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Capacity Efficiency and Influencing Factors Study of Chinese Listed Airlines

LIU Dan, LIN Shanshan, ZHENG Yuting

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (6): 13-22. DOI: 10.16097/j.cnki.1009-6744.2025.06.002

Abstract （606）

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Under carbon emission constraints, enhancing capacity efficiency to gain a competitive edge in the market has become a common focus among airline executives. This paper selects the panel data of 6 listed airlines in China from 2017 to 2021, incorporates carbon emissions as an undesirable output into the indicator system, and uses a window-based network DDF (Directional Distance Function) model to measure the capacity efficiency of listed airlines. Capacity inefficiency is decomposed into technical inefficiency and capacity utilization inefficiency to identify the key constraints behind the low capacity efficiency of listed airlines in China. Additionally, the dual methodology of panel regression and threshold effect analysis is used to investigate the impact of government subsidies on the capacity efficiency of listed airlines under the regulatory effect of equity concentration. The results show that the capacity efficiency of the 6 listed Chinese airlines under carbon emission constraints is generally low, jointly influenced by both technological level and capacity utilization. All listed airlines need to reduce carbon emissions during flight operations. The impacts of various factors on the capacity efficiency of Chinese listed airlines exhibit heterogeneity, with government subsidies, ownership concentration, enterprise age, and flight hours are the key driving factors in improving capacity efficiency of listed airlines in China. Furthermore, under the regulatory effect of equity concentration, the impact of government subsidies on the capacity efficiency of listed airlines exhibits a single-threshold regulatory effect, which maintains a promoting effect when the ownership concentration is less than or equal to 86.79%, but becomes an inhibitory effect when it is greater than 86.79%. Therefore, listed airlines should advance technological innovation, optimize input allocation, and maintain a reasonable level of ownership concentration. The government should facilitate technological upgrades, foster energy conservation and carbon reduction, and optimize the subsidy allocation mechanism.

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Drone Delivery: A Systematic Review on Technology, Efficiency, and Applications

WU Jingqiong, DIAN Ran, ZI Taisheng, LI Yunqi

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (6): 34-49. DOI: 10.16097/j.cnki.1009-6744.2025.06.004

Abstract （951）

PDF （2284KB）（450）

With the rapid development of e-commerce and a surge in demand for instant delivery, drone delivery, as an innovative solution in the logistics sector, is driving profound transformations in the logistics system. This paper synthesizes findings from 74 relevant articles published between 2015 and 2024, comprehensively examining drone delivery research advancements across key dimensions including critical technologies, economic benefits, environmental sustainability, application potential, and system synergy. The results indicate that drone delivery primarily relies on path planning algorithms, energy management, and multi-drone collaboration as its core technologies. Related optimization research has evolved from single-objective to multi-objective coordination, with algorithms transitioning from classical heuristics to intelligent approaches, effectively reducing solution time and optimizing costs. However, nonlinear effects of payload and wind resistance, along with adaptability to harsh weather conditions, remain bottlenecks. In terms of economic benefits, drone-vehicle collaborative systems can significantly reduce customer waiting time, delivery costs, and labor demands through optimized path planning and resource scheduling. Integration with public transit systems (bus/subway) effectively expands service coverage while reducing energy consumption. Multi-objective optimization models dynamically balance energy consumption, cost, and timeliness to further enhance synergistic benefits. Nevertheless, economic viability remains constrained by payload and range limitations, showing greater advantages in short distance, lightweight deliveries, particularly for emergency cargo deliveries. Environmental benefit analyses demonstrate that the operational phase of drone delivery exhibits significantly lower carbon emissions than traditional transportation methods, though a comprehensive lifecycle assessment encompassing manufacturing, operation, and recycling phases is required. Regarding applications, drone delivery technology demonstrates unique value in medical supply distribution, emergency logistics, and urban "last- mile" delivery, with particular advantages in remote areas and urgent emergency scenarios. However, challenges persist regarding safety risks, technological innovation gaps, limited social acceptance, and imperfect policy regulations. Future research should prioritize battery technology breakthroughs, intelligent path planning optimization, privacy/security safeguards, and cross regional policy coordination to accelerate drone delivery commercialization.

