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A lane-changing process is complicated due to multiple factors in the driving environment, and unsafe lane-changing behaviour may lead to a severe crash. This study proposes a method for the driving angle prediction of lane changes based on extremely randomized decision trees. First, the harmonic potential is defined to characterize the interaction between the lane-changing vehicle and the surrounding vehicles. Next, we construct extremely randomized decision trees to predict driving angles considering relative velocity, relative acceleration, and potential as input variables. Then, the NGSIM dataset is used to verify the method proposed, and the lane-changing process is divided into two stages by different environments. Furthermore, a comparison of prediction performance with several traditional machine learning methods further demonstrates the superior learning ability of the proposed method. Finally, we conduct a sensitivity analysis on the significant variables and discuss the effects of these variables on the prediction results.
Zhe Wang; Helai Huang; Jinjun Tang; Jaeyoung Lee; Xianwei Meng. Driving angle prediction of lane changes based on extremely randomized decision trees considering the harmonic potential field method. Transportmetrica A: Transport Science 2021, 1 -25.
AMA StyleZhe Wang, Helai Huang, Jinjun Tang, Jaeyoung Lee, Xianwei Meng. Driving angle prediction of lane changes based on extremely randomized decision trees considering the harmonic potential field method. Transportmetrica A: Transport Science. 2021; ():1-25.
Chicago/Turabian StyleZhe Wang; Helai Huang; Jinjun Tang; Jaeyoung Lee; Xianwei Meng. 2021. "Driving angle prediction of lane changes based on extremely randomized decision trees considering the harmonic potential field method." Transportmetrica A: Transport Science , no. : 1-25.
Surrogate Safety Measures (SSM) are important for safety performance evaluation, since crashes are rare events and historical crash data does not capture near crashes that are also critical for improving safety. This paper focuses on SSM and their applications, particularly in Connected and Automated Vehicles (CAV) safety modeling. It aims to provide a comprehensive and systematic review of significant SSM studies, identify limitations and opportunities for future SSM and CAV research, and assist researchers and practitioners with choosing the most appropriate SSM for safety studies. The behaviors of CAV can be very different from those of Human-Driven Vehicles (HDV). Even among CAV with different automation/connectivity levels, their behaviors are likely to differ. Also, the behaviors of HDV can change in response to the existence of CAV in mixed autonomy traffic. Simulation by far is the most viable solution to model CAV safety. However, it is questionable whether conventional SSM can be applied to modeling CAV safety based on simulation results due to the lack of sophisticated simulation tools that can accurately model CAV behaviors and SSM that can take CAV’s powerful sensing and path prediction and planning capabilities into crash risk modeling, although some researchers suggested that proper simulation model calibration can be helpful to address these issues. A number of critical questions related to SSM for CAV safety research are also identified and discussed, including SSM for CAV trajectory optimization, SSM for individual vehicles and vehicle platoon, and CAV as a new data source for developing SSM.
Chen Wang; Yuanchang Xie; Helai Huang; Pan Liu. A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling. Accident Analysis & Prevention 2021, 157, 106157 .
AMA StyleChen Wang, Yuanchang Xie, Helai Huang, Pan Liu. A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling. Accident Analysis & Prevention. 2021; 157 ():106157.
Chicago/Turabian StyleChen Wang; Yuanchang Xie; Helai Huang; Pan Liu. 2021. "A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling." Accident Analysis & Prevention 157, no. : 106157.
Cargo two- or three-wheeled vehicles (TTWs), as a new form of micro-mobility, have become a popular mode of urban cargo transportation in China. Cargo TTW riders’ psychological factors and risky behaviors lead to a number of accidents. A questionnaire is designed by comprehensively considering these factors and behaviors of cargo TTW riders that includes eleven risk factors to quantitatively analyze the risky behaviors based on structural equation modeling (SEM). One thousand three hundred nineteen participants reported using cargo TTWs on a questionnaire distributed across the country. The characteristics of riding behavior data are analyzed to verify the three-layer risk theoretical framework of “Psychological factors (Personality traits/specific factors) - Psychological acceptability of risks (confidence/perception/attitude) - Risky behaviors”. The results show that anger has a strong direct effect on riding violations, while normlessness and altruism have a direct effect on riding errors. Workload has a weak but direct effect on risky behaviors, and riding feedback has a weak and mixed effect. In addition, high-risk groups are identified by analysis of variance (ANOVA) with rider population attributes. These quantitative analyses can help guide safety countermeasures to mitigate accidents involving cargo TTWs.
