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Mr. Pengpeng Xu is a PhD student in the Deparment of Civil Engineering, The University of Hong Kong (2016-present). His research interests mainly include transport and health, traffic injury prevention, spatial data analysis, and applied Bayesian statistical models. He was rewarded as the Top Peer Reviewer 2019 by WoS and the Outstanding Reviewer of Accident Analysis & Prevention, and has completed around 100 reviewer assignments for nearly 20 journals.
This study aims to establish a stochastic link-based fundamental diagram (FD) with explicit consideration of two available sources of uncertainty: speed heterogeneity, indicated by the speed variance within an interval, and rainfall intensity. A stochastic structure was proposed to incorporate the speed heterogeneity into the traffic stream model, and the random-parameter structures were applied to reveal the unobserved heterogeneity in the mean speeds at an identical density. The proposed stochastic link-based FD was calibrated and validated using real-world traffic data obtained from two selected road segments in Hong Kong. Traffic data were obtained from the Hong Kong Journey Time Indication System operated by the Hong Kong Transport Department during January 1 to December 31, 2017. The data related to rainfall intensity were obtained from the Hong Kong Observatory. A two-stage calibration based on Bayesian inference was proposed for estimating the stochastic link-based FD parameters. The predictive performances of the proposed model and three other models were compared using K-fold cross-validation. The results suggest that the random-parameter model considering the speed heterogeneity effect performs better in terms of both goodness-of-fit and predictive accuracy. The effect of speed heterogeneity accounts for 18%–24% of the total heterogeneity effects on the variance of FD. In addition, there exists unobserved heterogeneity across the mean speeds at an identical density, and the rainfall intensity negatively affects the mean speed and its effect on the variance of FD differs at different densities.
Lu Bai; S.C. Wong; Pengpeng Xu; Andy H.F. Chow; William H.K. Lam. Calibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity. Transportation Research Part B: Methodological 2021, 150, 524 -539.
AMA StyleLu Bai, S.C. Wong, Pengpeng Xu, Andy H.F. Chow, William H.K. Lam. Calibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity. Transportation Research Part B: Methodological. 2021; 150 ():524-539.
Chicago/Turabian StyleLu Bai; S.C. Wong; Pengpeng Xu; Andy H.F. Chow; William H.K. Lam. 2021. "Calibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity." Transportation Research Part B: Methodological 150, no. : 524-539.
One challenge faced by the random-parameter count models for crash prediction is the unavailability of unique coefficients for out-of-sample observations. The means of the random-parameter distributions are typically used without explicit consideration of the variances. In this study, by virtue of the Taylor series expansion, we proposed a straightforward yet analytic solution to include both the means and variances of random parameters for unbiased prediction. We then theoretically quantified the systematic bias arising from the omission of the variances of random parameters. Our numerical experiment further demonstrated that simply using the means of random parameters to predict the number of crashes for out-of-sample observations is fundamentally incorrect, which necessarily results in the underprediction of crash counts. Given the widespread use and ongoing prevalence of the random-parameter approach in crash analysis, special caution should be taken to avoid this silent pitfall when applying it for predictive purposes.
Pengpeng Xu; Hanchu Zhou; S.C. Wong. On random-parameter count models for out-of-sample crash prediction: Accounting for the variances of random-parameter distributions. Accident Analysis & Prevention 2021, 159, 106237 .
AMA StylePengpeng Xu, Hanchu Zhou, S.C. Wong. On random-parameter count models for out-of-sample crash prediction: Accounting for the variances of random-parameter distributions. Accident Analysis & Prevention. 2021; 159 ():106237.
Chicago/Turabian StylePengpeng Xu; Hanchu Zhou; S.C. Wong. 2021. "On random-parameter count models for out-of-sample crash prediction: Accounting for the variances of random-parameter distributions." Accident Analysis & Prevention 159, no. : 106237.
A new dynamic model is established to formulate the cascading failure in the urban rail transit network based on the disaster spreading theory. Firstly, the weighted urban rail transit network by considering the time cost of each effective path is established, the transfer station and turn back station on the topological network are handled specifically, the Dijkstra algorithm is designed to solve the shortest path of each Origin-Destination. Then, the cascading failure model based on disaster spreading theory is established. Five factors including the failure evolution process with time, self-recovery ability of the nodes, failures spreading mechanism, passenger volume changes and the internal random noises by other influence factors are fully considered in this model. Finally, a real-world case study is conducted by using Chengdu Metro Network as the background. Eight simulation scenarios are established, the output is statistical number of failed stations. The results show that, the failed stations number has the greatest scale when fixed transfer stations are attacked. There is no obvious functional relationship between the scale of failure stations and self-recovery factor, and there is a positive correlation between self-recovery factor and cascading failure scale. Based on the results, five emergency resources allocation strategies are proposed.
