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Resource sharing (RS) integrated into the optimization of multi-depot pickup and delivery problem (MDPDP) can greatly reduce the logistics operating cost and required transportation resources by reconfiguring the logistics network. This study formulates and solves an MDPDP with RS (MDPDPRS). First, a bi-objective mathematical programming model that minimizes the logistics cost and the number of vehicles is constructed, in which vehicles are allowed to be used multiple times by one or multiple logistics facilities. Second, a two-stage hybrid algorithm composed of a k-means clustering algorithm, a Clark-Wright (CW) algorithm, and a nondominated sorting genetic algorithm II (NSGA-II) is designed. The k-means algorithm is adopted in the first stage to reallocate customers to logistics facilities according to the Manhattan distance between them, by which the computational complexity of solving the MDPDPRS is reduced. In the second stage, CW and NSGA-II are adopted jointly to optimize the vehicle routes and find the Pareto optimal solutions. CW algorithm is used to select the initial solution, which can increase the speed of finding the optimal solution during NSGA-II. Fast nondominated sorting operator and elite strategy selection operator are utilized to maintain the diversity of solutions in NSGA-II. Third, benchmark tests are conducted to verify the performance and effectiveness of the proposed two-stage hybrid algorithm, and numerical results prove that the proposed methodology outperforms the standard NSGA-II and multi-objective particle swarm optimization algorithm. Finally, optimization results of a real-world logistics network from Chongqing confirm the applicability of the mathematical model and the designed solution algorithm. Solving the MDPDPRS provides a management tool for logistics enterprises to improve resource configuration and optimize logistics operation efficiency.
Yong Wang; Lingyu Ran; Xiangyang Guan; Yajie Zou. Multi-Depot Pickup and Delivery Problem with Resource Sharing. Journal of Advanced Transportation 2021, 2021, 1 -22.
AMA StyleYong Wang, Lingyu Ran, Xiangyang Guan, Yajie Zou. Multi-Depot Pickup and Delivery Problem with Resource Sharing. Journal of Advanced Transportation. 2021; 2021 ():1-22.
Chicago/Turabian StyleYong Wang; Lingyu Ran; Xiangyang Guan; Yajie Zou. 2021. "Multi-Depot Pickup and Delivery Problem with Resource Sharing." Journal of Advanced Transportation 2021, no. : 1-22.
Climate change and the extreme weather have a negative impact on road traffic safety, resulting in severe road traffic accidents. In this study, a negative binomial model and a log-change model are proposed to analyse the impact of various factors on fatal traffic accidents. The dataset used in this study includes the fatal traffic accident frequency, social development indicators and climate indicators in California and Arizona. The results show that both models can provide accurate fitting results. Climate variables (i.e., average temperature and standard precipitation 24) can significantly affect the frequency of fatal traffic accidents. Non-climate variables (i.e., beer consumption, rural Vehicle miles travelled ratio, and vehicle performance) also have a significant impact. The modelling results can provide decision-making guidelines for the transportation management agencies to improve road traffic safety.
Yajie Zou; Yue Zhang; Kai Cheng. Exploring the Impact of Climate and Extreme Weather on Fatal Traffic Accidents. Sustainability 2021, 13, 390 .
AMA StyleYajie Zou, Yue Zhang, Kai Cheng. Exploring the Impact of Climate and Extreme Weather on Fatal Traffic Accidents. Sustainability. 2021; 13 (1):390.
Chicago/Turabian StyleYajie Zou; Yue Zhang; Kai Cheng. 2021. "Exploring the Impact of Climate and Extreme Weather on Fatal Traffic Accidents." Sustainability 13, no. 1: 390.