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Intermodal Transportation Network Design Optimization Considering Demand Uncertainty Under "Dual Carbon" Background

HUANG Rui, ZHAO Xu, WANG Jingyun

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (6): 1-12. DOI: 10.16097/j.cnki.1009-6744.2025.06.001

Abstract （859）

PDF （2693KB）（416）

A central challenge in modern intermodal transportation planning is the simultaneous consideration of "Dual Carbon" goals and growing fluctuations in freight demand. To address this challenge, this study presents an optimization model for intermodal transport network design. First, a bi-level bi-objective optimization model is developed, with the strategy planner serving as the upper-level leader and shippers as the lower-level followers. The upper level jointly determines the capacity expansion investment, low-carbon investment, and subsidy policies, with the objective of maximizing total revenue while minimizing total carbon emissions. The lower layer solves the network cargo flow allocation under user equilibrium based on generalized transportation costs. Then, the theory of real options is introduced, and geometric Brownian motion is used to describe the stochastic process of transportation demand fluctuations. This enables the quantification of the option value of delayed optimization to determine the optimal timing for strategy implementation. Based on the model characteristics, a nested Frank Wolfe multi-objective evolutionary algorithm based on decomposition (MOEA/D) is designed to solve the deterministic model, combined with a least squares Monte Carlo simulation algorithm to get the optimal implementation timing. Empirical analysis along the Western Land-Sea New Corridor shows that the proposed method simultaneously balances the economic, low-carbon, and operational efficiency optimization goals, which results in a 16.58% decrease in unit transportation costs, a 27.11% decrease in total carbon emissions, and a robust 5.41% increase in total revenue. Under demand uncertainty, delaying the implementation of optimization strategies can generate additional option value. In the case study, delaying to the third period can increase expected revenue by 4.70% and reduce total carbon emissions by 5.03%.

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Optimization of Collaborative Distribution of Light Trucks and Buses Considering Carbon Emission Costs

ZHANG Zhijian, ZHANG Ting, DI Zhen, GUO Junhua

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (6): 265-275. DOI: 10.16097/j.cnki.1009-6744.2025.06.024

Abstract （575）

PDF （2229KB）（110）

In order to cope with the traffic pressure and carbon emission caused by the expansion of the network in urban logistics and distribution, this paper proposes an optimum model of light trucks-public transportation collaborative distribution considering the cost of carbon emissions. The model integrates the fixed bus routes, customer demand time windows, carbon emission factors and transportation costs. It systematically depicts the complex constraints and multi-factor decision-making problems in the collaborative distribution of light truck-public transportation with the optimization goal of minimizing the total cost. In view of the characteristics of the problem, such as high dimensionality, complexity, and easy to fall into local optimum, this paper designs an improved genetic algorithm. It generates high-quality initial populations through the mileage-saving method, and introduces championship selection, adaptive crossover and mutation strategies, which significantly improves the convergence speed and global search ability of the algorithm. Experimental results show that the proposed algorithm achieves a cost optimization of 19.48% on the 60-node scale problem. Under four different node sizes, the maximum, minimum and average costs of running data for 20 times are effectively reduced. Compared with the mode of single distribution, the mode of public transport coordination can effectively reduce the costs of transportation, carbon emission and time window penalty within a certain range, and improve distribution efficiency and service quality.

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Emission Reduction Potential and Multi-path Synergy of Sustainable Aviation Fuel in China's Civil Aviation

TIAN Lijun, CHEN Xuegong, WANG Qi, XU Xinzhe, WANG Yating, QU Xixi

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (5): 365-374. DOI: 10.16097/j.cnki.1009-6744.2025.05.033

