Estimating effectiveness of speed reduction measures for pedestrian crossing treatments using an empirically supported speed choice modeling framework

Pedestrian crossing treatments, such as Pedestrian Hybrid Beacons (PHBs) and Rectangular Rapid-Flashing Beacons (RRFBs), are traffic control devices implemented to help pedestrians safely cross busy or higher-speed roadways at midblock crossings and uncontrolled intersections. This study aims to assess the effectiveness of the PHB and RRFB by analyzing drivers’ speeding behavior under different roadway types with real-life traffic conditions. In order to understand the effect of pedestrian crossing treatments (i.e., PHB and RRFB) have on drivers’ speeding behavior, this paper analyzes four zones (i.e., one upstream zone and three consecutive downstream zones). For the four analysis zones, different indexes were computed which corresponds to the differences in drivers’ speed when encountered with the pedestrian crossing treatments. A grouped random effect hurdle beta regression model is estimated for the indexes, with a fixed effect hurdle beta model used for comparison and validating the importance of considering the grouped random heterogeneity across participants. The proposed analysis framework was validated by the means of an empirical driving simulator study, based on two urban roads in the Central Florida region, North Alafaya Trail (SR-434) and South Orange Blossom Trail (US-441). The results revealed that the proposed modeling framework reflects drivers’ difference in speed for the different pedestrian crossing treatments. The results suggest that with proper understanding of the PHB, the PHB can reduce drivers speed even beyond the location of the PHB. Meanwhile, the RRFB does have some effect in speed reduction beyond the location of the RRFB, however many drivers failed to acknowledge the RRFB. It is suggested that when drivers’ have proper education on the use of the PHB to reduce speed safely and for the installation of RRFB be on roads with two or less lanes and a speed limit less than 40 mph. While the main purpose of the pedestrian crossing treatments is to help pedestrians cross safely, speed reduction can be considered a byproduct as revealed in this study.     

Spatio-temporal pattern of vulnerable road user’s collisions hot spots and related risk factors for injury severity in Tunisia

High risk of vulnerable road users (VRUs) injuries and fatalities have received higher interest nowadays in Tunisia. By using VRUs crash record (from January 1, 2001 to December 31, 2013), we describe the spatial pattern of VRUs collisions according to different temporal scales such as (a.m. vs p.m. rush hours VRUs collisions, working days vs non-working days VRUs collisions, daytime vs nighttime VRUs collisions) and investigate the influence of personal and environmental factors for VRUs injuries severity within the Center-East region in Tunisia. The empirical results are of great variety: spatial clustering pattern of each subtype of VRUs collisions according to temporal scale were clearly observed with the exception of daytime VRUs collisions, which shows a random tendency. All time-based subtypes of VRUs collisions also were found to be clustered along the national highways and regional highways especially in the regions of Sousse and Sfax. Results from VRUs severity model suggest that the degree of injury severity is higher for male than for female victim. The Tunisian VRUs are more likely to be involved in severe collision than non-Tunisian VRUs. Among driver contributory factors, the change of direction and hazardous overtaking increase the probability of sustaining fatal accidents compared to other driver contributory factors. The season factor shows that accident severity during the summer season is higher. From a policy view point, this kind of analysis can certainly help Tunisian public authorities to develop appropriate safety measures that can possibly reduce the number of VRUs injuries and fatalities.     

Meta‐CART: A tool to identify interactions between moderators in meta‐analysis

In the framework of meta‐analysis, moderator analysis is usually performed only univariately. When several study characteristics are available that may account for treatment effect, standard meta‐regression has difficulties in identifying interactions between them. To overcome this problem, meta‐CART has been proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta‐analysis to test the significance of moderator effects. The previous version of meta‐CART has its shortcomings: when applying CART, the sample sizes of studies are not taken into account, and the effect sizes are dichotomized around the median value. Therefore, this article proposes new meta‐CART extensions, weighting study effect sizes by their accuracy, and using a regression tree to avoid dichotomization. In addition, new pruning rules are proposed. The performance of all versions of meta‐CART was evaluated via a Monte Carlo simulation study. The simulation results revealed that meta‐regression trees with random‐effects weights and a 0.5‐standard‐error pruning rule perform best. The required sample size for meta‐CART to achieve satisfactory performance depends on the number of study characteristics, the magnitude of the interactions, and the residual heterogeneity.     

