Archive

25 December 2023, Volume 23 Issue 6 Previous Issue    Next Issue

---

For Selected:

Download Citations EndNote Ris BibTeX

Toggle Thumbnails

Select

Freight Carbon Emissions in Yangtze River Delta Region Based on Impact of Freight Volume and Energy Intensity WU Lan, LU Hao-dong, YIN Chao-ying, CHENG Ying-ji, REN Si-qi 2023, 23(6): 1-10.  DOI: 10.16097/j.cnki.1009-6744.2023.06.001 Abstract ( )   PDF (1555KB) ( )   To investigate the relationship between freight carbon emissions and freight volume as well as energy intensity in the Yangtze River Delta (YRD) region, an autoregressive distributed lagged error correction model (ARDL-ECM) was established to examine the long-run and short-run relationships between freight volume, GDP, energy intensity, and freight carbon emissions in the YRD region over the past 30 years. The Granger test was used to analyze the existence of the three data sets and their relationship with freight carbon emissions. Our findings reveal that freight carbon emissions increase by 2.369% , 1.394% , and 2.198% , respectively, when freight volume, GDP, and energy intensity increase by 1% in the long term. In the short term, we observe a 3.285% increase in freight carbon emissions when freight volume increased by 1%, and a 0.935% increase when energy intensity increased by 1%. The Granger test confirms a unidirectional causal link between energy intensity and freight carbon emissions. Therefore, we recommend the promotion of new energy-efficient freight transportation methods to reduce energy intensity, enhance energy utilization efficiency, and improve the multimodal freight transport network for the goal of reducing carbon emissions from freight transport. References | Related Articles | Metrics

Select

Critical Transportation Distance Analysis for Express Goods Transportation Modes Considering Low Carbon Emissions SUN Zong-sheng, SHUAI Bin, XU Min-hao 2023, 23(6): 11-21.  DOI: 10.16097/j.cnki.1009-6744.2023.06.002 Abstract ( )   PDF (2176KB) ( )   PDF(English version) (808KB) ( 46 )   The increasing demand of express goods transportation has also brought a surge in carbon emissions. Adjustment of transportation structure and technological progress are the main approaches to reduce carbon emissions in the field of transportation. This paper proposes a market share rate model based on the Logit model, which includes service attributes such as economy, timeliness, stability, safety, convenience and environmental sustainability. The purpose is to analyze the competitive relationships and transportation distance among the array of express goods transportation modes. Based on the share rate model, the interaction relationship between the critical transportation distance and the speed changes of high-speed railway is analyzed, and the feasibility of the model is verified by examples. The results indicate that the absolute advantage of high-speed railway express transportation at 250 km·h-1 is 700 km to 1500 km, and the advantageous transportation time is 2.8 hours to 6.0 hours. When considering carbon emissions, the transportation distance of 600 km and above is the absolute dominant range of high-speed railway express transportation, with a dominant transportation time of 2.4 hours. The absolute advantage distance of high-speed railway over highways will expand by 100 kilometers on the left boundary of the interval for every 0.1 increase in carbon emission weight coefficient. Under the speeds of 200 km · h- 1 , 250 km · h- 1 , 300 km · h- 1 , and 350 km · h- 1 , the maximum increase in critical distance between highways and high-speed railway is achieved at 250 km · h- 1 , which is 50% higher than the critical distance under 200 km·h-1 . When the carbon emission factor of air express transportation is reduced by half, the left boundary of its advantageous distance range will expand by 23%. References | Related Articles | Metrics

Select

Evaluation of Road Network Density Based on Structure, Efficiency, Fairness and Resilience DENG Mao-ying, DENG Ce-fang 2023, 23(6): 22-32.  DOI: 10.16097/j.cnki.1009-6744.2023.06.003 Abstract ( )   PDF (2327KB) ( )   Road network density serves as a critical indicator reflecting the state of transportation and urban development. It plays a pivotal role in territorial spatial planning and urban assessment. However, in practical planning, the evaluation of road network density often relies on recommended specifications or benchmarks from advanced international cities, lacking quantitative standards and methods. This study first identifies four fundamental dimensions for evaluating road network density: road network structure, road network efficiency, road network fairness, and road network resilience. A comprehensive road network density evaluation system is established that comprises 10 distinct indicators based on these four dimensions. In response to the challenge of determining weights for these multidimensional and multi-indicator dimensions, an enhanced CRITIC weighting method is employed to categorize and assign weights to the constructed indicator system. The final comprehensive evaluation result is obtained by solving the first and second level weights. Taking the Pearl River New Town Financial City area in Tianhe District of Guangzhou as a case study, we create three typical road network density development models and construct a road network model for quantitative evaluation and comparative analysis using the developed evaluation system. The results indicate that efficiency and fairness indicators carry the highest weight in the road network evaluation of the CBD area. Simultaneously, the dimensional assessment reveals that excessively high road network density can hinder efficiency. As road network density increases to a certain point, the improvements in road network structure and resilience show a diminishing trend. Consequently, there exists an optimal road network density for CBD areas, as opposed to a 'more is better' network. This multi- dimensional indicator system overcomes the limitations of a single evaluation metric and enables a comprehensive assessment of road networks in various functional areas. References | Related Articles | Metrics