Abstract （359）

PDF （2031KB）（197）

To address the contradiction between the rapid growth of China's civil aviation and deep decarbonization, the single path of sustainable aviation fuel (SAF) is confronted with the predicament of technical ceiling and offset by demand growth. This study constructs the FLEET model integrating mixed integer programming (MIP) and system dynamics (SD), establishes a two-way feedback mechanism of "micro-operation-macro-policy", and quantifies the synergistic emission reduction effect of multiple paths (SAF, new technology aircraft, and operation optimization). The core findings include: (1) Potential and limitations of SAF: When the SAF blending ratio is in the range of 0~30%, every 10% increase can reduce emissions by 12.7%; after exceeding 30%, the marginal benefit decreases significantly, which is restricted by raw material gap and PIL technical bottlenecks. (2) Necessity of multi-path synergy: under the mandatory blending scenario (SAF 50% + GDP annual growth rate 5.5%), carbon emissions in 2050 would still increase by 73% compared with 2019, and the increase is affected by the fluctuations in transport volume (±0.5%) and the actual blending efficiency of SAF (±5%). The optimal combination scenario (SAF 65% + new technology aircraft 40% + operation optimization 30% + market mechanism 15%) can achieve a 49.19% emission reduction in 2050. The emission reduction per unit policy incentive is 2.3 times of the single SAF path, forming technical complementarity, cost synergy, and emission reduction multiplier effect. (3) Carbon quota price threshold and heterogeneity: The carbon quota price exceeding 200 yuan per ton is the key threshold to trigger the technological leap of airlines; there are subject and regional heterogeneities in response (the emission reduction efficiency of eastern hubs is 1.35 tons per 10000 yuan larger than 0.87 tons per 10000 yuan of western branches). It is revealed that China's civil aviation emission reduction needs to break through the dependence on a single technology, and a three-stage "technology-raw material-policy" synergy roadmap is proposed (2025-2035 HEFA scale; 2035-2045 raw material diversification; 2045-2050 policy deepening), which provides a quantitative basis for phased policy design and engineering application.

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Assessment of Pollution and Carbon Reduction Potential of Medium-and Heavy-Duty Battery Electric Trucks

JIANG Zhijuan, CHEN Meiling, WEI Xintian, HE Jingwen

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (5): 353-364. DOI: 10.16097/j.cnki.1009-6744.2025.05.032

Abstract （314）

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The electrification of freight trucks is considered as an important measure for reducing emissions from road freight transport. However, the emission reduction potential of battery electric trucks (BETs) varies significantly depending on the electricity-generation method, weight class, and technical standard. A granular analysis of the electricity mix and vehicle parameters is essential for refining emission-reduction policies. This study evaluated the air-pollutant emissions of BETs and conventional diesel internal combustion engine trucks (diesel-ICETs) in China across their fuel-cycles to explore the potential of using BETs to reduce pollution and carbon emissions. Data on the crude oil mix, electricity mix, and truck technology of China were used in a life-cycle assessment (LCA) to analyze emission factors related to the energy production pathways and vehicle operations. The greenhouse gases, regulated emissions, and energy use in transportation (GREET) model was applied to assess the vehicle emission intensity (g·km ^-1) and freight emission intensity (g·t ^-1·km ^-1) for BETs and diesel-ICETs (China VI) of different weight classes. The results showed that replacing diesel-ICETs with BETs significantly reduces vehicle emission intensities for six pollutants: greenhouse gases (GHGs), volatile organic compounds (VOCs), CO, NO _x , PM _2.5 , and PM ₁₀ , as well as the freight emission intensities for four pollutants: GHG, CO, NO _x , and VOCs. However, due to electricity mix of China and technical limitations of BETs, the adoption of BETs increases SO _x emissions and exacerbates PM _2.5 and PM ₁₀ emissions in road freight.

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Multi-factor Equilibrium Allocation Model and Application of Container Freight Flow in Western Land-Sea New Corridor

JIANG Jun, HUANG Haoran, LI Huomei, FU Xiaona, OUYANG Fan

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (5): 114-123. DOI: 10.16097/j.cnki.1009-6744.2025.05.010

Abstract （308）

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The allocation study of container freight flows in the Western Land-Sea New Corridor can effectively enhance the corridor's transportation efficiency and achieve cost reduction and logistics efficiency improvement. In view of the current development of the container transport network along the corridor, this study identifies transportation cost, transit time, and carbon emissions as the core factors influencing freight flow allocation. Based on user equilibrium and system optimum theories, this paper develops a freight flow allocation model with the objective of minimizing multi-factor generalized transportation cost. An improved Frank-Wolfe algorithm is designed to solve the model, incorporating a dynamic impedance matrix to optimize the adjustment mechanism of path impedance. Key nodes along the corridor from Chongqing to ASEAN countries and connected regions are selected to construct a multi-node, multi-path container transport network. Freight volume is allocated based on actual transportation demand data from June 2024. The results show that the proposed model effectively balances transportation cost, transit time, and carbon emissions. Compared with the user equilibrium strategy and the actual distribution scheme, the system optimum allocation strategy reduces total generalized cost by 2.94% and 5.34%, respectively. Based on the findings, it is recommended that the corridor's operation platform appropriately increase the supply of rail-sea intermodal transport and adopt freight rate adjustment and other effective measures to guide container cargo shifting toward international rail transport modes.

