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We investigate the dynamic performance of traffic flow using a modified optimal velocity car-following model. In the car-following scenarios, the following vehicle must continuously adjust the following distance to the preceding vehicle in real time. A new optimal velocity function incorporating the desired safety distance instead of a preset constant is presented first to describe the abovementioned car-following behavior dynamically. The boundary conditions of the new optimal velocity function are theoretically analyzed. Subsequently, we propose an improved car-following model by combining the heterogeneity of driver’s sensitivity based on the new optimal velocity function and previous car-following model. The stability criterion of the improved model is obtained through the linear analysis method. Finally, numerical simulation is performed to explore the effect of the desired safety distance and the heterogeneity of driver’s sensitivity on the traffic flow. Results show that the proposed model has considerable effects on improving traffic stability and suppressing traffic congestion. Furthermore, the proposed model is compatible with the heterogeneity of driver’s sensitivity and can enhance the average velocity of traffic flow compared with the conventional model. In conclusion, the dynamic performance of traffic flow can be improved by considering the desired safety distance and the heterogeneity of driver’s sensitivity in the car-following model.
Lei Zhang; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Yan Huang. An Improved Car-Following Model considering Desired Safety Distance and Heterogeneity of Driver’s Sensitivity. Journal of Advanced Transportation 2021, 2021, 1 -12.
AMA StyleLei Zhang, Shengrui Zhang, Bei Zhou, Shuaiyang Jiao, Yan Huang. An Improved Car-Following Model considering Desired Safety Distance and Heterogeneity of Driver’s Sensitivity. Journal of Advanced Transportation. 2021; 2021 ():1-12.
Chicago/Turabian StyleLei Zhang; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Yan Huang. 2021. "An Improved Car-Following Model considering Desired Safety Distance and Heterogeneity of Driver’s Sensitivity." Journal of Advanced Transportation 2021, no. : 1-12.
This study aims to explore factors affecting passenger car and truck driver injury severity in passenger car-truck crashes. Police-reported crash data from 2007 to 2017 in Canada are collected. Two-vehicle crashes involving one truck and one passenger car are extracted for modeling. Different injury severities are not equally represented. To address the data imbalance issue, this study applies four different data imbalance treatment approaches, including over-sampling, under-sampling, a hybrid method, and a cost-sensitive learning method. To test the performances of different classifiers, five classification models are used, including multinomial logistic regression, Naive Bayes, Classification and Regression Tree, support vector machine, and eXtreme Gradient Boosting (XGBoost). In both the passenger car driver and truck driver injury severity analysis, XGBoost combined with cost-sensitive learning generates the best results in terms of G-mean, area under the curve, and overall accuracy. Additionally, the Shapley Additive Explanations (SHAP) approach is adopted to interpret the result of the best-performing model. Most of the explanatory variables have similar effects on passenger car and truck driver fatality risks. Nevertheless, six variables exhibit opposite effects, including the age of the passenger car driver, crash hour, the passenger car age, road surface condition, weather condition and the truck age. Results of this study could provide some valuable insights for improving truck traffic safety. For instance, properly installing traffic control devices could be an effective way to reduce fatality risks in passenger car-truck crashes. Besides, passenger car drivers should be extremely cautious when driving between midnight to 6 am on truck corridors.
Bei Zhou; Xiqing Wang; Shengrui Zhang; Zongzhi Li; Shaofeng Sun; Kun Shu; Qing Sun. Comparing Factors Affecting Injury Severity of Passenger Car and Truck Drivers. IEEE Access 2020, 8, 153849 -153861.
AMA StyleBei Zhou, Xiqing Wang, Shengrui Zhang, Zongzhi Li, Shaofeng Sun, Kun Shu, Qing Sun. Comparing Factors Affecting Injury Severity of Passenger Car and Truck Drivers. IEEE Access. 2020; 8 (99):153849-153861.
Chicago/Turabian StyleBei Zhou; Xiqing Wang; Shengrui Zhang; Zongzhi Li; Shaofeng Sun; Kun Shu; Qing Sun. 2020. "Comparing Factors Affecting Injury Severity of Passenger Car and Truck Drivers." IEEE Access 8, no. 99: 153849-153861.