Investigating temporal variations in pedestrian crossing behavior at semi-controlled crosswalks: A Bayesian multilevel modeling approach

“Semi-controlled” crosswalks are unsignalized, but have clear pavement markings and “yield to pedestrian” signs. At these locations, pedestrians and motorists frequently interact to determine who should proceed first. When interacting with drivers, pedestrian crossing decisions are complex events that involve a variety of human responses, as well as vehicle dynamics, traffic characteristics, and environmental conditions. In addition, these complexities can be subject to temporal effects. Without considering temporal variations in pedestrian-motorist interaction, statistical methods could lead to biased coefficient estimates and inaccurate conclusions.The study developed a Bayesian multilevel logistic regression (BMLR) model to capture heterogeneities in pedestrian interaction behavior during four different time periods. The proposed method incorporates time-specific effects that vary randomly between time-periods based on a weakly informative prior. The results indicate significant factors, some of which confirm previous research and some that are new ways to explain pedestrian behavior at the individual level. The identification of variables such as FlowOn and FlowWait sheds light on the interactions between pedestrians – providing more information than the single GroupSize measure.Some consequent safety implications are discussed from the perspectives of vehicle dynamics, vehicle flow rate and pedestrian volume. The more detailed metrics developed in this paper will provide a valuable starting point. for the design of crosswalk controls that will foster a higher degree of compliance and less delay.     

Modeling the influence of mobile phone use distraction on pedestrian reaction times to green signals: A multilevel mixed-effects parametric survival model

Understanding and modeling the influence of mobile phone use on pedestrian behaviour is important for several safety and performance evaluations. Mobile phone use affects pedestrian perception of the surrounding traffic environment and reduces situation awareness. This study investigates the effect of distraction due to mobile phone use (i.e., visual and auditory) on pedestrian reaction time to the pedestrian signal. Traffic video data was collected from four crosswalks in Canada and China. A multilevel mixed-effects accelerated failure time (AFT) approach is used to model pedestrian reaction times, with random intercepts capturing the clustered-specific (countries) heterogeneity. Potential reaction time influencing factors were investigated, including pedestrian demographic attributes, distraction characteristics, and environment-related parameters. Results show that pedestrian reaction times were longer in Canada than in China under the non-distraction and distraction conditions. The auditory and visual distractions increase pedestrian reaction time by 67% and 50% on average, respectively. Pedestrian reactions were slower at road segment crosswalks compared to intersection crosswalks, at higher distraction durations, and for males aged over 40 compared to other pedestrians. Moreover, pedestrian reactions were faster at higher traffic awareness levels.     

A latent class approach to fitting the weighted Euclidean model,clascal

A weighted Euclidean distance model for analyzing three-way proximity data is proposed that incorporates a latent class approach. In this latent class weighted Euclidean model, the contribution to the distance function between two stimuli is per dimension weighted identically by all subjects in the same latent class. This model removes the rotational invariance of the classical multidimensional scaling model retaining psychologically meaningful dimensions, and drastically reduces the number of parameters in the traditional INDSCAL model. The probability density function for the data of a subject is posited to be a finite mixture of spherical multivariate normal densities. The maximum likelihood function is optimized by means of an EM algorithm; a modified Fisher scoring method is used to update the parameters in the M-step. A model selection strategy is proposed and illustrated on both real and artificial data.The second author is supported as Bevoegdverklaard Navorser of the Belgian Nationaal Fonds voor Wetenschappelijk Onderzoek.     