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Utility Factor and Emission Reduction Benefits of Plug-in Hybrid Electric Vehicles

LEI Xue, FAN Pengfei, LIU Rui, LI Songsong, WU Yizheng, SONG Guohua

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (4): 337-348. DOI: 10.16097/j.cnki.1009-6744.2025.04.031

Abstract （165）

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The emission reduction benefits of plug-in hybrid electric vehicles (PHEVs) highly depend on their power mode, and assessing their reduction potential accurately requires a deep understanding of the interplay between various influencing factors. This study, based on over seventy million seconds of second-by-second driving data collected in Beijing, investigates the impacts of driving distance, charging behavior, and battery capacity on PHEV power mode selection. The results indicate that the utility factor (UF) exhibits nonlinear sensitivity to the number of charging piles per vehicle (CPPV). When CPPV is less than 0.6, increasing it by 0.1 can reduce daily CO2 emissions by approximately 637 tons; however, beyond this threshold, the reduction benefit declines to 81 tons per day. Furthermore, the study finds that under adequate charging accessibility, PHEVs with smaller battery capacities can still maintain a high proportion of electric driving. For example, a PHEV with a 20 kWh battery can achieve a UF of 0.82 when CPPV reaches 0.5, which is sufficient for daily electric travel demand. Therefore, deploying low-power charging infrastructure strategically in residential areas can enhance PHEV electrification levels effectively, reduce reliance on high-capacity batteries and high-power charging piles, and alleviate the load pressure on the power grid caused by concentrated charging.

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Integrated Control Model for Intersection Signal and Vehicle Trajectory Under Heterogeneous Traffic Flows

WANG Haiyong, ZHANG Dan, WANG Menglin, TIAN Aiai

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (4): 96-103. DOI: 10.16097/j.cnki.1009-6744.2025.04.010

Abstract （234）

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Under heterogeneous traffic conditions, this study proposes an integrated control model which simultaneously optimizes signals and trajectories to address the coordination problem between traffic signal control and vehicle trajectory planning. The model employs a Dueling Double Deep Q-Network (D3QN) through deep reinforcement learning approach to achieve the dual objectives of improving traffic efficiency and promoting eco-driving. The comprehensive validation of proposed model was conducted using the SUMO simulation platform. The simulation results show that, compared to the baseline model, although single-objective optimization strategies can partially enhance the performance of intersection, there are some limitations in overall efficiency improvement. In contrast, the proposed integrated control model effectively combines the optimization of macroscopic traffic flow with microscopic vehicle behavior adjustment, achieving a 66.99% reduction in average vehicle delay, an 11.26% decrease in fuel consumption, and significant reductions in CO2 and other pollutant emissions. Further sensitivity analyses reveal the performance of system trends under varying CAV penetration rate, indicating that performance gains gradually plateau beyond certain penetration thresholds. Moreover, the model demonstrates stable optimization effects under different traffic demand conditions, confirming its adaptability and robustness for urban intersection environments.

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Rail Transit and Land Use Supply and Demand Coordinated Development from Low-carbon Perspective

TAN Deming, CHEN Kepei, WU Dawei, LI Yanhuan, HU Sixin, ZHANG Caiping

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (4): 13-23. DOI: 10.16097/j.cnki.1009-6744.2025.04.002

