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Jie He
School of Transportation, Southeast University, 2 Si pai lou, Nanjing 210096, PR China

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Journal article
Published: 29 June 2021 in Analytic Methods in Accident Research
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Adverse weather could potentially increase the probability of driving errors and hazardous driving actions and it is necessary to explicitly understand the endogenous and exogenous mechanism of how adverse weather-related determinants influence crash-injury severities and explore their spatiotemporal stability. To investigate the heterogeneity and spatiotemporal stability of adverse-weather-related crash severity determinants, this paper estimated two groups of random parameters multinomial logit models with heterogeneity in the means and variances. Crash data from Ohio and California were utilized between January 1, 2013 and December 31, 2016. Three crash injury severity categories were investigated including no injury, minor injury, and severe injury, in terms of multiple factors that could be categorized as roadway characteristics, environmental characteristics, crash characteristics, temporal characteristics, vehicle characteristics and driver characteristics significantly influencing adverse weather-related crash injury outcomes. Additionally, the temporal stability and space transferability of the models were investigated through a series of likelihood ratio tests. Marginal effects were also adopted to analyze the spatiotemporal stability of the explanatory variables. The findings exhibited an overall spatiotemporal instability while some indicators were also observed to be of relative spatial or temporal stability such as insurance, overturning, proceeding and early morning over the four-year period considered. This paper provided some immediate recommendations targeted at preventing crashes under adverse weather conditions across different regions and could potentially facilitate the development of crash injury mitigation policies. More regions could be considered to provide observations for spatial instability tests in future research.

ACS Style

Xintong Yan; Jie He; Changjian Zhang; Ziyang Liu; Chenwei Wang; Boshuai Qiao. Spatiotemporal instability analysis considering unobserved heterogeneity of crash-injury severities in adverse weather. Analytic Methods in Accident Research 2021, 32, 100182 .

AMA Style

Xintong Yan, Jie He, Changjian Zhang, Ziyang Liu, Chenwei Wang, Boshuai Qiao. Spatiotemporal instability analysis considering unobserved heterogeneity of crash-injury severities in adverse weather. Analytic Methods in Accident Research. 2021; 32 ():100182.

Chicago/Turabian Style

Xintong Yan; Jie He; Changjian Zhang; Ziyang Liu; Chenwei Wang; Boshuai Qiao. 2021. "Spatiotemporal instability analysis considering unobserved heterogeneity of crash-injury severities in adverse weather." Analytic Methods in Accident Research 32, no. : 100182.

Journal article
Published: 09 June 2021 in Journal of Safety Research
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Freeway accidents are a leading cause of death in China, which also triggers substantial economic loss and an emotional burden to society. However, the internal mechanism of how microscopic kinetic parameters of vehicles influenced by road characteristics determine the occurrence of different types of accidents has not been explicitly studied. This research aimed to explore the “link role” of tire microscopic kinetic parameters in road characteristic variables and traffic accidents to aid in facilitating the traffic design and management, and thus to prevent traffic accident. Method: A mountain freeway in Zhejiang Province, China was used as the research object and the data used in this paper were obtained through a real-time vehicle experiment. Multiple estimation models, including the standard ordered logit (SOL) model, fixed parameters logit (FPL) model, and random parameters logit (RPL) model were established. Results: The findings show that road characteristics will affect the longitudinal kinetic characteristics of the vehicle and, consequently, map the level of risk of rear-end accidents. Driving compensation effects were also identified in this paper (i.e., the drivers tend to be more cautious in complicated driving circumstances). Another finding relating to the mountain freeway is that different tunnel characteristics (e.g., tunnel entrance and tunnel exit) have different effects on different types of traffic accidents. Practical Applications: The framework proposed in this article can provide new insight for researchers to enlarge the research subjects of both explanatory and outcome variables in accident analysis. Future research could be implemented to consider more driving conditions.

ACS Style

Changjian Zhang; Jie He; Xintong Yan; Ziyang Liu; Yikai Chen; Hao Zhang. Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions, road characteristics and accident types. Journal of Safety Research 2021, 78, 80 -95.

AMA Style

Changjian Zhang, Jie He, Xintong Yan, Ziyang Liu, Yikai Chen, Hao Zhang. Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions, road characteristics and accident types. Journal of Safety Research. 2021; 78 ():80-95.

Chicago/Turabian Style

Changjian Zhang; Jie He; Xintong Yan; Ziyang Liu; Yikai Chen; Hao Zhang. 2021. "Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions, road characteristics and accident types." Journal of Safety Research 78, no. : 80-95.