Perceiving temporal regularity in music

We address how listeners perceive temporal regularity in music performances, which are rich in temporal irregularities. A computational model is described in which a small system of internal self‐sustained oscillations, operating at different periods with specific phase and period relations, entrains to the rhythms of music performances. Based on temporal expectancies embodied by the oscillations, the model predicts the categorization of temporally changing event intervals into discrete metrical categories, as well as the perceptual salience of deviations from these categories. The model's predictions are tested in two experiments using piano performances of the same music with different phrase structure interpretations (Experiment 1) or different melodic interpretations (Experiment 2). The model successfully tracked temporal regularity amidst the temporal fluctuations found in the performances. The model's sensitivity to performed deviations from its temporal expectations compared favorably with the performers' structural (phrasal and melodic) intentions. Furthermore, the model tracked normal performances (with increased temporal variability) better than performances in which temporal fluctuations associated with individual voices were removed (with decreased variability). The small, systematic temporal irregularities characteristic of human performances (chord asynchronies) improved tracking, but randomly generated temporal irregularities did not. These findings suggest that perception of temporal regularity in complex musical sequences is based on temporal expectancies that adapt in response to temporally fluctuating input.     

Top-down control in time and space: Evidence from saccadic latencies and trajectories

Visual distractors disrupt the production of saccadic eye movements temporally, by increasing saccade latency, and spatially, by biasing the trajectory of the movement. The present research investigated the extent to which top-down control can be exerted over these two forms of oculomotor capture. In two experiments, people were instructed to make target directed saccades in the presence of distractors, and temporal and spatial capture were assessed simultaneously by measuring saccade latency and saccade trajectory curvature, respectively. In Experiment 1, an attentional control set manipulation was employed, resulting in the elimination of temporal capture, but only an attenuation of spatial capture. In Experiment 2, foreknowledge of the target location caused an attenuation of temporal capture but an enhancement of spatial capture. These results suggest that, whereas temporal capture is contingent on top-down control, the spatial component of capture is stimulus-driven.     

EXTENDED SIMPLES AND QUALITATIVE HETEROGENEITY

The problem of qualitative heterogeneity is to explain how an extended simple can enjoy qualitative variation across its spatial or temporal axes, given that it lacks both spatial and temporal parts. I discuss how friends of extended simples should address the problem of qualitative heterogeneity. I present a series of arguments designed to show that rather than appealing to fundamental distributional properties one should appeal to tiny and short-lived tropes. Along the way, issues relevant to debates about material composition, persistence over time and existence monism are discussed.         

Basic and applied research on extinction bursts

Discontinuation of the contingency between a response and its reinforcer sometimes produces a temporary increase in the response before its rate decreases, a phenomenon called the extinction burst. Prior clinical and basic studies on the prevalence of the extinction burst provide highly disparate estimates. Existing theories on the extinction burst fail to account for the dynamic nature of this phenomenon, and the basic behavioral processes that control response bursting remain poorly understood. In this paper, we first review the basic and applied literature on the extinction burst. We then describe a recent refinement of the concatenated matching law called the temporally weighted matching law that appears to resolve the above-mentioned issues regarding the extinction burst. We present illustrative translational data based conceptually on the model. Finally, we discuss specific recommendations derived from the temporally weighted matching law regarding procedures clinicians could implement to potentially mitigate or prevent extinction bursts.     

Roles of personal and environmental factors in the red light running propensity of pedestrian: Case study at the urban crosswalks

Pedestrian is vulnerable to mortality and severe injury in road crashes. Red light running violation of pedestrians is one of the leading causes to the crashes at signalized intersections, at which the crash involvement rates of pedestrians are high. Therefore, it is important to identify the factors that affect the propensity of red light running of pedestrian. In this study, effects of both personal factors (pedestrians’ demographics and behavior) and environmental factors (presence and behavior of other pedestrians, signal time, and traffic condition) on the individual decision of red light running violation are examined, using the video observation surveys at the signalized crossings that are prone to pedestrian-vehicle crashes and have moderate pedestrian and vehicular traffic volumes in the urban area. Crossing behaviors of 6320 pedestrians are captured. Results of a random parameter logit model indicate that pedestrian gender, age, number of lanes, presence of a companion, number of pedestrians around, presence of other violators in the same cycle, time to green, red time, traffic volume, and percentage of heavy vehicles all affect the propensity of red light running violation of pedestrians. Also, there are significant interaction effects by pedestrian’s gender and age, presence of other violators, with a companion, and traffic volume on the propensity. Findings are indicative to the development of effective engineering, enforcement and educational initiatives combating the red light running violation behavior of pedestrians. Therefore, pedestrian safety level at the signalized intersections can be enhanced.     

