2021 Vol. 39, No. 4

column
  Previous Issue | Next Issue 
Display Method:
2021, 39(4) doi: 10.3963/j.issn.1674-4861.2021.04.020
Abstract(160) PDF(1)
Abstract:
Overview
Frontiers and Research Trends of Aviation Safety Under the Global Epidemic
HE Peng, SUN Ruishan
2021, 39(4): 1-8. doi: 10.3963/j.jssn.1674-4861.2021.04.001
Abstract(445) HTML (300) PDF(31)
Abstract:
Safety is the key factor determining the competitiveness and sustainable development of the transportation system. The COVID-19 epidemic brings a tremendous impact on the global aviation industry, resulting in a severe safety situation of air transportation. The operation safety and new operation mode under the epidemic crisis are widely emphasized by industry and academia. Based on the analysis of the aviation safety situation, three hot issues are reviewed around the frontiers of aviation-safety research, including flight fatigue detection, safety data analysis, and the safety of single-pilot operation(SPO). According to the problems existing in the current safety research, the research trends in aviation safety in the future contain the cross-indicator rapid measurement technology of fatigue, data analysis technology based on safety Ⅱ and multi-source, multi-dimensional data fusion, and system reliability and validation studies of SPO.
Transportation Safety
A Study on Collision Avoidance Strategy for Vulnerable Road Users Under Visual Obstruction
WU Zixiang, HUANG Helai, CHEN Jiguang, ZHENG Dehong, ZHA Wuping
2021, 39(4): 9-15, 34. doi: 10.3963/j.jssn.1674-4861.2021.04.002
Abstract(306) HTML (166) PDF(37)
Abstract:
The traffic accident caused by visual obstruction is one of the main types of current urban-road traffic accidents. The work proposes an autonomous emergency-brake avoidance control strategy based on the collision time ratio and the safe braking distance, aiming at the crossroad traffic accident caused by a visual obstruction between vehicle and vulnerable road users(VRU). Firstly, the traffic state model of the vehicle and VRU is established to analyze the critical distance of braking to avoid a collision. Then the collision avoidance scenarios are divided into three types with different braking deceleration speeds adopted. The autonomous emergency collision-avoidance control strategy is proposed based on the critical distance and the collision time ratio between the vehicle and VRU in this scenario. Finally, the strategy and the traditional TTC braking algorithm are analyzed by the Euro NCAP test scenario. The results show that the vehicle can avoid collisions at a higher speed under ideal circumstances using this strategy in the Euro NCAP CPNC test scenario. Compared with the traditional TTC algorithm, it can reduce the collision speed more effectively in the high-speed driving condition where collisions cannot be avoided and reduce the risk of serious injury and death to improve the safety of vehicles.
A Method for Identifying Drivers' Risk Perception Based on LightGBM
LI Qing, JING Yunchao, ZHU Tong, ZHU Zishuo, LI Haimei
2021, 39(4): 16-25. doi: 10.3963/j.jssn.1674-4861.2021.04.003
Abstract(489) HTML (151) PDF(46)
Abstract:
Accident risk is related to risk perception, and a driving simulation is designed to evaluate the ability of drivers' risk perception considering hazard target' type(explicit/implicit)and amount(single/double). Data of driving behaviors and eye movement characteristics is collected under different risk scenarios. Mantel-Haenszel test is used to analyze differences of hazard target' factors and drivers' characteristics under different risk perception levels. The Spearman correlation test is used to study relationships among driving behaviors, eye movement characteristics, and risk perception ability. The results show that hazard target' factors are negatively correlated with risk perception. Driving age, vehicle speed, longitudinal acceleration, braking depth, braking response time, and response position are significantly related to risk perception. Drivers with poor risk perception would drive at higher speeds with faster acceleration and deeper brake. They need more time to react to emergencies. The evaluation set of risk perception ability is constructed, and the importance of features is ranked using the Random Forest algorithm. The model of risk perception is built based on LightGBM with the effects of different features analyzed. The results show that the identification effect of the model is the best based on LightGBM compared with SVM and AdaBoost. The F1 value reaches 86.07%with an accuracy of 86.14%, which can classify drivers with different risk perception levels.
