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Dynamic Differential Pricing of High-speed Railway Parallel Trains Considering Revenue Management CAI Jian-ming, OUYANG Shan 2020, 20(5): 1-8.  Abstract ( )   PDF (1635KB) ( )   Based on the Revealed Preference (RP) and Stated Preference (SP) survey data, this paper used the latent class model to subdivide railway passengers and obtained the passengers' preference for different service attributes of parallel trains such as train running time, departure time and comfort with quantified analysis. Then, a dynamic differential pricing model was presented for parallel trains based on revenue management, which set the goal as maximum total revenue of multiple trains. The simulated annealing algorithm was used to solve the model. The results from Beijing- Shanghai high- speed railway case study show that compared with the existing fixed pricing strategy, the proposed scheme can adapt to different passenger flow characteristics during peak and offpeak periods. It can also improve the total revenue from ticket sales, and provide reference for flexible pricing of high-speed railway parallel trains. References | Related Articles | Metrics

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Dynamic Optimal Supply Strategy of Parking Permits WANG Peng-fei, WANG An-ge, GUAN Hong-zhi, ZHAO Lei, ZHAO Peng-fei 2020, 20(5): 9-14.  Abstract ( )   PDF (1347KB) ( )   This study aims to clarify a dynamic optimal supply of parking permits considering the situation in which reservable and unreservable public parking facilities exist in an area simultaneously. We first mathematically established a stochastic optimal control model to minimize the expectation of the total time lost. We then analyzed the optimality condition, i.e., Hamilton-Jacobi-Bellman equation, by applying the dynamic programming principle. Finally, with the estimated parameters in the predetermined optimal value function, we obtained the analytic solution of the dynamic optimal supply strategy. The results show that the dynamic optimal supply strategy is a feedback control strategy, which means the proposed strategy is a function of queuing time. The dynamic optimal supply strategy can be divided into two patterns basing on the existence of queuing time. Taking the Guomao area in Beijing City as an example, the Monte-Carlo experiment results show that the dynamic optimal supply strategy can save 0.45~1.94 min/veh compared with the full supply strategy of parking permits. With the increase of the uncertainty in the parking permits holders' behavior, the average time-saving value increases, but the coefficient of variation decreases. References | Related Articles | Metrics

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Complete Decomposition Model of Demand of Transportation System in West under Background of Industrial Transfer ZUO Da-jie, XIAO Guo-sheng, WANG Meng-yun 2020, 20(5): 15-20.  Abstract ( )   PDF (1299KB) ( )   This paper studies the driving factors of the demand for the integrated transportation system in the western region of China to provide the basis for the transportation industry to formulate a reasonable development strategy. This paper takes industrial transfer as a factor to improve the original complete decomposition model. A complete decomposition model is developed, including four major factors, i.e., the total economic volume, intensity of transportation demand, industrial transfer, and overall change of industrial structure. A case study on the demand of the integrated transportation system in the western region based on the statistical data from 2002 to 2015 is carried out. The research shows that the economic growth is the main reason for the demand growth of the integrated transportation system. With the decrease of economic growth and the introduction of industrial transfer promotion policies, the impact of the transportation demand intensity and industrial transfer is gradually significant. Therefore, in the context of industrial transfer, the western region should do a good job in the comprehensive transportation system layout according to the development trend of industrial transfer, so as to create better conditions to undertake industrial transfer. References | Related Articles | Metrics

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Forecasting Volume of Urban Function Development of Existing Railway Station with Integrating Station-city Development FENG Tao, PENG Qi-yuan, TAO Si-yu, CHEN Xin-mei 2020, 20(5): 21-28.  Abstract ( )   PDF (2297KB) ( )   This paper proposed a method to forecast the urban function development volume for an existing railway station and its affected areas. The scientific and effective suggestions were then provided for station-city integration development due to the growing demand of urban Transit- Oriented Development (TOD). This paper analyzed the traffic residual capacity of roads around the station and the restriction of the plot ratio of urban planning, with the consideration of the proportion between the volume of the urban function and the traffic function. A volume forecasting model of the urban function was developed for the existing railway station and the model was solved by a commercial solver. The range of the overall development volume was obtained for various commerce formats. The Shapingba integrated transport hub in Chongqing, China was taken as an example to test the consistency between the solution results and the actual volume. The sensitivity of transportation function proportion and plot ratio were analyzed to verify the validity and applicability of the model. The results show that the method can effectively exploit the road loading capacity and achieve the coordinated development of rail hub and land- use that meets the overall urban development requirements. The proposed method provides a new perspective for investors to strengthen the intensive land use. References | Related Articles | Metrics

