High-resolution Event-based Data Research Articles

Modeling red-light running behavior using high-resolution event-based data: a finite mixture modeling approach

To effectively reduce the number of red-light violations and crashes, it is crucial to explore RLR behavior at local intersections, understand the contributing factors, and identify the riskiest intersections by estimating RLR frequency. In this study, a finite mixture modeling method was utilized to understand the contributing factors to RLR behavior and estimate this violating behavior. To develop the RLR estimation models, performance metrics and signal phasing data were collected from the Automated Traffic Signal Performance Measures (ATSPMs) system in two jurisdictions in Arizona: Pima County and the Town of Marana. The results from calibrated models showed that an increase in traffic flow, intersection delay, number of approach lanes, and split failure is associated with an increase in the likelihood of observing red-light violations. In addition, it was found that an increase in cycle length is associated with a decrease in the likelihood of observing the red-light violation. The results of comparing the proposed RLR estimation method with several conventional methods, the Poisson Generalized Linear Model (PGLM), Zero-inflated Poisson Regression Model (ZIPM), and Zero-inflated Negative Binomial Regression Model (ZINB) showed the proposed method outperforms all the models in terms of both model fit and accuracy. The application of the proposed method could be used to analyze the intersections with the highest number of red-light violations. Furthermore, the presented transferability results can be advantageous to transportation agencies within Arizona and urban areas with similar characteristics by providing insight into which model specifications may provide the best RLR estimation accuracy.

Network-level turning movement counts estimation using traffic controller event-based data

Accurate turning movement counts (TMCs) data collected from regional-wide signalized intersections is critical to regional transportation planning and simulation modeling. A variety of existing traffic sensors, configured at intersections for traffic detection and signal control, can generate a large amount of real-time high-resolution event-based data from traffic controllers but few of these sensors are configured to collect TMC. This paper proposes a methodology for estimating network-level TMC using existing traffic controller event-based data without installing additional sensors. First, relevant features that can indicate traffic arrival are extracted from existing event-based data, including detector occupancy time, detector-triggered count, and green time duration. With these features, a multi-output multilayer neural network model is developed to estimate TMC. To further improve network-level TMC estimation accuracy, intersection infrastructure data and point-of-interest (POI) data are included as exogenous variables for the proposed model. Ninety-three signalized intersections are chosen from the Pima County region, Arizona, to calibrate and verify the developed model. The validation results show that the proposed model can accurately estimate TMC, as indicated by a median Root Mean Square Error (RMSE) of 41 veh/15 min, 11 veh/15 min, and 12 veh/15 min for through movement, left-turn movement, and right-turn movement volume estimation, respectively. This research provides a new possibility of utilizing existing data sources to obtain network-level TMC data without additional infrastructure and labor costs for transportation agencies.

Modeling Real-Time Cycle-Level Crash Risk at Signalized Intersections Based on High-Resolution Event-Based Data

In the context of pro-active traffic management, real-time crash risk evaluation is one of the most critical components. Signalized intersections are well-known high-risk locations because of the variety of traffic movements, modes, and their interactions. Unlike access-controlled freeways, the traffic flow at signalized intersections presents cyclical characteristics, which are temporally separated by traffic signals. Therefore, the data preparation for real-time crash risk prediction at signalized intersections should be based on the signal cycle rather than a predefined fixed time interval (e.g., 5 minutes). In this research, the corresponding signal cycles where crashes occurred were verified based on high-resolution event-based data (i.e., Automated Traffic Signal Performance Measures (ATSPM)). Six types of real-time cycle-level factors were considered, including traffic volume, signal timing, headway and occupancy, traffic variation between upstream and downstream detectors, shockwave characteristics, and weather. Two undersampling strategies, matched case-control and random undersampling, were utilized to develop conditional logistic and binary logistic models, respectively. Model results indicate that the random undersampling performs better than the matched case-control method. It was found that higher cycle volume, overall average flow ratio across lanes, arrivals on yellow ratio, traffic volatility across approach sections, as well as longer cycle length and lower green ratio could significantly increase the odds of crash occurrence at signalized intersections. Moreover, longer maximum queue length, bigger shockwave, and higher absolute queuing shockwave speed tend to increase the odds of crash occurrence.