To promote the sustainable development and safety of bicycle traffic, survival analysis of the risk perception sensitivity of cyclists is proposed. The cumulative probability of survival serves as an index of risk perception sensitivity, and a Cox regression model is established. The proposed method is applied to middle school cyclists, and the factors of their risk perception are analyzed. Data are collected by questionnaire and traffic conflict survey and are quantified by factor analysis. The model results show that active and extroverted personality, negative peer influence, unsafe riding behavior intention, non-motor vehicle flow and speed, and a lack of separation facilities have negative correlations with risk perception sensitivity. Positive attitude towards traffic rules, good family education, heightened traffic safety awareness, motor vehicle flow and speed, pedestrian flow, and non-motorized lane width have positive correlations with risk perception sensitivity. The conflict type has no correlation with risk perception sensitivity. This study aims to improve the sensitivity of risk perception, prevent traffic conflicts and provide a theoretical basis for risk perception research on vulnerable traffic participants.
Dan Zhao; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Ling Yang. Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model. Sustainability 2020, 12, 2613 .
AMA StyleDan Zhao, Shengrui Zhang, Bei Zhou, Shuaiyang Jiao, Ling Yang. Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model. Sustainability. 2020; 12 (7):2613.
Chicago/Turabian StyleDan Zhao; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Ling Yang. 2020. "Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model." Sustainability 12, no. 7: 2613.
In intelligent transportation systems, vehicles can obtain more information, and the interactivity between vehicles can be improved. Therefore, it is necessary to study car-following behavior during the introduction of intelligent traffic information technology. To study the impacts of drivers’ characteristics on the dynamic characteristics of car-following behavior in a vehicle-to-vehicle (V2V) communication environment, we first analyzed the relationship between drivers’ characteristics and the following car’s optimal velocity using vehicle trajectory data via the grey relational analysis method and then presented a new optimal velocity function (OVF). The boundary conditions of the new OVF were analyzed theoretically, and the results showed that the new OVF can better describe drivers’ characteristics than the traditional OVF. Subsequently, we proposed an extended car-following model by combining V2V communication based on the new OVF and previous car-following models. Finally, numerical simulations were carried out to explore the effect of drivers’ characteristics on car-following behavior and fuel economy of vehicles, and the results indicated that the proposed model can improve vehicles’ mobility, safety, fuel consumption, and emissions in different traffic scenarios. In conclusion, the performance of traffic flow was improved by taking drivers’ characteristics into account under the V2V communication situation for car-following theory.
Shuaiyang Jiao; Shengrui Zhang; Bei Zhou; Zixuan Zhang; Liyuan Xue. An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment. Sustainability 2020, 12, 1552 .
AMA StyleShuaiyang Jiao, Shengrui Zhang, Bei Zhou, Zixuan Zhang, Liyuan Xue. An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment. Sustainability. 2020; 12 (4):1552.
Chicago/Turabian StyleShuaiyang Jiao; Shengrui Zhang; Bei Zhou; Zixuan Zhang; Liyuan Xue. 2020. "An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment." Sustainability 12, no. 4: 1552.
This paper introduces an improved car-following speed (CFS) model that simultaneously considers speed of the lead vehicle, vehicle spacing, and driver’s sensitivity to them. Specifically, the proposed model extends the Helbing-Tilch model and Yang et al. model developed based on the principle of grey relational analysis where vehicle spacing is considered as the primary factor contributing to car-following speed choices. A computational experiment is conducted for model calibration using vehicle spacing, speed, and acceleration data derived from vehicle trajectory data of the Next Generation Simulation (NGSIM) project sponsored by the Federal Highway Administration (FHWA). It shows that speed of the lead vehicle and vehicle spacing significantly affect speed of the lag vehicle. Further, model validation is carried out using an independent NGSIM dataset by comparing vehicle speed predictions made by the calibrated CFS model with Helbing-Tilch model and Yang et al. model as benchmarks. Compared with speed prediction results of the benchmark models, mean relative errors, root mean square errors, and equal coefficient of speed predictions of the CFS model have reduced by 72.41% and 61.85%, 70.14% and 57.99%, and 33.15% and 14.48%, respectively. The findings of model validation reveal that the CFS model could improve the accuracy of speed predictions in the car-following process.
Shuaiyang Jiao; Shengrui Zhang; Zongzhi Li; Bei Zhou; Dan Zhao. An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them. Journal of Advanced Transportation 2020, 2020, 1 -13.