Travel time reliability (TTR) is widely used to evaluate transportation system performance. Adverse weather condition is an important factor for affecting TTR, which can cause traffic congestions and crashes. Considering the traffic characteristics under different traffic conditions, it is necessary to explore the impact of adverse weather on TTR under different conditions. This study conducted an empirical travel time analysis using traffic data and weather data collected on Yanan corridor in Shanghai. The travel time distributions were analysed under different roadway types, weather, and time of day. Four typical scenarios (i.e., peak hours and off-peak hours on elevated expressway, peak hours and off-peak hours on arterial road) were considered in the TTR analysis. Four measures were calculated to evaluate the impact of adverse weather on TTR. The results indicated that the lognormal distribution is preferred for describing the travel time data. Compared with off-peak hours, the impact of adverse weather is more significant for peak hours. The travel time variability, buffer time index, misery index, and frequency of congestion increased by an average of 29%, 19%, 22%, and 63%, respectively, under the adverse weather condition. The findings in this study are useful for transportation management agencies to design traffic control strategies when adverse weather occurs.
Yajie Zou; Ting Zhu; Yifan Xie; Linbo Li; Ying Chen. Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai. Journal of Advanced Transportation 2020, 2020, 1 -11.
AMA StyleYajie Zou, Ting Zhu, Yifan Xie, Linbo Li, Ying Chen. Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai. Journal of Advanced Transportation. 2020; 2020 ():1-11.
Chicago/Turabian StyleYajie Zou; Ting Zhu; Yifan Xie; Linbo Li; Ying Chen. 2020. "Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai." Journal of Advanced Transportation 2020, no. : 1-11.
Examining the travel time variability (TTV) of buses, passenger cars and taxis is essential to obtain reliable travel time in urban daily trips. TTV analyses of three travel modes are conducted using travel time data collected on two urban arterial roads in Xi'an City. Firstly, the TTV is evaluated using statistical indexes. The results reveal that the TTV differs from vehicle to vehicle, period to period and site to site. Secondly, the finite mixture survival model is proposed to address the heterogeneity of travel time data by decomposing the population into several sub-populations. Wasserstein distance and Kolmogorov–Smirnov test are used to further compare the sub-populations of different vehicle types during different periods on different roads. Finally, based on the model analysis, it can be found that the finite mixture survival model is an accurate tool to examine the variability by capturing the heterogeneity of travel time data. The difference among the sub-populations suggests different travel behaviours. It concludes that more diverse travel behaviours result in higher TTV. An accurate investigation on TTV is valuable for travellers’ mode choices and transportation management agencies to obtain reliable travel time information and improve traffic efficiency.
Xinzhi Zhong; Yajie Zou; Zhi Dong; Shaoxin Yuan; Muhammad Ijaz. Finite mixture survival model for examining the variability of urban arterial travel time for buses, passenger cars and taxis. IET Intelligent Transport Systems 2020, 14, 1524 -1533.
AMA StyleXinzhi Zhong, Yajie Zou, Zhi Dong, Shaoxin Yuan, Muhammad Ijaz. Finite mixture survival model for examining the variability of urban arterial travel time for buses, passenger cars and taxis. IET Intelligent Transport Systems. 2020; 14 (12):1524-1533.
Chicago/Turabian StyleXinzhi Zhong; Yajie Zou; Zhi Dong; Shaoxin Yuan; Muhammad Ijaz. 2020. "Finite mixture survival model for examining the variability of urban arterial travel time for buses, passenger cars and taxis." IET Intelligent Transport Systems 14, no. 12: 1524-1533.
Annual fatal traffic accident data often demonstrate time series characteristics. The existing traffic safety analysis approaches (e.g., negative binomial (NB) model) often cannot accommodate the dynamic impact of factors in fatal traffic accident data and may result in biased parameter estimation results. Thus, a linear Poisson autoregressive (PAR) model is proposed in this study. The objective of this study is to apply the PAR model to analyze the dynamic impact of traffic laws and climate on the frequency of fatal traffic accidents occurred in a large time span (from 1975 to 2016) in Illinois. Besides, the NB model, NB with a time trend, and autoregressive integrated moving average model with exogenous input variables (ARIMAX) are also developed to compare their performances. The important conclusions from the modelling results can be summarized as follows. (1) The PAR model is more appropriate for analyzing the dynamic impacts of traffic laws on annual fatal traffic accidents, especially the instantaneous impacts. (2) The law that allows motorcycles and bicycles to proceed on a red light following the rules applicable after a “reasonable period of time” leads to an increase in the frequency of annual fatal traffic accidents by 14.98% in the short term and 30.69% in the long term. The climate factors such as average temperature and precipitation concentration period have insignificant impacts on annual fatal traffic accidents in Illinois. Thus, the modelling results suggest that the PAR model is more appropriate for annual fatal traffic accident data and has an advantage in estimating the dynamic impact of traffic laws.