A Detection Method for Drivers' Fatigue States Based on Normalization of Epidemic Prevention
HUANG Ling, HONG Peixin, WU Zerong, LIU Jianrong, HUANG Zixu, CUI Zuan
2021, 39(4): 26-34. doi: 10.3963/j.jssn.1674-4861.2021.04.004
Abstract(337) HTML (241) PDF(28)
Abstract:
The detection of fatigue driving is a research branch of traffic safety, and wearing masks in the COVID-19 situation poses a new challenge. Therefore, the driver's face is detected by the single-shot multi-box detector(SSD)model based on ResNet-10, and the MobileNet-V2 model is used to classify masks. The test set verifies that the classifier can reach an accuracy of 98.50%. The histogram of the oriented gradient(HOG)feature combined with the support vector machine(SVM)classifier is used to detect the driver's face without wearing a mask. In the subsequent processing, the cascade regress is used to locate the feature points and extract the fatigue indices in the time window. The second judgment is used to perform the text and sound warnings for the fatigue state, and the judgment thresholds are adjusted in the awaken state. The algorithm experimented on pre-collected videos and NTHU-DDD can achieve the accuracy of 92.65 and 86.09% at the overall speed of 18.42 fps, respectively. The proposed framework shows strong robustness against the variation of wearing glasses, facial posture, and illumination, considering the interference of mask and real-time performance.
Influences of Longitudinal Slopes of Highways on Drivers' Heart Rate and Driving Speeds on Rainy Days
WU Yanxia, LIU Jian, HUANG Shuai, QIAO Jian-gang
2021, 39(4): 35-42. doi: 10.3963/j.jssn.1674-4861.2021.04.005
Abstract(330) HTML (159) PDF(24)
Abstract:
The quantitative relationships between longitudinal gradients and driving speeds and those between longitudinal gradients and drivers' heart rate growth rate(HRGR)are studied under sunny and rainy weather. Basic data is collected through a field driving test to extract feature parameters for data fusion. Other factors are unchanged to compare similarities and differences between the vehicle speeds and the drivers' HRGR on sunny and rainy days. Based on these, this study feature points of the longitudinal slope section are determined. Four quantitative models are established by analyzing the performance rules of drivers' HRGR and driving speeds on sunny and rainy days. These four models are the relationships between driving speeds and slope gradients on sunny and rainy days and those between drivers' HRGR and slope gradients. The results show that under the same road conditions, the change trends of drivers' HRGR and driving speeds on sunny and rainy days are the same. However, the drivers' HRGR on rainy days changes more greatly, with more deceleration. With the increased gradient range [1.0, 4.0%], the drivers' HRGR satisfy the exponential growth model in the downhill section and the logarithmic growth model in the uphill section. The driving speeds show a negative exponential decline in the uphill section. In the downhill section, the driving speeds satisfy the exponential growth on sunny days. On rainy days, the quadratic polynomial of the relationship first increases and then decreases.
A Cause Analysis of Extraordinarily Severe Traffic Crashes Based on T-S Fuzzy Fault Tree and Bayesian Network
ZHENG Lai, GU Peng, LU Jian
2021, 39(4): 43-51+59. doi: 10.3963/j.jssn.1674-4861.2021.04.006
Abstract(1108) HTML (352) PDF(189)
Abstract:
The T-S fuzzy fault tree and Bayesian network are integrated for an in-depth analysis to identify the main causes of extraordinarily severe traffic crashes. A T-S fuzzy fault tree is established, with the extraordinarily severe traffic crash taken as the top event, the human, vehicle, road, and environmental factors taken as the intermediate events, and 24 sub-factors taken as the basic events. The fuzzy fault tree is transformed into a Bayesian network, and the importance and posterior probability of the basic events can be inferred biaxially to determine the main causes. The results show that the method of fusing T-S fuzzy fault tree and Bayesian network can improve the accuracy and reliability of the analysis results of the causes of extraordinarily severe traffic crashes through forward and reverse reasoning and can determine improper operation, speeding, imperfect protection facilities, and bending. Slope combination, slippery road surface, and failure to drive following regulations are the six major causes of extraordinarily severe traffic crashes. The six major causes are analyzed, revealing that improper operation and speeding are more critical for extraordinarily severe traffic crashes.