AMA StyleShuaiyang Jiao, Shengrui Zhang, Zongzhi Li, Bei Zhou, Dan Zhao. An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them. Journal of Advanced Transportation. 2020; 2020 ():1-13.
Chicago/Turabian StyleShuaiyang Jiao; Shengrui Zhang; Zongzhi Li; Bei Zhou; Dan Zhao. 2020. "An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them." Journal of Advanced Transportation 2020, no. : 1-13.
The popular real-time ridesharing service has promoted social and environmental sustainability in various ways. Meanwhile, it also brings some traffic safety concerns. This paper aims to analyze factors affecting real-time ridesharing vehicle crash severity based on the classification and regression tree (CART) model. The Chicago police-reported crash data from January to December 2018 is collected. Crash severity in the original dataset is highly imbalanced: only 60 out of 2624 crashes are severe injury crashes. To fix the data imbalance problem, a hybrid data preprocessing approach which combines the over- and under-sampling is applied. Model results indicate that, by resampling the crash data, the successfully predicted severe crashes are increased from 0 to 40. Besides, the G-mean is increased from 0% to 73%, and the AUC (area under the receiver operating characteristics curve) is increased from 0.73 to 0.82. The classification tree reveals that following variables are the primary indicators of real-time ridesharing vehicle crash severity: pedestrian/pedalcyclist involvement, number of passengers, weather condition, trafficway type, vehicle manufacture year, traffic control device, driver gender, lighting condition, vehicle type, driver age and crash time. The current study could provide some valuable insights for the sustainable development of real-time ridesharing services and urban transportation.
Bei Zhou; Xinfen Zhang; Shengrui Zhang; Zongzhi Li; Xin Liu. Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity. Sustainability 2019, 11, 3334 .
AMA StyleBei Zhou, Xinfen Zhang, Shengrui Zhang, Zongzhi Li, Xin Liu. Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity. Sustainability. 2019; 11 (12):3334.
Chicago/Turabian StyleBei Zhou; Xinfen Zhang; Shengrui Zhang; Zongzhi Li; Xin Liu. 2019. "Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity." Sustainability 11, no. 12: 3334.
Hit-and-run (HR) crashes refer to crashes involving drivers of the offending vehicle fleeing incident scenes without aiding the possible victims or informing authorities for emergency medical services. This paper aims at identifying significant predictors of HR and non-hit-and-run (NHR) in vehicle-bicycle crashes based on the classification and regression tree (CART) method. An oversampling technique is applied to deal with the data imbalance problem, where the number of minority instances (HR crash) is much lower than that of the majority instances (NHR crash). The police-reported data within City of Chicago from September 2017 to August 2018 is collected. The G-mean (geometric mean) is used to evaluate the classification performance. Results indicate that, compared with original CART model, the G-mean of CART model incorporating data imbalance treatment is increased from 23% to 61% by 171%. The decision tree reveals that the following five variables play the most important roles in classifying HR and NHR in vehicle-bicycle crashes: Driver age, bicyclist safety equipment, driver action, trafficway type, and gender of drivers. Several countermeasures are recommended accordingly. The current study demonstrates that, by incorporating data imbalance treatment, the CART method could provide much more robust classification results.
Bei Zhou; Zongzhi Li; Shengrui Zhang; Xinfen Zhang; Xin Liu; Qiannan Ma. Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment. Sustainability 2019, 11, 1327 .
AMA StyleBei Zhou, Zongzhi Li, Shengrui Zhang, Xinfen Zhang, Xin Liu, Qiannan Ma. Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment. Sustainability. 2019; 11 (5):1327.
Chicago/Turabian StyleBei Zhou; Zongzhi Li; Shengrui Zhang; Xinfen Zhang; Xin Liu; Qiannan Ma. 2019. "Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment." Sustainability 11, no. 5: 1327.
The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.
Xianglong Luo; Danyang Li; Yu Yang; Shengrui Zhang. Spatiotemporal Traffic Flow Prediction with KNN and LSTM. Journal of Advanced Transportation 2019, 2019, 1 -10.
AMA StyleXianglong Luo, Danyang Li, Yu Yang, Shengrui Zhang. Spatiotemporal Traffic Flow Prediction with KNN and LSTM. Journal of Advanced Transportation. 2019; 2019 ():1-10.
