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25 August 2023, Volume 23 Issue 4 Previous Issue    Next Issue

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Empirical Study on Carbon Dioxide Emissions and Atmospheric Environment Impact of Urban Public Passenger Transportation CHEN Dan, YU Hui, TANG Cheng, CHEN Zhi-xiong, TANG Miao 2023, 23(4): 1-10.  DOI: 10.16097/j.cnki.1009-6744.2023.04.001 Abstract ( )   PDF (1881KB) ( )   This paper proposes a novel medium-and long-term prediction method for carbon dioxide emissions and their atmospheric environmental impact on public passenger transportation, which can facilitate the improvement of transportation structure and the high-quality development of green low-carbon transportation. To handle the issue of unclear method and incomplete data accumulation of carbon emissions and their atmospheric environmental impact in China's public passenger transportation, a micro calculation model for carbon dioxide emissions from public passenger transportation is proposed in this paper. Then a new medium-and long-term prediction method for carbon dioxide emissions is presented based on a dynamic linear model, which can accurately capture the development trend of transportation demand and micro characteristics of transportation behavior. On this basis, a linear climate response model-based carbon dioxide emissions prediction method is proposed to forecast the medium-and long-term atmospheric environmental impact of the carbon emissions from China's public passenger transportation. Several major public passenger transportation modes, including urban rail transit, traditional & new energy cruise taxis, traditional & new energy buses, high-speed rail, and civil aviation, are studied to verify the effectiveness of the proposed method. The results show that carbon emissions from China's public passenger transportation will keep rising in the next 10 years, with the largest amount of carbon emissions from civil aviation accounting for 71.72%. Besides, the impact of the carbon emissions from public passenger transport on the atmospheric environment will increase rapidly as well, where civil aviation has the largest impact accounting for 69.26% , and urban rail transit has the smallest impact accounting for 1.35%. References | Related Articles | Metrics

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Substituted Relationship Between Ride-hailing and Public Transit and Emission Reduction Potential LV Ying, HE Lu-lu, SUN Hui-jun, XU Guang-tong 2023, 23(4): 11-23.  DOI: 10.16097/j.cnki.1009-6744.2023.04.002 Abstract ( )   PDF (3983KB) ( )   As a popular travel mode, ride-hailing has changed people's travel choices to some extent, which makes some passengers who used to take public transit turn to ride-hailing. The resulting substituted relationship between ride�hailing and public transit will have a certain impact on urban transport carbon emissions, and it is necessary to conduct in-depth research. Based on the comparison of utility functions, this paper uses the ride-hailing order data in Chengdu to infer whether the ride-hailing service has the chance to be replaced by public transit. From the OD (Origin Destination) level, this paper analyzes the characteristics of replaceable and irreplaceable trips by public transit and the emission reduction potential of the urban public transit system. The results show that 54.16% of all ride-hailing orders have a potential substituted relationship with public transit. Travel time and travel cost are the main factors that affect passengers' choices. When the public transit travel plan only includes subway travel or subway travel distance accounts for a relatively high proportion, it is more likely to show that there is a potential substituted relationship between ride�hailing and public transit. If public transit has the opportunity to replace some ride-hailing trips, it will lead to a reduction of about 45.59% in carbon emissions. The periods and areas with the greatest emission reduction potential are between 10:00 and 17:00, as well as areas near urban centers and subway lines. This study can provide decision support for relevant government departments to improve the service level and utilization efficiency of public transit by implementing effective means, and thus reduce the carbon emissions of the urban transport system. References | Related Articles | Metrics

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Empirical Study on Low-carbon and Rail-road Intermodal Transport in Middle West China WANG Chao, HUAI Xu, WU Jia-ni 2023, 23(4): 24-33.  DOI: 10.16097/j.cnki.1009-6744.2023.04.003 Abstract ( )   PDF (1825KB) ( )   The construction of modern integrated transportation system puts forward new requirements for high efficiency and green. In this paper, the rail- road intermodal transport in central and western China are selected as the research object. The entropy weight TOPSIS method is used to calculate the container freight demand of hub city logistics network, and a dual-objective optimization model of railway combined transport with minimum transport cost and carbon emission is developed. Four scenarios related to rail-road intermodal transport in central and western regions are simulated to solve the problem, and sensitivity analysis is carried out by discussing the influence of highway unit freight rate, carbon emission intensity and carbon tax rate on low-carbon transport. The results show that compared with the single highway transportation mode, the combined transportation of public and railway can achieve low carbon and low cost transportation. Transportation cost and carbon emission decrease with the increase of railway freight scale. Road transport, which is flexible, remains the main mode of transport. Compared with reducing the unit freight rate of highway, the government should formulate corresponding preferential policies for railway freight rate to encourage enterprises to give priority to railway transport. Encourage the use of new energy logistics distribution vehicles to reduce road carbon emission intensity, so as to reduce carbon emissions; Government departments should adjust the reasonable carbon tax rate according to local conditions to promote the transformation to low-carbon transportation. References | Related Articles | Metrics

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Dynamic Efficiency Measure in Yangtze River Delta Port Cluster Considering Carbon Emissions ZHENG Yan, BA Wen-ting, XIAO Yu-jie 2023, 23(4): 34-46.  DOI: 10.16097/j.cnki.1009-6744.2023.04.004 Abstract ( )   PDF (1860KB) ( )   This paper investigates the port dynamic and static efficiencies considering carbon emissions and major influencing factors. It explores an appropriate way to balance energy conservation and emission reduction with port development. This paper selects the panel data of 16 ports in the Yangtze River Delta port cluster in China from 2008 to 2020, combines the three-stage data envelopment analysis model based on slack variables and the distance disorientation function, and develops a model to measure the dynamic efficiency of ports considering unexpected output. The panel gravity model is applied to strip the effects of environmental factors and random noise. Combined with the improved Malmquist index, the total factor productivity and its decomposition considering unexpected output are calculated based on the relative distances of port input and output factors from the mapping points on the frontier surface. The results show that there are significant differences in efficiency between ports, and the third stage has a 3.474% increase in the mean efficiency over the first stage. The majority of port input indicators have increased but output indicators have not risen in the identical proportion, as the unexpected output indicator of port carbon emissions has increased annually. 1% increase in the level of freight would result in a corresponding reduction of 0.4192% in the overall number of berths, 0.0436% reduction of quay length, and 0.3862% reduction of, 10000-ton berth redundancy in ports. The average value of the third stage efficiency of the port cluster decreases by 0.062 after considering carbon emissions, and the ports with low efficiency values are more significantly inhibited by carbon emissions. From the perspective of dynamic changes, total factor productivity of the Yangtze River Delta port cluster increased by 1.4%, and the improvement of technical efficiency and pure technical efficiency was an important factor in promoting port development. The carbon emission output adversely affects the technological progress index, the pure technical efficiency change index and the Malmquist-Lenberger index, and the optimization of port structure and the implementation of low-carbon development strategy can help to improve the comprehensive dynamic efficiency of the port cluster. References | Related Articles | Metrics