Evaluation of Event-Based Sensors for Satellite Material Characterization

Broadband photometry has been used for many years to infer basic information about satellites; however, there has been limited success at remotely determining satellite surface materials, and the variability of brightness and spectral energy distribution of satellite reflections complicates satellite identification. This paper demonstrates the potential utility of event-based sensors for remotely characterizing satellites based on the reflectance properties of their surface materials. Event-based sensors offer three important advantages over traditional frame-based sensors, such as charge-coupled devices (CCDs) for characterizing satellite materials on orbit: very high temporal resolution, high dynamic range, and low data rates. This allows rapid, fine-resolution measurements over a broad range of intensities. An event-based camera was used to characterize the broadband reflectance properties of five common satellite materials over a range of illumination and observation angles in the laboratory. Some of the results are very distinctive, and have not previously been reported, demonstrating that event-based sensors might perform better than CCDs at satellite identification and material characterization. The results also show that different materials can exhibit quite different, and sometimes very distinctive, illumination/observation geometry-dependent reflectance characteristics. Using high angular resolution event-based data to assess broadband reflectance changes with changing geometry could be an effective means of unambiguously identifying satellites or determining the presence of specific satellite surface materials.

Estimating pedestrian delay at signalized intersections using high-resolution event-based data: a finite mixture modeling method

It has been widely shown that pedestrians’ level of frustration grows with the increase of pedestrian delay, and may cause pedestrians to violate the signals. However, for agencies seeking to use multimodal signal performances for signal operations, the pedestrian delay is not always readily available. To tackle this issue, this study proposed a finite mixture modeling method to estimate pedestrian delay using high-resolution event-based data collected from the smart sensors. The proposed method was used to estimate pedestrian delay at four signalized intersections on a major arterial corridor in Pima County, Arizona. The results showed the proposed method was able to capture and track the actual pedestrian delay fluctuations during the day at all the study intersections with average errors of 10 s and 13 s for mean-absolute-error and root-mean-square-error, respectively. In addition, the proposed model was compared with three conventional methods (HCM 2010, Virkler, Dunn) and the comparison results showed that the proposed method outperforms all the other methods in terms of both mean-absolute-error and root-mean-square-error. Furthermore, it was found that the proposed method is transferable and can be used as a network-wide delay estimation model for intersections with similar traffic patterns. The application of the proposed method could provide agencies with a more reliable, robust, and yet accurate approach for estimating pedestrian delay at signalized intersections where the pedestrian data are not readily available. In addition, it will allow system operators to quantitatively assess existing delays and enact changes to incorporate the better serve pedestrian needs.

Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based Data.

Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, which is a key element in RL-based traffic signal control, plays a vital role. However, the data used for state definition in the literature are either coarse or difficult to measure directly using the prevailing detection systems for signal control. This paper proposes a deep reinforcement learning-based traffic signal control method which uses high-resolution event-based data, aiming to achieve cost-effective and efficient adaptive traffic signal control. High-resolution event-based data, which records the time when each vehicle-detector actuation/de-actuation event occurs, is informative and can be collected directly from vehicle-actuated detectors (e.g., inductive loops) with current technologies. Given the event-based data, deep learning techniques are employed to automatically extract useful features for traffic signal control. The proposed method is benchmarked with two commonly used traffic signal control strategies, i.e., the fixed-time control strategy and the actuated control strategy, and experimental results reveal that the proposed method significantly outperforms the commonly used control strategies.

Volume Estimation using Traffic Signal Event-Based Data from Video-Based Sensors

Traffic volume data is one of the most critical variables for signal retiming. However, collecting traffic volume manually can be time-consuming and costly. In recent years, video-based sensor systems have been applied on signalized intersections for signal timing control. The detectors in video-based sensors generate large amounts of real-time high-resolution event-based data, including signal status and detection status data. The vehicle arrivals for each detection event is a stochastic process and has a relationship with the signal status and the detection duration (time occupancy). Therefore, a modified dynamic hidden Markov model (DHMM) is proposed to estimate vehicular volume by modeling the vehicle arrivals using event-based data collected at signalized intersections. The concept of an additional hidden state is introduced to make the vehicular volume finite by grouping volumes that have only a small probability of occurring into one hidden state. Additionally, a linear regression model is built to estimate the vehicular volume when the output of the DHMM is an additional hidden state. The resulting mean absolute percentage errors of the 15-min estimated volume are 14.1%, 10.3%, and 10.5%, respectively, at three study locations in Tucson, Arizona.