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Inferring Land Use Characteristics Using Travel Patterns ZHANG Zheng, CHEN Yan-yan, LIANG Tian-wen 2020, 20(5): 29-35.  Abstract ( )   PDF (1795KB) ( )   This paper proposes a land use inferring method based on the convolutional neural network (CNN), which can infer multiple lane use types at the traffic analysis zones (TAZs) simultaneously. The study combines public transport mobility dataset and online car- hailing mobility dataset for inferring land use type. Generation intensity, attraction intensity, and difference between generation and attraction intensity are extracted from the travel dataset, which are then used to train the CNN. The optimal network structure is determined by grid search. The TAZs within the 6th Ring Road of Beijing are taken as examples for the analysis. The results indicate that the proposed method is able to estimate the proportion distribution of several land use types at the same time within the TAZs, such as resident, workplace and leisure land uses. References | Related Articles | Metrics

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Vehicle Headlights Intention Recognition Model for Connected and Automated Vehicles in Mixed Traffic Flow LIANG Jun, QIAN Chen-yang, CHEN Long, WANG Wen-sa, ZHAO Tong-yang 2020, 20(5): 36-44.  Abstract ( )   PDF (2669KB) ( )   This study proposes a Vehicle Headlights Intention (VHI) recognition model to improve the communication between the Connected and Automated Vehicles (CAV) and Human- driven Vehicles (HV). The VHI recognition model is consist of light perception module, optical data processing module, and VHI recognition module. The light perception module is able to locate and track the HV that sent a light signal through the RedGreen-Blue (RGB) and Hue-Saturation-Value (HSV) color space, the Kanade-Lucas-Tomasi Tracking (KLT), and the vehicle matching algorithm. The optical data processing module calculates the optical radiant flux using optical channel gain algorithm. The VHI recognition module identifies the number of headlight flashing and vehicle driving status based on the Double- layer Hidden Markov Model(DHMM). The experimental results from three typical VHI scenarios indicate that the average accuracy of vehicle headlights perception is 96.8%, and the error of positioning and tracking is within 1 degree. The 1-second VHI recognition rate reaches 96.6%, which enables the driving intention recognition of CAVs and provides the basis for the automated driving decision of the CAV in mixed traffic flow. References | Related Articles | Metrics

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Calculation Method of Visual Information for Driver in Mountainous Highway MENG Yun-wei, CHEN Lei, LIU Bo-hang, CHEN Bing-yang, PAN Xiao-dong 2020, 20(5): 45-50.  Abstract ( )   PDF (1819KB) ( )   In order to quantify the amount of driving visual information in mountain highway, driving vision images are first segmented, the HSV color model is used to extract the hue, saturation and brightness values of the vision image, and the driving visual information calculation method of mountain highway is proposed based on driving vision psychological load. A real vehicle experiment is carried out, then the relevant data are collected, and the above calculation method is used and verified. The results show that the visual information received is the largest when driving in the semi closed space, and the one is the smallest in the closed space. The calculated results are consistent with the actual feelings of the test driver. The calculation method of driving visual information is feasible and can provide some technical reference for the reasonable layout of the road environment. References | Related Articles | Metrics

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Travel Intensity Influencing Factors Analysis Model Based on Signaling Data LEI Fang-shu 2020, 20(5): 51-55.  Abstract ( )   PDF (1679KB) ( )   This paper investigates the impact factors of urban travel intensity and the corresponding degree of impact. The land use and transportation infrastructure construction factors are considered in the analysis the 17 indicators involved in the analysis include land use mixing index, job-residential mixed ratio entropy index, public transportation stops 500-meter coverage, road network accessibility, so and so forth. Seven indicators with strong correlation with travel intensity were extracted based on correlation coefficients and goodness- of- fit analysis. A travel intensity multiple regression model in the central city of Beijing was developed based on the extracted indicators. Model results show that the work-resident ratio entropy index has the most significant impact on travel intensity. The impact of public transportation stop coverage rate on travel intensity is more obvious than road network density and accessibility. In addition, this paper also proposes a method of the outlier character analyze based on single land use or transportation infrastructure construction index fitting result, which is used to evaluate the balance between infrastructure supply and travel demand for different regions. References | Related Articles | Metrics

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Resident Travel Characteristics Analysis Method Based on Multi-source Data Fusion SU Yue-jiang, WEN Hui-ying, WEI Qing-bo, WU De-xin 2020, 20(5): 56-63.  Abstract ( )   PDF (2206KB) ( )   This study proposes an analysis method to investigate residents' travel characteristics based on multisource data fusion by using traditional household travel survey and transportation big data. The residents travel characteristics were initially analyzed through a combination of traditional household survey data analysis (age, occupation, vehicle ownership, population distribution) and mobile phone signaling data analysis (travel frequency distributions). Then, the residents' travel characteristics were further analyzed through mobile phone signaling, Intelligent Card (IC), Automatic Fare Collection (AFC), the Global Positioning System(GPS) and other big data. The analysis results include residents' travel time distribution, Origin- Designation (OD) distribution, and travel mode structure. The resident travel characteristics of Guangzhou, China was analyzed as an example. The study then compared the proposed method with traditional household survey data analysis methods. The results indicated that about 30% residents' trips were not recognized by traditional household sampling surveys; the proportion of travel rate of twice a day generated by these two methods were 39.5% different; and the differences generated by these two methods for non-commuting trips, bus trips in PM peak hours, and subway trips in PM peak hours were respectively 7.4%,8.1%and 12.6%. Compared with the traditional method, the multi- source data fusion analysis method is more effective to identify and analyze residents travel characteristics. It plays an important role to examine and balance residents' travel needs with the time and space distributions. References | Related Articles | Metrics