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Measurement of Road Automobile Transport Price and Analysis of Rail Competitiveness Considering Fuel Price Changes SHEN Jia-qi, LIU Hao, ZHANG Rong 2023, 23(4): 47-54.  DOI: 10.16097/j.cnki.1009-6744.2023.04.005 Abstract ( )   PDF (1365KB) ( )   Transportation structure adjustment, energy conservation and emission reduction require promoting a modal shift from road to rail for the automobile transport. This study proposes a method to measure the costs and prices of road automobile transport, solving the problems that the traditional method does not take into account return empty loads and is not accurate enough. The efficient design technique was used to efficiently collect the disaggregate freight choice behavior data of automobile shippers and a road/rail competitiveness model for automobile transport was developed based on Multinomial Logit and Mixed Logit models. The road automobile transport costs and prices measurement method and the road/rail competitiveness model were integrated to derive expressions for the elasticity of rail market share to fuel prices and to compare and analyze the impact of fuel price changes on the rail's competitiveness of different ODs (Origins and Destinations). The results show that the proposed method for measuring the costs and prices of road automobile transport can meet the requirements for practical use. The freight mode choice behavior model performs better than the traditional model in terms of fitness accuracy, and the freight value of time decreases with the increase of transport distance. When the fuel price increases by 20% , the rail market share of medium and long-distance transport increases by more than 30%, while the rail market share of short distance only increases by 0.01% . The longer the transport distance and the more uneven the demand for automobile transport between ODs, the greater the fuel price elasticity of rail market share. In addition, compared with the 1% increase in fuel prices, the carbon tax rate of 40 CNY ⋅ t -1 CO2 has a weaker effect on the promotion of modal shift from road to rail. References | Related Articles | Metrics

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Replacement of Hydrogen Fuel Cell Heavy Truck Fleet Study Based on Bi-level Programming WANG Zhi-huan, ZHANG Wen 2023, 23(4): 55-60.  DOI: 10.16097/j.cnki.1009-6744.2023.04.006 Abstract ( )   PDF (1463KB) ( )   Replacing the powertrain of conventional heavy trucks with hydrogen fuel cell is an important approach to achieve the goal of carbon peaking and carbon neutrality in road transportation. How the government formulate reasonable policies to promote enterprises to accelerate the replacement and how enterprises choose the optimal replacement strategy are two urgent problems to be solved. This paper proposes a mixed integer bi-level programming model considering factors such as carbon reduction target, enterprise costs and government policies for the fleet replacement problem. The government is set as the upper-level decision maker, with the goal of minimum total carbon emissions of the fleet, and the enterprise is the lower-level decision maker with the goal of maximum total cost of the fleet. Taking a transportation enterprise as an example, the planning period was set as 5 years and the fleet size was 50 vehicles. The column and constraint generation algorithm and Gurobi solver were used to calculate the optimal annual carbon emission ceiling of the government and the optimal replacement strategy of the enterprise fleet. The sensitivity analysis of emission tax, emission subsidy and purchase subsidy was carried out. The results show that there is a minimum threshold of emission tax, and whether the threshold is reached is the main factor that affects the decision of whether to replace vehicles. Based on this, setting emission subsidy can further promote enterprises to increase the number of vehicles to replace. In addition, appropriate adjustment of purchase subsidy according to government budgets can increase the proportion of vehicle replacement by enterprises, which is practical to reduce the carbon emissions. References | Related Articles | Metrics

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Pathway Towards Carbon Peak in Transportation Sector of Hunan Province FANG Han-xiao, LIU Can, JIANG Kang, XIAO Huai-xian, TANG yun 2023, 23(4): 61-69.  DOI: 10.16097/j.cnki.1009-6744.2023.04.007 Abstract ( )   PDF (1727KB) ( )   Transportation is a crucial element of the global economy, but it is also a significant source of carbon emissions. This study focuses on carbon emissions in the transportation sector of Hunan province. With the base year set as 2021, three scenarios are established: reference scenario, low- carbon scenario, and enhanced low- carbon scenario. The LEAP (Long- Range Energy Alternatives Planning System) model is employed to analyze and predict carbon emissions in Hunan's transportation sector from 2022 to 2035. The results show that in the reference scenario, carbon emissions in Hunan's transportation sector will increase rapidly, reaching 57.25 million tons in 2035, with no peak appearing during the forecast period. In the low-carbon and enhanced low-carbon scenarios, carbon emissions in Hunan's transportation sector are expected to reach their peak in 2033 and 2029, respectively, with peak amounts of about 44.81 million tons and 42.58 million tons. During the forecast period, energy consumption will only reach its peak under the enhanced low-carbon scenario, with a peak amount of 21.62 million tons of standard coal expected in 2030. It is recommended that Hunan province adopt strengthened energy-saving and emission reduction measures under the enhanced low-carbon scenario to promote the transportation sector's low-carbon sustainable development and achieve carbon peak as soon as possible. Finally, three main recommendations for green and low-carbon development in the transportation sector in Hunan Province are proposed based on emission reduction potential analysis: (1) It is recommended to adopt enhanced energy-saving and emission reduction measures under the enhanced low-carbon scenario to achieve carbon peaking as soon as possible. (2) The social vehicle sector is a key area for reducing emissions. It is recommended to promote new energy while constructing a clean power grid to maximize the benefits of emissions reduction. (3) Road freight transportation is an important focus for reducing emissions. Adjusting the transportation structure and reducing carbon emissions from heavy-duty trucks are the primary measures for reducing emissions from road freight transportation. References | Related Articles | Metrics