Yi He; Changxin Sun; Helai Huang; Liang Jiang; Ming Ma; Pei Wang; Chaozhong Wu. Safety of micro-mobility: Riders’ psychological factors and risky behaviors of cargo TTWs in China. Transportation Research Part F: Traffic Psychology and Behaviour 2021, 80, 189 -202.
AMA StyleYi He, Changxin Sun, Helai Huang, Liang Jiang, Ming Ma, Pei Wang, Chaozhong Wu. Safety of micro-mobility: Riders’ psychological factors and risky behaviors of cargo TTWs in China. Transportation Research Part F: Traffic Psychology and Behaviour. 2021; 80 ():189-202.
Chicago/Turabian StyleYi He; Changxin Sun; Helai Huang; Liang Jiang; Ming Ma; Pei Wang; Chaozhong Wu. 2021. "Safety of micro-mobility: Riders’ psychological factors and risky behaviors of cargo TTWs in China." Transportation Research Part F: Traffic Psychology and Behaviour 80, no. : 189-202.
Motorcycle is a popular mode of transportation in many developing countries, including Pakistan. Since the last decade, the registered number of motorcycles in Pakistan has increased by six times, constituting 74% of the total registered vehicles. However, limited research efforts have been made to investigate motorcycle-related safety issues in Pakistan. Thus, the relationship between potential risk factors and injury outcomes of motorcycle crashes is still unclear in the country. This study, therefore, established a random parameter logit model to examine the factors associated with the motorcycle injury severity. The analysis is based on two years (2014–2015) of data collected through the road traffic injuries surveillance system from Karachi city, Pakistan. The results indicate that the summer season, weekends, nighttime, elderly riders, heavy vehicle, and single-vehicle collisions are positively associated with fatalities, while the presence of pillion passengers and motorcycle-to-motorcycle crashes are negatively associated with fatalities. More importantly, in the specific context of Pakistan, morning hours, young riders, and female pillion passengers whose clothes stuck in the wheel significantly increase the fatal injury outcomes. Based on the findings, potential countermeasures to improve motorcycle safety are discussed, such as strict enforcement to control motorcyclists' risky behavior and speeding, provision of exclusive motorcycles lanes, and education of female pillion passengers. The findings from this study would increase awareness of motorcycle safety and can be used by the policymakers to enhance road safety in Pakistan, as well as in other developing countries with similar situations.
Amjad Pervez; Jaeyoung Lee; Helai Huang. Identifying Factors Contributing to the Motorcycle Crash Severity in Pakistan. Journal of Advanced Transportation 2021, 2021, 1 -10.
AMA StyleAmjad Pervez, Jaeyoung Lee, Helai Huang. Identifying Factors Contributing to the Motorcycle Crash Severity in Pakistan. Journal of Advanced Transportation. 2021; 2021 ():1-10.
Chicago/Turabian StyleAmjad Pervez; Jaeyoung Lee; Helai Huang. 2021. "Identifying Factors Contributing to the Motorcycle Crash Severity in Pakistan." Journal of Advanced Transportation 2021, no. : 1-10.
Lane-changing is a complicated task and has a high probability of accident occurrence. Although a large body of literature has used vehicle trajectories to microscopically understand and model lane-changing behavior, most of these studies focus on lane-changing decision making and lane changing's impacts on surrounding vehicles, not on traffic safety. The contributing factors to lane-changing risks have not been fully explored from the perspective of microscopic behavior using vehicle trajectory data. This study investigates the contributing factors to accident risks in different lane-changing patterns with taking unobserved heterogeneity into account. A vehicle trajectory dataset, HighD is used and 4842 lane-changing vehicle groups are extracted for analysis. These vehicle groups are divided into sixteen patterns according to the vehicle type, and three major patterns are examined. A lane-changing risk index (LCRI) is proposed to evaluate the risk level of each vehicle group. Two methods are developed and compared for exploring lane-changing risks of the three patterns including (1) establishing the random parameters fractional logit models; and (2) classifying LCRI by k-means algorithm and establishing random parameters ordered logit models with heterogeneity in means and variances. The modeling results show that the latter method performs better and the risk level of the vehicle group is strongly associated with (1) the mean and standard deviation of the gap distance between vehicles; (2) the longitudinal velocities and acceleration of vehicles; and (3) the lane-changing direction and duration. However, different patterns are found to have different contributing variables and effects. The effects of gap distances vary considerably across different vehicle groups and the longitudinal velocity of vehicles are associated with the means of random parameters for gap distance.