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Pedestrian Crossing Control Strategy for Parallel Flow Intersection AN Shi, SONG Lang, WANG Jian, YANG Lu 2020, 20(5): 64-71.  Abstract ( )   PDF (1943KB) ( )   This study focuses on solving the conflict between pedestrians and motor vehicles at parallel flow intersections and considers the alternate scramble game process between pedestrians and motor vehicles at the conflict point to find the gap crossing. The study redesigns the pedestrian crossing signal phase scheme, develops a pedestrian crossing mode with pedestrian vehicle overlapping phase, and then proposes two types of signal control strategies for pedestrian crossings with the consideration of the pedestrian exclusive phase crossing mode. The optimization model is developed for these two strategies and transformed equivalently to reduce the computational complexity. The results indicate that at the normal intersection in highly saturated state, the vehicle delay of parallel flow intersection is reduced by 73.8% and 50.3% respectively under the two pedestrian crossing modes, which shows more benefit from the pedestrian vehicle overlapping phases. Compared with the situation without pedestrian crossings, the vehicle delay caused by pedestrian vehicle overlapping phases only increases by 0.6 to 3.8 seconds per vehicle. The research results serve as a theoretical basis for pedestrian signal timing at parallel flow intersections. References | Related Articles | Metrics

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Information Release Strategy of Urban Rail Transit Based on Reinforcement Learning JIA Fei-fan, JIANG Xi, LI Hai-ying, YU Xue-qiao 2020, 20(5): 72-78.  Abstract ( )   PDF (1815KB) ( )   Guidance information can change the choice behavior of passengers and thus network passenger flow distribution. Information release is one of the key measures to alleviate the congestion problem from demand ideas. A method is proposed to generate an information release strategy based on reinforcement learning. The system state is extracted based on the load rate of passenger flow in each section in the network. The information release action is composed of the recommended paths of each OD. The reward value of implementing an information release action is evaluated according to system state change. By an urban rail transit dynamic passenger flow simulation system, the Q- learning algorithm is employed to obtain optimal information release strategy. A practical network is taken as an example to verify the proposed method. It was found that network congestion can be alleviated by using the proposed information release strategy. References | Related Articles | Metrics

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Method of Bus Left-turn Priority at Intersection Based on Variable Lane CHEN Yong-heng, LI Wan-ning, WU Chang-jian 2020, 20(5): 79-85.  Abstract ( )   PDF (2198KB) ( )   In order to reduce the delay of left-turn buses at signalized intersections and thus improve the traffic efficiency, a variable bus approach lane (VBAL) control method based on bus pre-signal is developed. Firstly, the geometric model and the main signal and pre-signal control model for the variable bus lane are proposed. Next, the single left- turn lane (SLTL) scheme is compared with the double left- turn lane (DLTL) scheme by vehicle cumulative curve, and the delay calculation model of left- turn bus and through car is established. And the sensitivity analysis of the model is carried out. The results show that the VBAL scheme can effectively reduce the delay of left-turn bus, and minimize the increase of the delay of the through vehicles. Finally, the feasibility of the VBAL method is proved by the case analysis. References | Related Articles | Metrics

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Analysis of Factors Influencing Fast Charging Behavior Based on Data of Connected Electric Vehicles YANG Ye, TAN Zhong-fu1 JIAO Gang-xin 2020, 20(5): 86-92.  Abstract ( )   PDF (1660KB) ( )   The research of this paper is based on the data of connected electric vehicles in Beijing. Firstly, electric vehicle trips are extracted with the type of charging behavior, and potential factors influencing the fast charging behavior are analyzed. Then, a logistic regression model is developed to identify the factors influencing the fast charging behavior, which includes available driving ranges, travel distance, and travel time. Finally, based on the significant influencing factors, a model is established to predict the fast charging behavior of private EVs. The prediction results show that the model has good prediction performance. The research results could help to optimize the charging behavior of private electric vehicles and improve the charging efficiency. References | Related Articles | Metrics