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Port Staff Trajectory Extraction Based on Deep Learning and Multi-level Matching Mechanism CHEN Xin-qiang, WANG Mei-lin, LI Chao-feng, YANG Yang, MEI Xiao-jun, ZHOU Ya-min 2023, 23(4): 70-79.  DOI: 10.16097/j.cnki.1009-6744.2023.04.008 Abstract ( )   PDF (2648KB) ( )   Due to the complex spatial layout of the port environment, the difficulty of accurate tracking of port staff exists under the interference of complex backgrounds such as container yards, lifting machinery, loading, unloading, and transportation equipment. This study proposes a trajectory extraction framework based on a Faster-RCNN detection algorithm and an improved Deep SORT tracking algorithm for port surveillance video. In this framework, an adaptive Gaussian noise reduction and histogram equalization algorithm were added, and the image enhancement technology and Person Re-identification network were integrated to extract the feature information of port images, to improve the rapidity and accuracy of the track extraction of port staff. The detection results of the port staff image sequence were output through the pre-feature extraction network, the candidate region suggestion network, the pool of interest area, and the full connection layer. The location information of port staff was matched by cascade matching and the Hungarian algorithm. Finally, the motion trajectory of port staff was predicted by the Kalman filter. The results show that the proposed method has good performance in the face of challenges such as different light changes, low visibility, and shadow interference in each typical port scene. The average values of EIDF1 , EIDR , ERCLL , and EMOTA are 98%, 97%, 97%, and 95%, respectively. The conclusion shows that the FRIMDS framework proposed in this study has certain accuracy and stability, and can provide technical support for the safety supervision of automated terminals. References | Related Articles | Metrics

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Vehicle Trajectory Prediction Based on Mixed Teaching Force Long Short-term Memory FANG Hua-zhen, LIU Li, XIAO Xiao-feng, GU Qing, MENG Yu 2023, 23(4): 80-87.  DOI: 10.16097/j.cnki.1009-6744.2023.04.009 Abstract ( )   PDF (2292KB) ( )   To improve the long-term trajectory prediction of the intelligent connected vehicle to the surrounding vehicles, this paper proposes an interaction-aware network framework based on mixed teacher forcing Long Short�Term Memory (LSTM) encoder-decoder. First, a trajectory prediction dataset is established through feature selection and trajectory sequence labeling. Then, the LSTM encoder-decoder model is developed. The encoder encodes the historical trajectory of the target vehicle, the information of surrounding vehicles, and the road environment into the context vector. The decoder adopts the mixed teaching mode to decode the context vector dynamically into the future trajectory. At last, the model is verified on the real road datasets NGSIM US101 and I-80 and compared with the traditional models. The experimental results show that the proposed model performs better than the traditional methods in long-term prediction. The 5 seconds final displacement error is 2.7 meters. The accuracy of the model after sparse sampling has been significantly improved compared with other methods, the 5 seconds final displacement error is 1.3 meters. References | Related Articles | Metrics

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Joint Optimization of Speed Guidance and Signal Priority Control for Connected Autonomous Truck Platoon at Intersections with Car and Truck Separation GAO Yun-feng, XI Jian-wei, SUN Ke 2023, 23(4): 88-101.  DOI: 10.16097/j.cnki.1009-6744.2023.04.010 Abstract ( )   PDF (2522KB) ( )   Trucks normally have longer braking distance and greater startup lost time compared to cars. Both speed guidance and signal priority control are beneficial to improving the efficiency and safety of truck flow on port access roads. To reduce the stop delay and exhaust emission of trucks, this study takes the connected autonomous truck platoon as the research object, and proposes a joint optimization model of speed guidance and signal priority control to play a comprehensive role in adjusting the following behavior in the connected autonomous truck platoon. First, the operation state of the connected autonomous truck platoon on a truck lane is analyzed in detail. Then, taking the truck speed, acceleration or deceleration rate, decision position and green time of the priority phase as decision variables, and taking the truck stop delay, exhaust emission and delay changes of public vehicles in the affected phase as the optimization objectives, an optimization model for the truck platoon operation on port access roads is formulated to achieve the goals of safety and efficiency. At last, the genetic algorithm is used to solve the proposed model, and the optimization results under different arrival time and different green time duration are obtained. The results show that the stop delay of the connected autonomous truck platoon is greatly reduced, and the delay of public vehicles in the affected phase is also within a reasonable range. References | Related Articles | Metrics

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Merging Decisions from Acceleration Lanes Considering Historical Vehicle Operating State Data GUO Ying-shi, GU Meng-lu, WANG Chang, SU Yan-qi, FU Rui, YUAN Wei 2023, 23(4): 102-110.  DOI: 10.16097/j.cnki.1009-6744.2023.04.011 Abstract ( )   PDF (2230KB) ( )   To investigate the impact of the historical vehicle operating state data on merging decisions of the merging vehicle in a highway acceleration lane, a merging decision model for the merging vehicle considering the vehicle historical operating state data in a time window was proposed by combining with GentleBoost (Gentle adaptive Boosting) ensemble learning algorithm. First, a roadside data acquisition platform was designed using high�definition cameras and millimeter radar devices, and real traffic interaction data at a typical merging zone with an acceleration lane in China were collected. Second, a merging decision model based on a GentleBoost algorithm considering current and historical vehicle operating state data in the merging scenario, and the impact of the remaining distance in the acceleration lane was proposed. Finally, a microscopic simulation merging scenario was built based on SUMO (Simulation of urban mobility) and MATLAB platforms, to test the GentleBoost merging decision model in an intelligent connected environment under different traffic flow conditions. The results showed that, as the length of the time window increased, the overall recognition accuracy of the model first increased and finally tended to be stable. The merging decision model reached its best performance when the time window length was 1.7 s. And the recognition accuracy was 98.9% for "Merge" events and 97.4% for "Non-merge" events. The simulation results showed that, compared to the LC2013 model embedded in SUMO, the proposed GentleBoost model considering the historical data obtained a higher merging success rate and a larger average traveling speed in the merging control zone. References | Related Articles | Metrics

