Keshuang Tang

Safety Evaluation for Intergreen Intervals at Signalized Intersections Based on Probabilistic Method

This study presents a probabilistic safety estimation approach based on traffic conflict technique. The study aims to investigate the impacts of the German signal control policy and intergreen design method on safety during intergreen intervals at signalized intersections in Japan. To facilitate application of the policy, the uncertainty of input variables and of safety itself, indicated by the postencroachment time, was analyzed first by means of field data collected at 12 typical intersections in Japan and Germany. A few case studies were then examined with Monte Carlo simulation. The results indicated that safety during the intergreen interval and its reliability might significantly drop if inter-green times were simply changed from the values based on the Japanese method to the values based on the German method without supplementary countermeasures. However, safety as well as reliability can be maintained if the Japanese control policy is completely replaced with the German one.

Design of Intergreen Times Based on Safety Reliability

This study proposes a stochastic approach for the design of intergreen times, that is, signal change and clearance intervals, at signalized intersections based on safety reliability. Unlike the conventional deterministic methods, the proposed method accounts for the uncertainty of input variables deriving intergreen times as well as driver decision randomness at the onset of yellow. The probability of risky behavior, including abrupt stop and red light running, was selected as the safety reliability measure for determining yellow time, and the probability of the occurrence of clearance failure was used as the measure for determining all-red time. A theoretical model of estimating safety reliability and deriving intergreen times was developed and was validated by the use of the Monte Carlo simulation technique. Comparisons with the current methods used in Japan, the United States, and Germany indicated that the proposed method captured the probabilistic nature of traffic and driving decision and thus provided a more rational design of intergreen times.

IMPACTS OF GROUP-BASED SIGNAL CONTROL POLICY ON DRIVER BEHAVIOR AND INTERSECTION SAFETY

In this paper, the authors evaluate the impacts of a group-based traffic signal control policy on driver behavior and intersection safety in Japan. Also known as movement-based, this policy refers to a control patten in which the controller is able to separately allocate time to each signal group instead of allocating based on traffic demand. Using data collected at an intersection before and after such a policy was implemented, the authors draw conclusions regarding the potential safety benefits of implementing such a policy. In addition, the authors discuss conclusion regarding the influence of group-based control policy on the occurrence probability and rate of red light running (RLR) and yellow entry time at intersections.

Behavior of Riders of Electric Bicycles at Onset of Green and Yellow at Signalized Intersections in China

In most Chinese cities, electric bicycles and electrically assisted bicycles (e-bikes) have drastically increased in recent years and currently constitute the largest proportion of the nonmotorized traffic at signalized intersections. Proper treatment of e-bikes has become a vitally important issue in improving the operational efficiency and safety performance of signalized intersections. However, fundamental knowledge of the unique operating characteristics and behavior of riders of e-bikes under various conditions is insufficient. This study statistically analyzed critical behavioral parameters of e-bike riders and empirically modeled their start-up behavior at the green onset following a 3-s red-and-yellow signal and their stop–pass decision behavior at the yellow onset following a 3-s flashing green. Distribution types and parameters of desired speed, start-up time, acceleration rate, perception–reaction time, and deceleration rate were investigated with the use of highly accurate trajectory data. A temporal–spatial model was developed to interpret the start-up curve, and three binary logistic regression models were built to predict the stop–pass decisions for different rider groups. It was found that the start-up curve of e-bikes could be well described by a quadratic function and that the red-and-yellow signal significantly induced a hurried start. The potential time to the stop line at the decision point was found to be the dominant independent factor explaining the stop–pass decision of e-bike riders; the flashing green signal seemed to enlarge the option zone, bring the indecision zone earlier, and result in more aggressive passing behavior.

Investigating Operational Benefits of Group-based Signal Control in Japan Through a Stochastic Approach

This study intends to investigate operational performance of the group-based signal control approach (GA) in Japan. A cycle-based stochastic approach incorporating the HCM 2000 delay model and Monte Carlo simulation was proposed. To facilitate its application, extensive field data was collected at 11 typical intersections in Germany and Japan to understand the variability of input variables and their correlations. A series of case studies were eventually conducted to investigate the applicability of GA under various conditions, i.e., degree of saturation (DS), split ratio (SR), and the coefficient of variation (COV) of traffic demand. Conclusions supported that the operational performance of GA deteriorated with increasing SR and decreasing DS. GA is apparently advantageous for SRs less than 0.8, COVs less than 0.2, and optimum cycle lengths greater than 90xa0s.

Signalized Intersection Design and Operations: Experiences in Germany and Japan

An official nationwide manual on intersection planning and design remains absent in China. Presently, a research group led by Tongji University is addressing its first version. Toward gaining more profound knowledge on the state of the art in intersection design and operations overseas while developing the Chinese manual, this paper thus presents German and Japanese experiences. Conclusions, future studies and efforts essential for China are discussed in the final section.