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Traffic Flow Data Imputation Method Based on Symmetrical Residual U-Net DAI Liang, MEI Yang, LI Shu-guang, QIAN Chao, WANG Gui-ping 2020, 20(5): 93-99.  Abstract ( )   PDF (2392KB) ( )   A large-scale road network traffic flow data imputation method based on the symmetrical Residual UNet (RU-Net) model is proposed for the traffic flow data missing caused by the scarcity or failure of traffic data collectors in the process of road network traffic data collection. By gridding the traffic flow data and channelizing the timing, then forming the tensor data format for convolution operation, this method uses the coding and decoding ability of RU-Net to encode the traffic flow data, and keeps the distortion small in the decoding process, to learn the internal multi-factor coupling characteristics of the traffic flow data. Residual learning can improve the signal-to-noise ratio of traffic flow data after coding, reduce the compression rate, and further improve the repair accuracy. Experimental results show that the RU-Net model can effectively repair the large-scale network traffic flow data under different data missing rates and different missing patterns by mapping relationship between the traffic flow characteristics learning history, non-fault collectors' data, and the data to be repaired. References | Related Articles | Metrics

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Mileage Prediction of Electric Vehicle Based on Multi Model Fusion HU Jie, WENG Ling-long, QIN Xiong-zhen, DU Yu-feng, GAO Zhang-bin 2020, 20(5): 100-106.  Abstract ( )   PDF (2222KB) ( )   The driving mileage prediction of pure electric vehicles is one of the most concerns for drivers. The existing regression models always have the drawbacks of low prediction accuracy and large relative error. This paper developed a machine learning method that combines segment regression prediction and single- point classification prediction to predict the mileage. The prediction method took real vehicle state parameters, environmental information as input, extracted the optimal feature set by clustering and filtering encapsulated feature selection, then selected a prediction method based on the sample size of driving segments, and layered coupling prediction of environmental temperature and battery health state (SOH) to improve the prediction accuracy of fragment regression. The final prediction result was further optimized by the model fusion of single point classification prediction and fragment regression prediction. The RMSRE relative error of the predicted result of the mileage test set is 0.035, and the average relative error is 1.71% , which can accurately and stably achieve the mileage prediction. References | Related Articles | Metrics

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A Real-time Prediction Method of Curbside Parking Occupancy Incorporating Dynamic Management Policies ZHAO Cong, ZHU Yi-fan, LI Xing-hua, DU Yu-chuan 2020, 20(5): 107-113.  Abstract ( )   PDF (1886KB) ( )   This paper proposes a machine learning method to predict curbside parking occupancy in dynamic parking policies. We apply the convolutional long short term memory neural network (ConvLSTM) to learn the temporal and spatial features of the data simultaneously. Based on the 4.92 million transaction records of parking meters in San Francisco, we train a policy model that incorporates the information of dynamic pricing and parking limits, and a non- policy model without other information. The results show that both the policy and non- policy model can predict the curbside parking occupancy, and the policy model has better performance in training efficiency and prediction accuracy. Meanwhile, the errors of the non-policy model increase with different parking policies, whereas the policy model can always obtain high prediction accuracy. References | Related Articles | Metrics

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Spatial Feature of Mandatory Lane Changing and Its Impact on Traffic Flow at Diverging Area ZHONG Yi-ying, CHEN Jian, SHAO Yi-ming, LI Rui 2020, 20(5): 114-120.  Abstract ( )   PDF (2300KB) ( )   To examine the vehicle following and lane- changing behaviors at multiple lane diverging area, this study classified the mandatory lane-changing behavior into aggressive and mild mandatory lane-changing behaviors. Based on the relation between the desire of lane-changing and vehicle position, this study quantified the transformation conditions of the aggressive and mild mandatory lane- changing behaviors. The rules of lanechanging behavior of diverging vehicle were proposed by optimizing the deceleration factor of car- following model. The car- following and lane-changing model for the multiple lane diverging area was then developed and the critical parameters were calibrated. The model was validated by the real data. The result shows that the spatial distribution of diverging vehicles at each lane have significant impact on the stability of traffic flow. When the leftmost lane has high density of diverging vehicles at, the middle two lanes would experience significant speed. The maximum reduction rate of vehicle speed was 51.4% , and takes longer time to restore the traffic flow stability. Based on the analysis of four different scenarios, the average speed could be significantly improved by reasonable spatial distribution of diverging vehicles. References | Related Articles | Metrics

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Evolution Mechanism of Congestion and Dissipation of Sudden Passenger Flow in Urban Rail Transit Based on Cellular Automaton JIANG Yang-sheng, LIU Wen-tao, YAO Zhi-hong 2020, 20(5): 121-127.  Abstract ( )   PDF (2098KB) ( )   To ensure the system safety and operational efficiency of urban rail transits under the condition of sudden passenger flow, this paper establishes a congestion propagation model of rail transit based on cellular automata. The propagation process of sudden passenger flow congestion is first analyzed, and the state parameter set is used to reflect the diversity of station congestion. A more accurate and reasonable evolution rule of passenger flow cellular automata is defined. Taking the rail transit network of Chengdu as an example, the evolution law of the passenger flow state is examined by simulating the sudden passenger flow with different values. The results show that the evolution speed from regional congestion to line congestion decreases with the improvement of sudden passenger flow, while the speed from line congestion to point congestion remains unchanged or slightly increases. In addition, a large number of sudden passenger flow will slow down the speed of congestion dissipation, and then cause regional congestion in a larger space- time range. The research results can provide decision support related to passenger evacuation for rail transit management departments. References | Related Articles | Metrics