Yue Zhang; Yajie Zou; Lingtao Wu; Jinjun Tang; Malik Muneeb Abid. Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency. Journal of Advanced Transportation 2020, 2020, 1 -9.
AMA StyleYue Zhang, Yajie Zou, Lingtao Wu, Jinjun Tang, Malik Muneeb Abid. Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency. Journal of Advanced Transportation. 2020; 2020 ():1-9.
Chicago/Turabian StyleYue Zhang; Yajie Zou; Lingtao Wu; Jinjun Tang; Malik Muneeb Abid. 2020. "Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency." Journal of Advanced Transportation 2020, no. : 1-9.
This paper investigates a novel design problem involving an optimal government lane expansion scheme for a battery electric vehicle (BEV) transportation network. A lane expansion model is established based on the BEV charging time, driver range anxiety and uncertain transportation demand. This model aims to minimize the total travel time (i.e., sum of the driving time and charging time) of all drivers in the transportation network and optimize the lane expansion scheme (i.e., the number and location of extended lanes in the network) under the established investment ceiling. To address demand uncertainty, an improved adjustable robust optimization method is proposed to relax the model by introducing two control parameters. Based on the framework of the active set algorithm, a local optimal solution algorithm is designed to effectively solve the abovementioned model. Sensitivity analyses are conducted for different control parameters and government investment scales. The results show that the model and algorithm we proposed can provide a theoretical basis for the government to improve the traffic efficiency of the BEV transportation network and achieve the goal of sustainable transport.
Kai Cheng; Yajie Zou; Xu Xin; Shuaiyu Gong. Optimal lane expansion model for a battery electric vehicle transportation network considering range anxiety and demand uncertainty. Journal of Cleaner Production 2020, 276, 124198 .
AMA StyleKai Cheng, Yajie Zou, Xu Xin, Shuaiyu Gong. Optimal lane expansion model for a battery electric vehicle transportation network considering range anxiety and demand uncertainty. Journal of Cleaner Production. 2020; 276 ():124198.
Chicago/Turabian StyleKai Cheng; Yajie Zou; Xu Xin; Shuaiyu Gong. 2020. "Optimal lane expansion model for a battery electric vehicle transportation network considering range anxiety and demand uncertainty." Journal of Cleaner Production 276, no. : 124198.
Freeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the impacts of different periodic functions on statistical and machine learning models. In this paper, our primary objective is to evaluate the performance of the six commonly used multistep ahead travel time prediction models (three statistical models and three machine learning models). In addition, we compared the impacts of three periodic functions on multistep ahead travel time prediction for different temporal scales (5-minute, 10-minute, and 15-minute). The results indicate that the periodic functions can improve the prediction performance of machine learning models for more than 60 minutes ahead prediction and improve the over 30 minutes ahead prediction accuracy for statistical models. Three periodic functions show a slight difference in improving the prediction accuracy of the six prediction models. For the same prediction step, the effect of the periodic function is more obvious at a higher level of aggregation.
Xu Miao; Bing Wu; Yajie Zou; Lingtao Wu. Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches. Journal of Advanced Transportation 2020, 2020, 1 -15.
AMA StyleXu Miao, Bing Wu, Yajie Zou, Lingtao Wu. Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches. Journal of Advanced Transportation. 2020; 2020 ():1-15.