Qinghong Chen; Helai Huang; Ye Li; Jaeyoung Lee; Kejun Long; Ruifeng Gu; Xiaoqi Zhai. Modeling accident risks in different lane-changing behavioral patterns. Analytic Methods in Accident Research 2021, 30, 100159 .
AMA StyleQinghong Chen, Helai Huang, Ye Li, Jaeyoung Lee, Kejun Long, Ruifeng Gu, Xiaoqi Zhai. Modeling accident risks in different lane-changing behavioral patterns. Analytic Methods in Accident Research. 2021; 30 ():100159.
Chicago/Turabian StyleQinghong Chen; Helai Huang; Ye Li; Jaeyoung Lee; Kejun Long; Ruifeng Gu; Xiaoqi Zhai. 2021. "Modeling accident risks in different lane-changing behavioral patterns." Analytic Methods in Accident Research 30, no. : 100159.
Background The nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the population and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations. Based on the model, we propose the data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs. Methods We develop a data-driven agent-based model for 7.55 million Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong is split into 4,905 500m×500m grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns. The general daily human mobility patterns are adopted from Google’s Community Mobility Report. The scenario without any NPIs is set as the baseline. By simulating the epidemic progression and human movement at the individual level, we proposed model-driven targeted interventions, which focus on the surgical testing and quarantine of only a small portion of regions instead of enforcing NPIs in the whole city. The efficacious of common NPIs and the proposed targeted interventions are evaluated by extensive Monte Carlo simulations. Findings Without NPIs, we estimate that there are 128,711 total infections (IQR 23,511-70,310) by the end of the 80-day simulation. The proposed targeted intervention averts 95.85% and 94.13% of baseline infections with only 100 (2.04%) and 50 (1.02%) grids being quarantined, respectively. Mild social distancing without testing results in 16,503 total cases (87.18% infections averted), rapid implementation of full lockdown and testing measures (such as the control measure in Mainland China) performs the best, with only 805 infections (99.37% infections averted). Testing-and-quarantining 10%, 20%, 50% of all symptomatic cases with 24-hour/48-hour avert 89.92%/ 87.78%, 95.47%/ 92.42%, and 97.93%/ 95.61% infections, respectively. Interpretation Big data-driven mobility modeling can inform targeted interventions, which are able to effectively contain the COVID-19 outbreak with much lower disruption of the city. It represents a promising approach to sustainable NPIs to help us revive the economy of the city and the world.
Hanchu Zhou; Qingpeng Zhang; Zhidong Cao; Helai Huang; Daniel Dajun Zeng. Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong. 2021, 1 .
AMA StyleHanchu Zhou, Qingpeng Zhang, Zhidong Cao, Helai Huang, Daniel Dajun Zeng. Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong. . 2021; ():1.
Chicago/Turabian StyleHanchu Zhou; Qingpeng Zhang; Zhidong Cao; Helai Huang; Daniel Dajun Zeng. 2021. "Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong." , no. : 1.