Research article
Published: 02 June 2021 in Environmental Science and Pollution Research
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Controlling the emission of urban passenger transport modes has become one of the most important tasks of governing urban air pollution. Most strategies only focused on carbon emission, whereas neglecting the influences of other pollutants (CO, HC, NOx, PM2.5), especially for upstream emissions from electricity generation caused by the electricity consumed during the operation of electrified transport modes. Based on the multinomial logit model (MNL), this study firstly calculated and evaluated the emission reduction effects brought about by the implementation of targeted emission taxes on different transport modes from the perspective of whole fuel cycle. Taking Jiangning District as an example, our research found that the policy implementing targeted emission tax for different transport modes can not only bring reduce 13.104 tons of CO, 0.327 tons of HC, 0.568 tons of NOx, and 0.140 tons of PM2.5, but also 26,726.82 (euro) of eco-environmental benefits for the treatment of air pollution. Our study can provide useful insights for shifting the structure of urban passenger transport modes, especially promoting the transfer of private cars to the urban green transport systems, to alleviate urban air pollution by formulating effective emission reduction strategies.

ACS Style

Boshuai Qiao; Jie He; Xintong Yan; Chunguang Bai; Changjian Zhang; Ziyang Liu. Assessing emission reduction effects from shifts of urban passenger transport modes by implementing targeted emission tax considering the whole fuel cycle. Environmental Science and Pollution Research 2021, 1 -17.

AMA Style

Boshuai Qiao, Jie He, Xintong Yan, Chunguang Bai, Changjian Zhang, Ziyang Liu. Assessing emission reduction effects from shifts of urban passenger transport modes by implementing targeted emission tax considering the whole fuel cycle. Environmental Science and Pollution Research. 2021; ():1-17.

Chicago/Turabian Style

Boshuai Qiao; Jie He; Xintong Yan; Chunguang Bai; Changjian Zhang; Ziyang Liu. 2021. "Assessing emission reduction effects from shifts of urban passenger transport modes by implementing targeted emission tax considering the whole fuel cycle." Environmental Science and Pollution Research , no. : 1-17.

Journal article
Published: 26 February 2021 in Accident Analysis & Prevention
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Single-vehicle crashes are more fatality-concentrated and have posed increasing challenges in traffic safety, which is of great research necessity. Tremendous previous studies have conducted relevant analysis with econometric modeling approaches, whereas the ability of non-parametric methods to predict crash severity is still smattering of knowledge. Consequently, the main objective of this paper is to conduct single-vehicle crash severity prediction with different tree-based and non-parameter models. An alternate aim is to identify the intrinsic mechanism of how contributing factors determine single-vehicle crash severity. By virtue of Grid-Search method, this paper conducted fine-tuning of different models to obtain the best performances based on five crash severity sub-datasets. For model evaluation, the accuracy indicators were calculated in training, validation and test sets, respectively. Besides, feature importance extraction was undertaken based on the results of model comparison. The finding indicated that these models didn’t exhibit a huge performance difference for crash severity prediction in the same severity level; however, the performances of the models did vary among different datasets, with an average training accuracy of 99.27 %, 96.4 %, 86.98 %, 86.84 %, 71.76 % in fatal injury, severe injury, visible injury, complaint of pain, PDO crash datasets, respectively. Additionally, it was found that in each severity dataset, the indicator urban freeways is a determinant factor that leads to the occurrence of crashes while rural freeways is more related to more severe crashes (i.e., fatal and severe crashes). This paper can provide valuable information for model selection and tuning in accident severity prediction. Future research could consider the influences that temporal instability of contributing features has on the model performances.

ACS Style

Xintong Yan; Jie He; Changjian Zhang; Ziyang Liu; Boshuai Qiao; Hao Zhang. Single-vehicle crash severity outcome prediction and determinant extraction using tree-based and other non-parametric models. Accident Analysis & Prevention 2021, 153, 106034 .

AMA Style

Xintong Yan, Jie He, Changjian Zhang, Ziyang Liu, Boshuai Qiao, Hao Zhang. Single-vehicle crash severity outcome prediction and determinant extraction using tree-based and other non-parametric models. Accident Analysis & Prevention. 2021; 153 ():106034.

Chicago/Turabian Style

Xintong Yan; Jie He; Changjian Zhang; Ziyang Liu; Boshuai Qiao; Hao Zhang. 2021. "Single-vehicle crash severity outcome prediction and determinant extraction using tree-based and other non-parametric models." Accident Analysis & Prevention 153, no. : 106034.

Journal article
Published: 26 February 2021 in Analytic Methods in Accident Research
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There is a great necessity to understand the endogenous and exogenous mechanism of how male and female driver characteristics determine the crash severity. Consequently, this paper estimated a group of random paramters logit models with heterogeneity in means and variances to investigate the heterogeneity and temporal stability of how male and female drivers affect crash severity. Using the crash data from California between January 1, 2013 and December 31, 2017, an extensive body of factors that could potentially affect crash injury severity were examined. Additionally, the temporal stability and gender transferability of the models were investigated through a series of likelihood ratio tests. Marginal effects were also adopted to analyze the temporal stability of the explanatory variables. Three crash injury severity categories were investigated including fatal injury, severe injury, and minor injury, in terms of roadway characteristics, environmental characteristics, crash characteristics, temporal characteristics and driver characteristics significantly influencing crash injury outcomes. Remarkable differences were observed between crashes involving male and female drivers, and both male and female related crashes exhibited statistically significant temporal instability over the five-year period considered. This paper could potentially be utilized to ameliorate highway safety aimed at male and female drivers respectively and facilitate the development of crash injury mitigation policies. Spatial stability may be also a valuable issue that should be further investigated in future research.