Chicago/Turabian StyleXu Miao; Bing Wu; Yajie Zou; Lingtao Wu. 2020. "Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches." Journal of Advanced Transportation 2020, no. : 1-15.
This study aims to incorporate survival theory into the estimation of crash modification factors (CMFs) and to examine the performance. A joint modeling framework, which considers crash counts and time intervals between crashes simultaneously, is proposed. To assess the performance of the joint model, this study collected roadway and crash data on 240 rural two-lane roadway segments in Texas, developed CMFs for a dummy treatment and further compared the results with those developed using the traditional empirical Bayes (EB) method. The main findings are summarized as follows: (1) The traditional EB method tends to overestimate the CMFs for the treatment, and underestimate the standard errors. In most cases, the results are biased; (2) The estimated CMF values with the joint model are closer to the true effect, and they have higher standard errors. The confidence intervals of the CMFs cover the CMF for the dummy treatment (i.e., 1.0), which is more realistic; (3) Temporal instability in traffic crashes are also observed in this study. Increasing the duration of the study period does not always increase the accuracy of CMF estimates. In addition to crash counts, safety analysts are encouraged to incorporate time intervals between crashes while estimating CMFs.
Lingtao Wu; Yi Meng; Xiaoqiang Kong; Yajie Zou. Incorporating survival analysis into the safety effectiveness evaluation of treatments: Jointly modeling crash counts and time intervals between crashes. Journal of Transportation Safety & Security 2020, 1 -21.
AMA StyleLingtao Wu, Yi Meng, Xiaoqiang Kong, Yajie Zou. Incorporating survival analysis into the safety effectiveness evaluation of treatments: Jointly modeling crash counts and time intervals between crashes. Journal of Transportation Safety & Security. 2020; ():1-21.
Chicago/Turabian StyleLingtao Wu; Yi Meng; Xiaoqiang Kong; Yajie Zou. 2020. "Incorporating survival analysis into the safety effectiveness evaluation of treatments: Jointly modeling crash counts and time intervals between crashes." Journal of Transportation Safety & Security , no. : 1-21.
Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.
Xiaoxue Yang; Yajie Zou; Jinjun Tang; Jian Liang; Muhammad Ijaz. Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models. Journal of Advanced Transportation 2020, 2020, 1 -16.
AMA StyleXiaoxue Yang, Yajie Zou, Jinjun Tang, Jian Liang, Muhammad Ijaz. Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models. Journal of Advanced Transportation. 2020; 2020 ():1-16.
Chicago/Turabian StyleXiaoxue Yang; Yajie Zou; Jinjun Tang; Jian Liang; Muhammad Ijaz. 2020. "Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models." Journal of Advanced Transportation 2020, no. : 1-16.
This paper investigates the outdoor non-work activity allocation behaviors of commuters in Xiaoshan District of Hangzhou, China, as well as the underlying relationship among different types of outdoor non-work activities. As per their commute and work schedules, commuters’ outdoor non-work activities are classified into six categories and considered as binary dependent variables for modeling analysis, including from home before work, on commute way from home to work, going home during work, going out (not going home) during work, on commute way from work back home, and from home after work. Independent variables include commute attributes, work schedules, sociodemographic attributes, and built-environmental attributes. A multivariate probit model is developed to explore the effects of explanatory variables and capture correlations among unobserved influential factors. The model estimation results show that daily work time, education years, and traffic zone have substantial impacts on commuters’ non-work activity allocations. As for the underlying relationship among unobserved factors, a positive correlation is found between the outdoor non-work activities on commute way to and from work, indicating a mutually promotive relationship. All other correlations are negative, indicating other types of non-work activities are mutually substitutive. These findings will help to better understand commuters’ behaviors of outdoor activity arrangement subject to the time-space constraint from fixed work schedules, and shed some light on the mechanism of complex work tour formation, so as to guide the development of activity-based travel demand models for commuters.
Xin Guan; Xin Ye; Cheng Shi; Yajie Zou. A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China. Sustainability 2019, 11, 5768 .
