Towards universal freeway incident detection algorithms

This paper reports the intensive test of the new transport systems centre (TSC) algorithm applied to incident detection on freeways. The TSC algorithm is designed to fulfil the universality expectations of automated incident detection. The algorithm consists of two modules: data processing module and incident detection module. The data processing module is designed to handle specific features of different sites. The Bayesian network based incident detection module is used to store and manage general expert traffic knowledge, and to perform coherent reasoning to detect incidents. The TSC algorithm is tested using 100 field incident data sets obtained from Tullamarine Freeway and South Eastern Freeway in Melbourne, Australia. The performance of the algorithm demonstrates its competitiveness with the best performing neural network algorithm which was developed and tested using the same incident data sets in an early research. Most importantly, both the detection rate and false alarm rate of the TSC algorithm are not sensitive to the incident decision threshold, which greatly improves the stability of incident detection. In addition, a very consistent algorithm performance is achieved when the TSC algorithm is transferred from Southern Expressway of Adelaide to both Tullamarine Freeway and South Eastern Freeway of Melbourne. No substantial algorithm retraining is required. A significant step towards algorithm universality is possible from this research.     

A new methodology for incident detection and characterization on surface streets

In this paper, a new methodology is presented for real-time detection and characterization of incidents on surface streets. The proposed automatic incident detection approach is capable of detecting incidents promptly as well as characterizing incidents in terms of time-varying lane-changing fractions and queue lengths in blocked lanes, lanes blocked due to incidents, and incident duration. The architecture of the proposed incident detection approach consists of three sequential procedures: (1) Symptom Identification for identification of incident symptoms, (2) Signal Processing for real-time prediction of incident-related lane traffic characteristics and (3) Pattern Recognition for incident recognition. Lane traffic counts and occupancy are the only two major types of input data, which can be readily collected from point detectors. The primary techniques utilized in this paper include: (1) a discrete-time, nonlinear, stochastic system with boundary constraints to predict real-time lane-changing fractions and queue lengths and (2) a pattern-recognition-based algorithm employing modified sequential probability ratio tests (MSPRT) to detect incidents. Off-line tests based on simulated as well as video-based real data were conducted to assess the performance of the proposed algorithm. The test results have indicated the feasibility of achieving real-time incident detection using the proposed methodology.     

Fuzzy-logic-based incident detection for signalized diamond interchanges

This paper documents a fuzzy-logic-based incident detection algorithm for signalized urban diamond interchanges. The model is capable of detecting lane-blocking incidents whose effects are manifested by patterns of deterioration in traffic conditions that require adjustments in signal control strategies. As a component of a real-time traffic adaptive control system for signalized diamond interchanges, the algorithm feeds an incident report (i.e., the time, location, and severity of the incident) to the system's optimization manager, which uses that information to determine the appropriate signal control strategy.The performance of the model was studied using a simulation of an actual diamond interchange. The simulation study evaluated the model's performance in terms of detection rate, false alarm rate, and mean time to detect. The model's performance was encouraging, and the fuzzy-logic-based approach is considered promising.     

Simulator evaluation of incident detection by transponder-equipped vehicles

This paper explores the efficacy of the driver-based incident detection using the vehicle-to-roadside communication (VRC) system. The proliferation of vehicle tags in the US for automatic toll collection, traffic monitoring, and vehicle navigation and information systems has created an infrastructure capable of supporting a driver-based incident detection system. The research reported herein investigated the use of "activatable" vehicle tags by drivers to send an incident signal to the Traffic Management Center through VRC reader stations spaced uniformly on a highway. The simulation results showed that good detection performance was achieved even at lower levels of market penetration of vehicle tags. The results further showed that detection performance is significantly affected by the severity of the incident in terms of number of lanes closed, the spacing of the VRC reader stations, traffic volume at the time of the incident, and the reporting propensity of the traveling public.The performance of the VRC-based incident reporting system was compared to the performance of two incident detection algorithms that rely on traffic data collected through the automatic vehicle identification (AVI) system. The comparison showed that the VRC-based incident reporting system attained shorter detection times and higher detection rates under fairly similar simulated conditions. The paper also discusses issues that need further study through simulation and field experimentation of the VRC-based incident reporting system.     