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Hierarchical and Distributed Control Optimization for Urban Network Traffic Signals HUANG Wei, HU Jing, HUANG Guo-yu, ZHOU Shao-rui 2023, 23(4): 111-123.  DOI: 10.16097/j.cnki.1009-6744.2023.04.012 Abstract ( )   PDF (3050KB) ( )   The control structure of network-wide traffic signal control is of great significance to improve the efficiency of urban traffic systems. Considering both the control performance and computational efficiency, this paper proposes an efficient hierarchical network signal control strategy. At the upper level, the cycle length optimization is conducted by using the Webster method. The lower level addresses the green split optimization with the objective function of minimizing the total time spent, which is formulated as a model predictive control (MPC). To decompose the network into intersections while mainlining optimal performance, a Benders decomposition method is introduced to decompose the problem into a Primal problem and a Master problem. The Primal problem solves the isolated intersection optimization independently while the Master problem tackles the flow interaction between adjacent intersections. For the solution approach, a Benders decomposition-based two-layer distributed signal control optimization algorithm is designed. To verify the performance of the proposed hierarchical control method, comparative experiments are set up for the two control levels on two test networks. The results show that the optimal solutions derived from the Benders Decomposition-based distributed MPC method are close to the global optimal solutions derived from the centralized control method. The errors of the network total time spent between the two methods are less than 3.26% . While maintaining the overall performance, the distributed MPC method can greatly improve computational efficiency; Compared with the centralized control method, the calculation time can be reduced up to 42.24% . In addition, the proposed distributed method outperforms the fixed-time control method. The reductions in the total travel time spent under different traffic conditions are about 9.40% to 20.57%. Moreover, by introducing the cycle optimization method at the upper level, the hierarchical distributed control method can further improve overall control performance. References | Related Articles | Metrics

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Induction Control Optimization Method Based on Dynamic Sensitivity GUAN De-yong, XU Yue, WANG Ke 2023, 23(4): 124-133.  DOI: 10.16097/j.cnki.1009-6744.2023.04.013 Abstract ( )   PDF (2279KB) ( )   To evaluate the practical application effect and value of the integrated detection method in the induction signal control, this paper develops an initial green light timing model based on the characteristics of the integrated detection method, and takes into account the number of real-time waiting vehicles and the starting loss time of different models and the time passing through the intersection area. By analyzing the change of unit green light extension time under different headway (sensitivity) detection response conditions, the paper proposes the induction control model based on dynamic sensitivity. The dynamic sensitivity selection scheme is determined using the reinforcement learning method and the traffic benefit combining the average number of vehicles, the average speed, the average loss time and the average maximum queue length. At last, the simulation model is established by the SUMO(Simulation of Urban Mobility) to verify the timing control, fixed sensitivity control (traditional induction control) and the proposed dynamic sensitivity control. The simulation results show that: ① The selection of dynamic sensitivity is affected by the space occupancy. ② Under the condition of 15% maximum traffic volume, compared with the timing control and the fixed sensitivity control, the proposed dynamic sensitivity control increases the average speed of the intersection by 31.69% and 5.05%, reduces the average loss time by 36.19% and 7.44%, and shortens the average maximum queue length by 45.17% and 7.78% . Before the traffic volume reaches 27% of the maximum traffic volume, the control effect of dynamic sensitivity is optimal. It shows that the proposed dynamic sensitivity induction control model has good performance and can provide a theoretical reference for the practical application of radar and vision integration in induction signal control. References | Related Articles | Metrics

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Trigger Model Predictive Control Based on Extended State Observers for Virtual Coupling LIN Jun-ting, NI Ming-jun 2023, 23(4): 134-146.  DOI: 10.16097/j.cnki.1009-6744.2023.04.014 Abstract ( )   PDF (4705KB) ( )   This paper proposes a trigger model prediction control method based on extended state observer to evaluate the real-time and resistance of the system for virtual coupling train control in the actual railway environment. First, a multi-train tracking model is developed based on the virtual coupled train dynamic equations, and the terminal function is designed to guarantee system stability. Then, to improve the system's real-time performance, this paper uses an event trigger mechanism to strengthen judgment conditions for the optimizations. This addresses the issue of low computational efficiency and resource waste in model prediction control. In addition, a front-end route extended state observer is introduced. This observer can estimate and correct the environmental disturbances in real time, decreasing the phenomenon of interference that leads to model inaccuracies and enhancing the model's resistance to distraction. To verify the effectiveness of the proposed method, four scenarios were built based on the MATLAB and Simulink simulation platforms and compared with conventional model prediction control methods. The results showed that compared to the traditional model prediction control methods, the three thresholds τ is 0.001,0.010,0.100 of the event trigger mechanisms increased the computational efficiency by 47% , 64% , and 73% respectively. The proposed algorithms increased the resistance by 33% in the conditions with external disturbances. References | Related Articles | Metrics

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Integrated Optimization of Multi-route for Battery Electric Buses Considering Vehicle and Line Matching DUAN Meng-yuan, QI Ge-qi, GUAN Wei, XU Xiao-han 2023, 23(4): 147-154.  DOI: 10.16097/j.cnki.1009-6744.2023.04.015 Abstract ( )   PDF (2202KB) ( )   To study the bus operation optimization problem, this paper establishes a life-cycle cost optimization model for electric buses considering battery capacity degradation, which takes vehicle and line matching, vehicle and battery renewal, and the number of service trips for buses as decision variables. Then we develop a rolling horizon scheduling optimization method and use GUROBI to solve it. With the operational data of multiple bus lines, the validity of the model is tested. Assuming that the planning horizon is 20 years, the optimal renewal scheme for vehicles and batteries, the matching scheme for vehicles and lines, and the number of trips for bus operations are obtained. The optimal scheme's life cycle cost is $35.27 × 107 , and the number of vehicle and battery replacements is 44 and 239, respectively. Based on the sensitivity analysis of different workloads and the matching strategies, the results show that considering the differences in workloads, the matching strategy of buses and lines can reduce the investment cost of vehicles and batteries and improve the operational efficiency of electric bus enterprises. References | Related Articles | Metrics

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Joint Optimization of Conventional Transit and Demand Responsive Transit for Ring-radial Cities LI Xin, QIAO Jing-yuan, LI Yan-hao, LIU Wan-ying, YUAN Yun 2023, 23(4): 155-163.  DOI: 10.16097/j.cnki.1009-6744.2023.04.016 Abstract ( )   PDF (2129KB) ( )   This paper focuses on the unbalanced utilization of network resources and long travel time of passengers for the conventional transit network during the ring-radical cities' expansion and proposes a modal network structure with a hierarchical layout of conventional transit and demand-responsive transit. Based on the continuous approximation method, this paper uses a mixed-integer program to minimize the sum of expected passenger cost and agency costs. A deterministic mode split and route choice model is embedded and cost components are derived based on passengers' groups. Then, decision variables such as the stop and line spacing of the conventional transit and the headway of bi�modal transit systems are optimized and a hybrid heuristic is proposed to solve the problem. At last, to verify the effectiveness of the designed network, a case study is conducted on a real-world ring-radial network within the fifth ring of Chengdu City. The results indicate that compared with the conventional transit network, the proposed system can reduce passenger costs by approximately 18.37% while effectively reducing operating costs by approximately 4.97%. The proposed system can reduce the construction cost by 31.85%, and reduce the travel distance by 24.36% for conventional buses. System cost decreases exponentially with the increase of average passenger flow density and the values of key design parameters under different conditions can be provided through the proposed method. References | Related Articles | Metrics