ACS Style

Xintong Yan; Jie He; Changjian Zhang; Ziyang Liu; Chenwei Wang; Boshuai Qiao. Temporal analysis of crash severities involving male and female drivers: A random parameters approach with heterogeneity in means and variances. Analytic Methods in Accident Research 2021, 30, 100161 .

AMA Style

Xintong Yan, Jie He, Changjian Zhang, Ziyang Liu, Chenwei Wang, Boshuai Qiao. Temporal analysis of crash severities involving male and female drivers: A random parameters approach with heterogeneity in means and variances. Analytic Methods in Accident Research. 2021; 30 ():100161.

Chicago/Turabian Style

Xintong Yan; Jie He; Changjian Zhang; Ziyang Liu; Chenwei Wang; Boshuai Qiao. 2021. "Temporal analysis of crash severities involving male and female drivers: A random parameters approach with heterogeneity in means and variances." Analytic Methods in Accident Research 30, no. : 100161.

Conference paper
Published: 12 February 2021 in IOP Conference Series: Earth and Environmental Science
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In recent years, with the popularization of electronic toll collection systems (ETC), expressway toll stations in China have developed into a mixed toll form jointly deployed by ETC toll lanes and MTC toll lanes, presenting a development trend based on ETC toll lanes. Since the toll collection system has the important function of "credit repayment", the relationship between operation benefit and cost will become an important indicator for measuring the toll station system. Taking the minimum cost and the maximum benefit of the toll station under normal operating condition as the optimization goals, a multi-objective nonlinear optimization model is established within the seek for balance between cost and benefit, so as to obtain an open plan with better toll lanes. The Nanjing-Hangzhou toll station is used as an example to verify the model. Furthermore, based on the trend of the future traffic flow and the increase in the proportion of ETC vehicles, the distribution law of the optimal opening schemes of the toll lane under different traffic volumes and different traffic flow structures is analyzed, which provides experience support for the construction and operation of toll stations in the future.

ACS Style

Hao Zhang; Cheng Cheng; Changjian Zhang; Jie He; Yang Xu; Yikai Chen; Qiong Hong. Optimization of Opening Scheme of ETC/MTC Toll Lane Based on Cost and Benefit Analysis. IOP Conference Series: Earth and Environmental Science 2021, 638, 012032 .

AMA Style

Hao Zhang, Cheng Cheng, Changjian Zhang, Jie He, Yang Xu, Yikai Chen, Qiong Hong. Optimization of Opening Scheme of ETC/MTC Toll Lane Based on Cost and Benefit Analysis. IOP Conference Series: Earth and Environmental Science. 2021; 638 (1):012032.

Chicago/Turabian Style

Hao Zhang; Cheng Cheng; Changjian Zhang; Jie He; Yang Xu; Yikai Chen; Qiong Hong. 2021. "Optimization of Opening Scheme of ETC/MTC Toll Lane Based on Cost and Benefit Analysis." IOP Conference Series: Earth and Environmental Science 638, no. 1: 012032.

Journal article
Published: 06 December 2020 in Accident Analysis & Prevention
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With the development and maturation of vehicle-based data acquisition technology, in-vehicle data is increasingly being used to explore road safety. This paper reports on research that analyzed the real-time tire force data (kinetic response) obtained from vehicle kinetic experiments, and constructed a new approach for identifying the high-risk of crashes on freeway segments with horizontal curvature. First, the road was divided into 1km units. Then, taking into account the characteristics of freeway alignment, each segment with horizontal curve was selected as the object of subsequent analysis. Automotive instrumentation was used to obtain a measure of tire force in the course of normal driving. The entire data set was preprocessed according to rate of change and the density of the data was reduced. By defining the outliers of the kinetic data and conducting factor analysis, two representative crash risk indicators of longitudinal and lateral stability were obtained. Negative binomial regression model (NBR model) and random effects negative binomial regression model (RENBR model) were constructed and jointly applied based on the new indicators to predict the risk value of horizontal curve segments. The method showed good prediction performance (71.8 %) for high-risk road segments with design flaws, but the predicted effect for low-risk road segments was not ideal. This study not only illustrated the effectiveness of in-vehicle data in assessing road crash risk by coupling multiple kinetic parameters, but also provided support for freeway safety research using surrogate measures of risk when there is a lack of crash statistics.

ACS Style

Changjian Zhang; Jie He; Mark King; Ziyang Liu; Yikai Chen; Xintong Yan; Lu Xing; Hao Zhang. A crash risk identification method for freeway segments with horizontal curvature based on real-time vehicle kinetic response. Accident Analysis & Prevention 2020, 150, 105911 .