Select

Car-following Model and Safety Characteristics of Connected Autonomous Vehicle Based on Molecular Force Field QU Da-yi, MENG Yi-ming, WANG Tao, SONG Hui, CHEN Yi-cheng 2023, 23(6): 33-41.  DOI: 10.16097/j.cnki.1009-6744.2023.06.004 Abstract ( )   PDF (2165KB) ( )   To describe the dynamic characteristics of multi-dimensional perception and interaction of heterogeneous traffic flow under the complex environment of human-vehicle-road more accurately, this paper proposes a vehiclefollowing model based on the molecular dynamics potential field function. The characteristics of self-driven particles and the safety characteristics of vehicle to vehicle interaction behavior of connected autonomous vehicles are analyzed from the perspective of molecular force field, which is helpful for systematically analyzing the synergy relationship and safety situation evolution law of networked heterogeneous vehicle groups. First, the connected autonomous vehicles under complex traffic conditions are taken as self-driven particles. The vehicle-following model for the connected autonomous vehicle is established based on the molecular dynamics potential field theory. The molecular force field model for the following behavior of the connected autonomous vehicles is developed by introducing the velocity synergy term. Then, the Artificial Bee Colony Algorithm is used to calibrate the parameters of the existing carfollowing model and the intelligent driver model using the High_D vehicle trajectory data. The rationality and safety of the molecular force field car- following model are verified. The numerical simulation is designed to verify the fitting effect and stability performance of the molecular force field model on the real vehicle following behavior. The results show that the Mean Absolute Error and Root Mean Squared Error of the vehicle acceleration results obtained by the molecular force field model were lower and the fluctuation was smaller than actual data when disturbed. The proposed model improves the safety and efficiency of the following behavior of the connected autonomous vehicles, and the macroscopic traffic flow operation has better stability. The proposed model can systematically describe the dynamic characteristics, microscopic car-following behavior and the vehicle to vehicle safety interaction relationship of connected heterogeneous vehicle groups, which provides a theoretical basis for improving driving safety and traffic operational efficiency. References | Related Articles | Metrics

Select

Combined Approximate Method for Traffic Data Imputation Under Multiple Missing Modes GUO Feng-xiang, HUANG Jin-tao, CHEN Yu-guang, GUO Yan-yong, LIU Pan 2023, 23(6): 42-50.  DOI: 10.16097/j.cnki.1009-6744.2023.06.005 Abstract ( )   PDF (2801KB) ( )   With the continuous expansion of the scale of basic data collected and applied in intelligent transportation systems, the importance of missing data imputation has become increasingly prominent. Aiming at the problem of random missing and continuous missing data in traffic dataset, this paper proposes a Combined Approximate Filling (CAF) method based on least squares support vector machine optimized by whale optimization algorithm. Considering the overall trend of the missing data and the fluctuation characteristics of the reference data, this paper uses the univariate imputation and multivariate imputation to fill the missing values based on the multiple imputation theory. The adaptive threshold segmentation method in image recognition is introduced to dynamically divide the difference values under different periods. The results of univariate imputation and multivariate imputation are combined through the dynamic dissimilarity threshold under different periods to complete the high- precision approximate imputation. Several experiments are designed to verify the performance of the imputation method, using a large number of real vehicle trajectory data in Yuxi City of Yunnan Province in China. The results show that the CAF imputation method can adapt to various occasions in small sample data. This method produces satisfied results under different missing rates, especially for random missing imputation. The maximum RMSE is 0.365. The experimental results also prove that the data imputation from the proposed method is more effective than some traditional methods under different missing types and different data dispersion conditions. References | Related Articles | Metrics

Select

Concurrent Optimization of Path Selection and Bandwidth-based Coordination for an Unclosed Traffic Network JING Bin-bin, LIN Yong-jie, YAN Zhu-hao, HUANG Zheng-jie 2023, 23(6): 51-62.  DOI: 10.16097/j.cnki.1009-6744.2023.06.006 Abstract ( )   PDF (1886KB) ( )   Bandwidth-based traffic signal coordination plays a crucial role in improving the efficiency of vehicles within urban traffic networks. It is widely adopted in the field of arterial and network traffic signal control. However, traditional bandwidth-based network control methods mostly focus on optimizing coordination variables, such as offsets and phase sequences, after the coordinated paths are determined. This neglects the simultaneous optimization of path selection and coordination variables, resulting in limited coordination effectiveness and challenges in catering to the varied control requirements of different coordination paths. To address these issues, this paper focuses on the concurrent optimization of path selection and bandwidth-based coordination within an unclosed traffic network. The coordination path is taken as a decision variable and optimized concurrently with offsets and phase sequences. The choice sets for coordination paths and coordination path pairs are determined through an analysis of the inflow and outflow relationships of traffic movements between adjacent intersections. The optimization objective is to maximize the sum of weighted bandwidth. To capture the spatial and temporal constraints involving bandwidth, coordinated path pairs, offsets, phase sequences, travel time, and red and green time, a time- space diagram is employed. In addition, binary variables are introduced to formulate optimization constraints for the coordination path pairs. The simultaneous optimization of path selection and bandwidth-based coordination is formulated as a mixed-integer nonlinear programming problem. The results of a numerical example demonstrate that, when compared to the traditional bandwidth- based model that focuses on coordinating through paths, the proposed model automatically selects key coordination paths with higher traffic volumes in the traffic network. Consequently, the sum of weighted bandwidths generated by this model at the network level increases by 14.16% , and the sum of weighted bandwidths on four arterials sees improvements of 17.26%, 20.68%, -0.29%, and 39.19%, respectively. References | Related Articles | Metrics

Select

Dynamic Control Method for Intersection Space Resources in Mixed Traffic Environment JIANG Xian-cai, XU Hui-zhi 2023, 23(6): 63-73.  DOI: 10.16097/j.cnki.1009-6744.2023.06.007 Abstract ( )   PDF (2452KB) ( )   Due to the essential difference of trajectory controllability between Connected-automated Vehicle (CAV) and Connected Human-driven Vehicle (CHV), the proposed signal control optimization methods for mixed-traffic of CAVs and CHVs at intersections do not consider the dynamic adjustment of approach lane utilization due to the change of CAV penetration rate. This paper proposes a dynamic CAV-dedicated lane allocation method to avoid using transitional or inefficient CAV- dedicated lanes. In addition, a collaborative optimization algorithm for CAV trajectory and signal control parameters is developed to save the start-up loss time and maximize the utilization of green time. The simulation results show that the proposed method can reduce the average delay per vehicle at intersections by 17.3% or more compared with that of a fully actuated signal control scheme. And it is necessary to drive the CAVs in one or more CAV-dedicated lanes when the CAV penetration rate exceeds 0.33. Compared with the optimization strategy by a previous study (Niroumand et al.), the proposed method is more suitable for multi-lane signalized intersection with high saturation and high CAV penetration rate. Further analysis shows that the length of road segment, CAV penetration rate and maximum speed are sensitive to the optimization results of the proposed method. References | Related Articles | Metrics