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Short-time Inflow and Outflow Prediction of Metro Stations Based on Hybrid Deep Learning ZHAO Jian-li, SHI Jing-shi, SUN Qiu-xia, REN Ling, LIU Cai-hong 2020, 20(5): 128-134.  Abstract ( )   PDF (1867KB) ( )   This paper proposes a prediction model (ResNet- CNN1D) combining convolutional neural network (CNN) and residual network (ResNet) for multi- station short- term passenger volume prediction of urban rail transit. The original passenger volume data is used as input of the model. The deep network composed of twodimensional CNN and ResNet is used to mine the spatial features between the stations. The one-dimensional CNN is used to mine the temporal features of the passenger flow. Based on the parametric matrix, the temporal and spatial features are weighted to obtain the multi-station inflow and outflow during the research period. The model is verified by the card-swiping data of the Qingdao No.3 metro line. Compared with existing traditional prediction models (ARIMA, SVR, LSTM, CLTFP, ConvLSTM), the proposed ResNet-CNN1D model in this paper has the best prediction accuracy. References | Related Articles | Metrics

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A Self-organized Equilibrium-oriented Relocation Optimization Method for Electric Vehicle Sharing YAO En-jian, HE Yuan-yuan, JIN Fang-lei, LU Tian-wei, PAN Long 2020, 20(5): 135-141.  Abstract ( )   PDF (1355KB) ( )   The Electric Vehicle(EV) sharing is facing the challenges of unbalanced vehicle distribution at different stations and the relocation operation cost is relatively high. This study proposes a self- organized equilibriumoriented relocation optimization method for the EV sharing. Using the sample data from questionnaire, the study develops a Multinominal Logit (MNL) model to describe users' choice of vehicle pickup stations and analyzes users' choice behaviors. The study then proposesa relocation optimization method which considers the selforganized supply and demand equilibrium for all operation stations. A dynamic discount strategy is included in the analysis to change users' most frequent choice that is using the nearest default stations. The goal of the vehicle relocation optimization model is minimizing the total relocation operation costs, and the tabu search algorithm is applied to solve the model. The effectiveness of the proposed method was verified throughthe case study of EV sharing stations in Haidian District of Beijing. The results indicated that compared with the non-dynamic discount strategy, the total relocation cost generated by the proposed model reduced 4.5% , the relocation cost of the personnel reduced 21.1%, and the relocation tasks reduced by 8.3%. References | Related Articles | Metrics

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Bus Dispatching Optimization of Multi-operators Considering Overlapping Interval SONG Xian-min, ZHANG Ming-ye, JIANG Jing-ling 2020, 20(5): 142-147.  Abstract ( )   PDF (1527KB) ( )   This study aims to address the problem of bus line scheduling of multiple operators in overlapping interval. Based on the analysis of the characteristics of overlapping interval, a bi- level programming model is proposed. The upper model represents the authority, and the objective is to minimize the total travel time of bus passengers. The decision variable is the bus line allocation plan. The lower model represents the operators, and each operator seeks for the maximum profit. The decision variables are the departure interval of the operating lines. The NSGA- II algorithm (Elitist Non-Dominated Sorting Genetic Algorithm) is applied to solve the model. Based on the bus line network of Nanguan District, Changchun City, a case study was performed. The experimental results show that the total travel time of bus passengers in the optimized network is reduced by 5.93%, which verifies the effectiveness of the proposed model. References | Related Articles | Metrics

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Multi-objective Optimization of Urban Public Transportation Network Differentiated Fare LI Xue-yan, ZHU Xin, LI Jing 2020, 20(5): 148-155.  Abstract ( )   PDF (1669KB) ( )   This study proposes the equalization algorithm of passenger flow OD matrix to make the pricing strategy of urban public transport network be more effective. The multiplier effect of social interaction and regret psychology are introduced in travelers' generalized cost analysis. The multi- objective optimization model is developed to reflect the public transport network's differentiated fare under fixed demand. The objectives of the model are the maximum profit of operational department and maximum utility for travelers. The distance-based fare, private car parking fee, and the departure frequency of public transport are variables in the model. The multiobjective optimization algorithm based on cluster intelligence is introduced to solve the model, the proposed model and algorithm are applied to the standard Mandl network. The results indicate that the distance- based public transport fare can reduce travel cost, and adjusting the fare by pareto optimal solution can promote travelers' choice behavior transfer to advantage equilibrium. References | Related Articles | Metrics