Chicago/Turabian StyleXianglong Luo; Danyang Li; Yu Yang; Shengrui Zhang. 2019. "Spatiotemporal Traffic Flow Prediction with KNN and LSTM." Journal of Advanced Transportation 2019, no. : 1-10.
With the implementation of the freeway free policy during the holidays, traffic congestion in the freeway becomes a common phenomenon. In order to alleviate traffic pressure, traffic flow prediction during the holidays has become a problem of great concern. This paper proposes a hybrid prediction methodology combining discrete Fourier transform (DFT) with support vector regression (SVR). The common trend in the traffic flow data is extracted using DFT by setting an appropriate threshold, which is predicted by extreme extrapolation of the historical trend. The SVR method is applied to predict the residual series. The experimental results with measured data collected from the toll stations in Jiangsu province of China show that the proposed algorithm has higher accuracy compared with the traditional method, and it is an efficient method for traffic flow prediction during the holidays.
Xianglong Luo; Danyang Li; Shengrui Zhang. Traffic Flow Prediction during the Holidays Based on DFT and SVR. Journal of Sensors 2019, 2019, 1 -10.
AMA StyleXianglong Luo, Danyang Li, Shengrui Zhang. Traffic Flow Prediction during the Holidays Based on DFT and SVR. Journal of Sensors. 2019; 2019 ():1-10.
Chicago/Turabian StyleXianglong Luo; Danyang Li; Shengrui Zhang. 2019. "Traffic Flow Prediction during the Holidays Based on DFT and SVR." Journal of Sensors 2019, no. : 1-10.
A hit-and-run (HR) crash occurs when the driver of the offending vehicle flees the crash scene without reporting it or aiding the victims. The current study aimed at contributing to existing literatures by comparing factors which might affect the crash severity in HR and non-hit-and-run (NHR) crashes. The data was extracted from the police-reported crash data from September 2017 to August 2018 within the City of Chicago. Two multinomial logistic regression models were established for the HR and NHR crash data, respectively. The odds ratio (OR) of each variable was used to quantify the impact of this variable on the crash severity. In both models, the property damage only (PDO) crash was selected as the reference group, and the injury and fatal crash were chosen as the comparison group. When the injury crash was taken as the comparison group, it was found that 12 variables contributed to the crash severities in both HR and NHR model. The average percentage deviation of OR for these 12 variables was 34%, indicating that compared with property damage, HR crashes were 34% more likely to result in injuries than NHR crashes on average. When fatal crashes were chosen as the comparison group, 2 variables were found to be statistically significant in both the HR and the NHR model. The average percentage deviation of OR for these 2 variables was 127%, indicating that compared with property damage, HR crashes were 127% more likely to result in fatalities than NHR crashes on average.
Bei Zhou; Zongzhi Li; Shengrui Zhang. Comparison of Factors Affecting Crash Severities in Hit-and-Run and Non-Hit-and-Run Crashes. Journal of Advanced Transportation 2018, 2018, 1 -11.
AMA StyleBei Zhou, Zongzhi Li, Shengrui Zhang. Comparison of Factors Affecting Crash Severities in Hit-and-Run and Non-Hit-and-Run Crashes. Journal of Advanced Transportation. 2018; 2018 ():1-11.
Chicago/Turabian StyleBei Zhou; Zongzhi Li; Shengrui Zhang. 2018. "Comparison of Factors Affecting Crash Severities in Hit-and-Run and Non-Hit-and-Run Crashes." Journal of Advanced Transportation 2018, no. : 1-11.
Bei Zhou; Arash M. Roshandeh; Shengrui Zhang. Corrigendum to “Safety Impacts of Push-Button and Countdown Timer on Nonmotorized Traffic at Intersections”. Mathematical Problems in Engineering 2018, 2018, 1 -1.
AMA StyleBei Zhou, Arash M. Roshandeh, Shengrui Zhang. Corrigendum to “Safety Impacts of Push-Button and Countdown Timer on Nonmotorized Traffic at Intersections”. Mathematical Problems in Engineering. 2018; 2018 ():1-1.
Chicago/Turabian StyleBei Zhou; Arash M. Roshandeh; Shengrui Zhang. 2018. "Corrigendum to “Safety Impacts of Push-Button and Countdown Timer on Nonmotorized Traffic at Intersections”." Mathematical Problems in Engineering 2018, no. : 1-1.