Select

Nested Logit Model of Elderly Travel Mode Choice Based on Boundedly Rationality ZHANG Bing, TAO Wen-kang, LIU Jian-rong, XUE Yun-qiang, DENG Ming-jun 2023, 23(6): 74-82.  DOI: 10.16097/j.cnki.1009-6744.2023.06.008 Abstract ( )   PDF (1656KB) ( )   To study the travel mode choice behavior of the elderly group and improve the travel mode convenience and accessibility for the elderly people, this paper proposes a travel mode choice model that meets the needs of the elderly group. Based on the traditional Logit model, the nested Logit model is used to improve the assumption of maximum traveler utility in the model. Taking the elderly travel group as the research object, the model is hypothesized based on the bounded rational satisfactory decision criterion, and the indifference threshold is introduced to establish the bounded rational nested Logit model that represents the elderly travel group. Then, taking the travel mode choice strategy data of the elderly group in Nanchang city as an example, the double-nested continuous average algorithm is used to solve and analyze the model. The results show that: (1) The elderly traveler group does not always choose the transportation mode with the lowest travel cost, and its mode choice behavior is affected by their degree of rationality and travel preferences. (2) The no-difference thresholds between nests of different travel modes interact with each other and gradually stabilize the mode choice probability between nests as the no- difference threshold increases; (3) The mode choice behavior of elderly travelers is affected by their rationality and preference, and when the travel cost differential is in one of the undifferentiated intervals of whether older people are able to make limited rational judgments, the probability of choosing public transportation changes with the propensity coefficient and stabilizes gradually as the propensity coefficient increases. References | Related Articles | Metrics

Select

Effect of Cell Phone Use on Pedestrian Crossing Risk During Waiting Stage PENG Jin-shuan, YUAN Hao, SONG Zhen, FAN Zi-bin, YANG Xiang-hao, ZHANG Lei 2023, 23(6): 83-89.  DOI: 10.16097/j.cnki.1009-6744.2023.06.009 Abstract ( )   PDF (2361KB) ( )   This paper investigates the effect of cell phone use on pedestrian crossing risk during the waiting stage. We conducted data collection at a typical signal-controlled crosswalk to monitor pedestrian crossing behaviors. Utilizing the random forest algorithm, we developed a model for classifying pedestrian violations and extracted key parameters to represent crossing risk. Subsequently, we constructed a pedestrian crossing risk assessment model using the CRITIC (Criteria Importance Through Intercriteria Correlation) weighting method. The results show that several factors significantly affect crossing violations, including pedestrian crossing waiting time, location choice, green light response time, startup speed, average speed and the rate of failing to achieve the target speed. Cell phone use during the waiting stage has a limited impact on location choice but results in slower pedestrian responses, reduced average speed, higher startup speed, and an increased rate of failing to achieve the target speed. The rate of failing to achieve the target speed, crossing location choice, and green light response time have a substantial impact on crossing risk, and thus cell phone use increases crossing risk by 26.1% compared to pedestrians who do not use cell phones. The research results can provide important insights for the enhancement and optimization of street crossing facilities, as well as the improvement of pedestrian safety education effect. References | Related Articles | Metrics

Select

Evaluation Model for Pedestrian Vehicle Conflict Degree at Signalized Intersections ZHANG Wen-hui, XU Hai-bin, ZHOU Ge, WEN Wen 2023, 23(6): 89-99.  DOI: 10.16097/j.cnki.1009-6744.2023.06.010 Abstract ( )   PDF (2569KB) ( )   In order to investigate the key influencing factors affecting the severity of pedestrian-vehicle conflicts at urban signalized intersections and to improve intersection safety management, typical urban road signalized intersections are selected. In this paper, UAV aerial photography is used to obtain traffic flow videos. Conflict point information parameters and location distribution characteristics are obtained through manual observation and Tracker software analysis and processing. To quantify the degree of conflict, post intrusion time, collision area speed, and potential collision distance are used as evaluation indicators for the severity of pedestrian vehicle conflicts. The K-means clustering algorithm is used to iteratively classify street crossing conflicts according to their severity, and 21 explanatory variables were determined in terms of people, vehicles, and roads. The multivariate ordered Logistic model is screened by Pearson correlation analysis. The AUC (Area Under Curve) of the model's estimated classification probability for the severity level of conflicts was 0.971, which was verified through the ROC (Receiver Operating Characteristic) curve. The results showed that the distance between pedestrians and the conflict point (0.364), the tendency of vehicles in front of the conflict point (stopping to yield: -4.22; slowing down to yield: -0.937), whether pedestrians ran red lights (0.818), the number of motor vehicle lanes (0.29), the length of waiting time for red lights (0.012), the age group of pedestrians (- 0.869), and the color of pedestrians' clothing (0.673) are significant factors affecting the severity of pedestrian vehicle conflict. The research results of this article can provide certain reference value for the formulation of traffic strategies for pedestrian crossing safety. References | Related Articles | Metrics

Select

Multilayer Fuzzy Estimation Method for Road Traffic Capacity Affected by Traffic Incidents WANG Jia-wen, SUN Chen-chen, ZHAO Jing, HANG Jia-yu 2023, 23(6): 100-110.  DOI: 10.16097/j.cnki.1009-6744.2023.06.011 Abstract ( )   PDF (3158KB) ( )   Traffic incidents often lead to regional congestion of the road network. Researching methods to estimate the capacity of road sections during traffic incidents is crucial for implementing accurate policies, mitigating traffic congestion, and improving the level of service. This paper proposes a multi-layered fuzzy-based method for estimating capacity. The method addresses the complex factors that affect capacity during traffic incidents, as well as the shortage of sample data. Firstly, the interrelationships between factors affecting capacity are analyzed macroscopically. Based on these relationships, a fuzzy logic system is constructed to estimate capacity. The system quantifies various traffic incident impacts and converts them into multiple feature values. Secondly, a multi-layered fuzzy estimation method is developed for capacity using traffic incident impact quantification feature values as input and outputting a reduced section capacity rate. Finally, the effectiveness of the proposed model is verified through practical cases, which compared the measured capacity during traffic incidents, classical capacity calculation methods, and the proposed method. The comparison results show that this method is applicable in various traffic incident scenarios, with a total average estimation error of 5.43%, significantly improving precision compared to the traditional HCM lane reduction method. Supported by dynamic detection data during traffic incidents, this research can provide support for intelligent management and control during non-normal conditions for traffic management departments. References | Related Articles | Metrics