AMA Style

Changjian Zhang, Jie He, Mark King, Ziyang Liu, Yikai Chen, Xintong Yan, Lu Xing, Hao Zhang. A crash risk identification method for freeway segments with horizontal curvature based on real-time vehicle kinetic response. Accident Analysis & Prevention. 2020; 150 ():105911.

Chicago/Turabian Style

Changjian Zhang; Jie He; Mark King; Ziyang Liu; Yikai Chen; Xintong Yan; Lu Xing; Hao Zhang. 2020. "A crash risk identification method for freeway segments with horizontal curvature based on real-time vehicle kinetic response." Accident Analysis & Prevention 150, no. : 105911.

Journal article
Published: 25 October 2020 in Transportation Letters
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ACS Style

Ziyang Liu; Jie He; Changjian Zhang; Xintong Yan; Hao Zhang. Optimal off-ramp terminal locating strategy based on traffic safety and efficiency. Transportation Letters 2020, 1 -12.

AMA Style

Ziyang Liu, Jie He, Changjian Zhang, Xintong Yan, Hao Zhang. Optimal off-ramp terminal locating strategy based on traffic safety and efficiency. Transportation Letters. 2020; ():1-12.

Chicago/Turabian Style

Ziyang Liu; Jie He; Changjian Zhang; Xintong Yan; Hao Zhang. 2020. "Optimal off-ramp terminal locating strategy based on traffic safety and efficiency." Transportation Letters , no. : 1-12.

Conference paper
Published: 22 October 2020 in MATEC Web of Conferences
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ETC and MTC lanes of China’s hybrid toll stations have various setting modes. When the vehicles passed through the toll stations, they would face a more complicated lane change process. If the ETC sign was set in the appropriate position in advance, the traffic safety in front of the toll stations would be effectively improved. The paper analyzed the process of lane change in 18 scenes by taking one-way three-lane highway at the upstream of toll station with six lanes as an example. Based on the definition of driver’s reaction distance, reading distance and safe distance of action, the theoretical calculation model of ETC sign preposition distance was established. It revealed the functional relationship between ETC lane layout schemes and sign preposition distance, and explored the reasonable setting position of the ETC sign in the full scenes of the lane layout. Finally, a case study of Nanjing toll station on Shanghai-Nanjing Expressway was carried out.

ACS Style

Hao Zhang; Changjian Zhang; Ying Zhang; Jinhang Ma; Jie He; Ziyang Liu; Xintong Yan; Qiong Hong. Calculation Model of Preposition Distance of Electronic Toll Collection Signs Based on Lane Changing Behavior. MATEC Web of Conferences 2020, 325, 01003 .

AMA Style

Hao Zhang, Changjian Zhang, Ying Zhang, Jinhang Ma, Jie He, Ziyang Liu, Xintong Yan, Qiong Hong. Calculation Model of Preposition Distance of Electronic Toll Collection Signs Based on Lane Changing Behavior. MATEC Web of Conferences. 2020; 325 ():01003.

Chicago/Turabian Style

Hao Zhang; Changjian Zhang; Ying Zhang; Jinhang Ma; Jie He; Ziyang Liu; Xintong Yan; Qiong Hong. 2020. "Calculation Model of Preposition Distance of Electronic Toll Collection Signs Based on Lane Changing Behavior." MATEC Web of Conferences 325, no. : 01003.

Conference paper
Published: 22 October 2020 in MATEC Web of Conferences
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In order to cope with the growing environmental pollution triggered by traditional disposable express packing box, the design and promotion of recycling express packing boxes (REPB) is of necessity and feasibility. However, relevant research is still lacking. To address the aforementioned issues, this paper attempted to propose a comprehensive method for designing REPB based on five intelligent functions of REPB, i.e., information processing, transportation environmental state perception, transportation environment interaction, human-machine interaction, and cloud interaction. Additionally, to ensure the reliability and safety of REFB, this paper discussed the material and structure of REPB based on shock absorption, as well as the intelligent functions to protect REFB from being stolen and lost. This paper can aid in the establishment of framework for REPB design pertaining to intelligence, safety, feasibility and reliability. Future research could further explore the standardization of REFB.

ACS Style

Jian Gong; Xingyue Lu; Jie He; Jiajia Li; Xintong Yan; Hao Zhang; Changjian Zhang. Intelligent and safe design of recycling express packing boxes. MATEC Web of Conferences 2020, 325, 02003 .

AMA Style

Jian Gong, Xingyue Lu, Jie He, Jiajia Li, Xintong Yan, Hao Zhang, Changjian Zhang. Intelligent and safe design of recycling express packing boxes. MATEC Web of Conferences. 2020; 325 ():02003.