Real-time queue length estimation using event-based advance detector data

ABSTRACTReal-time queue length information at signalized intersections is useful for both performance evaluation and signal optimization. Previous studies have successfully examined the use of high-resolution event-based data to estimate real-time queue lengths. Based on the identification of critical breakpoints, real-time queue lengths can be estimated by applying the commonly used shockwave model. Although breakpoints can be accurately identified using lane-by-lane detection, few studies have investigated queue length estimation using single-channel detection, which is a common detection scheme for actuated signal control. In this study, a breakpoint misidentification checking process and two input-output models (upstream-based and local-based) are proposed to address the overestimation and short queue length estimation problems of breakpoint-based models. These procedures are integrated with a typical breakpoint-based model framework and queue-over-detector identification process. The proposed framework was evaluated using field-collected event-based data along Speedway Boulevard in Tucson, Arizona. Significant improvements in maximum queue length estimates were achieved using the proposed method compared to the breakpoint-based model, with mean absolute errors of 35.7 and 105.6 ft., respectively.

Analysis of yellow-light running at signalized intersections using high-resolution traffic data

Many accidents occurring at signalized intersections are closely related to drivers’ decisions of running through intersections during yellow light, i.e., yellow-light running (YLR). Therefore it is important to understand the relationships between YLR and the factors which contribute to drivers’ decision of YLR. This requires collecting a large amount of YLR cases. However, existing data collection method, which mainly relies on video cameras, has difficulties to collect a large amount of YLR data. In this research, we propose a method to study drivers’ YLR behaviors using high-resolution event-based data from signal control systems. We used 8months’ high-resolution data collected by two stop-bar detectors at a signalized intersection located in Minnesota and identified over 30,000 YLR cases. To identify the possible reasons for drivers’ decision of YLR, this research further categorized the YLR cases into four types: “in should-go zone”, “in should-stop zone”, “in dilemma zone”, and “in optional zone” according to the driver’s location when signal turns to yellow. Statistical analysis indicates that the mean values of approaching speed and acceleration rate are significantly different for different types of YLR. We also show that there were about 10% of YLR drivers who cannot run through intersection before traffic light turns to red. Furthermore, based on a strong correlation between hourly traffic volume and number of YLR events, this research developed a regression model that can be used to predict the number of YLR events based on hourly flow rate. This research also showed that snowing weather conditions cause more YLR events.

Sequence Optimization at Signalized Diamond Interchanges Using High-Resolution Event-Based Data

Signalized diamond interchanges are pairs of ramp intersections characterized by interlocked left turns and relatively close spacing. This paper describes a series of performance measures derived from high-resolution signal controller event data that can be used to optimize the internal phase sequence and offset to improve traffic flows within diamond interchanges and to assess the progression of the interior movements qualitatively and quantitatively. The new heuristic developed in this paper improves on traditional green band optimization techniques by incorporating actual demand profiles measured in the field. A field analysis was performed on a diamond interchange at I-69 and 96th Street in northwest Indianapolis, Indiana, where the existing sequence data were collected and used to model the alternative sequences to identify the optimal sequence. Interior operations were improved under the optimized settings: the percentage of vehicle arrivals on green increased by 19% during the 09:00-to-15:00 midday plan. Video observations were used to corroborate the data and are included in a video synthesis of the time–space trajectories.

Using high-resolution event-based data for traffic modeling and control: An overview

Research on using high-resolution event-based data for traffic modeling and control is still at early stage. In this paper, we provide a comprehensive overview on what has been achieved and also think ahead on what can be achieved in the future. It is our opinion that using high-resolution event data, instead of conventional aggregate data, could bring significant improvements to current research and practices in traffic engineering. Event data records the times when a vehicle arrives at and departs from a vehicle detector. From that, individual vehicle’s on-detector-time and time gap between two consecutive vehicles can be derived. Such detailed information is of great importance for traffic modeling and control. As reviewed in this paper, current research has demonstrated that event data are extremely helpful in the fields of detector error diagnosis, vehicle classification, freeway travel time estimation, arterial performance measure, signal control optimization, traffic safety, traffic flow theory, and environmental studies. In addition, the cost of event data collection is low compared to other data collection techniques since event data can be directly collected from existing controller cabinet without any changes on the infrastructure, and can be continuously collected in 24/7 mode. This brings many research opportunities as suggested in the paper.