Select

Identification of Traffic Operation Status in Freeway Weaving Segments Considering Weather Effects LI Yan, CHEN Jiang-hui, ZENG Ming-zhe, XU Jin-hua, WANG Fan 2023, 23(6): 111-119.  DOI: 10.16097/j.cnki.1009-6744.2023.06.012 Abstract ( )   PDF (3091KB) ( )   This paper proposes a revised k-prototype method to improve the accuracy of the classification of traffic operational status at freeway weaving segments under adverse weather conditions by introducing the weather factors to traditional traffic flow indicators. The study determines the impact of weather on traffic flow status by analyzing the variation of traffic flow indicators under various levels of rainfall, visibility, and wind speed. The random forest model is used to select the influential variables of traffic flow operating status for each lane of the weaving sections. The information entropy is then introduced to measure the dissimilarity of the k-prototype algorithm and a clustering effectiveness evaluation indicator is used to measure the effectiveness of the status. The results show that the operation status in the lane level of the freeway weaving segments can be divided into seven categories. Corresponding to the Level of Service (LOS) at all levels in the Highway Capacity Manual, the LOS under adverse weather conditions would decrease significantly. The LOSs for lanes 1 and 3 decrease with an average of 4 levels, and the LOSs for lanes 2 and 4 decrease with an average of 3 levels. Under moderate weather conditions, each lane experiences a decrease of LOS by 2 to 3 levels. Under the same LOS, the minimum speed in lanes 1 and 3 would experience a decrease ranging from 11.2 to 17.4 km · h- 1 , and the minimum speed decreases in lanes 2 and 4 range from 21.2 to 27.4 km · h- 1 . The findings provide a theoretical basis for refined traffic management under adverse weather conditions, which helps to improve the traffic LOS at freeway weaving segments. References | Related Articles | Metrics

Select

Identification and Spatiotemporal Evolution Analysis of Air Traffic Congestion in Terminal Area LI Shan-mei, WANG Yu-xin, LEI Qing-lei, SONG Si-ni, WANG Chao 2023, 23(6): 120-132.  DOI: 10.16097/j.cnki.1009-6744.2023.06.013 Abstract ( )   PDF (5054KB) ( )   The identification of air traffic congestion in terminal areas has traditionally relied on the subjective judgment of air traffic controllers, lacking a scientific foundation to understand the evolution of congestion. This has often led to inaccuracies in congestion assessment. To enhance the accuracy of congestion assessment by controllers, this study proposes a methodology for identifying air traffic congestion based on Kernel density estimation and the traffic flow fundamental diagram, and investigates the spatiotemporal evolution characteristics of air traffic congestion. Considering the characteristics of air traffic control behavior and aircraft trajectories, a parameter identification method is proposed for determining air traffic flow parameters based on the fundamental diagram theory. By combining traffic flow parameter data with air traffic control experiences, a congestion recognition method is developed using Gaussian Kernel density estimation. The effectiveness of the proposed method is demonstrated using data from the Beijing terminal area. The results indicate that the relative speed thresholds for dividing traffic states are 6.5 km · min- 1 and 9.8 km·min-1 . In congested states, controllers exhibit more pronounced radar guidance behavior, and flights have longer durations, greater distances, and more turns, reflecting increased traffic complexity. The spatiotemporal analysis of air traffic congestion in the Beijing terminal area reveals a significant imbalance in congestion distribution. Congestion periods are mainly observed around 9:00 am, 2:00 pm, and 7:00 pm, with the central region experiencing the highest levels of congestion. These findings contribute to improving the precision and scientific basis for controllers' understanding of congestion scenarios. References | Related Articles | Metrics

Select

Illegal Parking Behavior Management Based on Spatial-temporal Analysis and Social Cognitive Theory LIN Jian-xin, LIU Yi-ni, ZHU Xue-chao, LIU Bo 2023, 23(6): 133-140.  DOI: 10.16097/j.cnki.1009-6744.2023.06.014 Abstract ( )   PDF (2170KB) ( )   This paper investigates the management strategy for illegal parking behavior using the illegal parking data in Guanganmenwai Street in Xicheng District of Beijing, China. The spatial-temporal analysis method and social cognitive theory are used to summarize parking supply. The road traffic management and driver difference are used as exogenous latent variables. Then the parking demand and illegal parking behavior are extracted as endogenous latent variables. The structural equation model is developed to reveal the path of illegal parking behavior and the mechanism of each factor. The results show that the latent variable of road traffic management has the greatest impact on illegal parking behavior. The observed variables such as parking rate, seeking time and parking time have significant impact on illegal parking behavior, and the utility values are respectively 0.83, 0.70 and 0.70. Improving road traffic management measures, activating parking resources, and improving the utilization rate of parking resources can effectively improve the current situation of illegal parking on roads, and provide a decision-making basis for the illegal parking management in cities. References | Related Articles | Metrics

Select

Rolling Time Domain Allocation of Regional Shared Parking Considering Potential Demand SUN Chao, YIN Hao-wei, SONG Mao-can, ZHANG Peng 2023, 23(6): 141-152.  DOI: 10.16097/j.cnki.1009-6744.2023.06.015 Abstract ( )   PDF (3460KB) ( )   This paper investigates the impact of urban road traffic conditions and potential parking demand on the selection of regional shared parking spaces. An integer linear programming model is proposed for dynamic reservation and allocation of regional parking spaces. The potential parking demand is considered in the model and the historical parking data is used to estimate the potential parking demand. The goal of the model is to maximize the comprehensive benefits considering the platform revenue and user costs. The method considers the potential parking demand when processing the requests from the reserved parking users prior to allocation. A portion of requests from reserved parking users might be rejected due to the record of poor turnover and low utilization of parking spaces. At the beginning of the allocation, both the reserved user requests and the new requests are analyzed, and the rolling time domain allocation is performed in consideration of the real-time travel delays. The case study results show that when the parking demand is high, considering the potential demand in the real-time allocation of reserved parking users can improve the parking allocation revenue. Compared to the traditional shared parking mode, the proposed method can improve the parking lot revenue by 10% on average, improve the parking space utilization rate by 20% , and decrease the rejection rate by 8.5%. The system revenue can be significantly affected by the number of users rejected due to poor parking turnover and low utilization of parking space and the length of parking cycle. The model can provide reference for shared parking operation and management with multiple shared parking lots and multiple destinations in the region. References | Related Articles | Metrics