情境模型中时间和空间维度更新的相互影响

运用移动窗口技术考察情境模型中时间和空间维度同时或序列转变时二者间的相互关系。结果发现：(1)时间和空间同时转变时, 二者存在易化关系, 但时间对空间的易化作用更大; (2)当二者序列转变时, 以中文为材料的结果只发现时间对空间的易化关系, 而以英语为材料的结果则发现存在双向易化关系, 但时间对空间的易化作用更大。据此, 情境模型中的时间和空间维度更新的捆绑－预期假设得到证明。     

Pedestrian compliance and cross walking speed adaptation due to countdown timer installations: A self report study

Although they are aware of the possible risk, a high number of pedestrians still violate the red light indication and cross the road illegally. This hazardous behaviour may cause incidents between them and the road vehicles. In order to reduce this illegal behaviour, the traffic signals are equipped with countdown timers, in order to provide more information and decrease pedestrians’ noncompliant behaviour. The main purpose of the present paper is to investigate the influence of countdown timers on pedestrians’ compliance regarding their crossing behaviour at intersections as well as to examine the parameters affecting walking speed adaptation. In the context of this analysis two regression models were developed. The first model is a binary logistic regression model which examines pedestrians’ self reported compliance. The results showed that the gender, the age, the perceived comfort and the seconds remaining for the onset of red light are the main parameters that affect compliance. The second model is an ordinal regression model which examines the factors that make pedestrians adapt their walking speed as they are crossing the road and are informed by the countdown timers about the second remaining for the termination of the green phase. The results of the second model revealed that the age, the compliance and the perceived assistance that the countdown timer provides for the walking speed adaptation affect the minimum remaining time before a pedestrian accelerates.     

Replies to comments on "Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?" by Yuan and Fang (2023)

Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and diagnosis of individuals/participants. But regression analysis with weighted composites has been known to yield attenuated regression coefficients when predictors contain errors. Contrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the signal-to-noise ratio (SNR). In particular, the SNR for the regression coefficient via the least squares (LS) method with equally weighted composites is mathematically greater than that by CB-SEM if the items for each factor are parallel, even when the SEM model is correctly specified and estimated by an efficient method. Analytical, numerical and empirical results also show that LS regression using weighted composites performs as well as or better than the normal maximum likelihood method for CB-SEM under many conditions even when the population distribution is multivariate normal. Results also show that the LS regression coefficients become more efficient when considering the sampling errors in the weights of composites than those that are conditional on weights.     

Normal Versus Noncentral Chi-square Asymptotics of Misspecified Models

In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences of ignoring spatial dependence. Specifically, we find inefficiency in the estimates of the factor score means and bias and inefficiency in the estimates of the corresponding covariance matrix. We apply the model to Schwartz value priority data obtained from 5 European countries. We show that the Schwartz motivational types of values, such as Conformity, Tradition, Benevolence, and Hedonism, possess high spatial autocorrelation. We identify several spatial patterns—specifically, Conformity and Hedonism have a country-specific structure, Tradition has a North–South gradient that cuts across national borders, and Benevolence has South–North cross-national gradient. Finally, we show that conventional factor analysis may lead to a loss of valuable insights compared with the proposed approach.     