Wencheng Huang; Bowen Zhou; Yaocheng Yu; Hao Sun; Pengpeng Xu. Using the disaster spreading theory to analyze the cascading failure of urban rail transit network. Reliability Engineering & System Safety 2021, 215, 107825 .
AMA StyleWencheng Huang, Bowen Zhou, Yaocheng Yu, Hao Sun, Pengpeng Xu. Using the disaster spreading theory to analyze the cascading failure of urban rail transit network. Reliability Engineering & System Safety. 2021; 215 ():107825.
Chicago/Turabian StyleWencheng Huang; Bowen Zhou; Yaocheng Yu; Hao Sun; Pengpeng Xu. 2021. "Using the disaster spreading theory to analyze the cascading failure of urban rail transit network." Reliability Engineering & System Safety 215, no. : 107825.
Ranking sites with promise is an essential step for cost-effective engineering improvement on roadway traffic safety. This study proposes a Bayesian multivariate spatio-temporal interaction model based approach for ranking sites. The severity-weighted crash frequency and crash rate are used as the decision parameters. The posterior expected rank and posterior mean of the decision parameters are adopted as the statistical criteria. The proposed approach is applied to rank road segments on Kaiyang Freeway in China, which is conducted via programming in the freeware WinBUGS. The results of Bayesian estimation and assessment indicate that incorporating spatio-temporal correlations and interactions into the crash frequency model significantly improves the overall goodness-of-fit performance and affects the identified crash-contributing factors and the estimated safety effects for each severity level. With respect to the ranking results, significant differences are found between those generated from the proposed approach and those generated from the naïve ranking approach and a Bayesian approach based on the multivariate Poisson-lognormal model. Besides, the ranks under the posterior mean criterion are found generally consistent with those under the posterior expected rank criterion.
Qiang Zeng; Pengpeng Xu; Xuesong Wang; Huiying Wen; Wei Hao. Applying a Bayesian multivariate spatio-temporal interaction model based approach to rank sites with promise using severity-weighted decision parameters. Accident Analysis & Prevention 2021, 157, 106190 .
AMA StyleQiang Zeng, Pengpeng Xu, Xuesong Wang, Huiying Wen, Wei Hao. Applying a Bayesian multivariate spatio-temporal interaction model based approach to rank sites with promise using severity-weighted decision parameters. Accident Analysis & Prevention. 2021; 157 ():106190.
Chicago/Turabian StyleQiang Zeng; Pengpeng Xu; Xuesong Wang; Huiying Wen; Wei Hao. 2021. "Applying a Bayesian multivariate spatio-temporal interaction model based approach to rank sites with promise using severity-weighted decision parameters." Accident Analysis & Prevention 157, no. : 106190.
As mobile device location data become increasingly available, new analyses are revealing the significant changes of mobility pattern when an unplanned event happened. With different control policies from local and state government, the COVID-19 outbreak has dramatically changed mobility behavior in affected cities. This study has been investigating the impact of COVID-19 on the number of people involved in crashes accounting for the intensity of different control measures using Negative Binomial (NB) method. Based on a comprehensive dataset of people involved in crashes aggregated in New York City during January 1, 2020 to May 24, 2020, people involved in crashes with respect to travel behavior, traffic characteristics and socio-demographic characteristics are found. The results show that the average person miles traveled on the main traffic mode per person per day, percentage of work trip have positive effect on person involved in crashes. On the contrary, unemployment rate and inflation rate have negative effects on person involved in crashes. Interestingly, different level of control policies during COVID-19 outbreak are closely associated with safety awareness, driving and travel behavior, and thus has an indirect influence on the frequency of crashes. Comparing to other three control policies including emergence declare, limits on mass gatherings, and ban on all nonessential gathering, the negative relationship between stay-at-home policy implemented in New York City from March 20, 2020 and the number of people involved crashes is found in our study.
Jie Zhang; Baoheng Feng; Yina Wu; Pengpeng Xu; Ruimin Ke; Ni Dong. The effect of human mobility and control measures on traffic safety during COVID-19 pandemic. PLOS ONE 2021, 16, e0243263 .
AMA StyleJie Zhang, Baoheng Feng, Yina Wu, Pengpeng Xu, Ruimin Ke, Ni Dong. The effect of human mobility and control measures on traffic safety during COVID-19 pandemic. PLOS ONE. 2021; 16 (3):e0243263.
Chicago/Turabian StyleJie Zhang; Baoheng Feng; Yina Wu; Pengpeng Xu; Ruimin Ke; Ni Dong. 2021. "The effect of human mobility and control measures on traffic safety during COVID-19 pandemic." PLOS ONE 16, no. 3: e0243263.
