2018 No. 2

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2018, 36(2)
Abstract(199) PDF(0)
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2018, 36(2) doi: 10.3963/j.issn.1674-4861.2018.02.019
Abstract(229) PDF(0)
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Transportation Safety
Influences of Track Irregularity Wavelength and Amplitude on Dynamic Performance of High-speed EMU
YUAN Xuancheng, WANG Kaiyun, GE Xin, ZHAI Wanming
2018, 36(2): 1-9. doi: 10.3963/j.issn.1674-4861.2018.02.001
Abstract(635) PDF(3)
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In order to understand track irregularity of high-speed railways,and to guaranteeitsoperation safety,dy-namic simulations of one type of high-speed electric multiple unit(EM U)are carried out.Specifically,the influences of track irregularity wavelength and amplitude on vehicle dynamic performance are analyzed to determine the ranges of irreg-ularity wavelength,which requires special attention during track maintenance.For simplification,the track irregularity is converted to vertical profile,track alignment,track cross-level,and track twist irregularities of harmonic wave with dif-ferent wavelengths and amplitudes,and simulations are carried out based on a vehicle-track coupled dynamic model.The results show that the irregularities withinshort and mid wavelength range havesignificant impacton wheel/rail dynamic in-teraction and the safety and smoothness of train operation,which is mainly caused by the irregularities within mid and long wavelength range.The vehicle dynamic force has a linear positive relationship with the irregularity amplitude,and the change of irregularity amplitude undersensitive wavelength has significant influences on system dynamic.Analysis re-sults determine the sensitive wavelength ranges of track irregularity and corresponding influence of its amplitudeof the specific type of high-speed EMU,which highlights the key points of track maintenance,andhelpful for improvingmainte-nance efficiency as well as operation quality of EMU.
A Safety Evaluation of Lane Change Based on Relevance Vector Machine
JIA Xiaolong, SONG Dingbo, WANG Chang, SHAN Yan, HE Aisheng, JIA Bingshuo
2018, 36(2): 10-17. doi: 10.3963/j.issn.1674-4861.2018.02.002
Abstract(372) PDF(0)
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The accuracy and reliability are the major problems that have existed in traditional of lane change warning algorithms.Therefore,a test vehicle is set by using millimeter-wave radar,AWS vision sensor,vehicle gyroscope and other devices aiming at solving the above-mentioned problems.A field tests is conducted on highways and 19 subjects are recruited.Nearly 1 000 lane change samples are extracted from recorded driving test data.Based on a three-stage lane change trajectory model,the statistical value of yaw rate is regarded as the basis to determine radius of curvature in each stage.The upper limit for acceptable safety threshold is determined according to the yaw rate quantile when α=0.05.A series of control points are formed by analyzing lane change processes for which lasted more than 12s and the maximum value of lane change duration is selected as the benchmark.B-spline curve planning is adopted to determine the lower limit of the acceptable safety threshold.Parameters in lane change are estimated by Relevance Vector Machine(RVM),and 7th polynomial model is used to fit lane change trajectory.The area enclosed by the fitted trajectories and the upper(or lower)limit of the acceptable safety threshold is used as the warning parameters.The ratio is used as a warning parameter to evaluate safety of lane change.Actual data is used to verify this algorithm.The evaluation results of this algorithm can reflect actual safety level of the lane change,and have strong correlation with operating behaviors of drivers.
An Analysis of Impact Factors of Accident Severity for Water Transport Based on Supporting Vector Machine
WANG Feixiang, YANG Yangdong, TIAN Shubing, HUANG Liwen
2018, 36(2): 18-23,32. doi: 10.3963/j.issn.1674-4861.2018.02.003
Abstract(405) PDF(0)
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In order to identify influencing factors for accident severity and to reduce casualties and economic loss in maritime traffic accidents,factors are extracted by developing a database of accidents information based on statistical anal-ysis of maritime accident data from 2015 to 2016.The factors mainly include ship type,accident location,time,gross tonnage of ships,visibility,and wind force,etc.According to the number of casualties and the amount of economic loss caused by maritime accidents,their consequences are divided into three levels,and a three-class model based on support vector machines(SVM)is established.Then cross-validation and a grid search algorithm are used to optimize penalty pa-rameters and kernel function parameters of the SVM model.An optimal classification model is developed.After that, SVM-RFE algorithm is used to calculate the weights of accident severity of the influencing factors.Furthermore,the fac-tors that have the greatest impacts on the consequences are identified.The results indicate that the overall classification accuracy of the three-class SVM model is larger than 70%.Self-sinking,accidents of fishing vessels,and accidents hap-pen during the autumn period are more likely to result in more casualties.Hazardous chemical ships,inland river acci-dents,and fishing vessels tend to have larger economic loss.