Select

Bus Stop Ridership Impact Factors Analysis Considering Spatial Effects WANG Yi-fan, CHEN Xue-wu 2023, 23(6): 153-164.  DOI: 10.16097/j.cnki.1009-6744.2023.06.016 Abstract ( )   PDF (3160KB) ( )   Investigating the impact factors of bus ridership is important to public transit planning and management. Using multi-source data from Nanjing city in China, this paper analyzes the impact factors of bus ridership at the bus stops. The study considers the spatial effects arising from both spatial dependence and spatial heterogeneity, and develops a spatial Durbin model and a geographically weighted regression model. The study examines the impact of land use, transport infrastructure, stop attributes, and socio-economic factors on bus ridership during morning and evening peak hours during weekdays. The results indicate that, from a global perspective, there is a significant spatial dependence and aggregation feature among bus stop ridership. The spatial Durbin model outperforms multiple regression models, spatial error models, and spatial lag models. The direct effect of each variable on ridership during peak hours in the morning and evening follows the commuting law. The spatial spillover effect of public service land density and the number of bus stops is the most significant and exhibits a siphon effect. From a local perspective, each impact factor exhibits significant spatial heterogeneity, with land use variables displaying the largest variation. The headway is negatively correlated with bus stop ridership, and the influence degree decreases outward from the center of Xuanwu Lake in Nanjing city. The number of routes is positively correlated with bus ridership, and the degree of impact increases from the old urban area to the peripheral areas. References | Related Articles | Metrics

Select

Bus Operation Optimization Control Strategy Considering Speed Regulation and Boarding Guidance WENG Jian-cheng, LI Wen-jie, LIN Peng-fei, DI Xiao-jian, XU Li-quan 2023, 23(6): 165-175.  DOI: 10.16097/j.cnki.1009-6744.2023.06.017 Abstract ( )   PDF (1906KB) ( )   PDF(English version) (1258KB) ( 15 )   Improving the uniformity of bus arrival intervals and operational service reliability through effective control strategies is an important way to enhance the quality of bus services and the satisfaction of passengers. First, a bus operation model was developed in consideration of the fluctuation of passenger boarding demand at stops. Second, a bus interval speed regulation strategy was proposed based on travel time deviation feedback mechanism, and then a station passenger information guidance strategy was designed based on the boarding choice behavior model. To minimize passenger travel costs and bus headway deviations, this paper proposed a combination optimization control strategy integrating speed regulation and boarding guidance for bus operation and the solution method. Taking the bus Route No. 57 in Beijing as an example, this paper performed the numerical simulation comparison experiments for four scenarios. The results show that the combination control strategy has the best optimization effect. Compared with the no control strategy, the stability of bus operation has been improved by 47.7% through the proposed method, and the bus bunching on the route was avoided. The comprehensive cost considering travel time and congestion has been reduced by 18.71%. The study also compared the optimization effects of strategies under different passenger income levels and passenger guidance information compliance rates. The results show that the effect of information guidance strategies on reducing total travel costs reduce as income levels increase. When the passenger guidance information compliance rate is 0.7. The combination control strategy has the most significant improvement effect on bus operation and passenger travel. This study can provide important support for the bus operation reliability and service quality improvement. References | Related Articles | Metrics

Select

Allocation Algorithm of Bidding Validity Right for Shared Spaces of Multiple Parking Lots Under Flexible Incentives GAO Liang-peng, ZHENG Yue, JIAN Wen-liang, MA Xin-wei, JI Yan-jie 2023, 23(6): 176-184.  DOI: 10.16097/j.cnki.1009-6744.2023.06.018 Abstract ( )   PDF (2217KB) ( )   To improve the operational benefit of flexible parking incentive mechanism, this paper introduces the multilevel inventory management model in queuing theory, and transforms the process of shared parking space recovery in multiple parking lots into a turnover and transfer process of bidding validity right between various parking lots. An allocation algorithm of bidding validity right is also proposed based on the Jackson network. The steady-state distribution solution of the available parking space and its constraint conditions are derived by characterizing the route selection decision of each parking lot bidding validity right into preliminary determination stage and secondary adjustment stage. The case study was performed using the University of California, Berkeley Campus as an example. The study analyzes the optimal allocation of each sub-region parking lot under the evolution of external parking demand. The results show that the allocation algorithm of bidding validity right based on Jackson network could enable parking lots in different sub regions to calculate the optimal allocation number of bidding validity right based on the external parking demand and self-owned shared parking spaces when operating shared parking policies in linkage. If the allocation of shared parking spaces in all sub-regions is sufficient, the bidding validity rights would be allocated to the parking central area according to the demand priority, in order to maintain the overall occupancy rate of parking spaces in the range of 0.65 to 0.80. With the continuous occupation of parking spaces, when the number of available parking spaces in each parking lot is lower than the constraint threshold, the bidding validity right would be increased immediately under the route decision so that the occupancy rate of parking spaces in parking lot maintained in the range of 0.90 to 1.00. This method avoids the oversaturation of the shared parking space and reduces the additional cost caused by excessive vacancy parking spaces. References | Related Articles | Metrics