Chicago/Turabian Style

Jian Gong; Xingyue Lu; Jie He; Jiajia Li; Xintong Yan; Hao Zhang; Changjian Zhang. 2020. "Intelligent and safe design of recycling express packing boxes." MATEC Web of Conferences 325, no. : 02003.

Research article
Published: 30 June 2020 in Discrete Dynamics in Nature and Society
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Crash severity prediction has been raised as a key problem in traffic accident studies. Thus, to progress in this area, in this study, a thorough artificial neural network combined with an improved metaheuristic algorithm was developed and tested in terms of its structure, training function, factor analysis, and comparative results. Data from I5, an interstate highway in the Washington State during the period of 2011–2015, were used for fitting and prediction, and after setting the theoretical three-layer neural network (NN), an improved Particle Swarm Optimization (PSO) method with adaptive inertial weight was offered to optimize the NN, and finally, a comparison among different adaptive strategies was conducted. The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. The method developed in this study can be applied to a big data analysis of traffic accidents and be used as a fast-useful tool for policy makers and traffic safety researchers.

ACS Style

Chen Zhang; Jie He; Yinhai Wang; Xintong Yan; Changjian Zhang; Yikai Chen; Ziyang Liu; Bojian Zhou. A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis. Discrete Dynamics in Nature and Society 2020, 2020, 1 -13.

AMA Style

Chen Zhang, Jie He, Yinhai Wang, Xintong Yan, Changjian Zhang, Yikai Chen, Ziyang Liu, Bojian Zhou. A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis. Discrete Dynamics in Nature and Society. 2020; 2020 ():1-13.

Chicago/Turabian Style

Chen Zhang; Jie He; Yinhai Wang; Xintong Yan; Changjian Zhang; Yikai Chen; Ziyang Liu; Bojian Zhou. 2020. "A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis." Discrete Dynamics in Nature and Society 2020, no. : 1-13.

Journal article
Published: 30 May 2020 in Journal of Advanced Transportation
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The primary objective of this study is to predict the short-term metro passenger flow using the proposed hybrid spatiotemporal deep learning neural network (HSTDL-net). The metro passenger flow data is collected from line 2 of Nanjing metro system to illustrate the study procedure. A hybrid spatiotemporal deep learning model is developed to predict both inbound and outbound passenger flows for every 10 minutes. The results suggest that the proposed HSTDL-net achieves better prediction performance on suburban stations than on urban stations, as well as generating the best prediction accuracy on transfer stations in terms of the lowest MAPE value. Moreover, a comparative analysis is conducted to compare the performance of proposed HSTDL-net with other typical methods, such as ARIMA, MLP, CNN, LSTM, and GBRT. The results indicate that, for both inbound and outbound passenger flow predictions, the HSTDL-net outperforms all the compared models on three types of stations. The results suggest that the proposed hybrid spatiotemporal deep learning neural network can more effectively and fully discover both spatial and temporal hidden correlations between stations for short-term metro passenger flow prediction. The results of this study could provide insightful suggestions for metro system authorities to adjust the operation plans and enhance the service quality of the entire metro system.

ACS Style

Hao Zhang; Jie He; Jie Bao; Qiong Hong; Xiaomeng Shi. A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction. Journal of Advanced Transportation 2020, 2020, 1 -12.

AMA Style

Hao Zhang, Jie He, Jie Bao, Qiong Hong, Xiaomeng Shi. A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction. Journal of Advanced Transportation. 2020; 2020 ():1-12.

Chicago/Turabian Style

Hao Zhang; Jie He; Jie Bao; Qiong Hong; Xiaomeng Shi. 2020. "A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction." Journal of Advanced Transportation 2020, no. : 1-12.

Journal article
Published: 11 April 2020 in Accident Analysis & Prevention
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This study investigates the traffic conflict risks at the upstream approach of toll plaza during the vehicles’ diverging period from the time of arrival at the diverging area to that of entering the tollbooths. Based on the vehicle’s trajectory data extracted from unmanned aerial vehicle (UAV) videos using an automated video analysis system, vehicles’ collision risk is computed by extended time to collision (TTC). Then, two time-varying mixed logit models including time-varying random effects logistic regression (T-RELR) model and time-varying random parameters logistic regression (T-RPLR) are developed to examine the time varying effects of influencing factors on vehicle collision risk, and four models including the standard random effects logistic regression (S-RELR) model, standard random parameters logistic regression (S-RPLR) model, distance-varying random effects logistic regression (D-RELR) model and distance-varying random parameters logistic regression (D-RPLR) are developed for model performance comparison. The results indicate that the T-RPLR model has the highest prediction accuracy. Eight influencing factors including following vehicle’s travel distance, following vehicle’s initial lane, following vehicle’s toll collection type, leading vehicle’s toll collection type, distance between two vehicles’ centroids, and following vehicle’s speed, are found to have time-varying effects on collision risk. Meanwhile, the first six factors are found to exhibit heterogeneous effects over the travel time. Another important finding is that the vehicle that comes from the innermost lane has an increasing trend to be involved in traffic conflicts, whereas the collision risks of other vehicles decrease as the travel time increases. Moreover, vehicles with higher speed have a decreasing probability to be involved in crashes over the travel time. Interestingly, the results of D-RPLR model are similar with that of T-RPLR model. These findings provide helpful information for accurate assessment of collision risk, which is a key step toward improving safety performance of the toll plazas’ diverging areas.