A Study of Safety-oriented Evaluation Model for Road Maintenance Program
LUO Yuexin, LU Jian
2018, 36(2): 24-32. doi: 10.3963/j.issn.1674-4861.2018.02.004
Abstract(443) PDF(0)
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In a view of deficiencies of the present evaluation system of road safety maintenance and management,in-tersections and sections are chosen as evaluation objects according to road differences.Five major impact factors are incor-porated in a four-level analytic hierarchy method,in order to derive a more objective and comprehensive evaluation sys-tem.Evaluation criteria are established for each indicator and their rationality is verified by Kendall′s W method.An eval-uation model is thus developed based on comprehensive indices,and parameters are calibrated by principal component a-nalysis and weight factor judgment table.Field data are collected from Wenchuan road,Bao′an road,and S20 road in Shanghai.This model is further validated through field evaluations and expert scores,a correlation coefficient of 0.737 is obtained.In comparison with other models,the R2indicates that the model yields better results with higher coefficient of determination.
An Analysis of Navigation Safety of Cruise Ships Based on Cloud Model and Entropy Weights:A Case Study of Qingdao Port
SUN Xing, LU Hongliang
2018, 36(2): 33-38. doi: 10.3963/j.issn.1674-4861.2018.02.005
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Navigation safety of cruise ships is one of the hot issues as its risk level is higher than other types of mar-itime transportation.According to navigational conditions,factors for safety evaluation of cruise ships at Qingdao port are classified into three first-grade indices:human,ship,and environment.Each first-grade index is further divided into sec-ond-grade indices.A cloud model is used to evaluate navigation safety of cruise ships in order to perform uncertainty map-ping analysis between evaluation factors and comments.The entropy weight method is used to define the weights of indi-ces.One cruise ship at Qingdao port is selected as a case study to apply the approach based on the cloud model and the en-tropy weight method.The results show that the drops of the cloud model for the target cruise are mainly distributed in EX=83.73,followed the scope of"relatively safe".This approach reconciles both fuzziness and randomness.Its intuition and effectiveness presents a new insight to analyze safety problems of cruise ships.
Transportation Information Engineering and Control
High-accuracy Vision-based Indoor Positioning Using Building Safety Evacuation Signs
TAO Qianwen, HU Zhaozheng, HUANG Gang, CAI Hao, WU Zhipeng
2018, 36(2): 39-46,60. doi: 10.3963/j.issn.1674-4861.2018.02.006
Abstract(413) PDF(0)
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As GPS signals are blocked in indoor environments,a vision-based accurate indoor positioning algorithm is proposed referring to fire safety evacuation signs which are widely and evenly distributed in indoor environments.The algorithm aims at calculating distance to the nearest fire safety evacuation sign in the map from the pose of current posi-tion.Color character of fire safety evacuation signs is used for color threshold segmentation.Histogram of Oriented Gra-dient(HOG)features and Support Vector Machine(SVM)are combined to check whether the candidate box contains a fire safety evacuation sign.Holistic Speeded Up Robust Features(SURF)is used for matching,and K-Nearest Neighbor (KNN)method is uses to select nearest K positions as candidate locations.SURF local feature is used for feature matc-hing,a location with the largest number of local feature matches is selected as the result of image-level positioning,and the pose of the current location is calculated in the map.Through the field test in an underground parking lot and an office building,the results show that the proposed method can meet the requirements of accurate indoor positioning,with the accuracy is above 96%,and the average positioning error is below 0.6 m.The results show that this proposed method provides a robust and accurate solution for indoor positioning.