Abstract （317）

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As vital components of urban spatial governance systems, rail transit and land use play critical roles in advancing urban green low-carbon development. This study proposes a rail transit and land use supply-demand model using population as the intermediary variable, with two scenarios established: natural development and low-carbon development. Integrating multi-source data including station Point of Interest (POI) data, Land Use and Land Cover (LULC) data, and population thermal dynamics, this study uses a passenger flow potential model and BP neural network to predict supply-demand relationships of passenger flow. Through Voronoi polygon spatial analysis and coupling degree modeling, this study comparatively examines the rail transit and land use coupling characteristics in Shenzhen under both scenarios and identifies mismatch causes. The results demonstrate that: the mean coupling degree between rail transit and land use remains stable within [0.7, 0.8] under both scenarios, exhibiting spatial patterns of "higher values in western regions versus lower values in eastern regions" and "central agglomeration with peripheral dispersion". Coupling mismatch primarily manifests as insufficient rail transit supply capacity relative to population travel demand. Compared with the natural development scenario, 58.63% of Voronoi units achieve superior coupling degrees under the low carbon development scenario, demonstrating its effectiveness in enhancing rail transit passenger flow and reducing transportation carbon emissions. However, decreased population density in central urban areas under the low-carbon scenario generates more mismatched stations (Z<1) characterized by weaker population travel demand relative to rail transit supply capacity. These findings provide strategic references for megacities to coordinate rail transit construction with intensive land use while achieving carbon emission reduction in transportation systems.

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Review of Literature on Air-rail Intermodality Focusing on Passenger Travel Behaviour

ZHANG Xiaoqiang, ZHOU Huixuan, WU Xiaoyu

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (3): 5-21. DOI: 10.16097/j.cnki.1009-6744.2025.03.002

Abstract （703）

PDF （1731KB）（280）

PDF(English version) （619KB）（6）

Comprehensive transportation is the development direction of intelligent, green and safe transportation in recent years. Through comprehensive development and utilization of aviation, railway, highway, waterway and pipeline transportation modes, it builds a transportation system with advanced transportation technology, reasonable layout and structure. Air-rail intermodality is one type of comprehensive transportation, which not only reduces the waste of resources through the reasonable use of existing transportation infrastructure, but also improves transportation accessibility through low cost of transportation mode shift. Air-rail intermodality is an effective strategy to ease airport congestion, expand the scope of airport radiation, reduce carbon emissions and meet the diversity of travel demand. In recent years, related research on air-rail intermodality has included research on competition and cooperation of air-rail intermodality, air-rail intermodality network, and evaluation methods of air-rail intermodality at macro level, then research on of air-rail intermodality at the middle level, and research on passenger travel behavior of air-rail intermodality at the micro level. This paper summarizes and the representative studies in domestic and international level of air-rail intermodality from 2000 to 2024, and sorts out the research results under different operation backgrounds and different transportation network scales. It is found that domestic scholars are more concerned on transport passenger travel behavior, emphasize passenger travel demand, and scholars in other countries tend to focus on air-rail intermodality social benefits and environmental benefits, emphasize the sustainable development of air-rail intermodality. The reasons of the research differences at home and abroad are analyzed from the social background, technical background and policy support. In the future, to explore practical collaborative research, including conflicts of interest between operators and between operators and passengers, the conflict between social and environmental benefits is a valuable research direction. Systematic research on the construction of air rail intermodal network, the location of transit nodes to network optimization, construct the evaluation method system of air-rail intermodality, it has guiding significance to the development of air-rail intermodality. Expanding the study area for air-rail intermodality with the considerations of the diverse needs of travelers, coping strategies that incorporate uncertainty factors, such as providing flight delay insurance, is a very meaningful research topic. It is a practical work to deeply integrate the theoretical research and practical application of passenger travel behavior of air-rail intermodality. Atlast, the future development of air-rail intermodality should use advanced science and technology to reduce the data acquisition cost of the whole travel chain of air-rail intermodality passengers and build a travel service platform of air-rail intermodality including travel path planning, one-stop ticket purchase, connection process visualization, connection time prediction and other services.

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Joint Optimization of Berth-quay Crane-shore Power Allocation Under Time-of-use Pricing

WANG Xiaokun, DONG Zejin, WANG Yuwei, XIAO Hong

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (2): 314-327. DOI: 10.16097/j.cnki.1009-6744.2025.02.029