The Optimized Scheduling of Emergency Supplies in Highways Under Emergencies
DOU Xuejing, WANG Aihui, SUN Feifei
2021, 39(4): 52-59. doi: 10.3963/j.jssn.1674-4861.2021.04.007
Abstract(310) HTML (167) PDF(14)
Abstract:
The optimized scheduling of emergency supplies in highways is studied to reduce personal injury and property loss caused by emergencies. This paper considers the situations of road damage after accidents, uncertain demand for emergency supplies, and different demand urgency of each accident point. According to the states of road damage after an accident, the revised driving speed of the rescue vehicle is determined. The fuzzy emergency supplies demand of the accident spots is transformed into a determined value using triangular fuzzy theory. Based on the traditional TOPSIS method, subjective and objective methods are used to determine the weight of indicators. The type-B correlation degree is used to objectively describe the distance between the evaluation object and ideal solutions. The demand urgency coefficients of each accident point are obtained by the above method to ensure the fairness of scheduling. On this basis, a multi-objective optimization model of emergency supplies schedule is established for the threelevel dispatching network. Taking Ya'an Earthquake as a case study, the fast non-dominant sequential genetic algorithm with the elite strategy(NSGA-Ⅱ)is used to solve the model. The results show that introducing demand urgency into the emergency supplies schedule can realize the priority distribution of accident points with high demand urgency.The emergency supplies scheduling scheme saves the delivery time by 21%, reduces the vehicle transportation cost by 25%, and meets the supplies demand by 100%. Considering the post-disaster road damage and fuzzy demand, the schedule optimization of emergency supplies is more consistent with the actual situation.
Transportation Information Engineering and Control
A Method for Extracting Regular Bus Parking Stops of Road Passenger Transport Based on Trajectory Data
LI Jun, JIE Chao, WANG Lin, GAO Zhongling
2021, 39(4): 60-67. doi: 10.3963/j.jssn.1674-4861.2021.04.008
Abstract(278) HTML (141) PDF(16)
Abstract:
It is of great significance to identify and extract the locations of the parking stops of regular buses of road passenger transport, providing support for optimizing the station location, setting the stops for customized travel, and the travel information service in road passenger transport. However, the current methods to obtain the location of parking stops of regular buses have problems of high cost and long cycle. The DBSCAN algorithm is used to detect point clusters located within parking stops and extract the parking stop location of regular buses by analyzing the typical characteristics of the track data of parking stops and taking those as the data source. Meanwhile, the DBSCAN algorithm is improved by establishing a special spatial grid index to decrease time complexity. Based on the track data of 136 regular routes of passenger service in the Beijing-Tianjin-Hebei region, this paper makes an empirical analysis.The results show that the improved DBSCAN algorithm can improve the execution efficiency, with the average execution time reduced by 59.72%. The number of parking stops is consistent with those generated by the traditional algorithm. Among the 282 regular bus parking stops extracted, 256 are real regular bus-parking stops with an accuracy rate of parking stops extraction of 90.78%.