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Robust Modeling Method for Track Irregularity of Complicated Deterioration Trend YANG Ya-qin, XU Peng, LI Ye, SUN Quan-xin 2020, 20(5): 156-162.  Abstract ( )   PDF (1836KB) ( )   To precisely describe the complicated track irregularity deterioration under the varying circumstances, this paper proposes a rail track deterioration adaptive segmentation framework based on minimum description length principle, referred as MDL-RTDAS. In MDL-RTDAS, the identification of the maintenance activities that result inmutations in the deterioration process is reformulated as a model selection problem. The algorithm is also proposed to solve the models. The effectiveness of MDL- RTDAS is verified by using the recent five years measurement data from the mileage K21+184 to K220+30 on Nanchang-Fuzhou railway. The MDL-RTDAS is compared with other similar algorithms in the accuracy, fitness and robustness. As the results indicate, under the conditions that the information of maintenance operations is incomplete and inaccurate, MDL-RTDAS is able to overcome the interference of contaminated measurements, precisely identify the mutations in deterioration rate caused by maintenance activities, and create a piecewise fitting model for track irregularity deterioration. Compared to other algorithms, MDL- RTDAS owns better performances in rail track deterioration adaptive segmentation.To precisely describe the complicated track irregularity deterioration under the varying circumstances, this paper proposes a rail track deterioration adaptive segmentation framework based on minimum description length principle, referred as MDL-RTDAS. In MDL-RTDAS, the identification of the maintenance activities that result inmutations in the deterioration process is reformulated as a model selection problem. The algorithm is also proposed to solve the models. The effectiveness of MDL- RTDAS is verified by using the recent five years measurement data from the mileage K21+184 to K220+30 on Nanchang-Fuzhou railway. The MDL-RTDAS is compared with other similar algorithms in the accuracy, fitness and robustness. As the results indicate, under the conditions that the information of maintenance operations is incomplete and inaccurate, MDL-RTDAS is able to overcome the interference of contaminated measurements, precisely identify the mutations in deterioration rate caused by maintenance activities, and create a piecewise fitting model for track irregularity deterioration. Compared to other algorithms, MDL- RTDAS owns better performances in rail track deterioration adaptive segmentation. References | Related Articles | Metrics

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Interchange Basic Segment Capacity Impact Factor Analysis Based on Panel Data ZHOU Yue-er, GONG Hua-feng, ZHAO Cong-xiao, XU Xiao-tong, HUANG Bo-ya 2020, 20(5): 163-168.  Abstract ( )   PDF (1563KB) ( )   This study aims to identify the capacity impact factors (CIFs) of basic interchange segments in mountainous cities and examine the co- relation of the major CIFs with the capacity. Using the traffic data and roadway geometric data collected from basic interchange segments located in multiple districts in Chongqing, China, the study conducted statistical analysis and developed a panel data model to describe the relationship of capacity and CIFs. The sensitivity of the CIFs to the capacity of basic interchange segments was analyzed and ranked. The results indicate the major CIFs include radius of vertical curve, design speed, radius of horizontal curve, and heavy vehicle percentage. Among these factors, radius of vertical curve is the most significant CIF, and the sensitivity is up to 20.66%. The roadway grade doesn't appear as a major CIF due to the small impact weight. The heavy vehicle percentage is identified as a major impact factor. References | Related Articles | Metrics

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Traffic Accident Location Clustering Based on Improved DBSCAN Algorithm HUANG Gang, QU Wei-bin, XU Hui-ying 2020, 20(5): 169-176.  Abstract ( )   PDF (1821KB) ( )   Traffic accident characteristics are significantly affected by regional distribution. In this paper, traffic accident characteristics are clustered by the optimized density- based spatial clustering of applications with noise (DBSCAN) clustering method. The 2019 traffic accident data in Wuxi, China is used as a case study. The open map API is used to obtain the longitude and latitude of the accident location as an input for the proposed method. The traditional DBSCAN clustering algorithm normally requires accurate input of the distance threshold and sample number threshold. This paper develops the DBSCAN clustering model with an adaptive search distance threshold and sample number threshold. The comparison results of the proposed algorithm with traditional algorithm show that the optimized algorithm is more intelligent in determining parameters and more accurate in dividing clusters; the recognition of noise points is more reasonable than the traditional algorithm. The applicability of the algorithm in the geographical location clustering for urban road traffic accidents is proved by calculating the model score of the silhouette coefficient in machine learning. References | Related Articles | Metrics

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Daily Dynamic Freight Train Service Optimization LI Sheng-dong, LV Hong-xia, LV Miao-miao, XU Chang-an, NI Shao-quan 2020, 20(5): 177-184.  Abstract ( )   PDF (1993KB) ( )   To compensate for the change of freight traffic arising in daily operations, this paper aims to construct a daily freight train service plan, which determines the origin and destination stations, number, formation, and time periods of trains to operate. This paper constructs a train service time-space network based on the formation plan and timetable. An integer programming model is constructed to minimize the total costs of wagon flow movement, train operation, and shipment delay with considering the constraints of wagon flow paths, flow conservation, operating number of trains, and transit time. An improved simulated annealing algorithm is designed to improve the solving efficiency by increasing the solution number to achieve parallel search as well as by introducing multiple neighborhood structures. A real case built on the Menghua railway illustrates the effectiveness of the model and algorithm proposed in this paper. References | Related Articles | Metrics