AMA StyleXin Guan, Xin Ye, Cheng Shi, Yajie Zou. A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China. Sustainability. 2019; 11 (20):5768.
Chicago/Turabian StyleXin Guan; Xin Ye; Cheng Shi; Yajie Zou. 2019. "A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China." Sustainability 11, no. 20: 5768.
Two common types of animal-vehicle collision data (reported animal-vehicle collision (AVC) data and carcass removal data) are usually recorded by transportation management agencies. Previous studies have found that these two datasets often demonstrate different characteristics. To accurately identify the higher-risk animal-vehicle collision sites, this study compared the differences in hotspot identification and the effect of explanation variables between carcass removal and reported AVCs. To complete the objective, both the Negative Binomial (NB) model and the generalized Negative Binomial (GNB) are applied in calculating the Empirical Bayesian (EB) estimates using the animal collision data collected on ten highways in Washington State. The important findings can be summarized as follows. (1) The explanatory variables have different effects on the occurrence of carcass removal data and reported AVC data. (2) The ranking results from EB estimates when using carcass removal data and reported AVC data differ significantly. (3) The results of hotspot identification are different between carcass removal data and reported AVC data. However, the ranking results of GNB models are better than those of NB models in terms of consistency. Thus, transportation management agencies should be cautious when using either carcass removal data or reported AVC data to identify hotspots.
Xiaoxue Yang; Yajie Zou; Lingtao Wu; Xinzhi Zhong; Yinhai Wang; Muhammad Ijaz; Yichuan Peng. Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification. Journal of Advanced Transportation 2019, 2019, 1 -13.
AMA StyleXiaoxue Yang, Yajie Zou, Lingtao Wu, Xinzhi Zhong, Yinhai Wang, Muhammad Ijaz, Yichuan Peng. Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification. Journal of Advanced Transportation. 2019; 2019 ():1-13.
Chicago/Turabian StyleXiaoxue Yang; Yajie Zou; Lingtao Wu; Xinzhi Zhong; Yinhai Wang; Muhammad Ijaz; Yichuan Peng. 2019. "Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification." Journal of Advanced Transportation 2019, no. : 1-13.
Wildlife‒vehicle collision (WVC) data usually contain two types: the reported WVC data and carcass removal data. Previous studies often found a discrepancy between the number of reported WVC and carcass removal data, and the quality of both datasets is affected by underreporting. Underreporting means the number of WVCs is not fully recorded in the database; neglecting the underreporting in WVC data may result in biased parameter estimation results. In this study, a copula regression model linking wildlife‒vehicle collisions and the underreporting outcome was proposed to consider the underreporting in WVC data. The WVC data collected from 10 highways in Washington State were analyzed using the copula regression model and the Negative Binomial (NB) model. The main findings from this study are as follows: (1) the Gaussian copula model can provide different modeling results when compared with the conventional modeling approach; (2) the hotspot identification results indicate that the Gaussian copula-based Empirical Bayes (EB) method can more accurately identify hotspots than the NB-based EB method. Thus, the proposed copula model may be a better alternative to the conventional NB model for modeling underreported WVC data.
Yajie Zou; Xinzhi Zhong; Jinjun Tang; Xin Ye; Lingtao Wu; Muhammad Ijaz; Yinhai Wang. A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis. Sustainability 2019, 11, 418 .
AMA StyleYajie Zou, Xinzhi Zhong, Jinjun Tang, Xin Ye, Lingtao Wu, Muhammad Ijaz, Yinhai Wang. A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis. Sustainability. 2019; 11 (2):418.
Chicago/Turabian StyleYajie Zou; Xinzhi Zhong; Jinjun Tang; Xin Ye; Lingtao Wu; Muhammad Ijaz; Yinhai Wang. 2019. "A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis." Sustainability 11, no. 2: 418.