Travel Destination Prediction of Public Transport Commuters by Integrating XGBoost Algorithm and Graph Adjustment Method
LIANG Quan, WENG Jiancheng, HU Juanjuan, HAN Bing
2021, 39(4): 68-76. doi: 10.3963/j.jssn.1674-4861.2021.04.009
Abstract(528) HTML (245) PDF(32)
Abstract:
Accurate grasp of the destinations of public transport commuters can clarify travel needs of passengers and improve public transport service. The data of public transport in one-month and the revealed preference(RP)survey in Beijing are collected. The travel chain of 563 public transport commuters is obtained through the association analysis of smart card numbers, transaction data, and network data. A total of 302 public transport commuters with high, medium, and low public travel stability are identified by association rules. The XGBoost integrated learning algorithm is introduced to develop a prediction model of the next travel destination for individual public transport commuters with different travel stabilities. The factors significantly influencing travel destinations are input variables. The following trip destination is the output variable. The prediction model is constructed by adjusting and optimizing parameters repeatedly. The destination prediction accuracy of passengers with high, medium, and low stability is 90%, 66.67%, and 50%, respectively. Besides, the transfer probability of the graph is utilized to revise the predicted results. The prediction accuracy is improved to 91.2%, 83.21%, and 69.5%. The transfer probability of the graph can improve the prediction accuracy of the passengers' travel destinations with medium and low stability. The destination data from the bus metropolitan system is used to compare and verify the aggregation results of destination prediction for the next trip.The absolute percentage error of the predicted value and the true value-changing gradient is less than 10%. Thus, the method of travel destination prediction by combining XGBoost and travel graph correction based on dividing public transport commuters' travel stability has high accuracy.
A Prediction Method for Short-term Parking Demands in Variable Interval Based on Particle Swarm Optimization and LSTM Model
LIU Donghui, XIAO Xue, ZHANG Jue
2021, 39(4): 77-83. doi: 10.3963/j.jssn.1674-4861.2021.04.010
Abstract(229) HTML (114) PDF(23)
Abstract:
An intelligent parking guidance system is widely considered to solve the problem of difficult parking at present, providing management, traffic participants, and parking operators with immediate and future parking information. A prediction method for short-term parking demands in variable interval based on particle swarm optimization and LSTM model is studied due to the importance of parking information. Based on the birth and death of the Markov process, the characteristics of the temporal parking demand are analyzed. It is formulated as a combination of the arrival rate and departure rate of parking calibrated by the temporal parking quantity. Dynamic prediction intervals are determined according to the calibrated arrival rate and departure rate. The improved LSTM network is used as the basic prediction model, and the network parameters are optimized by the particle swarm optimization algorithm. The parking lot in the Nanling campus of Jilin University is selected as a research object, and its parking data are predicted and compared with other prediction models. The results show that the MAE and MSE of the proposed parking demand prediction model are 2.53 vehicles and 11.89 vehicles in working days, respectively. For non-business days, the MAE is2.32 vehicles and the MSE is 10.89 vehicles. Therefore, a predictable prediction model of parking demands proposed in the work can predict the real-time and future parking demands, providing a reliable reference for management, traffic participants, and parking operators.
An Energy-saving Optimization Method of High-speed Trains Based on Time Deviation Penalty During Train Operation
MA Yangyang, MENG Xuelei, JIA Baotong, REN Yuanyuan, QIN Yongsheng
2021, 39(4): 84-91. doi: 10.3963/j.jssn.1674-4861.2021.04.011
Abstract(263) HTML (86) PDF(8)
Abstract:
Reasonable arranging the operation mode of the train in the section can reduce the energy consumption of train operation. A determination strategy of train operating conditions based on the speed limit of the interval is adopted to determine the operating condition of the train. The energy consumption of the train is used as the optimization objective, and the distance, time, and speed limit of the train are used as the constraints. Time deviation penalty during train operation is added to the objective function to develop a mathematical model of energy-saving optimization for high-speed railway train operation, and the improved artificial bee colony algorithm based on Gaussian mutation and chaotic disturbance is used to solve the optimization model. The model and algorithm are verified with CRH3-350 multi-unit data as an example, the solution results show that the energy consumption can be saved by 2.5% when time deviation penalty during train operation is considered. Compared with the basic artificial bee colony algorithm and particle swarm algorithm, the improved artificial bee colony algorithm has improved the target value by 4.2% and 4.1%, respectively. Adopting the determinative strategy based on the interval speed limit combined with the energy-consumption optimization model can meet the required train operation conditions under different speed limits and different intervals. It shows that the established model and the designed algorithm have good problem-solving efficiency and optimized quality.
