Safety problems in urban cycling mobility. A quantitative risk analysis at urban intersections

The attention to the most vulnerable road users has grown rapidly in recent decades. The experience gained reveals an important number of cyclist fatalities due to road crashes; most of which occur at intersections. In this study, dispersion of trajectories in urban intersections has been considered to identify the whole conflict area and the largest conflict areas between cars and bicycles, and the speeds have been used to calculate exposure time of cyclists and reaction time available to drivers to avoid collision. These data allow the summary approach to the problem, while a risk probability model has been developed to adopt an elementary approach analysis. A quantitative damage model has been proposed to classify each conflict point, and a probabilistic approach has been defined to consider the traffic volume and the elementary unit of exposure. The combination of damage and probability, permitted to assess the risk of crash, at the examined intersection. Three types of urban four-arm intersection, with and without bike paths, were considered. For each scheme, the authors assessed the risk of collision between the cyclist and the vehicle. The obtained results allowed the identification of the most hazardous maneuvers and highlighted that geometry and kinematics of traffic movements cannot be overlooked, when designing an urban road intersection. The strategy proposed by the authors could have a significant impact on the risk management of urban intersections. The obtained results and the proposed hazard estimation methodology could be used to design safer intersections

Analysis On Illegal Crossing Behavior of Pedestrians At Signalized Intersections Based On Bayesian Network

Pedestrians do not always comply with the crossing rules of when and/or where to cross the road at signalized intersections. This risky behavior tends to undermine greatly the effectiveness of safety countermeasures at such locations. Thus, it is very important to understand illegal behavior to develop more effective and targeting measures. In order to address the problem, this paper aimed to analyze characteristics of illegal crossings and their impact on behavior choice. Firstly, illegal crossing behaviors at signalized intersections were classified into two categories, including “crossing at a red light” and “crossing outside of a crosswalk.” Secondly, two sets of data were collected to understand the behaviors. One set of data was collected from video-based observation conducted at 3 signalized intersections in Guangzhou, China, capturing 3334 valid illegal crossing cases in total. Another set of data, from a questionnaire survey conducted online, resulted in 275 valid responses. Finally, presentational characteristics of illegal crossings at signalized intersection were analyzed and two Bayesian network-based behavior models were developed to investigate the characteristics and their impacts on the two types of illegal crossing behaviors, “crossing at a red light” and “crossing outside of a crosswalk,” respectively. Findings reveal that, (i) illegal crossings occur at various types of signalized intersections, with a higher probability for “crossing outside of a crosswalk” compared to “crossing at a red light;” (ii) Arc routing crossing has the highest probability to occur at signalized intersections compared to other types of out-side-crosswalk crossings. (iii) The location of origin and destination of a pedestrian has a significant effect on crossing outside of a crosswalk, the location of origin and destination of “one is inside of a crosswalk and another is outside of a crosswalk” has a highest proportion. These findings provide better understanding of illegal crossings and their impact factors so that the effectiveness of management and control of pedestrians at signalized intersections can be improved

ESTIMATION METHOD OF LEFT TURN LANE CAPACITY UNDER THE INFLUENCE OF PEDESTRIANS AND CROSSWALK LAYOUT AT SIGNALIZED CROSSWALKS

The left turn lane capacity is highly influenced by the presence of pedestrians under shared signal phasing.  Pedestrian crossing time can be highly influenced by pedestrian platoon maneuver under the influence of traffic signal settings, crosswalk length and bidirectional interactions of pedestrian, due to that left turning vehicles movement may be significantly affected. However, existing capacity estimation methods of left turn lanes did not consider those factors. In addition, the influence of crosswalk layout did not properly consider in the existing methods and researches. This study aims to propose a method to estimate the left turn lane capacity, considering the characteristics of crossing pedestrians and crosswalk layout variations. Waiting pedestrian presence-time is estimated by applying pedestrian presence probability model. In addition, the left turning vehicles discharge flow rate under different crosswalk layout and pedestrian demand condition are investigated and modeled. The left turn lane capacity estimation equation is proposed by characterizing the observed discharge flow rate of left turning vehicles under the influence of crossing pedestrians. It was found that the proposed capacity estimation method can represent the realistic influence of pedestrians and crosswalk layout on the left turn lane capacity at signalized crosswalks