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A Rescheduling Optimization Method for Metro Trains Under Cross-line Operation ZHANG Xi-ran, CHEN Shao-kuan, ZHAO Xing-dong, WANG Zhuo 2023, 23(4): 164-174.  DOI: 10.16097/j.cnki.1009-6744.2023.04.017 Abstract ( )   PDF (2358KB) ( )   PDF(English version) (1827KB) ( 32 )   The cross-line operation makes emergency train timetable rescheduling complex. In order to handle a reduction of passing capacity in a line section, train timetables of metro lines are rescheduled by integrating five strategies including short-turning, service cancellation/insertion, and cross-line cancellation/restoration. A train timetable rescheduling optimization model is proposed to minimize the delay of trains and the traveling time of passengers, in which operational safety, occupation of sidings, circulation of rolling stocks, and dynamic passenger flow are considered. A solution algorithm incorporating the non-dominated sorting genetic algorithm Ⅱ and timetable recalculation algorithm under cross-line operation is addressed to solve the proposed model, and the effectiveness of the proposed model and algorithm are verified by a case study. The results from the case study show that: compared with rescheduled timetables in which the original cross-line operation scheme is completely maintained or changed to an independent operation, the proposed method is able to effectively reduce the passenger traveling time by 3.86% and reduce the train delay by 21.07%. The stability of train rescheduling will be increased if the buffer time of cross-line operation is long, and the average passenger transfer waiting time and train delay are further reduced by 4.06% and 3.77% respectively. References | Related Articles | Metrics

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Collaborative Optimization of Urban Rail Transit Timetable and Rolling Stock Circulation Under Tidal Passenger Flow LI Si-jie, SHANG Yu-xin, LIU Zhi-gang 2023, 23(4): 175-183.  DOI: 10.16097/j.cnki.1009-6744.2023.04.018 Abstract ( )   PDF (2413KB) ( )   Due to the tidal phenomenon of passenger flow demand in urban rail transit, this paper studies the cooperative schedule of train timetable and rolling stock circulation under the unpaired transportation organization mode. Given a two-depot rail transit line with the unbalanced temporal-spatial distribution of passenger flow, a mixed integer nonlinear programming model is constructed with objectives of minimizing the total passenger waiting time cost, train fixed use cost, and train succession walking cost. The model takes the train departure time, train succession relationship, and depot entry and exit of rolling stocks as the decision variables, and considers timetable constraint, rolling stock circulation constraint, and passenger flow balance constraint. It is solved by Gurobi after linearization. Taking a metro line in Shanghai as an example to verify the effectiveness of this model, the results show that the total cost of passengers and enterprises decreased by 6.06%, 10.45%, and 6.35%, respectively, compared with the step-by�step solution scheme, the balanced departure scheme, and the paired operation scheme. The matching between train capacity distribution and passenger demand is improved, and the waiting time of passengers in the main passenger flow direction is reduced. The model helps to improve the enterprise transportation efficiency and passenger service level simultaneously. References | Related Articles | Metrics

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Metro Station Classification Based on Boarding and Alighting Passenger Flows and Ridership Impact Factors PANG Lei, REN Li-jian, ZHANG Zhe-hao, YUN Ying-xia 2023, 23(4): 184-193.  DOI: 10.16097/j.cnki.1009-6744.2023.04.019 Abstract ( )   PDF (2200KB) ( )   Existing studies have deep analysis on the metro ridership characteristics and its the impact factors, however, the impact factors of passenger flow for different types of metro stations can be further investigated. This paper utilized a time series clustering method which introduced boarding and alighting passenger flow characteristics to classify metro stations in Tianjin city of China, and developed an index system of effect factors according to the built environment, socio-economic, station attributes, and complex network characteristics based on multi-source geographic big data. Three regression models, Ordinary least squares, Geographically Weighted Regression and Multi-Scale Geographically Weighted Regression were used to analyze the factors that affect the ridership and the degree of impactfor different types of stations. The case study in Tianjin indicated that: (1) there are three main categories of stations based on the time-varying characteristics of passenger flow, residential-oriented, employment-oriented and commercial-residential balance stations. The spatial distribution and surrounding land use characteristics of each station were significantly different. (2) For the ridership impact factors at the residential-oriented stations, the MGWR model showed best fitting results. However, for employment-oriented and commercial-residential balance stations, the OLS model demonstrated better fitting results but the differences were marginal compared to other models. (3) The ridership impact factors for different types of stations were significantly different, and the differences were also shown in the direction and intensity of the impact factors. (4) The influence of bus station density and opening hours on the ridership of residential-oriented stations had significant spatial heterogeneity. The study results provided a planning guidance onstation classification and zoning and further improvement of the effectiveness of rail transit operation and development of TOD at rail transit stations in Tianjin. References | Related Articles | Metrics

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Analysis of Spatio-temporal Heterogeneity Impact of Built Environment on Rail Transit Passenger Flow XU Xin-yue , KONG Qing-xue, LI Jian-min, LIU Jun, SUN Qi 2023, 23(4): 194-202.  DOI: 10.16097/j.cnki.1009-6744.2023.04.020 Abstract ( )   PDF (2177KB) ( )   It is of great significance to study the influence of various built environment characteristics on passenger flow for urban rail transit network planning and operational passenger flow control. This paper considers the influence of four types of built environment characteristics on rail transit passenger flow, including population economic characteristics, station characteristics, external traffic characteristics and land use characteristics. A hybrid model (GTWR-RF) is proposed, which combines the geographically and temporally weighted regression (GTWR) and random forest (RF). The model is used to capture the spatio-temporal heterogeneity and nonlinearity of the effects of built environment characteristics on passenger flow. First, the statistical indicators of built environment are refined and improved by collecting multi-source data. The GTWR was used to calculate the influence coefficient of built environment on rail transit passenger flow, and to analyze the spatio-temporal heterogeneity of the influence of built environment on passenger flow. Then, the influence coefficient is input into the RF model for training, to analyze the nonlinearity of the influence of the built environment on passenger flow. Using the GTWR-RF model, the study completed the passenger flow prediction and determined the mean relative importance of the built environment characteristics on passenger flow prediction. A case study in Beijing shows that the GTWR-RF model can describe both the spatio-temporal heterogeneity and nonlinearity of the impact of built environment characteristics on passenger flow. Of all the built environment features, the number of working population has the most significant influence on the forecast of passenger flow, followed by the number of bus connections. In the morning peak passenger flow forecast, the determination coefficient of GTWR-RF model is increased by 5.7% compared to the OLS method, increased by 6.3% compared to the RF method, increased by 0.5% compared to the GBRT method, increased by 10.1% compared to the XGBoost method, increased by 7.3% compared to the GTWR method. References | Related Articles | Metrics