A Traffic Control Method for Freeways Using Variable Speed Limits under Foggy Weather
ZHANG Shan, ZHANG Cunbao, LI Wei
2018, 36(2): 47-53. doi: 10.3963/j.issn.1674-4861.2018.02.007
Abstract(472) PDF(5)
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Fog leads to a decline of visibility on freeways,which decreases safety and efficiency of traffic.A variable speed limit(VSL)control scheme for freeways under foggy weather conditions is proposed,including system design, workflow,and VSL operation strategy,etc.The speed limit of target sections is determined based on data of real-time road visibility and traffic flow,factors including the maximum safe speed,traffic state and non-compliance behaviors of drivers,etc are considered.Data collected from the G4 freeway and simulations are analyzed by Vissim.The results show that compared with no control scheme,the proposed scheme can increase the lowest speed in advection(agglomerate)fog by 27.24%(28.54%)and decrease the maximum speed difference of adjacent link by 26.42%(41.91%).This VSL control scheme is more effective in agglomerate.Furthermore,it ensures traffic safety and improves efficiency of freeways in foggy weather.
A Comparative Study of Driving Behaviors under Different Green-Yellow-Phase-switching Modes in Signalized Intersections
JIANG Zehao, WANG Tao, YANG Xiaoguang
2018, 36(2): 54-60. doi: 10.3963/j.issn.1674-4861.2018.02.008
Abstract(359) PDF(0)
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This study is intended to investigate influences of different phase-switching modes at the end of green light on traffic control at signalized intersections in China.Driving simulations are designed to compare and analyze driving behaviors under two typical phase-switching modes-green signal flash(GSF)and green signal countdown(GSC).Moreo-ver,decision behaviors of"passing/stopping"under two modes are developed.The results show that,driving behaviors during phase-switching interval of different modes have complexities,which are closely related to traffic parameters(i.e. driving speed,distance between the vehicle and stop line at the onset of amber light)and personal data of drivers(i.e. sex,age,and driving experience).In terms of traffic parameters,the GSC mode has a strong urging effect on drivers, under which the average passing rate is 10% ~20% higher,and the probability of being caught into dilemma zone is 7. 2% higher than that under the GSF mode.The passing rate is negatively correlated to the distance between the vehicle and stop line at the onset of amber light.The relationship between the passing rate and driving speed at the onset of am-ber light shows opposite trends under different phase-switching modes:positively correlated under the GSC mode,while negatively correlated under the GSF mode.In terms of personal data of drivers,the passing rate of male drivers who are 20~30 years old,and have 3~4 years driving experience is significantly higher than other groups.
A Comparative Analysis of Data Imputation Methods for Missing Traffic Flow Data
MENG Hongcheng, CHEN Shuyan
2018, 36(2): 61-67. doi: 10.3963/j.issn.1674-4861.2018.02.009
Abstract(731) PDF(30)
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To deal with the missing data problem in traffic flow datasets,a variety of missing data estimation meth-ods,including temporal correlation based methods,spatial correlation based methods,and spatial-temporal correlation based methods,are studied in this paper.The temporal correlation based methods include historical data based method, moving average method,exponential smoothing method,and linear regression method.The spatial correlation based method uses data collected from adjacent lanes and detectors to complete the missing data,while the spatial-temporal cor-relation based method considers both temporal and the spatial correlation of traffic flow.These methods are evaluated by actual traffic data collected from the freeway I-880 in California,USA.The results show that the method of exponential smoothing with smooth coefficient α=0.1,and the weighted average method based on the data of adjacent lanes outper-formed others.
A Fuzzy Method for Identifying Urban Traffic State Using RFID and Video Monitoring System
CHEN Zheng, LIU Zhao, WANG Jing, GUO Jianhua
2018, 36(2): 68-75. doi: 10.3963/j.issn.1674-4861.2018.02.010
Abstract(337) PDF(0)
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In order to identify the traffic state of urban road networks for proactively supporting the traffic manage-ment,traffic guidance and control,a traffic fuzzy identification method is proposed using the radio frequency identification (RFID)based traffic monitoring system together with the video surveillance system.In this method,traffic state is deter-mined using the travel time of vehicles obtained from the RFID system and the vehicle speed collected from the video sur-veillance system.Due to the actual traffic state can be directly observed by people from the video stream,actual traffic state threshold can be calibrated.This calibrated threshold can then be used to evaluate the performance of the proposed method.Based on the RFID and video based traffic monitoring system installed in Nanjing,the empirical results show that the proposed method is practical.The future work is recommended to promote the application of integrated traffic da-ta collection,especially RFID data in traffic management.