With the emergence of connected vehicle (CV) technology, there is a doubt whether CVs can improve driver intentions and behaviors, and thus protect them from accidents with the provision of real-time information. In order to understand the possible impacts of the real-time information provided by CV technology on drivers, this paper aims to develop a model which considers the heterogeneity between drivers with the aid of the extended theory of planned behavior. At the uncontrolled non-signalized intersections, a stated preference (SP) questionnaire survey was conducted to build the dataset consisting of 1001 drivers. Based on the collected dataset, the proposed model examines the relationships between subjective norms, attitudes, risk perceptions, perceived behavioral control and driving intentions, and studies how such driving intentions are simultaneously related to driver characteristics and experiences in the CV environment. Furthermore, driver groups which are homogenous with respect to personality traits are formed, and then are employed to analyze the heterogeneity in responses to driving intentions. Four key findings are obtained when analyzing driver responses to the real-time information provided by CV technology: 1) the proposed H-ETPB model is verified with a good fitness measure; 2) irrespective to driver personality traits, attitudes and perceived behavioral control have a direct and indirect association with driving intentions to accelerate; 3) driving intentions of high-neurotic drivers to accelerate are significantly related to subjective norms, while that of low-neurotic drivers are not; 4) elder high-neurotic drivers, and low-neurotic drivers who have unstable salaries or ever joined in online car hailing service have a strong intention in accelerating. The findings of this study could provide the theoretical framework to optimize traffic performance and information design, as well as provide in-vehicle personalized information service in the CV and CAV environments and assist traffic authorities to design the most acceptable traffic rules for different drivers at an uncontrolled non-signalized intersection.
Wenjing Zhao; Mohammed Quddus; Helai Huang; Qianshan Jiang; Kui Yang; Zhongxiang Feng. The extended theory of planned behavior considering heterogeneity under a connected vehicle environment: A case of uncontrolled non-signalized intersections. Accident Analysis & Prevention 2021, 151, 105934 .
AMA StyleWenjing Zhao, Mohammed Quddus, Helai Huang, Qianshan Jiang, Kui Yang, Zhongxiang Feng. The extended theory of planned behavior considering heterogeneity under a connected vehicle environment: A case of uncontrolled non-signalized intersections. Accident Analysis & Prevention. 2021; 151 ():105934.
Chicago/Turabian StyleWenjing Zhao; Mohammed Quddus; Helai Huang; Qianshan Jiang; Kui Yang; Zhongxiang Feng. 2021. "The extended theory of planned behavior considering heterogeneity under a connected vehicle environment: A case of uncontrolled non-signalized intersections." Accident Analysis & Prevention 151, no. : 105934.
Accessibility has attracted wide interest from urban planners and transportation engineers. It is an important indicator to support the development of sustainable policies for transportation systems in major events, such as the COVID-19 pandemic. Taxis are a vital travel mode in urban areas that provide door-to-door services for individuals to perform urban activities. This study, with taxi trajectory data, proposes an improved method to evaluate dynamic accessibility depending on traditional location-based measures. A new impedance function is introduced by taking characteristics of the taxi system into account, such as passenger waiting time and the taxi fare rule. An improved attraction function is formulated by considering dynamic availability intensity. Besides, we generate five accessibility scenarios containing different indicators to compare the variation of accessibility. A case study is conducted with the data from Shenzhen, China. The results show that the proposed method found reduced urban accessibility, but with a higher value in southern center areas during the evening peak period due to short passenger waiting time and high destination attractiveness. Each spatio-temporal indicator has an influence on the variation in accessibility.
Helai Huang; Jialing Wu; Fang Liu; Yiwei Wang. Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data. Sustainability 2020, 13, 112 .
AMA StyleHelai Huang, Jialing Wu, Fang Liu, Yiwei Wang. Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data. Sustainability. 2020; 13 (1):112.
Chicago/Turabian StyleHelai Huang; Jialing Wu; Fang Liu; Yiwei Wang. 2020. "Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data." Sustainability 13, no. 1: 112.
This study aims to investigate contributing factors to potential collision risks during lane-changing processes from the perspective of vehicle groups and explore the unobserved heterogeneity of individual lane-changing maneuvers. Vehicular trajectory data, extracted from the Federal Highway Administration’s Next Generation Simulation dataset, are utilized and 579 lane-changing vehicle groups are examined. Stopping distance indexes are developed to evaluate the potential collision risks of lane-changing vehicle groups. Three mixed binary logit models and three mixed logit models with heterogeneity in means and variances are established based on different perception reaction time. Model estimation results show that several variables significantly affect the risk status of lane-changing vehicle groups, including the mean values of clearance distance and speed differences between the leading vehicle in the current lane and the subject vehicle, standard deviations of clearance distance, and speed differences between these two vehicles, as well as standard deviations of the speed difference between the subject vehicle and the following vehicle in the target lane. Interestingly, the influences of the last three variables differ considerably across the observations and the mean of the random parameter for standard deviations of clearance distance between CLV and SV is associated with the mean speed difference between CLV and SV. Since one of the explanations is individual heterogeneity, personalized designs for advanced driver assistance system would be an effective measure to reduce the risk.