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Connectivity Reliability Evaluation of Subway Systems with Spatially Variable Seismic Intensity ZHAO Mi, SONG Jun, MIAO Hui-quan, ZHONG Zi-lan, DU Xiu-li 2023, 23(4): 203-210.  DOI: 10.16097/j.cnki.1009-6744.2023.04.021 Abstract ( )   PDF (1966KB) ( )   With the aim of post-earthquake traffic capacity assessment of the subway system, this paper presents a new method for seismic connectivity reliability evaluation considering the spatially variable seismic intensity. First, based on the historical earthquake records and the seismic intensity attenuation model, the seismic hazard analysis of the target site was conducted to generate a peak ground acceleration field with spatial variability. Second, the graph theory modeling method was adopted to construct the subway network topology model, and the seismic motion field was input into the network. The seismic failure probability of subway stations and interval tunnels was estimated based on the seismic fragility models and simulated the post- earthquake state of the subway network. Finally, the concept of effective connectivity was introduced to establish three indicators: network connectivity reliability, station connectivity reliability, and line failure rate. The seismic connectivity reliability of the subway system was analyzed from different perspectives based on the Monte Carlo simulation. The subway system of the six districts of Beijing was used as an example to analyze the seismic connectivity reliability based on the above process. The results showed that the spatially variable seismic intensity has a significant effect on the connectivity reliability of the system. 88% of the stations can connect effectively under the seismic with a probability of exceeding 10% in 50 years; The closed loops formed by interlocking subway lines can effectively improve the connectivity reliability of the system. References | Related Articles | Metrics

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Demand-responsive Dynamic Scheduling Considering Passengers' Spatio Temporal Flexibility for Passenger and Freight Transportation WU Wei-tiao, ZHOU Xiao, ZHU Yan-chen, LI Peng, ZOU Hong-hui, LI Yu 2023, 23(4): 211-227.  DOI: 10.16097/j.cnki.1009-6744.2023.04.022 Abstract ( )   PDF (2793KB) ( )   Demand- responsive transit, a representative mode of a shared mobility system, faces the challenge of efficiently handling travel demand and low vehicle utilization during peak hours. Previous research investigates the optimization of operational scheduling based on the immobile travel demand in the spatiotemporal dimension and has not yet explored the potential improvements in terms of human flexibility. The development of e-commerce has resulted in the rapid growth of freight quantity, but it also faces the problems of truck restrictions in the urban area and low time efficiency of parcel delivery. This paper focuses on designing a new mode of the shared mobility system, and proposes a demand-responsive transit service mode that accommodates both passenger and freight transportation and considers passengers' spatiotemporal mobility. First, the study introduces a new reservation management mechanism for passengers' spatiotemporal flexibility, and formulates the dynamic scheduling process of demand- responsive passenger and freight combined transportation as a Markov decision process in a rolling horizon framework. Each vehicle is considered as an agent, while the information of stops, requests, and vehicles in the environment is integrated as a state of reinforcement learning. The action space is designed to meet the constraints of time, capacity and spatiotemporal mobility adjustment. The reward function of the adaptation model is developed according to the dynamic scheduling characteristics. The method minimizes the total system cost and solves the Markovian decision process model with the Qtran_alt framework based on multi-agent reinforcement learning algorithm. A test was performed in the southern area of Huangpu District, Guangzhou City, and six performance indicators were designed to validate the overall performance of the model and algorithm. The performance was compared between the Qtran_alt and three other multi- agent reinforcement learning algorithms. And the sensitivity analysis was performed for spatiotemporal mobility, time slice interval, overtime penalty factor and freight- to- passenger ratio. The computational results demonstrate that our model can serve 50% of additional freight requests without an increase in total system cost of more than 5.37%, and reach a request service rate of 95.19% with strong universal applicability. References | Related Articles | Metrics

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Airport Departure Floor Curbside Capacity Evaluation Based on Improved Space-time Trajectory Theory BAI Qiang, JIANG Ying, LI Yi-fan, XU Zhi-man 2023, 23(4): 228-236.  DOI: 10.16097/j.cnki.1009-6744.2023.04.023 Abstract ( )   PDF (1819KB) ( )   The curbside of the departure floor of the airport terminal is a place for departure vehicles to park and drop off passengers and is an important component of the airport land-side transportation system. The curbside capacity of the departure floor is a basic parameter for airport curbside settings, which directly affects the operational efficiency of the airport land-side transportation system. The existing methods for the curbside capacity calculation can be improved. This paper uses the space-time trajectory theory, quantifies the factors that affect curbside capacity, and proposes a curbside capacity evaluation model considering pedestrians pass the curbside and vehicles merge into the driving lane from parking lanes. Field survey data and monitoring video data of Kunming Changshui International Airport are used for analysis. The result was compared with those of quick-estimation method, space-time trajectory theory method, and AnyLogic simulation method. The results show that the error between the estimated curbside capacity of Kunming Changshui International Airport using the proposed method and actually measured value is 0.38%. Compared with the results of quick-estimation method, AnyLogic simulation method, and the space-time trajectory method, the error of the method proposed in this paper has been respectively reduced by 127.73% , 8.57% , 11.06% . It indicates that the proposed method can effectively evaluate the departure floor curbside capacity of airport terminals and provides a theoretical reference for airport land-side traffic management. References | Related Articles | Metrics