Microscopic decision model for pedestrian route choice at signalized crosswalks

In this paper, two-tier mathematical models were developed to simulate the microscopic pedestrian decision-making process of route choice at signalized crosswalks. In the first tier, a discrete choice model was proposed to predict the choices of walking direction. In the second tier, an exponential model was calibrated to determine the step size in the chosen direction. First, a utility function was defined in the first-tier model to describe the change of utility in response to deviation from a pedestrian's target direction and the conflicting effects of neighboring pedestrians. A mixed logit model was adopted to estimate the effects of the explanatory variables on the pedestrians' decisions. Compared with the standard multinomial logit model, it was shown that the mixed logit model could accommodate the heterogeneity. The repeated observations for each pedestrian were grouped as panel data to ensure that the parameters remained constant for individual pedestrians but varied among the pedestrians. The mixed logit model with panel data was found to effectively address inter-pedestrian heterogeneity and resulted in a better fit than the standard multinomial logit model. Second, an exponential model in the second tier was proposed to further determine the step size of individual pedestrians in the chosen direction; it indicates the change in walking speed in response to the presence of other pedestrians. Finally, validation was conducted on an independent set of observation data in Hong Kong. The pedestrians' routes and destinations were predicted with the two-tier models. Compared with the tracked trajectories, the average error between the predicted destinations and the observed destinations was within an acceptable margin. Copyright © 2016 John Wiley & Sons, Ltd.postprin

Pedestrian perception-based level-of-service model at signalized intersection crosswalks

"jats:p"Pedestrian level of service (PLOS) is an important measure of performance in the analysis of existing pedestrian crosswalk conditions. Many researchers have developed PLOS models based on pedestrian delay, turning vehicle effect, etc., using the conventional regression method. However, these factors may not effectively reflect the pedestrians’ perception of safety while crossing the crosswalk. The conventional regression method has failed to estimate accurate PLOS because of the primary assumption of an arbitrary probability distribution and vagueness in the input data. Moreover, PLOS categories in existing studies are based on rigid threshold values and the boundaries that are not well defined. Therefore, it is an important attempt to develop a PLOS model with respect to pedestrian safety, convenience, and efficiency at signalized intersections. For this purpose, a video-graphic and user perception surveys were conducted at selected nine signalized intersections in Mumbai, India. The data such as pedestrian, traffic, and geometric characteristics were extracted, and significant variables were identified using Pearson correlation analysis. A consistent and statistically calibrated PLOS model was developed using fuzzy linear regression analysis. PLOS was categorized into six levels ("jats:italic"A"/jats:italic"–"jats:italic"F"/jats:italic") based on the predicted user perception score, and threshold values for each level were estimated using the fuzzy "jats:italic"c"/jats:italic"-means clustering technique. The developed PLOS model and threshold values were validated with the field-observed data. Statistical performance tests were conducted and the results provided more accurate and reliable solutions. In conclusion, this study provides a feasible alternative to measure pedestrian perception-based level of service at signalized intersections. The developed PLOS model and threshold values would be useful for planning and designing pedestrian facilities and also in evaluating and improving the existing conditions of pedestrian facilities at signalized intersections. Document type: Articl

Bayesian Approach on Quantifying the Safety Effects of Pedestrian Countdown Signals to Drivers

Pedestrian countdown signals (PCSs) are viable traffic control devices that assist pedestrians in crossing intersections safely. Despite the fact that PCSs are meant for pedestrians, they also have an impact on drivers’ behavior at intersections. This study focuses on the evaluation of the safety effectiveness of PCSs to drivers in the cities of Jacksonville and Gainesville, Florida. The study employs two Bayesian approaches, before-and-after empirical Bayes (EB) and full Bayes (FB) with a comparison group, to quantify the safety impacts of PCSs to drivers. Specifically, crash modification factors (CMFs), which are estimated using the aforementioned two methods, were used to evaluate the safety effects of PCSs to drivers. Apart from establishing CMFs, crash modification functions (CMFunctions) were also developed to observe the relationship between CMFs and traffic volume. The CMFs were established for distinctive categories of crashes based on crash type (rear-end and angle collisions) and severity level (total, fatal and injury (FI), and property damage only (PDO) collisions). The CMFs findings, using the EB approach indicated that installing PCSs result in a significant improvement of driver’s safety, at a 95% confidence interval (CI), by a 8.8% reduction in total crashes, a 8.0% reduction in rear-end crashes, and a 7.1% reduction in PDO crashes. In addition, FI crashes and angle crashes were observed to be reduced by 4.8%, whereas a 4.6% reduction in angle crashes was observed. In the case of the FB approach, PCSs were observed to be effective and significant, at a 95% Bayesian credible interval (BCI), for a total (Mean = 0.894, 95% BCI (0.828, 0.911)), PDO (Mean = 0.908, 95% BCI (0.838, 0.953)), and rear-end (Mean = 0.920, 95% BCI (0.842, 0.942)) crashes. The results of two crash categories such as FI (Mean = 0.957, 95% BCI (0.886, 1. 020)) and angle (Mean = 0.969, 95% BCI (0.931, 1.022)) crashes are less than one but are not significant at the 95 % BCI. Also, discussed in this study are the CMFunctions, showing the relationship between the developed CMFs and total entering traffic volume, obtained by combining the total traffic on the major and the minor approaches. In addition, the CMFunctions developed using the FB indicated the relationship between the estimated CMFs with the post-treatment year. The CMFunctions developed in this study clearly show that the treatment effectiveness varies considerably with post-treatment time and traffic volume. Moreover, using the FB methodology, the results suggest the treatment effectiveness increased over time in the post-treatment years for the crash categories with two important indicators of effectiveness, i.e., total and PDO, and rear-end crashes. Nevertheless, the treatment effectiveness on rear-end crashes is observed to decline with post-treatment time, although the base value is still less than one for all the three years. In summary, the results suggest the usefulness of PCSs for drivers