Qinghong Chen; Ruifeng Gu; Helai Huang; Jaeyoung Lee; Xiaoqi Zhai; Ye Li. Using vehicular trajectory data to explore risky factors and unobserved heterogeneity during lane-changing. Accident Analysis & Prevention 2020, 151, 105871 .
AMA StyleQinghong Chen, Ruifeng Gu, Helai Huang, Jaeyoung Lee, Xiaoqi Zhai, Ye Li. Using vehicular trajectory data to explore risky factors and unobserved heterogeneity during lane-changing. Accident Analysis & Prevention. 2020; 151 ():105871.
Chicago/Turabian StyleQinghong Chen; Ruifeng Gu; Helai Huang; Jaeyoung Lee; Xiaoqi Zhai; Ye Li. 2020. "Using vehicular trajectory data to explore risky factors and unobserved heterogeneity during lane-changing." Accident Analysis & Prevention 151, no. : 105871.
Exploring characteristics in traffic flow and predicting its variation patterns are the main steps to realize applications of Intelligent Transportation Systems. Most existing studies focused on highway or freeway traffic flow predictions and achieved good performance. However, due to the intermittent characteristics and intense fluctuation on short-term scales, it is a challenge to accurately predict future variation patterns of traffic flow on the urban road network. A hybrid model: Genetic Algorithm with Attention-based Long Short-Term Memory (GA-LSTM), combining with spatial-temporal correlation analysis, is proposed in this study to predict traffic volume on urban roads. In the temporal correlation modeling, the attention mechanism is introduced to represent the significance of historical observations in the LSTM model. In the spatial correlation modeling, a new weight matrix is constructed to combine the volume transition matrix estimated from vehicle trajectories and network weight matrix quantified from different detectors. Finally, the Genetic Algorithm is improved to optimize the attention weight in the deep learning structure. In the experiment, traffic flow data collected from License Plate Recognition (LPR) devices located at 19 urban intersections in Changsha City, China, is utilized to validate the effectiveness of the established model. A model comparison is further conducted with several widely used prediction models to show the superiority of the proposed model with higher accuracy and better stability under different prediction steps, especially advantage in capturing intense fluctuation of traffic volume at the short-term interval.
Jinjun Tang; Jie Zeng; Yuwei Wang; Hang Yuan; Fang Liu; Helai Huang. Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm. Transportmetrica A: Transport Science 2020, 17, 1217 -1243.
AMA StyleJinjun Tang, Jie Zeng, Yuwei Wang, Hang Yuan, Fang Liu, Helai Huang. Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm. Transportmetrica A: Transport Science. 2020; 17 (4):1217-1243.
Chicago/Turabian StyleJinjun Tang; Jie Zeng; Yuwei Wang; Hang Yuan; Fang Liu; Helai Huang. 2020. "Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm." Transportmetrica A: Transport Science 17, no. 4: 1217-1243.
Underreporting and spatial correlations are two important issues in traffic safety analysis. To deal with them simultaneously, this study proposes a Bayesian underreporting conditional autoregressive (CAR) model for analyzing crash frequency. In the formulation of the proposed model, a latent reporting process is incorporated into the crash counting process, and residual terms with CAR priors are added into the two processes to account for their respective spatial correlations. The seasonal crash data collected from Kaiyang Freeway, China in 2014 are used to verify the performance of the proposed model. It is estimated and compared with a traditional CAR model via Bayesian methods. The superiority of the underreporting model is indicated by its better model fit, more reasonable estimation results, and statistical significance of the spatial terms in the counting and reporting processes. Estimation results show that more crashes are expected to occur on longer freeway segments with larger traffic volume, smaller proportion of large truck/bus, greater horizontal curvature, and higher vertical grade. It is also shown that light traffic, traffic with more medium truck/bus or less large truck/bus, smaller horizontal curvature, bridge, and segments without ramps tend to increase the likelihood of crash reporting. These results are generally consistent with the findings in existing literature and engineering experience, which further support the proposed model as a good alternative for crash frequency analyzing.