Both left-driving (LD) and right-driving (RD) rules are used around the world. When traveling to places with different driving rules, pedestrians are likely to make mistakes. To investigate the frequency of such mistakes, a case study was conducted with pedestrians in Hong Kong, which follows LD rules, i.e., traffic drives on the left. The study aimed to probe the effects of hometown driving rules and length of stay on pedestrians’ right-looking habit and maladaptation to the Hong Kong LD system and determine the mediating effect of the right-looking habit. A face-to-face survey was conducted with 581 respondents at seven locations in Hong Kong. A structural equation model was applied to determine the relationship among hometown driving rules, length of stay, right-looking habit, and maladaptation. The model exhibited good fitness (χ2/degreesoffreedom=2.154; comparativefitindex=0.989; Tucker-LewisIndex=0.980; and rootmeansquareerrorofapproximation=0.045). The results revealed that hometown driving rules and length of stay had positive effects on the right-looking habit, and hometown driving rules had a direct negative effect on maladaptation. The right-looking habit partially mediated the effect of hometown driving rules and fully mediated the effect of length of stay on maladaptation to the Hong Kong LD system. It was found that when foreign pedestrians were in areas with unfamiliar driving rules, they tended to practice their hometown looking habits, especially foreign pedestrians who had stayed only for a short time; this behavior differed significantly from that of local pedestrians, and it led to more severe maladaptation. The findings of this study provide empirical evidence of pedestrians’ looking habits and maladaptation in areas with unfamiliar driving systems and have significant implications for improving the safety of foreign pedestrians.
Yun Ye; S.C. Wong; Fanyu Meng; Pengpeng Xu. Right-looking habit and maladaptation of pedestrians in areas with unfamiliar driving rules. Accident Analysis & Prevention 2020, 150, 105921 .
AMA StyleYun Ye, S.C. Wong, Fanyu Meng, Pengpeng Xu. Right-looking habit and maladaptation of pedestrians in areas with unfamiliar driving rules. Accident Analysis & Prevention. 2020; 150 ():105921.
Chicago/Turabian StyleYun Ye; S.C. Wong; Fanyu Meng; Pengpeng Xu. 2020. "Right-looking habit and maladaptation of pedestrians in areas with unfamiliar driving rules." Accident Analysis & Prevention 150, no. : 105921.
Although numerous efforts have been devoted to exploring the effects of area-wide factors on the frequency of pedestrian crashes in neighborhoods over the past two decades, existing studies have largely failed to provide a full picture of the factors that contribute to the incidence of zonal pedestrian crashes, due to the unavailability of reliable exposure data and use of less sound analytical methods. Based on a crowdsourced dataset in Hong Kong, we first proposed a procedure to extract pedestrian trajectories from travel-diary survey data. We then aggregated these data to 209 neighborhoods and developed a Bayesian spatially varying coefficients model to investigate the spatially non-stationary relationships between the number of pedestrian–motor vehicle (PMV) crashes and related risk factors. To dissect the role of pedestrian exposure, the estimated coefficients of models with population, walking trips, walking time, and walking distance as the measure of pedestrian exposure were presented and compared. Our results indicated substantial inconsistencies in the effects of several risk factors between the models of population and activity-based exposure measures. The model using walking trips as the measure of pedestrian exposure had the best goodness-of-fit. We also provided new insights that in addition to the unstructured variability, heterogeneity in the effects of explanatory variables on the frequency of PMV crashes could also arise from the spatially correlated effects. After adjusting for vehicle volume and pedestrian activity, road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes. The use of population or population density as a surrogate for pedestrian exposure when modeling the frequency of zonal pedestrian crashes is expected to produce biased estimations and invalid inferences. Spatial heterogeneity should also not be negligible when modeling pedestrian crashes involving contiguous spatial units.
Ni Dong; Fanyu Meng; Jie Zhang; S.C. Wong; Pengpeng Xu. Towards activity-based exposure measures in spatial analysis of pedestrian–motor vehicle crashes. Accident Analysis & Prevention 2020, 148, 105777 .
AMA StyleNi Dong, Fanyu Meng, Jie Zhang, S.C. Wong, Pengpeng Xu. Towards activity-based exposure measures in spatial analysis of pedestrian–motor vehicle crashes. Accident Analysis & Prevention. 2020; 148 ():105777.
Chicago/Turabian StyleNi Dong; Fanyu Meng; Jie Zhang; S.C. Wong; Pengpeng Xu. 2020. "Towards activity-based exposure measures in spatial analysis of pedestrian–motor vehicle crashes." Accident Analysis & Prevention 148, no. : 105777.
A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.
Fanyu Meng; Pengpeng Xu; Cancan Song; Kun Gao; Zichu Zhou; Lili Yang. Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach. International Journal of Environmental Research and Public Health 2020, 17, 5623 .