This paper explores public bicycle choice behavior for urban travelers and potential travelers who may use public bicycle systems. Factors influencing public bicycle choice behavior and current public bicycle usage in China were analyzed. Then primary factors of public bicycle choice behavior among those using different travel modes were studied using the binary logistic model. Influential factors were divided into internal and external factors. High prediction accuracy of the proposed model shows that BLM can predict the public bicycle choice behavior of urban residents. Results show that travel distance, convenience of transferring to public transit, and cycling safety are the three main factors that determine whether private car owners would choose the public bicycles. For walkers, walking distance and red light waiting time are the main factors. For users of public transit, travel distance, walking distance to public transit transfer, and automobile exhaust are the three main influential factors.
Jiangling Wu; Shengrui Zhang; Amit Kumar Singh; Zhenjun Zhu; Jun Zeng. Public Bicycle Choice Behavior of Urban Travelers in China. CICTP 2018 2018, 1 .
AMA StyleJiangling Wu, Shengrui Zhang, Amit Kumar Singh, Zhenjun Zhu, Jun Zeng. Public Bicycle Choice Behavior of Urban Travelers in China. CICTP 2018. 2018; ():1.
Chicago/Turabian StyleJiangling Wu; Shengrui Zhang; Amit Kumar Singh; Zhenjun Zhu; Jun Zeng. 2018. "Public Bicycle Choice Behavior of Urban Travelers in China." CICTP 2018 , no. : 1.
The traffic flow prediction plays a key role in modern Intelligent Transportation Systems (ITS). Although great achievements have been made in traffic flow prediction, it is still a challenge to improve the prediction accuracy and reduce the operation time simultaneously. In this paper, we proposed a hybrid prediction methodology combined with improved seasonal autoregressive integrated moving average (ISARIMA) model and multi-input autoregressive (AR) model by genetic algorithm (GA) optimization. Since traffic flow data has strong spatio-temporal correlation with neighboring stations, GA is used to select those stations which are highly correlated with the prediction station. The ISARIMA model is used to predict the traffic flow in test station at first. A multi-input AR model with traffic flow data in optimal selected stations is built to predict the traffic flow in test station as well. The final prediction result can be gained by combining with the results of ISARIMA and multi-input AR model. The test results from traffic data provided by TDRL at UMD Data Center demonstrate that proposed algorithm has almost the same prediction accuracy with artificial neural networks (ANNS). However, its operation time is almost the same with SARIMA model. It is proved to be an effective method to perform traffic flow prediction.
Xianglong Luo; Liyao Niu; Shengrui Zhang. An Algorithm for Traffic Flow Prediction Based on Improved SARIMA and GA. KSCE Journal of Civil Engineering 2018, 22, 4107 -4115.
AMA StyleXianglong Luo, Liyao Niu, Shengrui Zhang. An Algorithm for Traffic Flow Prediction Based on Improved SARIMA and GA. KSCE Journal of Civil Engineering. 2018; 22 (10):4107-4115.
Chicago/Turabian StyleXianglong Luo; Liyao Niu; Shengrui Zhang. 2018. "An Algorithm for Traffic Flow Prediction Based on Improved SARIMA and GA." KSCE Journal of Civil Engineering 22, no. 10: 4107-4115.
The two-step left-turn control model of ring-intersection (RI) has been widely applied to many cities in China. It is still unclear which control model is more suitable when the RI must be reformed due to being unsatisfied with the rapid development of traffic demand. This paper presents a method based on the characteristic of traffic flow in RI considering the efficient performance of the vehicles. Next, a more accurate capacity calculation formula of the left-turn lane is proposed using the stop line method. Then, this method is effectively tested in a typical RI, in the Hunan Province utilizing VISSIM platform.
Si Qin; Shengrui Zhang; Xinchao Chen; Jiangling Wu. Research on Traffic Control Mode Selection of Ring-Intersection. CICTP 2017 2018, 1 .
AMA StyleSi Qin, Shengrui Zhang, Xinchao Chen, Jiangling Wu. Research on Traffic Control Mode Selection of Ring-Intersection. CICTP 2017. 2018; ():1.
Chicago/Turabian StyleSi Qin; Shengrui Zhang; Xinchao Chen; Jiangling Wu. 2018. "Research on Traffic Control Mode Selection of Ring-Intersection." CICTP 2017 , no. : 1.