Qiang Zeng; Huiying Wen; Helai Huang; Jie Wang; Jinwoo Lee. Analysis of crash frequency using a Bayesian underreporting count model with spatial correlation. Physica A: Statistical Mechanics and its Applications 2019, 545, 123754 .
AMA StyleQiang Zeng, Huiying Wen, Helai Huang, Jie Wang, Jinwoo Lee. Analysis of crash frequency using a Bayesian underreporting count model with spatial correlation. Physica A: Statistical Mechanics and its Applications. 2019; 545 ():123754.
Chicago/Turabian StyleQiang Zeng; Huiying Wen; Helai Huang; Jie Wang; Jinwoo Lee. 2019. "Analysis of crash frequency using a Bayesian underreporting count model with spatial correlation." Physica A: Statistical Mechanics and its Applications 545, no. : 123754.
Weather is well recognized as a significant environmental factor contributing to higher risk of road crashes. In the conventional road safety studies, weather effects had been set out either based on the instant weather conditions recorded by the police officer attained or the average of meteorological observations over a relatively long time period, such as daily, weekly or even monthly, etc. To the best of our knowledge, it is rare that the lag effect of weather in the preceding period on the crash risk in the current period was attempted. With the use of high-resolution meteorological data in very short time interval, it is possible to evaluate the role of lagged weather effect on safety. In this study, we propose a novel distributed lag non-linear model (DLNM), integrated with case-crossover design, to evaluate the lag effect of weather on crash incidence. The proposed modelling framework could describe the non-linear relationship between weather and crash and the lag effects. Also, the possible over-dispersion and autocorrelation of the time-series weather and crash data can be controlled for. The model was estimated using an integrated meteorological, traffic and crash dataset in Hong Kong. For instances, high resolution data on temperature, humidity, rain intensity and wind speed in 1-hour interval was available. The bi-dimensional exposure-lag-response surfaces are established to visualize the varying effects of possible weather factors on crash risk, with respect to the lag size. Such relationship between effect size and lag size is often overlooked in the literatures. Results indicate that model with 4 degrees of freedom for both weather condition (knots at equal spaces) and lag time (knots at equal intervals) best fit with the observations, in accordance to Quasi-likelihood Akaike information criterion (Q-AIC). Then, stratified analyses are conducted to evaluate the difference in the association among different clusters. Findings should shed light on the modelling of non-linear exposure-response relationship and lag effects in traffic safety time series analysis.
Fen Xing; Helai Huang; Zhiying Zhan; Xiaoqi Zhai; Chunquan Ou; N.N. Sze; K.K. Hon. Hourly associations between weather factors and traffic crashes: Non-linear and lag effects. Analytic Methods in Accident Research 2019, 24, 100109 .
AMA StyleFen Xing, Helai Huang, Zhiying Zhan, Xiaoqi Zhai, Chunquan Ou, N.N. Sze, K.K. Hon. Hourly associations between weather factors and traffic crashes: Non-linear and lag effects. Analytic Methods in Accident Research. 2019; 24 ():100109.
Chicago/Turabian StyleFen Xing; Helai Huang; Zhiying Zhan; Xiaoqi Zhai; Chunquan Ou; N.N. Sze; K.K. Hon. 2019. "Hourly associations between weather factors and traffic crashes: Non-linear and lag effects." Analytic Methods in Accident Research 24, no. : 100109.
This study applies mixture components in a multivariate random parameters spatial model for zonal crash counts. Three different modeling formulations are employed to demonstrate the effects of mixture components and spatial heterogeneity in the goodness-of-fit in a multivariate random parameter model. The models are built for injury (i.e., possible, non-incapacitating, incapacitating, and fatal injury) and non-injury crashes using the data from 738 traffic analysis zones (TAZs) in Hillsborough County of Florida during a three-year period. The Deviance Information Criteria (DIC) is used to evaluate the performances of these models indicate the proposed model outperforms the rests. According to the estimated results, various traffic-related, demographics, and socioeconomic factors affect the occurrences of crashes for different severity levels. With regard to the effect of mixture components, it identifies two homogeneous sub-classes labeled as “stable pattern” and “unstable pattern” to better capture the heterogeneity. The standard deviation (SD) and correlation across injury and non-injury crashes are both very high in the “stable pattern” compared with its “unstable pattern” counterpart. On the other hand, the results of model comparison reveal that: (i) adding one more mixture component has no significant influences on the spatial heterogeneity and spatial correlation of different kinds of crash frequency and (ii) the consideration of spatial effects improves the accuracy of estimate results. Moreover, the multivariate random parameters spatial model with mixture components was compared with its univariate form to highlight the validity of applying multivariate structure.