AMA StyleFanyu Meng, Pengpeng Xu, Cancan Song, Kun Gao, Zichu Zhou, Lili Yang. Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach. International Journal of Environmental Research and Public Health. 2020; 17 (15):5623.
Chicago/Turabian StyleFanyu Meng; Pengpeng Xu; Cancan Song; Kun Gao; Zichu Zhou; Lili Yang. 2020. "Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach." International Journal of Environmental Research and Public Health 17, no. 15: 5623.
Introduction: Although public buses have been demonstrated as a relatively safe mode of transport, the number of injuries to public bus passengers is far from negligible. Existing studies of public bus safety have focused primarily on injuries caused by collisions. Surprisingly, limited effort has been devoted to identifying factors that increase the severity of passenger injuries in non-collision incidents. Method: Our study therefore investigated the injury risk of public bus passengers involved in collision incidents and non-collision incidents comparatively, based on a police-reported dataset of 17,383 passengers injured on franchised public buses over a 10-year period in Hong Kong. A random parameters logistic model was established to estimate the likelihood of fatal and severe injuries to passengers as a function of various factors. Results: Our results indicated substantial inconsistences in the effects of risk factors between models of non-collision injuries and collision injuries. The severity of passenger injuries tended to increase significantly when non-collision incidents occurred due to excessive speed of bus drivers, on double-decker buses, in less urbanized areas, in winter, in heavy rains, during daytime, and at night without street lighting. Elderly female passengers were also found more likely to be fatally or severely injured in non-collision incidents if they lost their balance while boarding, alighting from, or standing on a bus. In comparison, the following factors were associated with a greater likelihood of fatal or severe injuries in collision incidents: elderly female passengers, standing passengers who lost balance, buses out of driver control, double-decker buses, collisions with vehicles or objects, and less urbanized areas. Practical Applications: Based on our comparative analysis, more targeted countermeasures, namely “4E” (engineering, enforcement, emergency, and education) and “3A” (awareness, appreciation, and assistance), were recommended to mitigate collision injuries and non-collision injuries to public bus passengers, respectively.
Hanchu Zhou; Chen Yuan; Ni Dong; S.C. Wong; Pengpeng Xu. Severity of passenger injuries on public buses: A comparative analysis of collision injuries and non-collision injuries. Journal of Safety Research 2020, 74, 55 -69.
AMA StyleHanchu Zhou, Chen Yuan, Ni Dong, S.C. Wong, Pengpeng Xu. Severity of passenger injuries on public buses: A comparative analysis of collision injuries and non-collision injuries. Journal of Safety Research. 2020; 74 ():55-69.
Chicago/Turabian StyleHanchu Zhou; Chen Yuan; Ni Dong; S.C. Wong; Pengpeng Xu. 2020. "Severity of passenger injuries on public buses: A comparative analysis of collision injuries and non-collision injuries." Journal of Safety Research 74, no. : 55-69.
The rate of road traffic fatalities has long served as a regular indicator to evaluate and compare road safety performance for different administrative divisions. This article introduces a novel method known as the Markov chain spatial model to incorporate the spatial effects into the temporal dynamic of the fatality rates. Compared to the traditional Markov chain model, the proposed spatial Markov chain model can quantify the influence of neighboring sites explicitly in the transition process. A case study using a long duration dataset, from 1975 to 2015 in the 48 lower states of the United Sates, was conducted to illustrate the proposed model. The fatality rates were measured as the number of traffic fatalities per 100 million vehicle miles or per 10,000 residents. The results show that the probability of transition for one state between different levels of traffic fatality risks depends largely on the context of its surrounding neighbors. Another important finding is that relative to the estimates of traditional Markov chain models, states surrounded by neighborhoods with relatively low fatality rates take a longer time to transform to a higher level of fatality risk in the spatial Markov chain model. On the other hand, those with high-risk neighborhoods takes less time to deteriorate. These findings confirm that it is imperative to incorporate spatial effects when modeling the temporal dynamic of safety indicators to assess and monitor the safety trends in the areas of interest.
Hanchu Zhou; Helai Huang; Pengpeng Xu; Fangrong Chang; Mohamed Abdel-Aty. Incorporating spatial effects into temporal dynamic of road traffic fatality risks: A case study on 48 lower states of the United States, 1975-2015. Accident Analysis & Prevention 2019, 132, 105283 .
AMA StyleHanchu Zhou, Helai Huang, Pengpeng Xu, Fangrong Chang, Mohamed Abdel-Aty. Incorporating spatial effects into temporal dynamic of road traffic fatality risks: A case study on 48 lower states of the United States, 1975-2015. Accident Analysis & Prevention. 2019; 132 ():105283.