Survival analysis is used to analyze site data to explore vehicles’ mandatory lane changing behaviors and related influential factors. The traffic data on the mandatory lane changing durations of vehicles were collected by an unmanned aerial vehicle (UAV) in a freeway maintenance construction area. A multiplicative hazard model of mandatory lane change was established using the semi-parametric method of survival analysis. Meanwhile, the lane change data has been analyzed by Cox regression analysis. The results show that about three quarters of vehicles’ MLC duration is less than 10 sec. There is no significant evidence showing that different vehicle types have an effect on the duration of mandatory lane changing. The cumulative survival rate of MLC duration the off-peak period has been found to be significantly lower than that in the peak period and transition period. As it turns out, the cumulative survival rate of vehicles’ MLC duration in the transition period is the highest.
Jiangling Wu; Shengrui Zhang; Amit Kumar Singh; Si Qin. Hazard-Based Model of Mandatory Lane Change Duration. CICTP 2017 2018, 1 .
AMA StyleJiangling Wu, Shengrui Zhang, Amit Kumar Singh, Si Qin. Hazard-Based Model of Mandatory Lane Change Duration. CICTP 2017. 2018; ():1.
Chicago/Turabian StyleJiangling Wu; Shengrui Zhang; Amit Kumar Singh; Si Qin. 2018. "Hazard-Based Model of Mandatory Lane Change Duration." CICTP 2017 , no. : 1.
Zhendong Sun; Shengrui Zhang; Yun Li; Wenjing Zhao; Jiangling Wu. The choice of commuter path of private cars under ticket trade constraints. JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING 2018, 35, 1 .
AMA StyleZhendong Sun, Shengrui Zhang, Yun Li, Wenjing Zhao, Jiangling Wu. The choice of commuter path of private cars under ticket trade constraints. JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING. 2018; 35 (2):1.
Chicago/Turabian StyleZhendong Sun; Shengrui Zhang; Yun Li; Wenjing Zhao; Jiangling Wu. 2018. "The choice of commuter path of private cars under ticket trade constraints." JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING 35, no. 2: 1.
Walking speed is an important parameter for signal timing and safety analysis at signalized intersections. Walking speed varies significantly at different location and over time, and need to be re-calibrated for safer and efficient intersection designs. This paper documents a study performed to analyze pedestrian’s speed characteristics at signalized intersections in new urban district communities. Field data were obtained through video camera at four typical 4-leg intersections in the high-tech metropolitan area of Xi’an, China. Statistical descriptions and the distribution fittings were conducted by Statistical Product and Service Solutions (SPSS). Results shows that the pedestrian crossing speed characteristics are influenced by both the pedestrian’s own characteristics and an intersection's geometric and operational characteristics. Male and female walking speed distributions are all in line with the normal distribution, and the youth, middle-aged, and elderly pedestrians speed distributions follow log-normal distribution. The average walking speed varies significantly with the variance of the crosswalk lengths at the same intersection.
Jiang-Ling Wu; Sheng-Rui Zhang; Amit Kumar Singh; Qiu-Ping Wang. Pedestrian Walking Speed Characteristics at Signalized Intersections in New Urban District Communities. DEStech Transactions on Engineering and Technology Research 2017, 1 .
AMA StyleJiang-Ling Wu, Sheng-Rui Zhang, Amit Kumar Singh, Qiu-Ping Wang. Pedestrian Walking Speed Characteristics at Signalized Intersections in New Urban District Communities. DEStech Transactions on Engineering and Technology Research. 2017; (ictim):1.
Chicago/Turabian StyleJiang-Ling Wu; Sheng-Rui Zhang; Amit Kumar Singh; Qiu-Ping Wang. 2017. "Pedestrian Walking Speed Characteristics at Signalized Intersections in New Urban District Communities." DEStech Transactions on Engineering and Technology Research , no. ictim: 1.