High-Resolution Event-Based Data at Diamond Interchanges

Signalized diamond interchanges are unique pairs of intersections characterized by interlocked left turns and relatively close spacing between ramps. A diamond interchange has four external entry points (origins) and four external exit points (destinations). Examination of the external origin–destination paths and evaluation of their impact on the interior storage and progression are critical for operating a diamond interchange effectively. This paper describes a series of performance measures derived from high-resolution signal controller data that can be used to (a) assess the quality of progression of the interior movements qualitatively and quantitatively and (b) optimize the internal offset to improve traffic flows within the interchange. Additional performance measures for identifying internal and ramp queuing are discussed. The integration of graphical performance measures into controller front panel displays and central systems is recommended to assist engineers in tuning and maintaining efficient operation of diamond interchanges.

Use of Event-Based Traffic Data in Generating Time–Space Diagrams for Evaluation of Signal Coordination

The time–space (TS) diagram is a popular visualization tool in evaluating progression quality for signalized arterials, and most signal optimization software products (such as Synchro) can generate TS diagrams as part of the optimization output. During the signal retiming process, TS diagrams generated by optimization software need to be validated by field observations, and minor changes will be made to signal control parameters if a discrepancy is observed. The validation process is time-consuming and costly. Through the use of high-resolution event-based traffic data collected from existing traffic signal controllers, a practical procedure for constructing TS diagrams for signalized arterials is proposed. The diagrams can be used as a convenient visualization tool in evaluating the performance of traffic signals and in identifying opportunities for fine-tuning in a timely manner. Reasonable agreement was found between the TS diagram and vehicle trajectory data collected from the field. A field experiment was carried out to illustrate how signal parameter changes could be made by intuitive evaluation of the TS diagram. Recommendations and limitations of the proposed approach are discussed.

Roundabout Critical Headway Measurement Based on High-Resolution Event-Based Data from Wireless Magnetometers

Critical headway is an important parameter for roundabout design, particularly with regard to analytical modeling approaches. The models that have been developed over the past 30 years were typically based on data obtained from manually reduced video or field observations. This paper reports on the application of wireless magnetometers to collect point presence detection for calculating the rejected critical headways. Data were collected at a single-lane roundabout in Carmel, Indiana. Carmel has had more than 60 roundabouts in operation for several years: it represented a community that was highly experienced with roundabouts. More than 260,000 entering vehicles were observed at one of the single-lane roundabouts over a 2-week period with more than 45,000 rejected headways analyzed. For the roundabout studied, 75% of the rejected headways were found to be less than 3.0 s. The rejected headway values were somewhat lower than the values reported in NCHRP Report 572, perhaps because of the evolving driver familiarity with roundabouts. Although this community had a particularly large number of roundabouts, the rejected headway characteristics observed suggested that as roundabouts became more common throughout the United States, it was appropriate to revisit some of the basic traffic engineering parameters used for analysis, much as the traffic signal community did with saturation flow rate in the 1990s. The techniques presented in this paper could be scaled to several roundabouts with varying geometrics and traffic to diversify the data set necessary to update some of the values developed during the past decade before roundabouts were common in the United States.

High-Resolution Detector and Signal Data to Support Crash Identification and Reconstruction

Traffic crashes may not always result in severe or fatal injuries, but they can have a nontrivial impact on system performance, particularly during heavy traffic conditions. One way to reduce the frequency of such incidents is first to identify the necessary circumstances that resulted in the collision. However, road accidents, particularly intersection-related crashes, are complex phenomena and may result from various combinations of causal factors. Recently, methods for recording high-resolution arterial traffic data have been developed. Traffic safety engineers should explore such high-resolution data to understand the causes of crashes. A study was done to illustrate, for a particular intersection crash resulting from a signal violation, how high-resolution event-based data obtained from loop detectors could be used to identify the incident and the vehicles involved in the crash. How high-resolution data could support a traditional reconstruction of this crash was also illustrated. A Monte Carlo simulation technique was used to estimate the most probable combination of driver behaviors that resulted in the collision. Excessive speed of the vehicle that violated the red light was the most critical factor contributing to the crash.