보행자 거동 및 운전자 주행 특성 기반의 자율주행 종방향 거동 계획

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 이경수.본 연구는 보행자의 미래 거동 방향에 대한 불확실성을 고려한 보행자 모델을 제안하고, 보행자 대응 시의 운전자 주행 특성을 반영하여 자율주행 차량의 종방향 모션을 계획하는 알고리즘을 제시한다. 도심 자율 주행을 가능하게 하기위해서는 보행자와의 상호적인 주행이 필수적이다. 그러나, 보행자는 거동 방향 전환이 쉽게 일어나기 때문에 미래 거동을 예측하기가 어렵고, 이에 대응하는 자차의 거동을 결정짓는 데도 어려움이 있다. 이러한 보행자의 거동 불확실성이 존재함에도 자율 주행 차량이 보행자의 안전성을 확보하고 휴먼 운전자와 같이 거동하기 위해서는, 보행자의 거동 불확실성을 반영하는 보행자 모델이 우선적으로 필요하다. 해당 연구에서는 보행자 거동 특성을 조사하여 보행자 거동 확률 모델을 정의하고, 보행자 대응 상황에서의 운전자의 거동을 조사하여 자율주행 차량의 종방향 거동 계획에 적용한다. 해당 논문은 크게 보행자 모델 정의, 예측 기반 충돌 위험 평가 그리고 보행자 대응 종방향 거동 계획의 세 가지 주요 파트로 이루어져 있다. 첫 번째 파트에서 보행자 모델 정의의 핵심 이론은 보행자의 거동 속도와 방향을 전환하는 거동 사이에는 특정 상관관계를 가지고 있다는 것이다. 보행자의 거동 특성은 자율 주행 차량에 부착된 라이다 센서와 전방 카메라를 통해 획득한 보행자 데이터를 통계적으로 분석한 결과로 도출되었다. 해당 데이터를 통해 속도에 따라 보행자가 모든 방향에 대해서 거동할 확률이 도출되고, 보행자의 미래 거동 범위는 도출된 확률 분포에서 유효 시그마 범위를 설정하여 구획된다. 이는 보행자가 일정 시간 동안 특정 확률로 거동할 영역을 고려하여, 위험이 존재할 수 있는 보행자에 대해서 미리 차량의 움직임을 계획할 수 있도록 한다. 두 번째 파트로 보행자와 자 차량의 일정 시간 동안의 위치 정보를 예측하여 충돌 위험성을 평가한다. 보행자 예측은 앞서 도출한 보행자 유효 예측 거동 범위 내에서 가장 위험성이 큰 방향으로 움직인다고 가정한다. 또한, 자 차량의 경우 주어진 로컬 경로를 따라 움직인다는 가정을 하는 차선 유지 모델을 사용한다. 예측 결과를 통해 현재 추가적인 감속도를 가하지 않았을 때, 충돌 위험이 존재하는지 확인한다. 마지막으로, 타겟이 되는 보행자에 대한 종방향 거동을 결정한다. 우선적으로 보행자 대응 상황에서 적절한 감속도와 감속 시점을 결정하기 위해 휴먼 운전자 주행 데이터를 분석한다. 이를 통해 주행에서 핵심적인 파라미터들이 정의되고, 해당 파라미터들은 종방향 거동 계획에 반영된다. 따라서 최종적으로 보행자 예측 거동 영역에 대해서 자율 주행 차량의 추종 가속도이 결정된다. 제시된 알고리즘은 실차 테스트를 통해 성능이 확인된다. 테스트 결과, 도출한 보행자 모델과 예측 모델을 바탕으로 한 감속 결정 시점과 감속도의 궤적이 동일 상황들에 대해서 능숙한 운전자와 유사함이 확인되었다.This paper presents a pedestrian model considering uncertainty in the direction of future movement and a human-like longitudinal motion planning algorithm for autonomous vehicle in the interaction situation with pedestrians. Interactive driving with pedestrians is essential for autonomous driving in urban environments. However, interaction with pedestrians is very challenging for autonomous vehicle because it is difficult to predict movement direction of pedestrians. Even if there exists uncertainty of the behavior of pedestrians, the autonomous vehicles should plan their motions ensuring pedestrian safety and respond smoothly to pedestrians. To implement this, a pedestrian probabilistic yaw model is introduced based on behavioral characteristics and the human driving parameters are investigated in the interaction situation. The paper consists of three main parts: the pedestrian model definition, collision risk assessment based on prediction and human-like longitudinal motion planning. In the first section, the main key of pedestrian model is the behavior tendency with correlation between pedestrians speed and direction change. The behavior characteristics are statistically investigated based on perceived pedestrian tracking data using light detection and ranging(Lidar) sensor and front camera. Through the behavior characteristics, movement probability for all directions of the pedestrian is derived according to pedestrians velocity. Also, the effective moving area can be limited up to the valid probability criterion. The defined model allows the autonomous vehicle to know the area that pedestrian may head to a certain probability in the future steps. This helps to plan the vehicle motion considering the pedestrian yaw states uncertainty and to predetermine the motion of autonomous vehicle from the pedestrians who may have a risk. Secondly, a risk assessment is required and is based on the pedestrian model. The dynamic states of pedestrians and subject vehicle are predicted to do a risk