As an important part of urban public transportation systems, the feeder bus fills a service gap left by rail transit, effectively extending the range of rail transit’s service and solving the problem of short-distance travel and interchanges. By defining the potential demand of feeder bus services and considering its relationship with the traffic demands of corresponding staging areas, the distance between road and rail transit, and the repetition factor of road bus lines, this paper established a potential demand model of roads by opening feeder bus services and applying a logit model for passenger flow distribution. Based on a circular route model, a route starting and ending at urban rail transit stations was generated, and a genetic algorithm was then applied to solve it. The Wei-Fang community of Shanghai was selected as the test area. Per the model and algorithm, the feeder route length was conformed to a functional orientation of short-distance travel and the feeder service of a feeder bus; the route mostly covered where conventional bus lines were fewer, which is a finding that is in agreement with the actual situation; the feasibility of the model and algorithm was verified.
Zhenjun Zhu; Xiucheng Guo; Jun Zeng; Shengrui Zhang. Route Design Model of Feeder Bus Service for Urban Rail Transit Stations. Mathematical Problems in Engineering 2017, 2017, 1 -6.
AMA StyleZhenjun Zhu, Xiucheng Guo, Jun Zeng, Shengrui Zhang. Route Design Model of Feeder Bus Service for Urban Rail Transit Stations. Mathematical Problems in Engineering. 2017; 2017 ():1-6.
Chicago/Turabian StyleZhenjun Zhu; Xiucheng Guo; Jun Zeng; Shengrui Zhang. 2017. "Route Design Model of Feeder Bus Service for Urban Rail Transit Stations." Mathematical Problems in Engineering 2017, no. : 1-6.
Hit-and-run crashes are accidents where drivers of striking vehicles fail to stop after crashes. Without helping victims or reporting accidents to associated authorities could increase the likelihood of serious injuries and even fatalities. In order to reduce hit-and-run crashes, it is important to understand factors contributing to decisions of fleeing crash scenes. In current study, various factors which could affect occurrences of hit-and-run crashes were thoroughly investigated against six different improper driving behaviors. Logistic regression models were established to facilitate the analysis. Police-reported crash data within Cook County, Illinois, USA between 2004 and 2012 were used in this study. The results showed that variables contributing to hit-and-run crashes varied for different improper driving behaviors. Among six established models, “following too closely” and “distraction by phone” models had most statistically significant variables. This study also concluded that following variables would increase the likelihood of hit-and-run crashes in at least one model: multiple vehicle crash, weekend, population of 2,500 – 5,000, population of 5,000 – 10,000, national highway system, traffic signal, yield sign, shoulder, darkness, and less than three lanes. The results of current study could offer important insights for reducing hit-and-run crashes in both planning and operational levels.
Bei Zhou; Arash M. Roshandeh; Shengrui Zhang; Zhuanglin Ma. Analysis of Factors Contributing to Hit-and-Run Crashes Involved with Improper Driving Behaviors. Procedia Engineering 2016, 137, 554 -562.
AMA StyleBei Zhou, Arash M. Roshandeh, Shengrui Zhang, Zhuanglin Ma. Analysis of Factors Contributing to Hit-and-Run Crashes Involved with Improper Driving Behaviors. Procedia Engineering. 2016; 137 ():554-562.
Chicago/Turabian StyleBei Zhou; Arash M. Roshandeh; Shengrui Zhang; Zhuanglin Ma. 2016. "Analysis of Factors Contributing to Hit-and-Run Crashes Involved with Improper Driving Behaviors." Procedia Engineering 137, no. : 554-562.
The design of human-based traffic systems should consider resident characteristics, traffic facilities, management techniques, and other aspects. Due to limitations imposed by topography, the road network layout and resident travel characteristics of mountain cities are different from other cities. Based on the characteristics of mountain city traffic systems, this paper discusses key problems of human-based traffic system construction in mountain cities and clarifies how to solve them, such as road network, three-dimensional traffic, walking traffic, and high-efficiency public traffic. This paper looks at the construction of the New Yan'an Northside District human-based traffic system as a concrete example of external connections, three-dimensional traffic, walking systems, traffic management, and other aspects.
Hongxia Feng; Shengrui Zhang. Study on Construction of Human-Based Traffic System for Mountain City. CICTP 2012 2012, 1 .
AMA StyleHongxia Feng, Shengrui Zhang. Study on Construction of Human-Based Traffic System for Mountain City. CICTP 2012. 2012; ():1.
Chicago/Turabian StyleHongxia Feng; Shengrui Zhang. 2012. "Study on Construction of Human-Based Traffic System for Mountain City." CICTP 2012 , no. : 1.