ACS Style

Lu Xing; Jie He; Mohamed Abdel-Aty; Yina Wu; Jinghui Yuan. Time-varying Analysis of Traffic Conflicts at the Upstream Approach of Toll Plaza. Accident Analysis & Prevention 2020, 141, 105539 .

AMA Style

Lu Xing, Jie He, Mohamed Abdel-Aty, Yina Wu, Jinghui Yuan. Time-varying Analysis of Traffic Conflicts at the Upstream Approach of Toll Plaza. Accident Analysis & Prevention. 2020; 141 ():105539.

Chicago/Turabian Style

Lu Xing; Jie He; Mohamed Abdel-Aty; Yina Wu; Jinghui Yuan. 2020. "Time-varying Analysis of Traffic Conflicts at the Upstream Approach of Toll Plaza." Accident Analysis & Prevention 141, no. : 105539.

Journal article
Published: 05 April 2020 in Sustainability
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Over the past decade, the rapid development of e-commerce and express industries in China has resulted in huge environmental costs. Compared with manufacturing industries, the values of green innovation are less recognized in logistics industries. To promote the green practices in logistic enterprises, it is imperative to have a thorough understanding of the determinants of green innovation adoption. To this end, this paper performs an empirical investigation into the intentions to adopt green innovation from 196 Chinese express companies. The determinant variables were constructed from the perspective of technology characteristics (perceived green usefulness and perceived integration ease of use), stakeholder pressure (government, customer, and platform pressures), and social influence. Then, a 20-item scale was designed based on the literature review and expert opinions. The results revealed the significant positive effects of technology characteristics and social influence on the intentions to adopt green innovation. Meanwhile, only the platform pressure was significant with the adopting intentions among the variables from stakeholder pressure. Moreover, variables from technology characteristics were found to have meditation effects between social influence and adopting intentions. Based on the findings, theoretical and practical implications are proposed to promote the green and sustainable development of express companies in China.

ACS Style

Hao Zhang; Jie He; Xiaomeng Shi; Qiong Hong; Jie Bao; Shuqi Xue. Technology Characteristics, Stakeholder Pressure, Social Influence, and Green Innovation: Empirical Evidence from Chinese Express Companies. Sustainability 2020, 12, 2891 .

AMA Style

Hao Zhang, Jie He, Xiaomeng Shi, Qiong Hong, Jie Bao, Shuqi Xue. Technology Characteristics, Stakeholder Pressure, Social Influence, and Green Innovation: Empirical Evidence from Chinese Express Companies. Sustainability. 2020; 12 (7):2891.

Chicago/Turabian Style

Hao Zhang; Jie He; Xiaomeng Shi; Qiong Hong; Jie Bao; Shuqi Xue. 2020. "Technology Characteristics, Stakeholder Pressure, Social Influence, and Green Innovation: Empirical Evidence from Chinese Express Companies." Sustainability 12, no. 7: 2891.

Journal article
Published: 02 April 2020 in Mathematical Problems in Engineering
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In the past decades, despite considerable attention having been paid to third-party logistics (3PL) owing to its specialized service, sophisticated operation, and reduced cost, research on quantitative methods for estimating the efficiency of 3PL companies is still lacking, especially for those with small or medium scale. Therefore, the purpose of this study was to establish a quantitative evaluation method to measure the efficiency of the individual nongovernmental 3PL firms and explore the valuable information for the management of 3PL business with Apriori and K-means. Taking TopChains (an emerging nongovernmental 3PL company) as an example, the monthly supply and demand (S&D) level and matching degree were evaluated via the integrating data mining algorithm (i.e., Apriori and K-means) and TOPSIS entropy weight method based on historical data. The findings demonstrate that the S&D level varied with time and space, and the customer demand in February tended to reduce substantially. Besides, the outcome of S&D matching degree in June is undesirable, indicating the unsatisfactory efficiency in resource management. The evaluation maneuver stated in this study can serve as a valuable tool to measure individual nongovernmental 3PL enterprises’ efficiency in terms of S&D, and for reference, the results can aid in rational enterprise investment plan. Besides, this attempt broadened the direction of ARM and K-means being applied in the logistics field.

ACS Style

Xintong Yan; Jian Gong; Jie He; Hao Zhang; Changjian Zhang; Ziyang Liu. Integrated Data Mining and TOPSIS Entropy Weight Method to Evaluate Logistics Supply and Demand Efficiency of a 3PL Company. Mathematical Problems in Engineering 2020, 2020, 1 -12.