Speed Trajectory Optimization of Connected Autonomous Vehicles at Signalized Intersections
CHEN Zhuangzhuang, LUO Lihua
2021, 39(4): 92-98, 156. doi: 10.3963/j.jssn.1674-4861.2021.04.012
Abstract(535) HTML (195) PDF(48)
Abstract:
The work studies the optimal control strategy of the speed trajectory for the connected autonomous vehicle(CAV)platoon at urban signalized intersections. Based on the optimal control theory, the automatic driving model is utilized to describe the interaction among vehicles. With the total fuel consumption for the CAV platoon considered as the optimization objective, the constraints of the model are established according to the timing phase of the traffic signal. All the CAVs in the platoon can pass through the intersection with the minimized total fuel consumption by optimizing the speed trajectory for the leading CAV. The necessary conditions for the optimal solution are obtained based on Pontryagin's minimum principle to solve the proposed optimal controller. Then, the numerical solving algorithm is developed utilizing the resilient backpropagation(RPROP)solution algorithm. The simulation results for multiple representative scenarios show that the whole CAV platoon can pass through the signalized intersection without any stop and avoid stopping and starting caused by reaching the stop line at the red time window. Moreover, the total amount of fuel consumption can be decreased by 69.74% at most. The proposed method which takes advantage of CAV technology can improve the traffic efficiency and fuel economy for urban transportation.
Optimization of Guide Signs in Subways Based on Pedestrian Cognition Laws
HAO Yarui, LEI Bin, ZHANG Yuan, SUN Yingya
2021, 39(4): 99-107. doi: 10.3963/j.jssn.1674-4861.2021.04.013
Abstract(215) HTML (112) PDF(13)
Abstract:
Guide signs in subways play an important role in guiding pedestrians during their travel. Many guides are disorderly set up and overloaded with information, resulting in a low cognitive rate. This paper takes pedestrian cognition laws as a starting point and designs indoor cognitive experiments for the forgetting laws of immediate memory and short-term memory of pedestrians to enable pedestrians to obtain information from guide signs more efficiently. The instantaneous memory capacity of identifying different block numbers is studied by dividing the guide identification information into blocks. Brown-Peterson method is used to study the information forgetting law in short-term memory.The results show that the immediate memory of pedestrians in 3 seconds is 4±1 information chunk, and pedestrians can keep information memory for 15-20 s without retelling. By the tracking test of pedestrians at the transfer station of Xi'an Metro Line 2 and Line 3 at Xiaozhai Station, the characteristics of pedestrians' behaviors are observed, with problems existing in the setting of guide signs analyzed. The results are consistent with indoor cognitive experiments, which verify the experiments.