assessment. In this section, the pedestrian behavior is predicted under the assumption of moving to the most dangerous direction in the effective moving area obtained above. The prediction of vehicle behavior is performed using a lane keeping model in which the vehicle follows a given path. Based on the prediction result, it is checked whether there will be a collision between the pedestrian and the vehicle if deceleration motion is not taken. Finally, longitudinal motion planning is determined for target pedestrians with possibility of collision. Human driving data is first examined to obtain a proper longitudinal deceleration and deceleration starting point in the interaction situation with pedestrians. Several human driving parameters are defined and applied in determining the longitudinal acceleration of the vehicle. The longitudinal motion planning algorithm is verified via vehicle tests. The test results confirm that the proposed algorithm shows similar longitudinal motion and deceleration decision to a human driver based on predicted pedestrian model.Chapter 1. Introduction 1 1.1. Background and Motivation 1 1.2. Previous Researches 3 1.3. Thesis Objective and Outline 5 Chapter 2. Probabilistic Pedestrian Yaw Model 8 2.1. Pedestrian Behavior Characteristics 9 2.2. Probability Movement Range 11 Chapter 3. Prediction Based Risk Assessment 13 3.1. Lane Keeping Behavior Model 15 3.2. Subject Vehicle Prediction 17 3.3. Safety Region Based on Prediction 19 Chapter 4. Human-like Longitudinal Motion Planning 22 4.1. Human Driving Parameters Definition 22 4.1.1 Hard Mode Distance 23 4.1.2 Soft Mode Distance and Velocity 23 4.1.3 Time-To-Collision 23 4.2. Driving Mode and Acceleration Decision 25 4.2.1 Acceleration of Each Mode 25 4.2.2 Mode Selection 26 Chapter 5. Vehicle Test Result 28 5.1. Configuration of Experimental Vehicle 28 5.2. Longitudinal Motion Planning for Pedestiran 30 5.2.1 Soft Mode Scenario 32 5.2.2 Hard Mode Scenario 35 Chapter 6. Colclusion 38 Bibliography 39 국문 초록 42Maste

Evaluation of Pedestrian Level of Service at Signalised Intersections from the Elderly Perspective

The crossing decisions and behaviour of elderly pedestrians are affected by the pedestrian level of service (PLOS). In this paper, an evaluation model was established to analyse the relationship between the traffic environment and the perceived evaluation of elderly pedestrians. Firstly, the characteristic parameters of the selected intersections and the perceived evaluation data of elderly pedestrians at the synchronisation scenery were extracted using manual recording and questionnaire-based truncation methods. The correlation between the perceived evaluation data of elderly pedestrians and the traffic parameters were tested with respect to the dimensions of safety, convenience and efficiency. Then, the significant parameters affecting PLOS were recognised. Based on the traffic characteristic parameters, the PLOS evaluation model from the elderly perspective was established using the fuzzy linear regression method. PLOS classification thresholds were obtained using the fuzzy C-means clustering algorithm. The data from two intersections were used to validate the model. The results show that the difference between the actual and the predicted PLOS values of the two crosswalks were 0.2 and 0.1, respectively. Thus, the proposed PLOS evaluation model in this paper can be used to accurately predict the PLOS from the elderly perspective using the traffic data of signalised intersections