Combined traffic assignment–signal control equilibrium is usually integrated into a non-cooperative games model between the network authority and road users. Unlike a pure Wardropian equilibrium, in reality there may be both competition and cooperation between authority and users. Authority has always been regarded as the upper level in classical bi-level formulations, but this placement may increase the difficulty of obtaining a global optimal solution between authority and users. This paper proposes a level-change Stackelberg (LC Stackelberg) model that embraces both authority–user and user–authority formulations. The model is calibrated by a model predictive control (MPC) controller. A route-choice probability model is used to estimate flow burden on two parallel routes. Meanwhile, the difference of route-choice probability between the two parallel paths is regarded as the level-change threshold. A generalized autoregressive conditional heteroscedasticity (GJR-GARCH) model is used as a triggering function in the MPC controller to fulfill the level-change procedure. A modified wavelet neural network algorithm is used to seek the global optimal solution. Cournot, Stackelberg, and Monopoly, combined with a fixed-time control policy based on the Webster method, were chosen as benchmarks in a numerical example to test model validity. The results show that the LC Stackelberg model obtains the minimum total travel time compared with other models. Furthermore, the level-change between authority and users could also decrease route choice probability on one specific path, indicating the model’s potential application in urban networks.
Hang Yang; Zhongyu Wang; Yajie Zou; Bing Wu; Xuesong Wang. Level-Change Stackelberg Games Model for the Combined Traffic Assignment–Signal Control Equilibrium on Networks. Transportation Research Record: Journal of the Transportation Research Board 2018, 2672, 24 -35.
AMA StyleHang Yang, Zhongyu Wang, Yajie Zou, Bing Wu, Xuesong Wang. Level-Change Stackelberg Games Model for the Combined Traffic Assignment–Signal Control Equilibrium on Networks. Transportation Research Record: Journal of the Transportation Research Board. 2018; 2672 (48):24-35.
Chicago/Turabian StyleHang Yang; Zhongyu Wang; Yajie Zou; Bing Wu; Xuesong Wang. 2018. "Level-Change Stackelberg Games Model for the Combined Traffic Assignment–Signal Control Equilibrium on Networks." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 48: 24-35.
Purpose It would take billions of miles’ field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event. Design/methodology/approach This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling (IS) techniques. Taking the typical cut-in scenario as example, the proposed method extracts the critical variables of the scenario. Then, the distributions of critical variables are statistically fitted. The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem. Considering the error of distribution fitting, the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world. Findings Based on the naturalistic driving data in Shanghai, the proposed method is validated by simulation. The result shows that compared with the existing methods, the proposed method improves the test efficiency by 35 per cent, and the accuracy of accelerated test result is increased by 23 per cent. Originality/value This paper has three contributions. First, the genetic algorithm is used to calculate IS parameters, which improves the efficiency of test. Second, the result of test is modified by the error correction parameter, which improves the accuracy of test result. Third, typical high-risk cut-in scenarios in China are analyzed, and the proposed method is validated by simulation.
Yiming Xu; Yajie Zou; Jian Sun. Accelerated testing for automated vehicles safety evaluation in cut-in scenarios based on importance sampling, genetic algorithm and simulation applications. Journal of Intelligent and Connected Vehicles 2018, 1, 28 -38.
AMA StyleYiming Xu, Yajie Zou, Jian Sun. Accelerated testing for automated vehicles safety evaluation in cut-in scenarios based on importance sampling, genetic algorithm and simulation applications. Journal of Intelligent and Connected Vehicles. 2018; 1 (1):28-38.
Chicago/Turabian StyleYiming Xu; Yajie Zou; Jian Sun. 2018. "Accelerated testing for automated vehicles safety evaluation in cut-in scenarios based on importance sampling, genetic algorithm and simulation applications." Journal of Intelligent and Connected Vehicles 1, no. 1: 28-38.