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Collaborative Evolution of Inter-airport Route Based on Two-level Game Model WU Wei, LIN Zhi-yi, CHEN Xu-mei 2023, 23(4): 237-250.  DOI: 10.16097/j.cnki.1009-6744.2023.04.024 Abstract ( )   PDF (2993KB) ( )   To effectively optimize the inter-airport route network and realize the proper functions of each airport in the airport system, this study aims to achieve the optimal match between airport subsidy policies and the required route network by investigating the relationship between airport subsidy policies and airline route adjustments. The game relationship among passengers, airlines, and airports is analyzed from the perspective of coordinated development of airport and a two-layer game model is developed. In the upper level of the model, an experience-weighted attraction model (EWA) is introduced to analyze the relationship between airline pricing strategies and passenger "escape" to determine the optimal pricing strategy for airlines. In the lower level of the model, the upper model results are used, and an asymmetric random response (QRE) equilibrium model is introduced to analyze the relationship between airport subsidies and airline route adjustments, and to determine the optimal subsidy policy for airports. The results show that: (1) The key factor that affect passengers travel behavior choices of is travel cost, which results in the effect of ticket price discounts exists a high sensitivity zone, an inert zone, and an ineffective zone. By improving ground transportation within airport clusters and controlling travel costs for passengers, it is possible to effectively guide passenger flows between airport clusters. (2) There are multiple peaks in the profits of the game between airlines and passengers, and the optimal discount interval is concentrated between 0.4 and 0.9, depending on the travel costs of passengers and the initial ticket prices. (3) Optimizing the airline network by providing subsidies within airport clusters leads to different efficiency levels of subsidy strategies between airports, including monopolistic, inefficient, and optimal collaborative intervals. (4) Integrating the dual factors of passenger travel choices and inter-airport collaborative subsidies can effectively enhance the profitability of airlines, promote the complementarity of airline routes within airport clusters, and effectively achieve coordinated development between airports. References | Related Articles | Metrics

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Route Network Modeling for Unmanned Aerial Vehicle in Complex Urban Environment HU Xiao-bing, YANG Chang-shu, ZHOU Jun 2023, 23(4): 251-261.  DOI: 10.16097/j.cnki.1009-6744.2023.04.025 Abstract ( )   PDF (2597KB) ( )   This paper focuses on the path planning problem of unmanned aerial vehicle (UAV) in complex urban environments and uses the three-dimensional(3D) visibility graph method for the road network modeling. First, the dense and irregular obstacle environment of the city is deformed and reorganized under the premise considering the safety margin of UAV flight, and then the discrete nodes are collected on the outer surface of the obstacles at different horizontal and vertical intervals. A complex urban low-altitude road network model is developed based on 3D visibility graphs. To reduce the potential conflicts and collision risks between UAVs, the concept of UAV maneuvering protection zone is introduced to further reduce the road network scale and optimize the road network structure. Combined with the requirements of UAV performance and smooth flight, an improved ripple spreading algorithm is proposed to solve the problem with the maximum heading angle change as the main constraint and the minimum path length as the goal. The simulation results show that: the interval of picking points in the 3D visibility graph directly determines the number of nodes and links in the road network model and has a significant impact on the optimal path and planning time. 1000 sets of simulation experiments show that the average length of the shortest path after considering the maneuvering protection zone increases by less than 1% compared with that without the maneuvering protection zone, while the computation time is reduced by nearly 70% . The simulation experiments verify that the introduction of UAV maneuvering protection zone and the limitation of heading angle change can effectively reduce the size of the road network, improve the computational efficiency, and help to obtain smooth paths and reduce the potential collision risk of UAVs. References | Related Articles | Metrics

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Construction and Validation of a General Calculation Model on Median Opening Length for Expressway Renovation and Expansion Project HAO Jia-tian, WU Zhong-guang, TIAN Wan-li, HAN Feng, LIU Bo-wen 2023, 23(4): 262-269.  DOI: 10.16097/j.cnki.1009-6744.2023.04.026 Abstract ( )   PDF (1705KB) ( )   In order to solve the median opening length setting problem in the expressway renovation and expansion project, we analyze the vehicle driving stability in the median opening section. The curve radius of the innermost lane in the multi-lane conversion without transverse slip was taken as the minimum turning radius of the multi-lane median opening section, and the geometric relationship of the road alignment at the multi-lane median opening section was used to establish the general calculation model of the multi-lane median opening length. Considering the driving safety and efficiency of vehicles at the multi-lane median opening section, the time occupancy rate, section saturation, speed consistency, and section trip delay were selected as evaluation indexes to establish the cloud model for evaluating the vehicle operation status. By VISSIM simulation, we obtain the evaluation index data of vehicle operation at the multi-lane median opening section and used the cloud model to determine the vehicle operation status level under different numbers of conversion lanes and different median opening lengths. The results show that, with the median opening length obtained by the proposed model, the comprehensive evaluation results of vehicle operation status under the conditions of 2-lane and 4-lane are at K1 grade, and the comprehensive evaluation results of vehicle operation status under the conditions of 5-lane and 6-lane are above K2 grade. It is proved that the proposed median opening length model can guarantee the vehicle driving state in the median opening section, and it can improve the overall vehicle running safety and efficiency of the section and ensure the driving stability of the innermost lane of the median opening conversion section. References | Related Articles | Metrics

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China Railway Express Network Flow Assignment in Blockchain Background HONG Zhi-chao, ZHANG Jin, SUN Wen-jie, SHEN Hao, ZHAO Gang, LIANG Hong-bin 2023, 23(4): 270-281.  DOI: 10.16097/j.cnki.1009-6744.2023.04.027 Abstract ( )   PDF (2640KB) ( )   PDF(English version) (881KB) ( 27 )   Network traffic analysis is the basis of traffic network optimization. This paper focuses on the China Railway Express, and constructs a two-objective network assignment model with the minimum total transportation cost and the shortest transportation time. For the transportation costs, considering the competition between China Railway Express liner and international liner and international air transport, the two-stage game is used to depict the change in the freight rate of the liner before and after the application of blockchain technology. For the transportation time, the proportion of customs clearance time before and after the application of blockchain technology is considered to affect the total time. The analysis results based on the one-week operation data of the China Railway Express in 2020 show that the application of blockchain technology has a significant impact on network traffic. After the application of blockchain technology, the traffic of the East and Middle Passages will decrease by 2.76% and 5.12% respectively, and the traffic of the West Passage will increase by 7.88%. A part of the cargo transportation tasks of the East and Middle Passages will be transferred to the West Passage. At the same time, the total transportation cost of the network was reduced by 17.08% , the total time was reduced by 8.27% , and the comprehensive transportation cost was reduced by 13.08% , which is beneficial to improving the economic benefits and attractiveness of the China Railway Express to customers. This analysis of the changes in the network traffic of the China Railway Express before and after the application of blockchain technology in this paper can provide an important reference for the adjustment of the train operation plan and the optimization of network facilities. References | Related Articles | Metrics