Chicago/Turabian StyleHanchu Zhou; Helai Huang; Pengpeng Xu; Fangrong Chang; Mohamed Abdel-Aty. 2019. "Incorporating spatial effects into temporal dynamic of road traffic fatality risks: A case study on 48 lower states of the United States, 1975-2015." Accident Analysis & Prevention 132, no. : 105283.
Perceived as a minor transportation mode mainly for recreation, cycling and its related safety issues have not been treated as a citywide concern in Hong Kong and have thus received inadequate research efforts. Our study aimed to illuminate the safety challenges faced by cyclists in Hong Kong. We examined the police crash records from 1998 to 2017 and developed a Bayesian Poisson state space model to evaluate the longitudinal change in traffic injuries to cyclists. We then used quasi-induced exposure to measure the annual relative risk of crash involvement for cycling. Based on an officially published travel characteristics survey, we further measured the risk of injury for cycling per minutes cycled. Between 1998 and 2017, Hong Kong witnessed a more than twofold increase in the number of cyclist injuries, with an average annual increase rate of 5.18% (95% CI: 0.53%–12.77%). By 2017, cyclists were 2.21 (1.82–2.69) times more likely to be involved in traffic crashes than in 1998. Per 10 million minutes, the injury rates for cycling were 28.64 (27.43–29.70) and 42.54 (41.07–44.02) on weekdays during 2001–2003 and 2010–2012. After adjusting for sex and age groups, cyclists were 1.95 (1.43–2.61) times more likely to be injured in 2010–2012 than in 2001–2003. Per minutes traveled, cyclists also sustained significantly higher risks of fatality and injury than pedestrians, private car drivers and passengers, taxi passengers, public bus passengers, and minibus passengers. A comparison of Hong Kong with other regions suggests that Hong Kong is among the most dangerous areas for cycling in terms of fatality rate per minutes cycled. Cyclist injuries have become a substantial public health burden in Hong Kong. A range of countermeasures with proven effectiveness should be promptly implemented to improve the safety of these vulnerable road users.
Pengpeng Xu; Ni Dong; S. C. Wong; Helai Huang. Cyclists injured in traffic crashes in Hong Kong: A call for action. PLOS ONE 2019, 14, e0220785 .
AMA StylePengpeng Xu, Ni Dong, S. C. Wong, Helai Huang. Cyclists injured in traffic crashes in Hong Kong: A call for action. PLOS ONE. 2019; 14 (8):e0220785.
Chicago/Turabian StylePengpeng Xu; Ni Dong; S. C. Wong; Helai Huang. 2019. "Cyclists injured in traffic crashes in Hong Kong: A call for action." PLOS ONE 14, no. 8: e0220785.
Due to the wide existence of heterogeneous nature in traffic safety data, traditional methods used to investigate motorcyclist rider injury severity always lead to masking of some underlying relationships which may be critical for the formulation of efficient safety countermeasures. Instead of applying one single model to the whole dataset or focusing on pre-defined crash types as done in previous studies, the present study proposes a two-step method integrating latent class cluster analysis and random parameters logit model to explore contributing factors influencing the injury levels of motorcyclists. A latent class cluster approach is first used to segment the motorcycle crashes into relatively homogeneous clusters. A mixed logit model is then elaborately developed for each cluster to identify its unique influential factors. The analysis was based on the police-reported crash dataset (2015–2017) of Hunan province, China. The goodness-of-fit indicators and the Receiver Operating Characteristic curves show that the proposed method is more accurate when modeling the riders’ injury severities. The heterogeneity found in each homogeneous subgroup supports the application of the random parameters logit model in the study. More importantly, the results demonstrate that segmenting motorcycle crashes into relatively homogeneous clusters as a preliminary step helps to uncover some important influencing factors hidden in the whole-data model. The proposed method is proved to have great potential for accounting for the source of heterogeneity. The injury risk factors identified in specific cases provide more reliable information for traffic engineers and policymakers to improve motorcycle traffic safety.
Fangrong Chang; Pengpeng Xu; Hanchu Zhou; Alan H.S. Chan; Helai Huang. Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model. Accident Analysis & Prevention 2019, 131, 316 -326.
AMA StyleFangrong Chang, Pengpeng Xu, Hanchu Zhou, Alan H.S. Chan, Helai Huang. Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model. Accident Analysis & Prevention. 2019; 131 ():316-326.
Chicago/Turabian StyleFangrong Chang; Pengpeng Xu; Hanchu Zhou; Alan H.S. Chan; Helai Huang. 2019. "Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model." Accident Analysis & Prevention 131, no. : 316-326.