This paper develops a semi-nonparametric Poisson regression model to analyze motor vehicle crash frequency data collected from rural multilane highway segments in California, US. Motor vehicle crash frequency on rural highway is a topic of interest in the area of transportation safety due to higher driving speeds and the resultant severity level. Unlike the traditional Negative Binomial (NB) model, the semi-nonparametric Poisson regression model can accommodate an unobserved heterogeneity following a highly flexible semi-nonparametric (SNP) distribution. Simulation experiments are conducted to demonstrate that the SNP distribution can well mimic a large family of distributions, including normal distributions, log-gamma distributions, bimodal and trimodal distributions. Empirical estimation results show that such flexibility offered by the SNP distribution can greatly improve model precision and the overall goodness-of-fit. The semi-nonparametric distribution can provide a better understanding of crash data structure through its ability to capture potential multimodality in the distribution of unobserved heterogeneity. When estimated coefficients in empirical models are compared, SNP and NB models are found to have a substantially different coefficient for the dummy variable indicating the lane width. The SNP model with better statistical performance suggests that the NB model overestimates the effect of lane width on crash frequency reduction by 83.1%.
Xin Ye; Ke Wang; Yajie Zou; Dominique Lord. A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data. PLOS ONE 2018, 13, e0197338 .
AMA StyleXin Ye, Ke Wang, Yajie Zou, Dominique Lord. A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data. PLOS ONE. 2018; 13 (5):e0197338.
Chicago/Turabian StyleXin Ye; Ke Wang; Yajie Zou; Dominique Lord. 2018. "A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data." PLOS ONE 13, no. 5: e0197338.
Jinjun Tang; Shen Zhang; Xinqiang Chen; Fang Liu; Yajie Zou. Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city—China. Physica A: Statistical Mechanics and its Applications 2018, 493, 430 -443.
AMA StyleJinjun Tang, Shen Zhang, Xinqiang Chen, Fang Liu, Yajie Zou. Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city—China. Physica A: Statistical Mechanics and its Applications. 2018; 493 ():430-443.
Chicago/Turabian StyleJinjun Tang; Shen Zhang; Xinqiang Chen; Fang Liu; Yajie Zou. 2018. "Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city—China." Physica A: Statistical Mechanics and its Applications 493, no. : 430-443.
Short-term travel time prediction is an essential input to intelligent transportation systems. Timely and accurate traffic forecasting is necessary for advanced traffic management systems and advanced traveler information systems. Despite several short-term travel time prediction approaches have been proposed in the past decade, especially for hybrid models that consist of machine learning models and statistical models, few studies focus on the over-fitting problem brought by hybrid models. The over-fitting problem deteriorates the prediction accuracy especially during peak hours. This paper proposes a hybrid model that embraces wavelet neural network (WNN), Markov chain (MAR), and the volatility (VOA) model for short-term travel time prediction in a freeway system. The purpose of this paper is to provide deeper insights into underlining dynamic traffic patterns and to improve the prediction accuracy and robustness. The method takes periodical analysis, error correction, and noise extraction into consideration and improve the forecasting performance in peak hours. The proposed methodology predicts travel time by decomposing travel time data into three components: a periodic trend presented by a modified WNN, a residual part modeled by Markov chain, and the volatility part estimated by the modified generalized autoregressive conditional heteroscedasticity model. Forecasting performance is investigated with freeway travel time data from Houston, Texas and examined by three measures: mean absolute error, mean absolute percentage error, and root mean square error. The results show that the travel times predicted by the WNN-MAR-VOA method are robust and accurate. Meanwhile, the proposed method is able to capture the underlying periodic characteristics and volatility nature of travel time data.
Hang Yang; Yajie Zou; Zhongyu Wang; Bing Wu. A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain. Canadian Journal of Civil Engineering 2018, 45, 77 -86.
AMA StyleHang Yang, Yajie Zou, Zhongyu Wang, Bing Wu. A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain. Canadian Journal of Civil Engineering. 2018; 45 (2):77-86.
Chicago/Turabian StyleHang Yang; Yajie Zou; Zhongyu Wang; Bing Wu. 2018. "A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain." Canadian Journal of Civil Engineering 45, no. 2: 77-86.