Abstract （232）

PDF （2379KB）（140）

In the context of the national drive to promote shore power applications at ports and the implementation of time-of-use electricity pricing mechanisms in various regions, this study investigates the continuous berth-quay crane-shore power joint allocation optimization problem, which considers terminal operational efficiency, ship energy costs, carbon emissions, and the physical requirements for ships to connect to shore power under a time-of-use electricity pricing regime. A bi-objective optimization model is constructed to minimize the total comprehensive cost and total carbon emissions. Specifically, the total comprehensive cost comprises waiting costs, delay costs, and penalty costs based on the ship's total stay time at the port, which reflect terminal operational efficiency. Ship energy costs consist of fuel costs and electricity costs. Total carbon emissions include emissions generated by auxiliary engines during docking, indirect emissions from using shore power, and emissions from quay crane operations. To solve this model, an improved NSGA-II algorithm is designed, integrating heuristic methods, a gene repair strategy, progressive elimination, and an alternate group population. A case study based on a real container terminal is conducted to test the model and analyze the impacts of electricity prices, peak-valley electricity price differences, and the proportion of retrofitted vessels and the shore power coverage rate at the terminal. The results indicate that the improved NSGA-II algorithm is better than traditional NSGA-II algorithm in terms of calculation results and performance, and can effectively solve the model. The off-peak electricity price was reduced by 45.45%, resulting in a 20.33% decrease in total costs and a 6.33% reduction in total carbon emissions, while the number of vessels using shore power increased by 23.81%. When the peak-to-valley electricity price difference increased from 3∶1 to 5∶1, time and energy costs rose by 7.69 and 4.49%, respectively, leading to an 5.16% increase in total comprehensive costs. An excessively large peak-to-valley electricity price ratio is not recommended. Increasing the shore power coverage rate to 50% and the proportion of retrofitted vessels to 70% is more beneficial for enhancing the port's economic and environmental benefits.

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Game Mechanism and Guiding Strategy of Intelligent Connected Transit Signal Priority

WEI Liying, FENG Mei, WU Runze

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (2): 95-107. DOI: 10.16097/j.cnki.1009-6744.2025.02.009

Abstract （244）

PDF （2006KB）（155）

The ongoing development of intelligent connected technology provides crucial support for achieving transit signal priority (TSP) and assisting the development of the intelligent connected public transit towards "precision public transit" and "safe public transit". This paper starts from the conflict game relationship between different phases, and constructs a TSP guiding strategy based on chicken game in the intelligent connected environment. Firstly, the chicken game theory is used to analyze the game behavior of priority and non-priority phases of public transit, establishing a game model with weighted delay as the benefit matrix. Then, adopting the active priority and speed guidance, a TSP guiding strategy and optimization process based on the proposed game model is proposed by considering factors such as punctuality, limitation of minimum green time, priority and non priority phase delay of priority transit. Finally, to validate the strategy, a case study is conducted using an actual intersection in Beijing, employing SUMO for simulation. The results show that the TSP guiding strategy can effectively improve the traffic efficiency of priority phases and reduce the negative impact on non-priority phases compared to the initial timing; under the condition of 50% penetration rate, compared to the implementation of strategy, 20% of priority buses have been optimized significantly for punctuality, and the traffic efficiency indicators such as average queue length, average parking times and delay are reduced by at least 33.27%. Additionally, fuel consumption and CO2 emissions are reduced by at least 12.20%, and the negative influence of non-priority phase indicators is less than 8%.

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Spatiotemporal Distribution Characteristics and Reduction Potential Assessment of Taxi Carbon Emissions

WANG Mingzhi, JIN Jingdong, DONG Chunjiao, LI Penghui, WANG Jing, WANG Junyue

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (1): 311-318. DOI: 10.16097/j.cnki.1009-6744.2025.01.029

Abstract （273）

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To investigate the spatiotemporal distribution characteristics of taxi carbon emissions, this study extracts parameters such as average speed and travel distance between trajectory points from taxi Global Positioning System (GPS) data and constructs the Computer Programme to Calculate Emissions from Road Transport (COPERT) micro- emission model to quantify taxi emissions. Based on this, distribution fitting is used to analyze the distribution characteristics of emissions over time, space, and among vehicles. Based on the analysis results, two traffic management measures—taxi restriction and speed control—are proposed, and their emission reduction potential is evaluated through numerical simulations. An empirical study in Changzhi City shows that the taxi industry exhibits a zero-sum game phenomenon, with emissions more evenly distributed between 8:00-13:00 and 14:00-22:00. Emissions aggregated at nodes and road segments follow a truncated power-law distribution, with the top 10% of nodes and road segments accounting for 95.59% and 74.71% of emissions, respectively. The evaluation results indicate that the taxi restriction policy can reduce emissions by up to 20.35%, maintaining a taxi speed of 15 m ⋅ s-1 results in the lowest emission factor, potentially reducing emissions by up to 21.43%. Implementing speed control on the top 10% of road segments with the highest emissions can reduce emissions by 16.23%, while randomly selecting the same number of segments for speed control can only reduce emissions by 2.37%. The results can support the development of refined carbon emission control strategies and energy-saving measures for urban transportation.