Transportation Planning and Management
Optimization on Train Operation Adjustment on Single-track Railway from Discrete Perspective
ZHANG Zhengkun, ZHU Changfeng, MA Wenhu
2021, 39(4): 108-116. doi: 10.3963/j.jssn.1674-4861.2021.04.014
Abstract(200) HTML (139) PDF(6)
Abstract:
A simulation model of discrete system is constructed from a perspective of system simulation to solve theproblem of adjusting multi-class train operation on a single-track railway , considering key constraints such as the typeof technical operation and the number of arrivals and departure tracks at each station. Based on the existing train advance strategy, an event transfer function is designed to avoid the repeated execution of the adjustment strategy duringinterval operation and the minimum stopping time of the train at the station. In this way,the system timeliness isstrengthened during the simulation of adjusting multi-class train operation.Considering the difficulties caused bymulti-class trains for conflict reliefs,a finite random strategy adjustment for same-class trains and a layered randomstrategy adjustment for multi-class trains are sequentially designed using the advantage of hierarchical decision making in predicting crossing stations and overtaking stations. It can improve the global searching ability of the discretesimulation system during adjusting train operation.Finally, the effectiveness and rationality of the simulation model ofthe discrete system,as well as the strategy adjustment,are verified through an example analysis. The results areshown as follows: ① The strategy adjustment designed in the work can improve the quality of the solution by about 5.77%.② The event transition function in the strategy adjustment can improve the timeliness of the system simulationby about 34.47%.③ Although the hierarchical strategy can ensure the timeliness of high-class trains,it needs to sacrifice the timeliness of low-class trains,losing the system's timeliness by about 45.3%
An Assignment Method of Commuter Flow of Private Cars Under Travel Reservation
BAI Zixiu, JIAO Pengpeng, CHEN Yue, LIN Kun, YUN Xu
2021, 39(4): 117-124. doi: 10.3963/j.jssn.1674-4861.2021.04.015
Abstract(192) HTML (79) PDF(15)
Abstract:
Travel reservation alleviates the supply-demand contradiction of urban transportation, which utilizes transportation resources adequately. For the traffic jams caused by private car commuting, the assignment method of the commuter flow of private cars under travel reservation is studied. It divides vehicles into the controllable reservation and uncontrollable non-reservation ones, and road status into reservable/non-reservable road status. The methods to calculate road state discrimination and vehicles' travel time are given to propose a travel reservation model for urban commuting private cars. The Nguyen-Dupuis network is used to evaluate the effect of implementing travel reservations in terms of both travel time and the number of reservation vehicles. The results show that when the travel reservation rate is increased from 0% to 100%, the path travel time is reduced by 20%-30%, and the average travel time is decreased from 610 s to 466 s. With a 30% reservation ratio, 80% of the benefits of the full reservation ratio can be obtained. There is still 2% of vehicles failing to reserve due to uneven demands for reservations even when all vehicles are expected to participate in the reservation. It is concluded that the expected congestion relief can be achieved when the reservation rate reaches 30%-40%.
Optimization of Container Train Service Route Based on Sea-Rail Intermodal Transportation
ZHANG Fengting, YANG Juhua, YU Jiang, QIN Yongsheng, SHEN Facai
2021, 39(4): 125-133. doi: 10.3963/j.jssn.1674-4861.2021.04.016
Abstract(470) HTML (174) PDF(27)
Abstract:
Since uncertain factors are affecting the operation of container trains in the process of sea-rail intermodaltransportation.Combined with the customers’ demand for a fixed time window , the uncertain planning interval is introduced to represent the range of time in container loading and unloading at each customer node.Meanwhile,the demand time window with timeliness requirements is set as a soft constraint. The penalty function is integrated into theobjective function of the transportation cost as a penalty term. A reasonable penalty coefficient is selected to constructa multi-objective optimization model of the train service path combined with the low transportation cost and less transportation time. For uncertain variables,the chance-constrained programming transformation model is used to obtain amulti-objective path optimization model considering fuzzy time. Then, the multi-objective problem is transformed intoa single objective problem by weighted summation, and the artificial bee colony algorithm is designed to solve the constructed model.The results of sea-rail intermodal transportation in Yantian Port show that:① The transportation timeis reduced by 88% in the constraint of hard time windows, but the cost is increased by 97%,fully showing the advantage of soft time windows.② When only the transportation cost is considered,the transportation time increases by5.3%. When only the transportation time is considered , the transportation cost increases by 67.8%.These experimental results confirm that the proposed model reduces the transportation cost and meets the needs of different transportation timeliness of different customers.