There are few research studies that addressed the impact of reduced visibility due to fog using real-time data. It is thus meaningful to conduct further investigation that can clearly describe the changes in driving behavior and traffic parameters under foggy conditions using real-time traffic and weather data. Field traffic and weather data were collected in this research and fog cases were selected and analyzed by comparing them with clear cases to identify the differences in traffic characteristics under the two different situations. Moreover, vehicles were classified into two types (i.e., passenger cars and trucks) to identify whether the impact of reduced visibility due to fog on traffic varies depending on vehicle types. Afterward, the traffic parameters under different visibility classes and the effects of reduced visibility on different lanes were analyzed using ANOVA. Finally, a matched case–control logistic regression model was applied to further confirm the relationship between traffic parameters and reduced visibility due to fog. It was concluded that the impact of fog on traffic varies by vehicle types and lanes. The impact was also different by visibility classes. The impact of reduced visibility on passenger cars is more significant compared with that on trucks. The effect of reduced visibility on traffic parameters is more significant on inner lanes than outer lanes. Under these weather conditions, drivers should pay more attention to the traffic because higher headway variance is more likely to result in the crash occurrence. The matched case–control logistic regression modeling results indicate that larger average headway, speed variance, headway variance, and occupancy were related to the increase of the likelihood of a reduced visibility. The results would be helpful to understand the change of traffic status and investigate the potential factors for higher crash frequency under foggy conditions.
Yichuan Peng; Mohamed Abdel-Aty; Jaeyoung Lee; Yajie Zou. Analysis of the Impact of Fog-Related Reduced Visibility on Traffic Parameters. Journal of Transportation Engineering, Part A: Systems 2018, 144, 04017077 .
AMA StyleYichuan Peng, Mohamed Abdel-Aty, Jaeyoung Lee, Yajie Zou. Analysis of the Impact of Fog-Related Reduced Visibility on Traffic Parameters. Journal of Transportation Engineering, Part A: Systems. 2018; 144 (2):04017077.
Chicago/Turabian StyleYichuan Peng; Mohamed Abdel-Aty; Jaeyoung Lee; Yajie Zou. 2018. "Analysis of the Impact of Fog-Related Reduced Visibility on Traffic Parameters." Journal of Transportation Engineering, Part A: Systems 144, no. 2: 04017077.
Yajie Zou; Xin Ye; Kristian Henrickson; Jinjun Tang; Yinhai Wang. Jointly analyzing freeway traffic incident clearance and response time using a copula-based approach. Transportation Research Part C: Emerging Technologies 2018, 86, 171 -182.
AMA StyleYajie Zou, Xin Ye, Kristian Henrickson, Jinjun Tang, Yinhai Wang. Jointly analyzing freeway traffic incident clearance and response time using a copula-based approach. Transportation Research Part C: Emerging Technologies. 2018; 86 ():171-182.
Chicago/Turabian StyleYajie Zou; Xin Ye; Kristian Henrickson; Jinjun Tang; Yinhai Wang. 2018. "Jointly analyzing freeway traffic incident clearance and response time using a copula-based approach." Transportation Research Part C: Emerging Technologies 86, no. : 171-182.
Jinjun Tang; Fang Liu; Wenhui Zhang; Ruimin Ke; Yajie Zou. Lane-changes prediction based on adaptive fuzzy neural network. Expert Systems with Applications 2018, 91, 452 -463.
AMA StyleJinjun Tang, Fang Liu, Wenhui Zhang, Ruimin Ke, Yajie Zou. Lane-changes prediction based on adaptive fuzzy neural network. Expert Systems with Applications. 2018; 91 ():452-463.
Chicago/Turabian StyleJinjun Tang; Fang Liu; Wenhui Zhang; Ruimin Ke; Yajie Zou. 2018. "Lane-changes prediction based on adaptive fuzzy neural network." Expert Systems with Applications 91, no. : 452-463.