AMA Style

Xintong Yan, Jian Gong, Jie He, Hao Zhang, Changjian Zhang, Ziyang Liu. Integrated Data Mining and TOPSIS Entropy Weight Method to Evaluate Logistics Supply and Demand Efficiency of a 3PL Company. Mathematical Problems in Engineering. 2020; 2020 ():1-12.

Chicago/Turabian Style

Xintong Yan; Jian Gong; Jie He; Hao Zhang; Changjian Zhang; Ziyang Liu. 2020. "Integrated Data Mining and TOPSIS Entropy Weight Method to Evaluate Logistics Supply and Demand Efficiency of a 3PL Company." Mathematical Problems in Engineering 2020, no. : 1-12.

Conference paper
Published: 24 March 2020 in Lecture Notes in Electrical Engineering
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The rapid mechanization in China results in excessive adverse effects recently, such as traffic congestion and air pollution. Affected by the negative effects, an increasing number of citizens decide to use their private cars only at a certain time, which leads to the urban private cars idling (UPCI) phenomenon. In order to investigate the UPCI behavior and its influence factors, this paper, taking Nanjing city in China as a case study, conducted a detailed survey including 279 private car owners. A logistic regression model was developed to investigate the impact factors related with UPCI. The result of regression indicated that the number of children in a family was an impeding factor which caused the fewer UPCI behaviors. The smaller job-housing distances and independence on vehicles, however, aggravated the UPCI phenomenon. The results of this study are beneficial to understand the UPCI behavior, and provide useful information for the effective urban transportation demand management (TDM) and necessary guidance for urban private car purchase and usage.

ACS Style

Lu Xing; Jie He; Chen Zhang; Ziyang Liu; Hao Zhang. Investigating Private Cars Idling Behavior in Urban Areas. Lecture Notes in Electrical Engineering 2020, 407 -415.

AMA Style

Lu Xing, Jie He, Chen Zhang, Ziyang Liu, Hao Zhang. Investigating Private Cars Idling Behavior in Urban Areas. Lecture Notes in Electrical Engineering. 2020; ():407-415.

Chicago/Turabian Style

Lu Xing; Jie He; Chen Zhang; Ziyang Liu; Hao Zhang. 2020. "Investigating Private Cars Idling Behavior in Urban Areas." Lecture Notes in Electrical Engineering , no. : 407-415.

Journal article
Published: 08 March 2020 in Energies
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Globally, the use of electric vehicles, and in particular the use of electric buses, has been increasing. The city of Nanjing leads China in the adoption of electric buses, supported by city policies and infrastructure. To lower costs and provide a better service, vehicle selection is crucial, however, existing selection methods are limited. Accordingly, Nanjing Bus Company developed a test method based on road tests to select a bus. This paper presents a detailed description of the test method and a case study of its application. The method included an organization structure, selection of eight test vehicles (four 10 m length, four 8 m length) from four brands (a total of 32 test vehicles), selection of indicators and selection of routes. Data was collected from repeated drives by 65 drivers over an 8-week period. Indicators included power consumption, charging duration, failure duration and driving distance. It is concluded that the road test method designed and conducted by the Nanjing Bus Company provides a good framework for the selection of pure electric buses. Furthermore, subsequent experience with selected buses supports the validity and value of the model.

ACS Style

Jian Gong; Jie He; Cheng Cheng; Mark King; Xintong Yan; Zhixia He; Hao Zhang. Road Test-Based Electric Bus Selection: A Case Study of the Nanjing Bus Company. Energies 2020, 13, 1253 .

AMA Style

Jian Gong, Jie He, Cheng Cheng, Mark King, Xintong Yan, Zhixia He, Hao Zhang. Road Test-Based Electric Bus Selection: A Case Study of the Nanjing Bus Company. Energies. 2020; 13 (5):1253.

Chicago/Turabian Style

Jian Gong; Jie He; Cheng Cheng; Mark King; Xintong Yan; Zhixia He; Hao Zhang. 2020. "Road Test-Based Electric Bus Selection: A Case Study of the Nanjing Bus Company." Energies 13, no. 5: 1253.

Comparative study
Published: 01 February 2020 in Accident Analysis & Prevention
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Toll plazas with both Electronic Toll Collection (ETC) lane(s) and Manual Toll Collection (MTC) lane(s) could increase crash risks especially at upstream diverging areas because of frequency lane-change behaviors. This study develops the logistic regression (LR) model and five typical non-parametric models including, K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees (DT), and Random Forest (RF) to examine the relationship between influencing factors and vehicle collision risk. Based on the vehicle trajectory data extracted from unmanned aerial vehicle (UAV) videos using an automated video analysis system, the unconstrained vehicle motion's collision risk can be evaluated by the extended time to collision (ETTC). Results of model performance comparison indicate that not all non-parametric models have a better prediction performance than the LR model. Specifically, the KNN, SVM, DT and RF models have better model performance than LR model in model training, while the ANN model has the worst model performance. In model prediction, the accuracy of LR model is higher than that of other five non-parametric models under various ETTC thresholds conditions. The LR model implies a pretty good performance and its results also indicate that vehicle yields the higher collision risk when it drives on the left side of toll plaza diverging area and more dangerous situations could be found for an ETC vehicle. Moreover, the vehicle collision risks are positively associated with the speed of the following vehicle and the angle between the leading vehicle speed vector and X axis. Furthermore, the results of DT model show that three factors play important roles in classifying vehicle collision risk and the effects of them on collision risk are consistent with the results of LR model. These findings provide valuable information for accurate assessment of collision risk, which is a key step toward improving safety performance of the toll plaza diverging area.