A Plan for Passenger Train Operation Based on the Dynamic Allocation of Seat Types
DAI Wanqing, YANG Xinfeng
2021, 39(4): 134-142. doi: 10.3963/j.jssn.1674-4861.2021.04.017
Abstract(179) HTML (136) PDF(5)
Abstract:
Dynamic seat allocation is considered to formulate a plan for passenger train operation meeting the diversified needs of passengers,thus improving the service quality in the railway departments and the travel experience ofrailway passengers.From passengers and railway operation departments,this paper takes the minimum generalizedtravel time of passenger dynamics and the maximum railway revenue as the objectives and the conservation laws of passenger flow,interval through ability,and passenger flow volume demand as the constraint conditions. Besides,themulti-objective planning model is established. A Logit model is used to obtain the fixed number of passengers and theshare rates in the hard seat , hard berth, and soft berth. In the solving process , the share rates in various seats are constantly updated by the generated operation plan to realize the dynamic allocation of the three categories of seats untilthe results are obtained.'The example is solved using the non-dominated sorting genetic algorithm with elite strategy(NSGA-II) , and then the analysis of the example is carried out. The results show that the establishment of the plan forpassenger train operation considering the dynamic allocation of seat types has increased the passenger-railway servicerate by 3.5% and the railway's revenue by 1.5%,realizing the passenger's choice of seats.
A Hierarchical Spatiotemporal Optimization Model of Customized Bus Routes Considering Time Windows
WEN Dong, ZHANG Mengmeng
2021, 39(4): 143-150. doi: 10.3963/j.jssn.1674-4861.2021.04.018
Abstract(276) HTML (136) PDF(17)
Abstract:
Studying the layout and scheduling optimization of a customized bus line network has important implications, enhancing the attractiveness of the public transport system and passenger travel. However, the distribution of customized bus passengers' demand points in time and space is discrete, which hinders the bus line design. A timespace hierarchical optimization model of customized buses considering time windows is constructed to solve this problem, and a genetic algorithm is designed to solve the model. The hot spots of demand points are identified in time and space by analyzing the fishing net and kernel density, with the cluster analysis of hot spots and the classification of bus pooling realized. Based on the set of bus pooling, a space optimization model of bus lines is constructed by the bus capacity, line length, and passenger travel distance. A time optimization model of bus lines is constructed by the minimum time cost of passengers. Jinan customized bused are used to evaluate the performance of the model. The results show that the routing scheme is optimized by the model, with an average service coverage rate of passengers of 96%, the average travel time saved by the single passenger in each period of the service area of 15 minutes, and an average load factor of public transport of 90%.
An Approach for Determining Seating Capacity of 12m City Buses Based on Passenger Density
YAN Shengyu, ZHAO Zhuanzhuan, ZHANG Kaichao, ZHANG Zijian
2021, 39(4): 151-156. doi: 10.3963/j.jssn.1674-4861.2021.04.019
Abstract(283) HTML (109) PDF(9)
Abstract:
The projected area of seated passengers on board is investigated to provide an optimal solution for the seating configuration to adapt to the passenger flow in the operational periods and improve the serviceability of city buses. The criteria regarding whether the current trip is a peak shift are defined, with a passenger density index proposed for optimizing the seating capacity in operational periods considering both standing and seated passengers. Besides, the work analyzes the effects of the number of people on board and the peak coefficient on the optimal solution of the capacity of seats. An approach is proposed to determine the optimal capacity of seats with a single seat configuration of all the 12 m city buses, with the correspondence between capacity of seats allocated to the bus lines and the operational periods discussed. The feasibility of the present approach is demonstrated through a case study. Moreover, the applicability of three main seat configurations to the operational periods and the attributes of the bus lines are compared. The results show that the projected area of a seated passenger is 0.35 m2 in the city buses in Xi'an and the peak coefficient has a more significant effects on the capacity of seats of the 12 m city buses. The recommended value of the capacity of seats of the 12 m city buses preferably ranges from 21 to 43 seats. When the deviation of the actual capacity of seats with the optimal solution is controlled within 2 seats and the specifications of seat configuration are considered, which adapted to the bus line can achieve the desired effects.

Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

Tel：027-86580355 Email：jtjsj@vip.163.com

Supported by: Beijing Renhe Information Technology Co. Ltd