Discrete dynamic models for freeway incident detection systems

This paper considers the problem of freeway incident detection within the general framework of computer‐based freeway surveillance and control. A new approach to the detection of freeway traffic incidents is presented based on a discrete‐time stochastic model of the form ARIMA (0, 1, 3) that describes the dynamics of traffic occupancy observations. This approach utilizes real‐time estimates of the variability in traffic occupancies as detection thresholds, thus eliminating the need for threshold calibration and lessening the problem of false‐alarms. Because the moving average parameters of the ARIMA (0, 1, 3) model change over time, these parameters can be updated occasionally. The performance of the developed detection algorithm has been evaluated in terms of detection rate, false‐alarm rate, and average time‐lag to detection, using a total of 1692 minutes of occupancy observations recorded during 50 representative traffic incidents.     

Extended bottlenecks, the fundamental relationship, and capacity drop on freeways

This paper presents evidence that the commonly used point bottleneck model is too simplistic for freeway bottlenecks, the actual mechanism appears to occur over an extended distance. We find evidence of subtle flow limiting and speed reducing phenomena more than a mile downstream of a lane drop bottleneck. These phenomena impact the fundamental relationship, FD. Close to the lane drop the free flow regime appears to come from a “parabolic” FD, but further downstream the relationship straightens to a “triangular” FD and throughput increases. We develop a theory to explain the underlying mechanisms. These insights should help resolve the decades long debate about the shape of the FD. The phenomena also provide a mechanism that may contribute to the empirically observed capacity drop often seen at bottlenecks. Although we study a lane drop, this work should be transferable to other bottlenecks where the capacity restriction persists for an extended distance, e.g., a corridor with a fixed number of lanes and an on-ramp bottleneck.     

A fuzzy-based system for incident detection in urban street networks

Detecting incidents on urban streets or arterials using loop detector data is quite challenging. The pattern of the incident could be quite similar to non-incident cases as intersections get congested. This paper describes the development of a fuzzy logic for incident detection. An Integrated System for Incident Management (

-sim) was developed. An integral component of such system is a microscopic simulator,

-sim-s, an object-oriented model that allows for virtual detector installations at different locations, modeling different intersection layouts, traffic control types and timing, and link characteristics.

-sim-s was utilized to generate various incident scenarios and extracting associated detectors’ accumulative counts. A data clustering technique was utilized to consolidate the various incident scenarios into a single data set for the development of the Fuzzy Logic for incident detection at intersections (

-sim-fl). The

-sim-fl uses the detector data as well as other link properties in flagging detecting incidents.The

-sim-fl can be used to indicate the possibility of an incident, a stalled vehicle, or a sort of traffic disturbance. The devised logic was validated using separate simulation-based incident scenarios.     

Simulation assessment of incident detection by cellular phone call-in programs

This research study was designed to assess by simulation the efficacy of incident detection by cellular phone call-in programs. The assessment was conducted by varying the proportion of drivers with cellular phones on the highway so as to mirror the cellular industry statistics that show a continued growth of ownership of cellular phones in the United States. An analytical model, which combined simulation and the limited field data available in the literature, was used to determine measures of effectiveness of the cellular phone-based detection system. The results showed that a cellular phone detection system offers fast incident detection times and higher detection rates for both shoulder and lane blocking incidents. For example, in moderate traffic flow (i.e. 1,550 vehicles per hour per lane), 90 percent of incidents blocking two lanes were detected in 1.5 minutes when the proportion of drivers with cellular phones was one out of 10 drivers, even with only 20 percent of them willing to report incidents. When the current proportion of cellular ownership, i.e. 1 out of 3, was used in the simulation, the detection time improved to 0.8 minutes. The simulation analysis of incident detection by cellular phones also showed that there is a direct relationship between the probability of detection and the detection time; that is, the specification of a higher detection rate resulted in slower detection times. This is in sharp contrast with the results of field study of automatic incident detection (AID) systems which demonstrated an inverse relationship between probability of detection and detection time. This revised version was published online in June 2006 with corrections to the Cover Date.     