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An Optimization Model for Gate Re-assignment under Flight Delays JIANG Yu, HU Zhi-tao, TONG Chu, LIU Zhen-yu, CHEN Li-li, ZHANG Hong-hai 2020, 20(5): 185-190.  Abstract ( )   PDF (1731KB) ( )   To resolve the airport gate usage conflicts resulting from flight delays, this paper proposes a multiobjective optimization model of aircraft-to-gate reassignment on the basis of flight delay classification. The model aims at minimizing apron conflict probability, passenger walking distance, and the number of passengers assigned to remote stands. A non-dominated sorting genetic algorithm with elitist strategy (NSGA-Ⅱ) is designed to solve the problem. Simulation on the actual operational data of a hub airport shows that, with the gate conflicts being resolved, average passenger walking distance and number of passengers assigned to remote stands reduces to 102.9 m and 0, respectively. On the premise of ensuring safe apron operation, the model proposed herein can effectively optimize passenger flight experience, improve apron operation efficiency, and provide decision support for gate assignment in busy airports. References | Related Articles | Metrics

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Optimization of Demand-response Container Train Timetables and Benders Decomposition JIANG Yu-xing, NIU Hui-min 2020, 20(5): 191-198.  Abstract ( )   PDF (1442KB) ( )   This study proposes a linear mixed integer programming model for the container train timetables to minimize the total tardiness of deliveries. The assignment between the container goods and the container trains is fully considered in terms of time and quantity. An approach based on Benders decomposition is developed to convert the model into a master problem of assignment scheme of container goods to trains as well as a timetabling subproblem. The cuts of the master problem are constantly generated by solving the dual subproblem. To overcome the disadvantage of low efficiency of the cut, an improved strategy is also proposed to produce multiple cuts and add them to the master problem in each iteration. A numerical example is provided to demonstrate the effectiveness of the model and algorithm. The results indicate that the improved strategy increases the calculation efficiency of the algorithm. The train timetables obtained by the proposed model and algorithm match well with the distribution of the quantity and times of the goods; the generated train timetables well meet the user demand. References | Related Articles | Metrics

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A Capacity Estimation Approach for Waterway Traffic Considering Operational Characteristics of Cruise Ships WENG Jin-xian, LIAO Shi-guan, FU Shan-shan, WANG Ya-li 2020, 20(5): 199-204.  Abstract ( )   PDF (2278KB) ( )   A new capacity estimation model for waterway traffic is derived from the classic traffic capacity model by considering the operational characteristics of cruise ships. According to the ship automatic identification system (AIS) data, a case study on Huangpu River Cruiseis applied to testify the applicability of the proposed model in estimating the traffic capacity of the core water area. Results show that the proposed model could provide an accurate estimation on the traffic capacity for this area. The traffic capacity reaches 76 ship/h under the current traffic compositions in the Huangpu River channel. When the ship traffic flow is larger than 69 ship/h, the maritime operators are suggested to take capacity management measures such as "Peak Restrictions". References | Related Articles | Metrics

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A Service Area Division Model for Emergency Facilities Based on Multiple Traveling Salesman Problem ZHAO Xing, JI Kang, SHEN Ke 2020, 20(5): 205-211.  Abstract ( )   PDF (1689KB) ( )   This paper develops a service area division model for emergency facilities based on the multiple traveling salesman problem. The model aims at minimizing the overall time cost of traversing all distribution centers. With constraints on the demand of distribution centers and the capacity of emergency facilities, the service area of each emergency facility can be determined. A hybrid algorithm is designed to obtain the optimal solution. Based on the P-median location model, an initial feasible solution is generated. The Tabu Search algorithm is then applied to search for the optimal solution combining with the LKH solver. A case study based on the realistic network topology of Beilun District, Ningbo, is carried out to verify the effectiveness of the proposed model and solution method. References | Related Articles | Metrics

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An Optimization Model for O2O Chain Stores Distribution Based on Order Splitting XIN Yu-chen, SHI Sheng-nan, YANG Hua-long 2020, 20(5): 212-217.  Abstract ( )   PDF (1705KB) ( )   This paper studied the problems of order allocation and logistics distribution in chain stores of retail enterprises under the O2O transformation background. Considering the factors such as the possession of various types of commodities of chain store and its inventory capacity, customer returns, an order splitting strategy that only allows the segmentation of commodity types and does not split the quantity was put forward. Based on the order splitting strategy, an O2O chain store distribution optimization model was established with the goal of minimizing the total distribution cost. A two- stage heuristic algorithm was designed based on the nearest order assignment and improved tabu search. The applicability and effectiveness of the proposed model and algorithm are tested on benchmark instances. Experimental results show that the fulfillment rate of customer orders could be increased significantly by splitting customer orders. Moreover, as long as the total inventory of goods in each store is not less than the total demand of customer orders, a customer order fulfillment rate of 100% can be achieved. References | Related Articles | Metrics