A Study of Eco-driving Strategy at Signalized Intersections
MENG Zhu, QIU Zhijun
2018, 36(2): 76-84,92. doi: 10.3963/j.issn.1674-4861.2018.02.011
Abstract(310) PDF(6)
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In order to reduce fuel consumption and exhaust emissions caused by frequently starting and idling vehi-cles at signalized intersections,the problem of eco-driving strategy at signalized intersection on energy-saving oriented is studied.With the help of the V2I communication system in intersection areas,GPS locations and moving states of vehi-cles,also signal phase and timing information are obtained,then optimal speed advisory scenarios of green lights are de-termined according to these data.Time and space trajectories of vehicles under each scenario are analyzed.Considering fu-el consumption of upstream and dow nstream areas of intersections,a unified optimization objective function is established with a goal of minimum average fuel consumption per kilometer.The optimal solution of eco-driving strategy is obtained, and speed advice is provided to the driver.A random simulation is carried out by Matlab.It shows that the eco-driving strategy can reduce fuel consumption by more than 10% comparing with non-eco-driving speed strategy.Among the six green light optimal speed advisory scenarios,the most significant fuel saving of the optimal eco-driving strategy appears in Case 2,which reaches 30% -60%,followed by Case 4,which reaches 25% -50%.It appears slightly worse in Case 3 and Case 5.In order to improve fuel consumption efficiency,vehicles should avoid stopping and waiting when passing sig-nalized intersections.They can also try to pass through intersections by appropriate acceleration and deceleration if necessary.
Transportation Planning and Management
Dynamic Routing of Vehicles with Known Duration of Non-recurrent Congestion
LI Manman, LU Jian, GUO Wenqian
2018, 36(2): 85-92. doi: 10.3963/j.issn.1674-4861.2018.02.012
Abstract(328) PDF(2)
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Dynamic routing of vehicles with known duration of non-recurrent congestion in actual urban road net-work is studied.The shortest travel time and initial delivery route between customers are obtained using a modified Dijk-stra algorithm.On the basis,the initial delivery route is updated in accordance with recurrent congestion information by the genetic algorithm.And then,adjusted delivery route is obtained by 2-opt algorithm and insertion algorithm consider-ing travel time influenced by non-recurrent congestion and its duration.The developed delivery route algorithm is finally applied in a car navigation system in real-time.A numerical experiment proves that driving routes can be updated based on congestion information to avoid traffic jam,and travel time decreases total by 0.65 -13.18 min.If the duration time of non-recurrent congestions is known further,the travel time decreases by 0.16-4.17 min additionally.The greater of the influences of non-recurrent congestion factors on urban network,the more travel time are saved using the developed algo-rithm.
An Analysis of Rider Density Distribution at Different Parts of Public Buses
YAN Shengyu, ZHAO Zhuanzhuan, BAI Xin
2018, 36(2): 93-98. doi: 10.3963/j.issn.1674-4861.2018.02.013
Abstract(323) PDF(1)
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To analyze inhomogeneity of density distributions in different areas of public buses,characteristics of standing density of passenger flow is studied through a investigation of 7 crowded bus routes in both off-peak hours and peak hours.Based on different layouts of seats,causes of inhomogeneity,distribution characteristics,and correlation within each other are analyzed.The parts have maximum standing density in each feature interval are verified.Tendency of passengers to choose standing areas are analyzed.The results show that,after get on a bus passengers firstly choose to settle down in area B or C,the possibility is 93.23%.When the average standing density ρin a bus exceeds 6 persons per m2,the maximum of standing density is in area B and C,as the layouts of seats are"1+1 on wheelbase,1+1 on posteri-or passage"or"1+1 on wheelbase,2+2 on posterior passage".When ρranges from 0 to 2 persons per meter2,propor-tion of standing density in area C decreases significantly.When it reaches 40%,the decrease tends to slow dow n.