Evaluation of forced flows on freeways with single‐loop detectors

This paper documents the development of a simple method for identifying and/or predicting freeway congestion using single loop detection systems. The proposed algorithm is simple and easy to incorporate into most freeway management systems. The Washington State Department of Transportation's Traffic Systems Management Center (TSMC) sponsored the original study. The investigation also led to a recommendation to replace the original TSMC definition of congestion or forced flow conditions with a more reliable indicator. Although, the TSMC has recently implemented a more advanced prediction system based on fuzzy set theory and neural networks to further identify patterns and rules for ramp metering strategies, the findings presented here continue to be constructive to freeway managers looking for quick and easy analyses that rely solely on single‐loop detection systems. The Seattle Area freeway study section used for the original study was the portion of mainline 1–5 northbound starting at the downtown Seattle Station 108 and ending at the Mountlake Terrace Station 193. Several days' worth of volume and lane‐occupancy data were collected for the afternoon time period from 2:30 p.m. to 6:30 p.m. Time intervals of 20 seconds were chosen for each data collection period. Important products of this research include the following:

• simple, and more reliable criterion for the definition of “bottleneck” or forced flow conditions than that originally used by the TSMC.
• simple, and reliable criterion for predicting impending “bottlenecks” or forced flow conditions.
• A proposed variable for improved selection of the appropriate metering rate. (Further analysis of the use of this variable for determining metering rates is recommended for future studies.

The proposed criteria are simple and easy to incorporate into current freeway management computer systems. Further investigation of freeway performance measurement using volume and occupancy data obtained from single‐loop systems is currently being performed.     

An application of the CUSUM algorithm to freeway incident detection based on two contiguous detectors

The CUSUM (cumulative sum of log‐likelihood ratio) algorithm is a detection algorithm that shows potential for the improvement of incident detection algorithms because it is designed to minimize the mean detect delay for a given false alarm constraint and it can also detect changes with different patterns. In this study, the CUSUM algorithm was applied to freeway incident detection by integrating traffic measurements from two contiguous loop detectors and the non‐stationarity of traffic flows. The developed algorithm was tested based on incident data from the PATH program, with consideration given to the impact of different geometric conditions on algorithm performance. It was also compared with two existing algorithms, in order to address the influence of traffic patterns. The evaluation results show that the CUSUM incident detection algorithm can perform equally well in comparison with the selected algorithms.     

Considering various ALINEA ramp metering strategies for crash risk mitigation on freeways under congested regime

This study evaluates the expected benefits of using the ALINEA ramp metering algorithm as a method for real-time safety improvement on an urban freeway. The objective of this research is to use ramp metering to produce a significant decrease in the risk of crashes on the freeway while avoiding any significant adverse effects on operation. This is achieved by simulating the freeway during the congested period in micro-simulation and testing various ramp metering configurations to determine which provides the best results. Statistical measures developed for the same stretch of freeway using loop detector data are used to quantify the risk of crashes as well as the benefits in each of the alternative strategies. The study concludes that there are significant benefits in metering multiple ramps when the feedback ramp metering algorithm is implemented at multiple locations. It was found that increasing the number of metered on-ramps produces increasing safety benefits. Also, a shorter cycle length for each of the meters and a higher critical occupancy value leads to better results.     

基于分布式实时数据库的SCADA系统初探

通过对现有SCADA系统的软硬件系统结构以及优、缺点的分析,引入分布式实时数据库的概念,并对该库和SCADA技术相结合,利用各自的优势,形成一种新的SCADA监控系统进行了一些初步的探讨。     

基于分布式计算机的智能相机在城市交通中的研究与应用

我国逐步开始将城市建设与互联网技术进行高度融合以打造智能城市。智能城市建设过程中,智能交通是重要一环,当前我国交通视频监控系统普遍是通过道路两旁的摄像头拍摄获得路况图像信息,这种方式虽然是解决了一些问题,但是不能满足智能城市的建设需求。本文总结和研究了分布式智能相机的一些关键技术,得出嵌入式设备高效率、低耗能的优点使其在交通监控领域得到了越来越广泛的应用,基于分布式计算机设计了智能相机的分布式智能城市交通系统,提出了一系列有益于提升系统运行的具有应用前景的算法,用来解决使用重叠视野进行跟踪和稀疏相机网络中的有效跟踪。     

Location configuration design for Dynamic Message Signs under stochastic incident and ATIS scenarios

This paper proposes a methodology for deploying permanent Dynamic Message Signs (DMS) in a vehicular traffic network. Of particular interest is the planning problem to optimize the number of DMS to deploy in conjunction with Advanced Traveler Information Systems (ATIS), operating and maintenance cost of DMS, and incident-related user cost under random traffic incident situations. The optimal DMS location design problem discussed herein is formulated as a two-stage stochastic program with recourse (SPR). A Tabu search algorithm combined with dynamic traffic simulation and assignment approaches are employed to solve this problem. A case study performed on the Fort-Worth, Texas network highlights the effectiveness of the proposed framework and illustrates the affect factors such as demand, network structure, DMS response rate, and incident characteristics have on the solution. The numerical results suggest that designing and deploying DMS and ATIS jointly is more cost-effective and efficient than the sequential build-out of the two from the system management perspective.     