Helai Huang; Fangrong Chang; Hanchu Zhou; Jaeyoung Lee. Modeling unobserved heterogeneity for zonal crash frequencies: A Bayesian multivariate random-parameters model with mixture components for spatially correlated data. Analytic Methods in Accident Research 2019, 24, 100105 .
AMA StyleHelai Huang, Fangrong Chang, Hanchu Zhou, Jaeyoung Lee. Modeling unobserved heterogeneity for zonal crash frequencies: A Bayesian multivariate random-parameters model with mixture components for spatially correlated data. Analytic Methods in Accident Research. 2019; 24 ():100105.
Chicago/Turabian StyleHelai Huang; Fangrong Chang; Hanchu Zhou; Jaeyoung Lee. 2019. "Modeling unobserved heterogeneity for zonal crash frequencies: A Bayesian multivariate random-parameters model with mixture components for spatially correlated data." Analytic Methods in Accident Research 24, no. : 100105.
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.
Qiang Zeng; Qiang Guo; Sze Chun Wong; Huiying Wen; Heilai Huang; Xin Pei. Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression. Transportmetrica A: Transport Science 2019, 15, 1867 -1884.
AMA StyleQiang Zeng, Qiang Guo, Sze Chun Wong, Huiying Wen, Heilai Huang, Xin Pei. Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression. Transportmetrica A: Transport Science. 2019; 15 (2):1867-1884.
Chicago/Turabian StyleQiang Zeng; Qiang Guo; Sze Chun Wong; Huiying Wen; Heilai Huang; Xin Pei. 2019. "Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression." Transportmetrica A: Transport Science 15, no. 2: 1867-1884.
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.
Research efforts have been made to understand tunnels’ traffic safety. Most of the previous studies have not considered the different features of tunnels with different ranges of length comprehensively. Generally, three- or four-zone approach has been adopted, with which the entrance and exit parts of a tunnel are considered symmetrical in the safety analysis. This study employs a seven-zone analytic approach for the safety investigation of 18 expressway tunnels with length ranging from 2 to 3 km. The results reveal that the crash rate increases firstly for the entrance zone, then decreases for the midzone and again increases at the exit zone. The high crash rates at the access, entrance, and transition zones are attributed to rear-end crashes. Although single-vehicle crashes take place in the mid and exit zones (1) at the tunnel entrance: failure to maintain safe distance; (2) in the midarea: failure to maintain safe distance, fatigue driving, overspeeding, and improper lane change; and (3) at the tunnel exit: overspeeding and improper lane change mainly contribute to the crash occurrence. Friedman test was performed to test the significance of the contributing factors. The crash occurrence mechanism is discussed for the selected long tunnels. Finally, engineering and policy countermeasures are recommended to improve traffic safety in expressway tunnels.
Amjad Pervez; Helai Huang; Jaeyoung Lee; Chunyang Han; Jie Wang; Xuan Zhang. Crash analysis of expressway long tunnels using a seven-zone analytic approach. Journal of Transportation Safety & Security 2019, 13, 108 -122.
AMA StyleAmjad Pervez, Helai Huang, Jaeyoung Lee, Chunyang Han, Jie Wang, Xuan Zhang. Crash analysis of expressway long tunnels using a seven-zone analytic approach. Journal of Transportation Safety & Security. 2019; 13 (1):108-122.
Chicago/Turabian StyleAmjad Pervez; Helai Huang; Jaeyoung Lee; Chunyang Han; Jie Wang; Xuan Zhang. 2019. "Crash analysis of expressway long tunnels using a seven-zone analytic approach." Journal of Transportation Safety & Security 13, no. 1: 108-122.
