incidents

A practical approach for predicting the congestion boundary due to traffic incidents was proposed. Based on the kinematic wave theory and Van Aerde single-regime flow model, a model for estimating the congestion propagation speed for the basic road segment was developed. Historical traffic flow data were used to analyze the time variant characteristics of the urban traffic flow for each road type. Then, the saturation flow rate was used for analyzing the impact of the traffic incident on the traversing traffic flow at the congestion area. The base congestion propagation speed for each road type was calculated based on field data, which were provided by the remote traffic microwave sensors(RTMS), floating car data(FCD) system and screen line survey. According to a comparative analysis of the congestion propagation speed, it is found that the expressway, major arterial, minor arterial and collector are decreasingly influenced by the traffic incident. Subsequently, the impact of turning movements at intersections on the congestion propagation was considered. The turning ratio was adopted to represent the impact of turning movements, and afterward the corresponding propagation pattern at intersections was analyzed. Finally, an implementation system was designed on a geographic information system(GIS) platform to display the characteristics of the congestion propagation over the network. The validation results show that the proposed approach is able to capture the congestion propagation properties in the actual road network.

A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

Advanced information and communication technolo-gies can be used to facilitate traffic incident management.If an incident is detected and blocks a road link,in order to reduce the incident-induced traffic congestion,a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work.Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information.A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled.Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles,operation time interval of the proposed strategy,traffic density in the traffic network,and the scope of the area where traffic incident information is delivered.The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

Modeling of optimal control for urban freeway corridor under incident conditions

Traffic control and management are effective measures to solve the problem of traffic congestion. The optimal control model for freeway corridor is developed under incident conditions, which is in the form of minimization of the sum of the square of the difference between traffic demand and capacity at each intersection and on the freeway bottleneck section. The model optimizes control parameters of phase splits at arterial intersections, off-ramp diversion rates at upstream off-ramps and on-ramp diversion rates at downstream on ramps. Finally, the objective function is discussed and it is showed that the optimal control model is simple and practical.

Estimation of Incident-Induced Congestion on Signalized Arteries Using Traffic Sensor Data

This paper describes how to derive quantitative information about the effect of traffic incidents on urban traffic flow from the raw measurements detected by sectional loop sensors. 3-he two critical parame- ters of the travel delay and queue length, which reflect the temporal and spatial properties of inci- dent-induced congestion, cannot be directly determined from commonly used loop sensors. The modified queuing diagram is used here to quantify incident-induced queues and travel delays on signalized arteries using sensor data from the targeted and upstream links. The method is tested using data generated by the VISSIM simulation model, with results indicating its efficiency and accuracy with an average relative travel delay error of 18.67% for all samples which falls to 8.07% for high volume and very high volume conditions.

用交通动力学法对高速公路事件影响分析的研究

应用交通动力学分析算法对高速公路偶发性事件影响进行研究,推导出偶发性拥挤下排队率、消散率、最大排队长度、总延时车时数、拥挤下的总通行车时数等参数,并将分析结果与确定性排队分析算法的结果进行了比较,认为本算法能更加准确的分析事件的影响。以算例对这种算法的具体应用、与确定性排队分析方法的具体差别作了说明。

偶发性事件下城市交通区域边界的最优控制

为了应对时变交通中偶发性事件对宏观交通流的影响,针对现有城市路网边界控制器迭代次数多,响应速度慢的问题,提出了基于宏观基本图(MFD)的城市交通区域边界的最优控制方法。在迭代学习控制边界控制器基础上加入BP神经网络算法,根据误差对控制器的微分增益进行在线学习调节。通过2种不同偶发性事件下的交通模拟仿真,结果表明:新的城市交通区域边界的最优控制方法不仅在正常路网中具有更好的跟踪精度和更快的迭代速度,并且当发生偶发性事件时,也能自适应跟踪期望曲线,保持较快的误差学习收敛速率,满足实际交通受限变化的需要。

论刑事被害人请求精神损害赔偿的合理性问题

精神损害赔偿制度的确立经历了一个漫长过程。在确立精神损害赔偿制度后,我国实务界对于刑事案件是否应当支持被害人的精神损害赔偿请求权有了不同看法,最高人民法院先后通过两个司法解释对此做出规定,均否定了当事人在刑事附带民事诉讼中请求精神损害赔偿的权利,但同时又在交通肇事案件中打开了口子,直接导致法院在司法裁判中裁判标准不一致。基于当然解释、比较法、法益侵害已经造成和公法私法责任并行不悖的立场,刑事被害人请求精神损害赔偿的权利应当得到支持。目前宜先采用司法解释和指导案例形式进行过渡,同时,未来我国《民法典》和《刑事诉讼法》应当明确支持刑事附带民事案件精神损害赔偿。