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Assessment of Impact Factors on Passenger Attraction of New Metro Line WEN Hui-min, ZHU Shan, SUN Jian-ping, ZHANG Jian-bo, ZHANG Jing-jing 2023, 23(4): 282-289.  DOI: 10.16097/j.cnki.1009-6744.2023.04.028 Abstract ( )   PDF (1668KB) ( )   Understanding the factors that impact the competitiveness of new metro lines can not only provide insight into passengers' commuting choices and enable us to anticipate the impact of future metro routes but also optimize the overall public transport capacity and service quality. This paper utilizes a classification and regression tree (CART) model to analyze the relationship between the new metro line's competitiveness and individual passengers' travel characteristics. At first, the individual passenger's travel characteristics, such as passenger types, travel habits, travel types, and home-work metro accessibility, are collected from each individual's long-term smart card data. Secondly, the competitiveness of the new metro line is represented by its ridership. The CART model is then built based on the above�mentioned characteristics and it is proven robust with an 82.6% classification accuracy. At last, a comprehensive analysis of the model structure and factors weights reveals the significance of the factors that impact the attractiveness of the new metro line. The results indicate that the distance between the residence and the new line is the most critical factor in the new line's competitiveness. The metro accessibility of the residence and the rate of multimodal traveling are two factors that have a minor impact. In addition, the trip's duration and distance have little impact, while the level of interest for older passengers differs from other age groups. The study's findings will undoubtedly improve the planning and operation of public transportation. References | Related Articles | Metrics

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Nonlinear and Threshold Effects of Built Environment on Origin-destination Flows of Urban Rail Transit XU Qi, LI Wen-xi, CHEN Yue, HU Jia-jun, LIANG Xiao 2023, 23(4): 290-297.  DOI: 10.16097/j.cnki.1009-6744.2023.04.029 Abstract ( )   PDF (1925KB) ( )   Studies on the dependency relationship between the origin-destination (OD) passenger flow of urban rail transit and built environment indicators are useful to enhance Transit-oriented development(TOD). Existing studies have extensively examined the effects of the built environment on ingress/egress passenger flow. The studies that investigate the impact of the built environment on OD passenger flow seldom consider the effects of the interaction of built environment determinants on OD passenger flow. To this end, this study first uses multi-source location-based big data to describe the indicators of the TOD built environment of urban rail transit and then applies the extreme gradient boosting model (XGBoost) to investigate the nonlinear relationship between OD flows of the Beijing subway and TOD built environment. The case study of Beijing indicates that XGBoost can be able to identify the nonlinear relationship between OD flows and the TOD built environment indicators with a more reliable estimate result. Its interpretation ability reaches 72.6%. The difference in the effects of the built environment on OD flows is significant. The importance of two group indicators, namely density and public transport accessibility, ranked the top two. The average importance of indicators in these two groups is 4.41% and 3.71% , respectively, which make 1.29 and 1.08 times all variables' average values. The nonlinear effect of key indicators on OD flows is significant and shows a threshold effect. And the bivariate partial dependence diagram shows that the movement of urban rail transit passengers is determined by the difference in the built environment of OD pairs and the corresponding interaction between them. Hence, developing TOD not only needs to examine the influence of the built environment on ingress/egress flow from the perspective of traffic generation but also needs optimization and coordination of land use around urban rail transit stations from the perspective of traffic distribution. References | Related Articles | Metrics

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Route Optimization of Fresh Food Distribution Under Time-varying Network and Hybrid Adjustment Strategy MA Chang-xi, XUE Fan-song, MA Cun-rui, LI Hai-jun 2023, 23(4): 298-306.  DOI: 10.16097/j.cnki.1009-6744.2023.04.030 Abstract ( )   PDF (1775KB) ( )   This paper proposes a vehicle routing optimization method based on hybrid adjustment strategy under time�varying road network. The cost minimization objective function include time window penalty cost, cargo damage cost, vehicle cost, carbon emission cost, and overall customer satisfaction cost. Then, the urban roads were divided by region, and the deep learning method was used to predict the vehicle speed under different regional roads. To ensure the balance between customer satisfaction and cost, the hybrid adjustment strategy was used to screen the customer points that may lead to large-scale delays in the distribution process. At last, a Solomon example and a lifetime fresh food distribution example in Guangzhou were performed. The results indicate that compared with the traditional genetic algorithm, the improved genetic algorithm can accelerate the convergence process of the optimal solution. Compared with the single adjustment strategy, the predicted speed can reduce the target cost by 17.29%, and the on-time rate reaches 95.65%. The hybrid adjustment strategy can reduce the target cost and improve the on-time rate of reaching customers, which provides theoretical reference for fresh food distribution cost analysis. References | Related Articles | Metrics

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Exploring High-income Trip Characteristics of Taxis NIAN Guang-yue, PAN Hai-xiao, SUN Jian 2023, 23(4): 307-314.  DOI: 10.16097/j.cnki.1009-6744.2023.04.031 Abstract ( )   PDF (1676KB) ( )   To improve the operational efficiency and service level of taxis, the correlation mechanism between taxi trip characteristics and taxi drivers' income per unit time (IPUT) is studied based on the central area of Chongqing. A random forest prediction model is constructed to analyze the relative importance and significance of trip characteristics on IPUT. The results show that trip characteristics can be used to predict drivers' IPUT with good accuracy, and the relative importance of delivery speed, search time, and the number of long orders is the greatest in predicting the average IPUT. The increase in search trip detour, delivery trip detour, and search mileage significantly increases the probability that the middle-income drivers fall to the low-income drivers, while the decrease in search trip detour, search area preference, search mileage, and delivery speed significantly increase the probability that the middle-income drivers rise to the high-income drivers. High-income drivers have the characteristics of being proactive in searching for passengers, not preferring specific areas, tending to anticipate short routes with high travel speeds, and favoring long orders but not deliberately pursuing them. This study bridges the gaps in previous research on the construction of correlations between trip characteristics and benefits, the characterization and prediction of diverse trip characteristics on IPUT, and the outlining of operating characteristics of high-income taxi drivers. The study may provide theoretical references and technical support for urban traffic management, taxi quantity regulation, and fare adjustment. References | Related Articles | Metrics