A field investigation of red-light-running in Shanghai,China

Red-Light-Running (RLR) is the major cause of severe injury crashes at signalized intersections for both China and the US. As several studies have been conducted to identify the influencing factors of RLR behavior in the US, no similar studies exist in China. To fill this gap, this study was conducted to identify the key factors that affect RLR and compare the contributing factors between US and China. Data were collected through field observations and video recordings; four intersections in Shanghai were selected as the study sites. Both RLR drivers and comparison drivers, who had the opportunity to run the light but did not, were identified. Based on the collected data, preliminary analyses were firstly conducted to identify the features of the RLR and comparison groups. It was determined that: around 57% of RLR crossed the stop line during the 0–0.4 second time interval after red-light onset, and the numbers of red light violators decreased as the time increased; among the RLR vehicles, 38% turned left and 62% went straight; and at the onset of red, about 88% of RLR vehicles were in the middle of a vehicle platoon. Furthermore, in order to compare the RLR group and non-RLR group, two types of logistic regression models were developed. The ordinary logistic regression model was developed to identify the significant variables from the aspects of driver characteristics, driving conditions, and vehicle types. It was concluded that RLR drivers are more likely to be male, have local license plates, and are driving passenger vehicles but without passengers. Large traffic volume also increased the likelihood of RLR. However, the ordinary logistic regression model only considers influencing factors at the vehicle level: different intersection design and signal settings may also have impact on RLR behaviors. Therefore, in order to account for unobserved heterogeneity among different types of intersections, a random effects logistic regression model was adopted. Through the model comparisons, it has been identified that the model goodness-of-fit was substantially improved through considering the heterogeneity effects at intersections. Finally, benefits of this study and the analysis results were discussed.     

A maximum likelihood method for latent class regression involving a censored dependent variable

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.     

Temporal isolation effects in recognition and serial recall

Recent temporal distinctiveness models of memory predict that temporally isolated items will be recalled better than temporally crowded items. The effect has been found in some tasks (free recall, memory for serial order when report order is unconstrained, running memory span) but not in others (forward serial recall). Such results suggest that the attentional weighting given to a temporal dimension in memory may vary with task demands. Here, we find robust temporal isolation effects in recognition memory (Experiment 1) and a smaller isolation effect in forward serial recall when an open pool of items is used (Experiment 2). Analysis of 26 temporal isolation effects suggests that the phenomenon occurs in a range of tasks but is larger when it is useful to attend to a temporal dimension in memory. The overall pattern of results is taken to favor memory models that rely on multiple weighted dimensions in memory, one of which is temporal.     

A method in modeling interactive pedestrian crossing and driver yielding decisions during their interactions at intersections

Investigating pedestrian crossing and driver yielding decisions should be an important focus considering the high risks of pedestrians in exposed to motorized traffic. Limitations, however, exist in previous studies – variables considered previously have been limited; how their behavior affect each other (defined as interactive impacts) were not sufficiently considered. This paper aims to provide a methodological approach for pedestrian crossing and driver yielding decisions during their interactions, considering of different variable types including interactive impact variables, traffic condition variables, road design variables, and environment variables. A Distance-Velocity (DV) framework proposed in an earlier study is introduced for definitions and concepts in studying pedestrian-vehicle interactions. Logistic regression, support vector machines, neural networks and random forests, are introduced as candidate models. A case study involving six crosswalk locations is conducted, focusing on interactions between pedestrians and right-turn vehicles. The proposed methodological approach is applied, with the performance of the four machine learning methods compared in terms of model generalization and confusion matrix. The model with the best performance is further compared to the typical gap-based model. Results show that random forest and logistic regression models performed the best in modeling pedestrian crossing and driver yielding decisions respective, in terms of model generalization. Besides, the DV-based modeling method (average accuracy of over 90% for pedestrians and 80% for drivers) outperformed the traditional gap-based method in all test seeds. As a key finding, interactive impacts from each other (the pedestrian and the driver) act as a key contributing variable on their decisions.     

Statistical inference based on latent ability estimates

The quality of approximations to first and second order moments (e.g., statistics like means, variances, regression coefficients) based on latent ability estimates is being discussed. The ability estimates are obtained using either the Rasch, or the two-parameter logistic model. Straightforward use of such statistics to make inferences with respect to true latent ability is not recommended, unless we account for the fact that the basic quantities are estimates. In this paper true score theory is used to account for the latter; the counterpart of observed/true score being estimated/true latent ability. It is shown that statistics based on the true score theory are virtually unbiased if the number of items presented to each examinee is larger than fifteen. Three types of estimators are compared: maximum likelihood, weighted maximum likelihood, and Bayes modal. Furthermore, the (dis)advantages of the true score method and direct modeling of latent ability is discussed.