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Electric Vehicle Route Optimization for Fresh Logistics Distribution Based on Time-varying Traffic Congestion ZHAO Zhi-xue, LI Xia-miao 2020, 20(5): 218-225.  Abstract ( )   PDF (1577KB) ( )   The electric vehicle routing planning for fresh logistics distributions based on time- varying traffic conditions is investigated in this paper. With the time- varying traffic network, the calculation method for travel time is designed based on a route section division strategy. A three-constraint decision factor method is designed to consider the minimum product freshness limit, vehicle load constraints, and electric vehicle power constraints. A distribution route optimization model for electric vehicles is established for the urban logistics distribution of fresh products to minimize the total distribution cost. An improved ant colony algorithm is proposed, in which its parameters are adjusted adaptively. Simulation results show that this method can properly arrange the departure time and plan the distribution route to avoid traffic congestion, according to the requirements of product freshness, customer attributes, and road network characteristics. Compared with other algorithms, the proposed model and algorithm can significantly reduce the distribution cost and improve the economic benefits of enterprises. References | Related Articles | Metrics

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Spatial Characteristics Analysis of Traffic Accessibility and City Economic Activity: A Case Study of Beijing ZHU Yu-ting, LIU Ying, XU Qi, GUO Ji-fu, CHEN Ying 2020, 20(5): 226-233.  Abstract ( )   PDF (1954KB) ( )   With the data collection from open data websites, global regression models with constant parameters and a local regression model with variable parameters were employed to analyze spatial characteristics between traffic accessibility and urban economic activities in the sixth- ring area of Beijing. The local regression model performs better than global regression models because of the significant spatial heterogeneity between traffic accessibility and urban economic activities. Results show that private traffic accessibility exhibits a multi- ring structure with diminishing from the center (Tian An Men Square) to the edge of the study area. And public traffic accessibility is more extensive along the rail transit lines. The spatial separation and matching phenomena between traffic accessibility and urban economic activities are coexisting. A high spatial matching appears in northwest and central regions. A sub- matching appears in the northeast and eastern regions, where assignificant separation appears in the southwest region. For spatial separation areas (e.g., Fengtai and Liangxiang), infrastructure development including road and rail lines should be paid more attention. For areas with the matching of private traffic accessibility but the separation of public traffic accessibility (e.g., Future Science City), the construction of rail lines and operation of buses should be considered. For spatial matching area (such as Zhongguancun Science City and Beijing Economic- Technological Development Area), the optimization policy for traffic management should be taken into account. References | Related Articles | Metrics

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Impact of Ride-hailing Service on Use of Public Transport in China's Cities ZHONG Jun, LIN Yan, HANG Yu 2020, 20(5): 234-239.  Abstract ( )   PDF (1215KB) ( )   In recent years, the rapid development of ride-hailing service in China's cities may have a significant impact on the traditional way of travel. An important issue is how it will affect public transport. Based on the rational choice theory, this paper makes an empirical study using the relevant data from 109 cities. The heterogeneity analyses are also conducted according to the time of ride- hailing service entering the city and the category of the city. The results show that: the ride-hailing service has a significant negative impact on the use of urban public transport; as time goes on, the negative impact of ride- hailing service shows a law of first strengthening and then weakening; the negative impact of ride-hailing service is more serious in megacities. This study reveals the influence of ride-hailing on public transport in China’s cities, which can be used as a reference for traffic managers to make relevant policies. References | Related Articles | Metrics

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Effects of Vehicle Load Characteristics on Distributions of Time-to-collision WANG Ying, FANG Zhi-chun, JIAN Zhu-qing, TU Hui-zhao, SZE Nang-ngai 2020, 20(5): 240-246.  Abstract ( )   PDF (1504KB) ( )   Time-to-collision (TTC), which is affected by vehicle load characteristics, is considered as an effective index for the risk assessment of the car- following process in collision avoidance systems. This research uses vehicle type, overweight and speeding to quantify vehicle load characteristics. Twelve types of car- following scenarios under free- flow conditions were decomposed with different load characteristics of the leading and following vehicles. The weigh-in-Motion (WIM) technique is applied to obtain traffic flow data that combines the load characteristics. The influence of vehicle load characteristics on TTC distribution in the 12 types of carfollowing scenarios was analyzed. The significance of TTC distribution was compared using KS test. Results show that the distribution of TTC cumulative frequency fits an exponential distribution. At the 5% level of significance, vehicle class and speeding do not show a significant effect on TTC distribution. Overweight of light vehicles has significant effect on TTC distribution. And overweight increases the potential conflict risk in the scenarios of a light vehicle following a heavy/light vehicle. In the case that the leading and following vehicles are not speeding, the potential conflict risk in the scenario of a light vehicle following a heavy vehicle is higher than the scenario of a light vehicle following a light vehicle. References | Related Articles | Metrics