Empirical Analysis and Modeling of Stop-Line Crossing Time and Speed at Signalized Intersections

In China, a flashing green (FG) indication of 3 s followed by a yellow (Y) indication of 3 s is commonly applied to end the green phase at signalized intersections. Stop-line crossing behavior of drivers during such a phase transition period significantly influences safety performance of signalized intersections. The objective of this study is thus to empirically analyze and model drivers’ stop-line crossing time and speed in response to the specific phase transition period of FG and Y. High-resolution trajectories for 1465 vehicles were collected at three rural high-speed intersections with a speed limit of 80 km/h and two urban intersections with a speed limit of 50 km/h in Shanghai. With the vehicle trajectory data, statistical analyses were performed to look into the general characteristics of stop-line crossing time and speed at the two types of intersections. A multinomial logit model and a multiple linear regression model were then developed to predict the stop-line crossing patterns and speeds respectively. It was found that the percentage of stop-line crossings during the Y interval is remarkably higher and the stop-line crossing time is approximately 0.7 s longer at the urban intersections, as compared with the rural intersections. In addition, approaching speed and distance to the stop-line at the onset of FG as well as area type significantly affect the percentages of stop-line crossings during the FG and Y intervals. Vehicle type and stop-line crossing pattern were found to significantly influence the stop-line crossing speed, in addition to the above factors. The red-light-running seems to occur more frequently at the large intersections with a long cycle length.

A Kalman Filter-Based Queue Length Estimation Method with Low-Penetration Mobile Sensor Data at Signalized Intersections

Queue length estimation is of great importance for signal performance measures and signal optimization. With the development of connected vehicle technology and mobile internet technology, using mobile sensor data instead of fixed detector data to estimate queue length has become a significant research topic. This study proposes a queue length estimation method using low-penetration mobile sensor data as the only input. The proposed method is based on the combination of Kalman Filtering and shockwave theory. The critical points are identified from raw spatiotemporal points and allocated to different cycles for subsequent estimation. To apply the Kalman Filter, a state-space model with two state variables and the system noise determined by queue-forming acceleration is established, which can characterize the stochastic property of queue forming. The Kalman Filter with joining points as measurement input recursively estimates real-time queue lengths; on the other hand, queue-discharging waves are estimated with a line fitted to leaving points. By calculating the crossing point of the queue-forming wave and the queue-discharging wave of a cycle, the maximum queue length is also estimated. A case study with DiDi mobile sensor data and ground truth maximum queue lengths at Huanggang-Fuzhong intersection, Shenzhen, China, shows that the mean absolute percentage error is only 11.2%. Moreover, the sensitivity analysis shows that the proposed estimation method achieves much better performance than the classical linear regression method, especially in extremely low penetration rates.

Real-Time Prediction of Vehicle Trajectories for Proactively Identifying Risky Driving Behaviors at High-Speed Intersections

At high-speed intersections in many Chinese cities, a traffic-light warning sequence at the end of the green phase—three seconds of flashing green followed by three seconds of yellow—is commonly implemented. Such a long phase transition time leads to heterogeneous decision-making by approaching drivers as to whether to pass the signal or stop. Therefore, risky driving behaviors such as red-light running, abrupt stop, and aggressive pass are more likely to occur at these intersections. Proactive identification of risky behaviors can facilitate mitigation of the dilemma zone and development of on-board safety altering strategies. In this study, a real-time vehicle trajectory prediction method is proposed to help identify risky behaviors during the signal phase transition. Two cases are considered and treated differently in the proposed method: a single vehicle case and a following vehicle case. The adaptive Kalman filter (KF) model and the K-nearest neighbor model are integrated to predict vehicle trajectories. The adaptive KF model and intelligent driver model are fused to predict the following vehicles’ trajectories. The proposed models are calibrated and validated using 1,281 vehicle trajectories collected at three high-speed intersections in Shanghai. Results indicate that the root mean square error between the predicted trajectories and the actual trajectories is 5.02u2009m for single vehicles and 2.33u2009m for following vehicles. The proposed method is further applied to predict risky behaviors, including red-light running, abrupt stop, aggressive pass, speeding pass, and aggressive following. The overall prediction accuracy is 95.1% for the single vehicle case and 96.2% for the following vehicle case.

Real-Time Queue Length Estimation for Signalized Intersections Using Vehicle Trajectory Data

Queue length is one of the most important performance measures for signalized intersections. Many methods for queue length estimation based on various data sources have been proposed in the literature. With the latest developments and applications of probe vehicle systems, cycle-by-cycle queue length estimation based only on probe data has become a promising research topic. However, most existing methods assume that information such as signal timing, arrival pattern, and penetration rate is known, an assumption that constrains their applicability in practice. The objective of this study was to propose a cycle-by-cycle queue length estimation method using only probe data without the foregoing assumption. Based on the shock wave theory, the proposed method is capable of reproducing the dynamic queue forming and dissipating process cycles at signalized intersections by using probe vehicle trajectories. To reproduce the queuing processes, the inflection points of probe vehicle trajectories representing the changes of arrival patterns are identified and extracted from the trajectory points of vehicles joining and leaving the queue. A piecewise linear function is then used to fit all the inflection points to estimate the stopping and discharging shock waves. Finally, signal timing data and queue lengths can be calculated on the basis of the estimated shock waves. Under both saturated and oversaturated traffic conditions, the performance of the method is comprehensively evaluated through 60 simulation scenarios, which cover sampling intervals from 5 s to 60 s and penetration rates ranging from 5% to 100%. Results show that compared with the method proposed by Ramezani and Geroliminis in 2015, the proposed method has more robustness for all the sampling intervals and displays more estimation accuracy of queue length and a higher success rate under conditions of low penetration rate.