Although the importance of human factors to crash occurrence has been demonstrated previously, the roles played by human factors in motorcycle killed and severely injured (KSI) crashes have remained unclear. One aim of our study is therefore to empirically determine the relative contribution of illegal behavior to motorcycle KSI crashes, conditional on real-world collisions between motorcycles and motor vehicles. Given that a crash is typically the synthetical result of human, vehicle, roadway, and environmental factors, another aim is to identify high-risk scenarios where inappropriate behavior is more likely to result in severe injuries for motorcyclists through interactions with other related factors. Based on a comprehensive dataset of 4587 police-reported crashes involving motorcycles during 2015–2017 in Hunan province, China, a data mining technique namely classification and regression tree was elaborately employed. Our results demonstrated the illegal behavior of the striking motor-vehicle drivers as one of the most dominant factors contributory to motorcycle KSI crashes, with a normalized importance value of 36.9%. We also confirmed collision object (i.e., collision with heavy or light vehicles) and helmet use of motorcyclists as determinants influencing motorcycle rider injury severities. Two types of extreme high-risk traffic scenarios were identified accordingly. A motorcycle rider was hit at weekends by a heavy motor-vehicle driver who was driving without license, driving a substantial vehicle, speeding, changing lanes illegally or driving in the wrong direction, and a motorcyclist was hit on weekdays by a heavy motor-vehicle driver aged 18–34 or 45–54, who was driving without license, driving a substantial vehicle, speeding, changing lanes illegally or driving in the wrong direction. Our findings are expected to shed more light on a deeper understanding of the illegal driving behavior as causation of motorcycle KSI crashes.
Fangrong Chang; Pengpeng Xu; Hanchu Zhou; Jaeyoung Lee; Helai Huang. Identifying motorcycle high-risk traffic scenarios through interactive analysis of driver behavior and traffic characteristics. Transportation Research Part F: Traffic Psychology and Behaviour 2019, 62, 844 -854.
AMA StyleFangrong Chang, Pengpeng Xu, Hanchu Zhou, Jaeyoung Lee, Helai Huang. Identifying motorcycle high-risk traffic scenarios through interactive analysis of driver behavior and traffic characteristics. Transportation Research Part F: Traffic Psychology and Behaviour. 2019; 62 ():844-854.
Chicago/Turabian StyleFangrong Chang; Pengpeng Xu; Hanchu Zhou; Jaeyoung Lee; Helai Huang. 2019. "Identifying motorcycle high-risk traffic scenarios through interactive analysis of driver behavior and traffic characteristics." Transportation Research Part F: Traffic Psychology and Behaviour 62, no. : 844-854.
In previous studies, the safety-in-numbers effect has been found, which is a phenomenon that when the number of pedestrians or cyclists increases, their crash rates decrease. The previous studies used data from highly populated areas. It is questionable that the safety-in-numbers effect is still observed in areas with a low population density and small number of pedestrians. Thus, this study aims at analyzing pedestrian crashes in a suburban area in the United States and exploring if the safety-in-numbers effect is also observed. We employ a Bayesian random-parameter Poisson-lognormal model to evaluate the safety-in-numbers effects of each intersection, which can account for the heterogeneity across the observations. The results show that the safety-in-numbers effect were found only at 32 intersections out of 219. The intersections with the safety-in-numbers effect have relatively larger pedestrian activities whereas those without the safety-in-numbers effect have extremely low pedestrian activities. It is concluded that just encouraging walking might result in serious pedestrian safety issues in a suburban area without sufficient pedestrian activities. Therefore, it is plausible to provide safe walking environment first with proven countermeasures and a people-oriented policy rather than motor-oriented. After safe walking environments are guaranteed and when people recognize that walking is safe, more people will consider walking for short-distance trips. Eventually, increased pedestrian activities will result in the safety-in-numbers effects and walking will be even further safer.
Jaeyoung Lee; Mohamed Abdel-Aty; Pengpeng Xu; Yaobang Gong. Is the safety-in-numbers effect still observed in areas with low pedestrian activities? A case study of a suburban area in the United States. Accident Analysis & Prevention 2019, 125, 116 -123.
AMA StyleJaeyoung Lee, Mohamed Abdel-Aty, Pengpeng Xu, Yaobang Gong. Is the safety-in-numbers effect still observed in areas with low pedestrian activities? A case study of a suburban area in the United States. Accident Analysis & Prevention. 2019; 125 ():116-123.
Chicago/Turabian StyleJaeyoung Lee; Mohamed Abdel-Aty; Pengpeng Xu; Yaobang Gong. 2019. "Is the safety-in-numbers effect still observed in areas with low pedestrian activities? A case study of a suburban area in the United States." Accident Analysis & Prevention 125, no. : 116-123.
Jie Wang; Helai Huang; Pengpeng Xu; Siqi Xie; Sze Chun Wong. Random parameter probit models to analyze pedestrian red-light violations and injury severity in pedestrian–motor vehicle crashes at signalized crossings. Journal of Transportation Safety & Security 2019, 12, 818 -837.