Select

Urban Rail Transit Freight Train Service Planning and Shipment Allocation Considering Rolling Stock Circulation XIAO Ya-ling, BAI Yun, CHEN Yao, LI Zhu-jun, WEN Fang 2023, 23(6): 185-195.  DOI: 10.16097/j.cnki.1009-6744.2023.06.019 Abstract ( )   PDF (2233KB) ( )   The passenger demand of an urban rail transit line is relatively small, and the departure interval is large during off peak hours. Using the spare capacity of rail transit lines to transport freight can improve line utilization and bring economic benefits. Without adjusting passenger train service planning, this paper uses passenger and special freight trains to transport freights simultaneously. The study optimizes the bilateral freight train timetable and shipment allocation while considers rolling stock utilization. A collaborative optimization model is developed with the objective of maximizing the net freight profit to determine train stopping plans, train formation, schedules, rolling stock circulation plans, shipment allocation, and freight storage capacity to be reserved at stations. The proposed method considers the constraints of train operation safety, rolling stock circulation plan, freight transportation time requirements, and so on. The model is transformed into a mixed-integer linear programming model by the linearization method and then is solved by Gurobi software. The case analysis shows that compared to the step-by-step optimization model that optimizes train service planning and shipment allocation before the rolling stock circulation plan, the proposed method can reduce the number of rolling stocks in operation by 3, reduce the operating cost of freight trains by about 23.6% , and increase the net freight profit by 10.3% . The sensitivity analysis indicates that compared with passenger trains, improving the freight space and loading/unloading efficiency of the freight trains can increase net freight profit more effectively. References | Related Articles | Metrics

Select

Collaborative Optimization of Train Skip-stop and Passenger Flow Control on Subway Line Considering Passenger Evacuation at Transfer Stations WANG Pei-heng, YANG Li-xing, LI Shu-kai, GAO Zi-you 2023, 23(6): 196-205.  DOI: 10.16097/j.cnki.1009-6744.2023.06.020 Abstract ( )   PDF (2114KB) ( )   During the morning rush hour, subway lines and transfer stations face enormous pressure on passenger transportation and evacuation in major cities. The paper first explores the strategy of trains skipping transfer stations to reduce congestion at the transfer stations and pressure on passenger evacuation. A collaborative optimization model is then developed to jointly consider train skip-stop strategies at transfer stations and passenger flow control for a subway line operating in two directions, with the objective of minimizing passenger waiting times and transfer delays. The model is transformed into a linear integer programming model by approximating and linearizing relevant nonlinear constraints. The effectiveness of the proposed collaborative optimization model is verified through numerical experiments conducted on Beijing subway line 5. The CPLEX optimization solver, utilizing the branch and bound algorithm, is employed to solve the model, confirming the practical effectiveness of the proposed approach. The experimental results indicated that, compared to the existing passenger flow control with all-stop scheme, the optimized scheme significantly increases the number of passengers by 2954 that is carried by a train within a 14-minute timeframe. Furthermore, it reduces delay times experienced outside stations, transfer delays, and the overall delay time by 58.9%, 16.9%, and 41.6%, respectively. References | Related Articles | Metrics

Select

Analysis of Differential Impact of Built Environment on Passenger Flow and Commuter Ridership Rate of Urban Rail Transit Station PANG Lei, REN Li-jian, YUN Ying-xia 2023, 23(6): 206-214.  DOI: 10.16097/j.cnki.1009-6744.2023.06.021 Abstract ( )   PDF (2087KB) ( )   There is a significant relationship between the built environment and the characteristics of urban rail transit ridership. However, existing studies mainly focus on the impact of built environment on passenger flow of urban rail transit stations, and rarely analyze the commuter ridership rate of stations. Station passenger flow reflects the transportation intensity of urban rail transit, while station commuter ridership rate reflects the sharing capacity of urban rail transit, both of which have an important impact on the operational effectiveness of passenger flow. This study proposed a method to measure the commuter ridership rate of urban rail transit stations based on urban rail transit smart card data and cell phone signaling data, and utilized a multi-scale geographically weighted regression (MGWR) model to investigate and compare the differences in the impact of the built environment on the passenger flow and commuter ridership rate of the stations. The study case in Tianjin of China indicated that: the passenger flow of urban rail transit stations showed a decreasing spatial distribution from the center of the city to the suburbs, while the commuter ridership rate showed an increasing spatial distribution from the center of the city to the suburbs. There were both significant differences and similarities in the built environment factors affecting station passenger flow and commuter ridership rate, with the average distance of a station from a transit stop being the global variable affecting both and having a significant negative effect. There existed spatial heterogeneity in the intensity and direction of the effect of local influence variables in the built environment on station passenger flow and commuter ridership rate. The differences in the effects of the built environment on station passenger flow and commuter ridership rate indicated that it was important to consider not only the differences in the types of built environment factors contributing to these two different indicators of passenger flow characteristics, but also the differences in the spatial effects of localized influences on the built environment. In the future planning, the classification of synergistic configuration, zoning level intervention of differentiated strategies, integrated activation of the built environment factors of the heterogeneous effect should be considered to comprehensively enhance the station passenger flow operational efficiency. References | Related Articles | Metrics

Select

Comprehensive Optimization Model and Algorithm of Operation Plan for Smart Port Station of Heavy Haul Railway WU Yi-di, HE Shi-wei, ZHOU Han 2023, 23(6): 215-226.  DOI: 10.16097/j.cnki.1009-6744.2023.06.022 Abstract ( )   PDF (1752KB) ( )   To improve the intelligent level of the port station scheduling, this paper focuses on the operation organization of the port station of heavy haul railway and analyzes the shunting operation scheme of different unloading systems under the mode of leading locomotive shunting operation. The train disintegration plan, unloading plan, shunting operation plan and train combination plan are used as the key parameters to describe the entire operation process of different types of heavy haul trains at the station. Considering the unloading capacity of the port station and the transport efficiency of the empty trains after unloading, this paper develops a hybrid integer linear programming model for the comprehensive optimization of operation plan of the port station of heavy haul railway. The objective function reflects the minimum residence time of the trains. The hybrid algorithm combining micro-evolution with heuristic strategy and adaptive neighborhood search is designed to solve the model. The case study in a port station of a heavy haul railway show that there is no idle waiting time in the equipment cooperative scheduling scheme, and the first 7 departure trains from the train allocation scheme satisfy the maximum combination number of empty trains. By comparing different shunting operation schemes, the mode of leading locomotive shunting operation can reduce 5 switch engines for port station. Compared with Gurobi solver, the proposed algorithm can save 97.32% of the computing time and reduce the gap to the optimal lower bound by 0.06%. References | Related Articles | Metrics