An investigation of incident frequency,duration and lanes blockage for determining traffic delay

Traffic delay caused by incidents is closely related to three variables: incident frequency, incident duration, and the number of lanes blocked by an incident that is directly related to the bottleneck capacity. Relatively, incident duration has been more extensively studied than incident frequency and the number of lanes blocked in an incident. In this study, we provide an investigation of the influencing factors for all of these three variables based on an incident data set that was collected in New York City (NYC). The information about the incidents derived from the identification can be used by incident management agencies in NYC for strategic policy decision making and daily incident management and traffic operation. In identifying the influencing factors for incident frequency, a set of models, including Poisson and Negative Binomial regression models and their zero‐inflated models, were considered. An appropriate model was determined based on a model decision‐making tree. The influencing factors for incident duration were identified based on hazard‐based models where Exponential, Weibull, Log‐logistic, and Log‐normal distributions were considered for incident duration. For the number of lanes blocked in an incident, the identification of the influencing factors was based on an Ordered Probit model which can better capture the order inherent in the number of lanes blocked in an incident. As identified in this study, rain is the only factor that significantly influenced incident frequency. For incident duration and the number of lanes blocked in an incident, various factors had significant impact. As concluded in this study, there is a strong need to identify the influencing factors in terms of different types of incidents and the roadways where the incidents occured.     

Pedestrian crossing detection based on evidential fusion of video-sensors

This paper introduces an online pedestrian crossing detection system that uses pre-existing traffic-oriented video-sensors which, at regular intervals, provide coarse spatial measurements on areas along a crosswalk. Pedestrian crossing detection is based on the recognition of occupancy patterns induced by pedestrians when they move on the crosswalk. In order to improve the ability of non-dedicated sensors to detect pedestrians, we introduce an evidential-based data fusion process that exploits redundant information coming from one or two sensors: intra-sensor fusion uses spatiotemporal characteristics of the measurements and inter-sensor fusion uses redundancy between the two sensors. As part of the EU funded TRACKSS project on cooperative advanced sensors for road traffic applications, real data have been collected on an urban intersection equipped with two cameras. The results obtained show that the data fusion process enhances the quality of occupancy patterns obtained and leads to high detection rates of pedestrian crossings with multi-purpose sensors in operational conditions, especially when a secondary sensor is available.     

Distributed work and travel behaviour: The dynamics of interactive agency choices between employers and employees

This paper develops a framework within which multiple agents make discrete choices in respect of a common objective – the determination of participation in distributed work, especially the opportunities and constraints associated with telecommuting. Ideas in discrete choice theory and game theory are combined to define a set of choice experiments in which employees and employers interact in arriving at a choice path in a distributed work context. A state choice experiment with offers and feedback, known as an interactive agency choice experiment (IACE), is empirically investigated in the context of telecommuting options with an exploratory sample of employees and employers in Sydney, Australia. The approach highlights the role of information and negotiation in breaking down the barriers to more flexible work activity, to deliver potential benefits to the transport system such as reduced traffic congestion and environmental sustainability. The paper identifies the types of incentives that an employee/er has to offer the employer/employee in securing effective telecommuting. This revised version was published online in June 2006 with corrections to the Cover Date.     

Motorway signals and the detection of incidents

A mathematical model of automobile trip tours is presented. Within a framework of eight common restrictions on automobile trip making, all travel behavior is assumed random and all of the ways in which tours can be arranged are assumed equally likely. Three probability distributions are derived from the model: (1) the probability that a household makes a given number of tours in a day; (2) the probability that a household makes a given number of trips in a day; and (3) the probability that a tour reaches a given number of destinations. It is shown that the model agrees with similar probability distributions generated from home‐interview data for Milwaukee.