ACS Style

Lu Xing; Jie He; Ye Li; Yina Wu; Jinghui Yuan; Xin Gu. Comparison of different models for evaluating vehicle collision risks at upstream diverging area of toll plaza. Accident Analysis & Prevention 2020, 135, 105343 .

AMA Style

Lu Xing, Jie He, Ye Li, Yina Wu, Jinghui Yuan, Xin Gu. Comparison of different models for evaluating vehicle collision risks at upstream diverging area of toll plaza. Accident Analysis & Prevention. 2020; 135 ():105343.

Chicago/Turabian Style

Lu Xing; Jie He; Ye Li; Yina Wu; Jinghui Yuan; Xin Gu. 2020. "Comparison of different models for evaluating vehicle collision risks at upstream diverging area of toll plaza." Accident Analysis & Prevention 135, no. : 105343.

Journal article
Published: 19 December 2019 in World Electric Vehicle Journal
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In China, the development of pure electric buses is an essential means to address energy and environmental concerns. Proper evaluation and selection of pure electric buses is a crucial step prior to introducing the buses into actual operation. This paper presents a multi-objective method for the selection of pure electric buses based on road driving tests. An index system for evaluating the operating performance through the fuzzy analytic hierarchy process is established, and to ensure the method is more systematic than those in previous studies, indicators are classified into three categories: qualitative, semi-qualitative and quantitative indicators, of which the nineteen indexes have been comprehensively considered and designed. To verify the advantage of the evaluation method proposed in the article, two typical buses were selected as the assessed objectives regarding reliability, economic, security, environmental adaptability, etc. The assessing process indicates that the method is easily implemented and of high practical value. Additionally, the results show satisfactory agreement with the actual scenario. Thus, it can be assumed that the method detailed herein provides a basis for the design, selection, and evaluation of pure electric buses.

ACS Style

Yanzhong Liu; Jie He; Wenhui Lu; Xintong Yan; Cheng Cheng. Evaluation Method to Select Pure Electric Buses Based on Road Operation Tests. World Electric Vehicle Journal 2019, 11, 4 .

AMA Style

Yanzhong Liu, Jie He, Wenhui Lu, Xintong Yan, Cheng Cheng. Evaluation Method to Select Pure Electric Buses Based on Road Operation Tests. World Electric Vehicle Journal. 2019; 11 (1):4.

Chicago/Turabian Style

Yanzhong Liu; Jie He; Wenhui Lu; Xintong Yan; Cheng Cheng. 2019. "Evaluation Method to Select Pure Electric Buses Based on Road Operation Tests." World Electric Vehicle Journal 11, no. 1: 4.

Research article
Published: 16 October 2019 in PLOS ONE
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In order to address the time pattern problems in free-floating car sharing, in this paper, the authors offer a comprehensive time-series method based on deep learning theory. According to car2go booking record data in Seattle area. Firstly, influence of time and location on the free-floating car-sharing usage pattern is analyzed, which reveals an apparent doublet pattern for time and dependence usage amount on population. Then, on the basis of the long-short-term memory recurrent neural network (LSTM-RNN), hourly variation in short-term traffic characteristics including travel demand and travel distance are modeled. The results were also compared with other different statistical models, such as support vector regression (SVR), Autoregressive Integrated Moving Average model (ARIMA), single and second exponential smoothing. It showed that (LSTM-RNN) shows better performance in terms of statistical analysis and tendency precision based on limited data sample.

ACS Style

Chen Zhang; Jie He; Ziyang Liu; Lu Xing; Yinhai Wang. Travel demand and distance analysis for free-floating car sharing based on deep learning method. PLOS ONE 2019, 14, e0223973 .

AMA Style

Chen Zhang, Jie He, Ziyang Liu, Lu Xing, Yinhai Wang. Travel demand and distance analysis for free-floating car sharing based on deep learning method. PLOS ONE. 2019; 14 (10):e0223973.

Chicago/Turabian Style

Chen Zhang; Jie He; Ziyang Liu; Lu Xing; Yinhai Wang. 2019. "Travel demand and distance analysis for free-floating car sharing based on deep learning method." PLOS ONE 14, no. 10: e0223973.