Select

Rail Transit Station Clustering and Its Impact on Passenger Flow Forecasting HU Zuo-an, DENG Jin-cheng, YANG Jiang-hao, ZHAO Yan 2023, 23(6): 227-238.  DOI: 10.16097/j.cnki.1009-6744.2023.06.023 Abstract ( )   PDF (3367KB) ( )   Urban rail transit stations have heterogeneity due to the interaction of multi-level factors. This paper classifies the rail transit stations using the passenger card data, interest point data and subway network data, and extracts passenger flow, land use and network properties. The passenger flow property considers the different status under weekdays, weekends and holidays. The land use property considers the land use intensity and balance of the radiation area of the station. The network property considers the characteristics of nodes and influence capabilities. The paper develops the clustering model of the K-means++ algorithm and uses comprehensive clustering evaluation indicators to determine the number of clusters, distinguish and analyze the multi- dimensional characteristics of different types of stations. It combines the land use of the station area and the characteristics of the station network to analyze the impact on travel activities, and designs the joint prediction within the cluster and the overall joint. Three multivariate time series forecasting methods are used to evaluate the impact of site clustering on forecasting performance. The results show that the rail transit station can be divided into 10 clusters when consider the characteristics of all passenger flow. The rail transit stations can be divided into 5 clusters when consider the characteristics of inbound passenger flow on weekdays. Fully mining the time-varying characteristics of passenger flow can obtain more refined clustering results. There is a certain feedback relationship between passenger flow distribution characteristics and its land use and network characteristics. Compared to the overall joint prediction, the proposed method captures spatial correlation through clustering and joint prediction of sites with strong correlation and can effectively improve the prediction performance. The average reduction in root mean square error was 9.04%, and the average reduction in mean absolute error was 4.94%. The study results provide a basis for the refined management of the station and the planning of station facility construction. References | Related Articles | Metrics

Select

Multi-trip Food Waste Collection Routing Optimization with Workload Balance ZHANG Yan, LI Zi-xin, LIU Jin-ping 2023, 23(6): 239-249.  DOI: 10.16097/j.cnki.1009-6744.2023.06.024 Abstract ( )   PDF (1705KB) ( )   The high cost of food waste collection restricts the implementation of waste separation management in the cities. This study determined the time periods and collection frequencies to balance the workload of food waste collection for each time period based on the amount and distribution of the wasted food. The study then modeled the problem as multi-trip vehicle routing problem with time windows and considered the drivers' workload balance. The mixed integer programming model was solved to obtain the optimal solution for the small-size problems. The threedimensional matrix code and the hybrid adaptive large neighborhood search algorithm (HALNS) for multi-trip vehicle routing was used to solve the large-size problems. Five different numerical examples were used to test the mathematical model and the HALNS algorithm. It was found that when considering workload balance constraints, the imbalance of workload among different truck drivers could be improved significantly with less than 2% cost increases. Then, the proposed method was applied in a real case in Dalian of China. The solutions obtained by the HALNS were compared to the existing waste collection optimization tool. The results showed that the collection route obtained by HALNS could reduce collection costs by 14.3% , decrease carbon emissions by 12.7% and improve the balance of driver workloads by 57.3%. References | Related Articles | Metrics

Select

Fresh Food Distribution Route Optimization of Mixed Fleets in Urban and Rural Areas Under Low Carbon Perspective SONG Li-ying, ZHAO Shi-chao, BIAN Qian, DU Peng, SHEN Peng-ju 2023, 23(6): 250-261.  DOI: 10.16097/j.cnki.1009-6744.2023.06.025 Abstract ( )   PDF (2356KB) ( )   The logistics industry is undergoing a rapid green transformation. Logistics companies increasingly employ mixed fleets consisting of both fuel-powered refrigerated trucks and electric refrigerated trucks for fresh food distribution. Taking into account environmental benefits, enterprise benefits, customer satisfaction, and fresh product characteristics, this paper establishes a fresh distribution route optimization model for mixed fleets. To illustrate the model, we analyze two distinct customer distribution scenarios: urban and rural areas. The results reveal significant cost savings when compared to pure fuel or pure electric vehicle fleets. In urban areas, the mixed fleet reduces total distribution costs by 32.9% to 39.4% , and in rural areas, by 12.1% to 21.4% . Furthermore, strategic placement of distribution centers and charging stations not only benefits enterprises but also enhances customer satisfaction and contributes positively to the environment. This paper provides as a valuable theoretical reference for logistics enterprises when determining fleet configurations and route planning tailored to specific distribution area characteristics. It also provides theoretical insights for government decision-makers when selecting locations for terminal cold chain distribution centers and designing electric refrigerated truck charging facilities, further advancing the green transformation of cold chain logistics. References | Related Articles | Metrics

Select

Distribution Routing Problem with Time Window Under Constraint of Pallet Loading LIU Yong, YUE Zhi-cheng, WANG Yong 2023, 23(6): 262-273.  DOI: 10.16097/j.cnki.1009-6744.2023.06.026 Abstract ( )   PDF (3189KB) ( )   To address issues such as chaotic delivery, this paper introduces pallets with adjustable support structure heights as carriers for loading and unloading in delivery services. Firstly, we design a pallet loading scheme inspired by the "wall building" theory and propose a route optimization strategy for delivery vehicles considering the constraint of three-dimensional pallet loading. We then formulate a two-objective optimization model that seeks for the highest average vehicle loading rate and the lowest total cost, which substitute the loading efficiency into the time cost. A solution algorithm, called 3DRP (Three-Dimensional Routing with Pallet), is designed, which combines the route optimization strategy with the constraint of three-dimensional pallet loading. To verify the effectiveness of our method, we test it on the LOH & NEE three-dimensional loading example, achieving a loading rate of 68.2%. Moreover, we validate our method using data from an express company in Chongqing, which shows that our method can achieve an average vehicle loading rate of 83.02% with no time penalty costs on some routes. By comparing our proposed method with the traditional 3D loading scheme, we conclude that the optimization of three-dimensional packing of express can balance the high loading rate of vehicles, reduce time and penalty costs by 97.5%, and improve vehicle utilization. References | Related Articles | Metrics