AMA StyleJie Wang, Helai Huang, Pengpeng Xu, Siqi Xie, Sze Chun Wong. Random parameter probit models to analyze pedestrian red-light violations and injury severity in pedestrian–motor vehicle crashes at signalized crossings. Journal of Transportation Safety & Security. 2019; 12 (6):818-837.
Chicago/Turabian StyleJie Wang; Helai Huang; Pengpeng Xu; Siqi Xie; Sze Chun Wong. 2019. "Random parameter probit models to analyze pedestrian red-light violations and injury severity in pedestrian–motor vehicle crashes at signalized crossings." Journal of Transportation Safety & Security 12, no. 6: 818-837.
This study intended to identify the potential factors contributing to the occurrence of pedestrian crashes at signalized intersections in a densely populated city, based on a comprehensive dataset of 898 pedestrian crashes at 262 signalized intersections during 2010–2012 in Hong Kong. The detailed geometric design, traffic characteristics, signal control, built environment, along with the vehicle and pedestrian volumes were elaborately collected. A Bayesian measurement errors model was introduced as an alternative method to explicitly account for the uncertainties in volume data. To highlight the role played by exposure, models with and without pedestrian volume were estimated and compared. The results indicated that the omission of pedestrian volume in pedestrian crash frequency models would lead to reduced goodness-of-fit, biased parameter estimates, and incorrect inferences. Our empirical analysis demonstrated the existence of moderate uncertainties in pedestrian and vehicle volumes. Six variables were found to have a significant association with the number of pedestrian crashes at signalized intersections. The number of crossing pedestrians, the number of passing vehicles, the presence of curb parking, and the presence of ground-floor shops were positively related with pedestrian crash frequency, whereas the presence of playgrounds near intersections had a negative effect on pedestrian crash occurrences. Specifically, the presence of exclusive pedestrian signals for all crosswalks was found to significantly reduce the risk of pedestrian crashes by 43%. The present study is expected to shed more light on a deeper understanding of the environmental determinants of pedestrian crashes.
S.Q. Xie; Ni Dong; Sze Chun Wong; Helai Huang; Pengpeng Xu. Bayesian approach to model pedestrian crashes at signalized intersections with measurement errors in exposure. Accident Analysis & Prevention 2018, 121, 285 -294.
AMA StyleS.Q. Xie, Ni Dong, Sze Chun Wong, Helai Huang, Pengpeng Xu. Bayesian approach to model pedestrian crashes at signalized intersections with measurement errors in exposure. Accident Analysis & Prevention. 2018; 121 ():285-294.
Chicago/Turabian StyleS.Q. Xie; Ni Dong; Sze Chun Wong; Helai Huang; Pengpeng Xu. 2018. "Bayesian approach to model pedestrian crashes at signalized intersections with measurement errors in exposure." Accident Analysis & Prevention 121, no. : 285-294.
This study investigated the impacts of zonal configurations on macro-level traffic safety analysis for crashes of different severity levels. Bayesian multivariate Poisson-lognormal models with multivariate conditional auto-regressive priors were developed to account for the spatial autocorrelation between adjacent geographical units and correlations among crash types of four ordinal severity levels, i.e. fatality, severe injury, slight injury and no injury. For the purpose of evaluating the effects of zonal configurations on macro-level traffic safety analysis, the proposed model was calibrated using crash data of four types of geographical units, i.e. block group, traffic analysis zone, census tract and zip code tabulation area, in Hillsborough County of Florida. The study empirically revealed the extensive presence and the significance of MAUP in macro-level safety analysis based on the existing zonal configurations. It gave out a warning and encouraged more research efforts on rational application of macroscopic safety analysis with different zonal configurations.
Xiaoqi Zhai; Helai Huang; Pengpeng Xu; Nang Ngai Sze. The influence of zonal configurations on macro-level crash modeling. Transportmetrica A: Transport Science 2018, 15, 417 -434.
AMA StyleXiaoqi Zhai, Helai Huang, Pengpeng Xu, Nang Ngai Sze. The influence of zonal configurations on macro-level crash modeling. Transportmetrica A: Transport Science. 2018; 15 (2):417-434.
Chicago/Turabian StyleXiaoqi Zhai; Helai Huang; Pengpeng Xu; Nang Ngai Sze. 2018. "The influence of zonal configurations on macro-level crash modeling." Transportmetrica A: Transport Science 15, no. 2: 417-434.
Pengpeng Xu; Helai Huang; Ni Dong. The modifiable areal unit problem in traffic safety: Basic issue, potential solutions and future research. Journal of Traffic and Transportation Engineering (English Edition) 2018, 5, 73 -82.