A Choice Model for Departure Time Based on Schedule Delay Costs
HU Wenjun, ZHOU Xizhao, SHEN Silin
2018, 36(2): 99-105,119. doi: 10.3963/j.issn.1674-4861.2018.02.014
Abstract(290) PDF(1)
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Uncertain travel time can lead to negative effects.One of which is the increasing of schedule delay cost. Transportation information provided to travelers can reduce the negative effects and the schedule delay cost.In order to e-valuate the negative effects and the value of transportation information,a model for departure time selection with uncer-tain travel time is developed.Assuming that the distribution of travel time is different for different departure time,travel-ers can obtain average travel conditions or traffic information based on daily status from Advanced Traffic Information System(ATIS).A traveler predicts his/her travel time and selects departure time based on perceived value of that infor-mation from ATIS.A case study verifies the influences of uncertain travel time,different information conditions,and the quality of forecasts on general travel costs of travelers.The results show that the benefits of traffic information should not only be reflected in saving of travel time,but also in reducing schedule delay costs by about 30% ~40%.
A Quantitative Method for Site Selection of Offshore Wind Farms in Far-reaching Sea Areas
TANG Zhengqi, JIANG Jianping, LI Zilin, SHI Qilin
2018, 36(2): 106-111. doi: 10.3963/j.issn.1674-4861.2018.02.015
Abstract(360) PDF(11)
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In order to promote construction of offshore wind farms in far-reaching sea areas and rational use of ma-rine resources,a decision-making model based on fuzzy logic is proposed for site selection of offshore wind farms in far-reaching sea areas.First,influencing factors are identified from existing studies,based on which a three-layer decision-making framework is established.Second,input and output variables are fuzzified,and four fuzzy logic boxes are estab-lished by using decision attributes,which are wind resources,natural environment,traffic environment,and fan condi-tions.Then associated reasoning rules are also developed.Third,weights of decision attributes are derived by using lin-guistic variables for final decision-making.Finally,take site selection of offshore wind farms in Shanghai as a case study, its scheme is determined by this method.
A Facility Location Model Based on Entropy and TOPSIS for Sea Drones
CHEN Junfeng, WENG Jianjun, WU Bing, ZHENG Dao, YUAN Dan
2018, 36(2): 112-119. doi: 10.3963/j.issn.1674-4861.2018.02.016
Abstract(320) PDF(3)
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In order to reasonably select the site of sea drones,an entropy and TOPSIS based model is developed. The influencing factors are identified from three aspects,which are natural conditions,water conditions,and traffic con-ditions,and the airspace,waters and land conditions are comprehensively considered.Afterwards,the site selection index is derived,and a TOPSIS-based method is utilized.Information entropy is applied to determine weights of each influen-cing factors.The site of the sea drone is selected by using both information entropy and TOPSIS.The proposed model is applied to the site selection of seaplane in Zhenjiang.In this case four candidate sites of seaplanes are in this lower reaches of the Yangtze River,and the relative closeness degree of each alternative to the ideal point is 0.469/0.682/0.326/0. 295,respectively.Alternative 2 gets the maximum relative closeness degree,which is 0.682 and the best site among these four alternatives,and it is consistent with the actual selected site.The proposed entropy and TOPSIS based model uses objective information for judgments,which can reduce the errors caused by subjective assessment and also increase the credibility of decisions.
An Optimization Model of Cargo Express Train with Consideration of Planned Delivery Time
YAN Yongchao, HE Shiwei, YIN Weichuan
2018, 36(2): 120-126. doi: 10.3963/j.issn.1674-4861.2018.02.017
Abstract(308) PDF(0)
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It is always challenging to guarantee transport time for the organization of cargo express train transporta-tion.Thus,an optimization model of cargo express train organization is proposed in the present study.In view of impact factors of express train transportation organization,a Bi-objective function is introduced for goal description as maximum capacity of cargo handling and minimum time to organize Inter-Bureau express train.IBM ILOG Cplex software is called from C# to derive accurate solution to Pareto solution set.The Jing-jin-ji cargo express train organization is further in-vestigated as a case study.In comparison with the actual operation schedule,the model results show that stop time of Jing-jin-ji cargo express train is effectively shortened under the premise of cargo loading requirements when Pareto solu-tion is 0.50 and 0.51,respectively.The express train X481 and X492 are respectively ahead of the schedule by 127.21 min and 200 min,w hich verifies the effectiveness of the proposed model.Suggestions are also provided to improve opera-tions of the relevant organizations.

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