Select

Energy-efficient Ship Route Optimization Considering Wind and Wave Impacts JI Ming-jun, HU Han-lin, GAO Zhen-di, FANG Wan-wei 2023, 23(6): 274-283.  DOI: 10.16097/j.cnki.1009-6744.2023.06.027 Abstract ( )   PDF (1996KB) ( )   As the global energy crisis intensifies, energy conservation and emission reduction have become important issues in the present era. Crude oil trade is crucial for the development of national economy, with shipping being the primary mode of transportation for crude oil. Very Large Crude Carriers (VLCC) have been widely used for the crude oil transportation. Due to the size of the vessels, VLCC are often affected by the weather conditions, particularly wind and waves. This paper developed two optimization models for VLCC energy-saving routes with the objective of minimizing total fuel consumption. The models consider limitations of meteorological environment, safety margins, time constraints, and obstacle avoidance. An improved ant colony algorithm is proposed to solve the models based on the principle of artificial potential fields. The computational experiments demonstrate that the proposed approach effectively reduces fuel consumption for the VLCC. By comparing the fuel consumption under different estimated arrival time, it was found that relaxing the estimated arrival time of vessels can effectively reduce fuel consumption. However, when the estimated arrival time was adjusted to a certain extent, the reduction in fuel consumption became minimal. Therefore, in actual operational processes, shipping companies need to consider the actual operations and balance vessel fuel consumption with estimated arrival time to achieve energy conservation and emission reduction goals. References | Related Articles | Metrics

Select

Susceptibility of Passenger Motion Sickness Based on Vehicle Motion Parameters Under Multiple Scenarios MA Li, FU Rui, SUN Qin-yu, GUO Ying-shi, WANG Chang, YUAN Wei 2023, 23(6): 284-295.  DOI: 10.16097/j.cnki.1009-6744.2023.06.028 Abstract ( )   PDF (2769KB) ( )   Motion sickness is a common symptom caused by vehicle movement, which significantly affects vehicle passengers' riding experiences. It is difficult to completely avoid motion sickness under existing vehicle control methods, and the level of motion sickness increases with the duration of the riding time in various conditions, it is therefore necessary to study the vehicle motion parameter threshold corresponding to the motion sickness status of vehicle passengers. This paper investigates the changes of motion sickness threshold under different scenarios and the sensitivity of motion sickness related vehicle motion parameters based on different motion sickness ratings (SoMSMP). First, a vehicle ride experiment is designed for four types of scenarios (different susceptibility to motion sickness and with or without non-drive related tasks), and the variation rule of motion sickness ratings is analyzed for the four scenarios. The acceleration and jerk under the four motion conditions are taken as the analysis object. The single factor variance and statistical method are used to make a lateral comparison of the motion sickness threshold change rule (lateral SoMS-MP) under the same scenario and different motion sickness levels, and a longitudinal comparison (longitudinal SoMS-MP) of the motion sickness threshold change rule under different scenarios with the same motion sickness rating. The results show that the motion sickness ratings increased with exposure time, and the differences exist in the motion sickness ratings in the four scenarios. The high motion sickness susceptibility participants have lateral SoMS-MP, while the low susceptibility participants have no lateral susceptibility, and longitudinal SoMS-MP can only be identified at moderate degree of motion sickness. The study results provide references for the motion sickness ratings and motion sickness threshold analysis, which can be used for the human driving or automatic driving control strategies to reduce motion sickness. References | Related Articles | Metrics

Select

Prediction Model for Residents Travelling OD in Urban Areas Based on Mobile Phone Signaling Data HU Bao-yu, LIU Xue 2023, 23(6): 296-306.  DOI: 10.16097/j.cnki.1009-6744.2023.06.029 Abstract ( )   PDF (3125KB) ( )   PDF(English version) (1242KB) ( 27 )   To reveal the travel pattern and OD generation principle of urban area residents, the destination selection mechanism of urban area residents is explored based on mobile phone signaling data. The position opportunity selection (POS) model was developed by considering the population and the number of POI. The mobile phone signaling travel data of Harbin residents, obtained from the Unicom Smart Footprint Platform, is used to validate the model. The analysis is conducted at both the traffic cell and traffic mid-zone levels, focusing on Harbin's second, third, and fourth ring roads. The results show that the POS model predictions were generally consistent with the actual data patterns in the traffic attraction capacity and travel distance distributions. At both the traffic cells and mid-zones scales, the model achieves a prediction accuracy of 67% ~72% and 75% ~83% , respectively. These results indicate an improvement of 13%~18% and 9%~20% over the opportunity priority selection model and a superiority of 57%~60% and 55%~60% over the radiation model, respectively. The advantage of the proposed model lies in its simplicity and lack of parameters. The input data are easily obtainable, and the model offers high prediction accuracy. The findings provide a theoretical reference for urban traffic planning. References | Related Articles | Metrics

Select

Estimation Algorithms of Passenger Boarding Stops Based on Smart Card Data GAO Wan-chen, LU Shi-chang, LI Dan 2023, 23(6): 307-318.  DOI: 10.16097/j.cnki.1009-6744.2023.06.030 Abstract ( )   PDF (3270KB) ( )   This paper design three estimation algorithms: the two-stage algorithm, the improved K-nearest neighbor algorithm, and the improved fuzzy C-means clustering algorithm, to address the issue of missing passenger boarding stops in urban public transit automatic fare collection systems. We compare the estimation results of these algorithms with the traditional time window algorithm, and evaluate their accuracy using the entropy rate method. The proposed algorithms are validated using smart card data from bus No. 18 in Zhuhai City. The results indicate that all three algorithms can successfully match all passenger boarding stops, and the matching rate is approximately 36.3% higher than that of the traditional time window algorithm. The accuracy of the passenger boarding stop estimation, based on the average entropy rate of the three- dimensional sample data, is ranked as follows: the two- stage algorithm, improved K-nearest-neighbor algorithm, and improved fuzzy C-means clustering algorithm. The difference in accuracy between the two-stage algorithm and the improved K-nearest neighbor algorithm is minor. The algorithm with the lowest entropy rate is selected to determine the final boarding stops of passengers, making it suitable for implementation in urban public transit system. References | Related Articles | Metrics