AMA StylePengpeng Xu, Helai Huang, Ni Dong. The modifiable areal unit problem in traffic safety: Basic issue, potential solutions and future research. Journal of Traffic and Transportation Engineering (English Edition). 2018; 5 (1):73-82.
Chicago/Turabian StylePengpeng Xu; Helai Huang; Ni Dong. 2018. "The modifiable areal unit problem in traffic safety: Basic issue, potential solutions and future research." Journal of Traffic and Transportation Engineering (English Edition) 5, no. 1: 73-82.
This study aimed to identify the factors affecting the crash-related severity level of injuries in taxis and quantify the associations between these factors and taxi occupant injury severity. Casualties resulting from taxi crashes from 2004 to 2013 in Hong Kong were divided into four categories: taxi drivers, taxi passengers, private car drivers and private car passengers. To avoid any biased interpretation caused by unobserved spatial and temporal effects, a Bayesian hierarchical logistic modeling approach with conditional autoregressive priors was applied, and four different model forms were tested. For taxi drivers and passengers, the model with space-time interaction was proven to most properly address the unobserved heterogeneity effects. The results indicated that time of week, number of vehicles involved, weather, point of impact and driver age were closely associated with taxi drivers' injury severity level in a crash. For taxi passengers' injury severity an additional factor, taxi service area, was influential. To investigate the differences between taxis and other traffic, similar models were established for private car drivers and passengers. The results revealed that although location in the network and driver gender significantly influenced private car drivers' injury severity, they did not influence taxi drivers' injury severity. Compared with taxi passengers, the injury severity of private car passengers was more sensitive to average speed and whether seat belts were worn. Older drivers, urban taxis and fatigued driving were identified as factors that increased taxi occupant injury severity in Hong Kong.
Fanyu Meng; Pengpeng Xu; S.C. Wong; Helai Huang; Y.C. Li. Occupant-level injury severity analyses for taxis in Hong Kong: A Bayesian space-time logistic model. Accident Analysis & Prevention 2017, 108, 297 -307.
AMA StyleFanyu Meng, Pengpeng Xu, S.C. Wong, Helai Huang, Y.C. Li. Occupant-level injury severity analyses for taxis in Hong Kong: A Bayesian space-time logistic model. Accident Analysis & Prevention. 2017; 108 ():297-307.
Chicago/Turabian StyleFanyu Meng; Pengpeng Xu; S.C. Wong; Helai Huang; Y.C. Li. 2017. "Occupant-level injury severity analyses for taxis in Hong Kong: A Bayesian space-time logistic model." Accident Analysis & Prevention 108, no. : 297-307.
ObjectiveTo advance the interpretation of the ‘safety in numbers’ effect by addressing the following three questions. How should the safety of pedestrians be measured, as the safety of individual pedestrians or as the overall safety of road facilities for pedestrians? Would intersections with large numbers of pedestrians exhibit a favourable safety performance? Would encouraging people to walk be a sound safety countermeasure?MethodsWe selected 288 signalised intersections with 1003 pedestrian crashes in Hong Kong from 2010 to 2012. We developed a Bayesian Poisson-lognormal model to calculate two common indicators related to pedestrian safety: the expected crash rate per million crossing pedestrians and the expected excess crash frequency. The ranking results of these two indicators for the selected intersections were compared.ResultsWe confirmed a significant positive association between pedestrian volumes and pedestrian crashes, with an estimated coefficient of 0.21. Although people who crossed at intersections with higher pedestrian volumes experienced a relatively lower crash risk, these intersections may still have substantial potential for crash reduction.ConclusionsConclusions on the safety in numbers effect based on a cross-sectional analysis should be reached with great caution. The safety of individual pedestrians can be measured based on the crash risk, whereas the safety of road facilities for pedestrians should be determined by the environmental hazards of walking. Intersections prevalent of pedestrians do not always exhibit favourable safety performance. Relative to increasing the number of pedestrians, safety strategies should focus on reducing environmental hazards and removing barriers to walking.
Pengpeng Xu; Siqi Xie; Ni Dong; Sze Chun Wong; Helai Huang. Rethinking safety in numbers: are intersections with more crossing pedestrians really safer? Injury Prevention 2017, 25, 20 -25.
AMA StylePengpeng Xu, Siqi Xie, Ni Dong, Sze Chun Wong, Helai Huang. Rethinking safety in numbers: are intersections with more crossing pedestrians really safer? Injury Prevention. 2017; 25 (1):20-25.
Chicago/Turabian StylePengpeng Xu; Siqi Xie; Ni Dong; Sze Chun Wong; Helai Huang. 2017. "Rethinking safety in numbers: are intersections with more crossing pedestrians really safer?" Injury Prevention 25, no. 1: 20-25.