Lane changing behavior generally expresses uncertainty due to the impact of environmental factors, and unreasonable lane changes can cause serious collisions. High precision prediction of lane changing intent is helpful to enhance proactivity in driving safety protection. This study proposed a lane-changing prediction model based on Fuzzy C-means clustering algorithm and adaptive Neural Network (FCMNN), which introduced a new prediction process: (1) Unsupervised learning method: categorize original dataset into different clusters according to their distribution features; (2) Supervised learning method: optimize sub-Neural Network structures and weighting parameters for each cluster or pattern. Through comparing with several traditional methods under different simulation scenarios, the proposed model effectively improve the prediction performance and stability. The results obtained in this study will be helpful to deeply analyze the intent recognition of driving behavior, improve the safety of lane-changing behavior, and provide key technology in driving prediction of Advanced Driver Assistance System (ADAS).
Jinjun Tang; ShaoWei Yu; Fang Liu; Xinqiang Chen; Helai Huang. A hierarchical prediction model for lane-changes based on combination of fuzzy C-means and adaptive neural network. Expert Systems with Applications 2019, 130, 265 -275.
AMA StyleJinjun Tang, ShaoWei Yu, Fang Liu, Xinqiang Chen, Helai Huang. A hierarchical prediction model for lane-changes based on combination of fuzzy C-means and adaptive neural network. Expert Systems with Applications. 2019; 130 ():265-275.
Chicago/Turabian StyleJinjun Tang; ShaoWei Yu; Fang Liu; Xinqiang Chen; Helai Huang. 2019. "A hierarchical prediction model for lane-changes based on combination of fuzzy C-means and adaptive neural network." Expert Systems with Applications 130, no. : 265-275.
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.
Recent advance in variable message signs (VMS) technology has made it viable to provide spatio-temporal information on traffic and network conditions to drivers. There is a debate whether VMS diverts drivers’ attention away from the road and may cause unnecessary distraction in their driving tasks due to inconsistent VMS contents and formats. There are also other external factors such as weather conditions, visibility and time of day that may affect the integrity and reliability of the VMS. In China, only about 23% drivers were persuaded by VMS to follow route diversion. In order to capture the full benefits of VMS, the aim of this paper is therefore to identify the factors affecting VMS by examining what kinds of VMS contents, formats and their interactions are more preferable to drivers, specifically in China. A revealed preference (RP) questionnaire and stated preference (SP) survey consisting of 1154 samples from private and taxi drivers was conducted and analyzed using discrete choice model. The results revealed that the information showed by amber-on-black on text format, white-on-blue on graph format or the suggested route diversion information showed by single line are preferred by drivers in fog weather. In addition, highly educated drivers or drivers with no occupation are more prone to the qualitative delay time on a text-graph format in fog weather. In normal weather, drivers with working trip purpose are mostly preferred to receive the information on a congested traffic condition with a reason on a text-only format. However, the congested traffic condition along with the information on the apparent causes shown by red-on-black or green-on-black on a text-only format was least preferred by drivers. Regarding current and adjacent road traffic information, drivers prefer to receive the suggested route diversion on a graph-only format in fog weather and the qualitative delay time on a text-graph format in normal weather. Irrespective to weather conditions, male drivers incline to the qualitative delay time on a text-graph format. The findings of this study could assist traffic authorities to design the most acceptable VMS for displaying traffic information for the purpose of improving road traffic efficiency and provide the theory evidence for the design of in-vehicle personalized information service system.
Wenjing Zhao; Mohammed Quddus; Helai Huang; Jaeyoung Lee; Zhuanglin Ma. Analyzing drivers’ preferences and choices for the content and format of variable message signs (VMS). Transportation Research Part C: Emerging Technologies 2019, 100, 1 -14.
AMA StyleWenjing Zhao, Mohammed Quddus, Helai Huang, Jaeyoung Lee, Zhuanglin Ma. Analyzing drivers’ preferences and choices for the content and format of variable message signs (VMS). Transportation Research Part C: Emerging Technologies. 2019; 100 ():1-14.
Chicago/Turabian StyleWenjing Zhao; Mohammed Quddus; Helai Huang; Jaeyoung Lee; Zhuanglin Ma. 2019. "Analyzing drivers’ preferences and choices for the content and format of variable message signs (VMS)." Transportation Research Part C: Emerging Technologies 100, no. : 1-14.