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Low Carbon Routing Optimization of Crowd-shipping Pickup and Delivery Distribution Considering Congestion

WU Xue, HU Dawei, WANG Yin

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (1): 188-201. DOI: 10.16097/j.cnki.1009-6744.2025.01.018

Abstract （293）

PDF （1937KB）（212）

Based on vehicle travel time calculation under different traffic congestion conditions, the time-dependent crowdshipping pickup and delivery problem (TD-CPDP) is proposed, and a mixed integer programming model is developed by considering joint optimization of routing and speed for crowd-shipping vehicles. An improved adaptive large neighborhood search (IALNS) algorithm, incorporating a free-flow speed optimization mechanism, is designed to integrate route and speed decisions. The algorithm features a novel returned optimal searching strategy to avoid local optima, destroy-repair operators for exploring the solution space, and an adaptive mechanism for operator selection to improve search efficiency. Comparative validation with perturbation-based, ant colony, variable neighbourhood search, and adaptive large neighbourhood search algorithms demonstrates the superiority of IALNS algorithm. Sensitivity analysis reveals that, compared to constant high or low vehicle speeds, free-flow speed optimization reduces distribution costs by 3.97% and 20.91% , respectively, while maintaining low carbon emissions. Distribution costs and emissions are sensitive to occasional driver detours and compensation pricing, but both can be kept low when vehicle travel time limits and compensation pricing are within reasonable ranges.

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Train Operation Plan of Green Urban Rail Transit Considering Transportation Capacity Utilization and Carbon Emissions

YANG Wenwen, MENG Xuelei, GAO Ruhu, LIN Li

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (1): 122-132. DOI: 10.16097/j.cnki.1009-6744.2025.01.013

Abstract （211）

PDF （2289KB）（179）

As the "dual carbon" goal of Carbon Peak and Carbon Neutrality rises to the national strategic level, the establishment of a green transportation system has become increasingly urgent. This paper proposes a train operation plan for urban rail transit that focuses on the core principle of "efficiency enhancement and carbon reduction" in green transportation. The operation plan is based on a multi-route, multi-type formation configuration, taking into account the benefits of resources, the environment, passengers, and enterprises. To investigate the impact of different routes on passenger flow distribution, passenger flows are classified based on their travel characteristics, and an analysis of the associated travel costs for each passenger group is conducted. A multi-objective optimization model is established with the objectives of maximizing train transportation resource utilization, minimizing carbon emissions during train operations, and reducing both the operational expenditures of enterprises and the time costs associated with passenger travel. The model is subject to various constraints such as line capacity, departure frequency, and the number of vehicles in operation. To solve the model, an improved Sparrow Search Algorithm (SSA) was proposed, with a comparative analysis conducted against a full-length route, single-type formation operation plan. Furthermore, the solution results were compared with those obtained from the traditional SSA and Particle Swarm Optimization (PSO) algorithms. The results demonstrate that the multi- route, multi-type formation operation plan performs better than full-length route, single-type formation plan in terms of capacity utilization, carbon emissions reduction, enterprise operational costs, and passenger travel time costs. Moreover, the improved SSA shows significant advantages over traditional algorithms in terms of solution efficiency and quality. Therefore, the method proposed effectively balances the interests of enterprises and passengers, and also enhances resource utilization and reduces carbon emissions, providing strong decision-making support for the green operation of urban rail transit systems.

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Integrated Optimization of Express and Local Train Schedules and Stopping Strategies Considering Carbon Trading

JIA Fuqiang, LI Kaiqiang, LI Yinzhen, MA Chengzheng, FENG Ziting

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (1): 113-121. DOI: 10.16097/j.cnki.1009-6744.2025.01.012

Abstract （270）

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To reduce carbon emissions during train operations and construct a sustainable and green urban rail transit system, this paper presents an optimization approach for the timetables and stop plans of express and local trains in urban rail transit, integrating carbon trading. First, the travel path selection mechanism of different passenger types is characterized and a calculation method for passenger travel time is proposed. Next, by computing the carbon emissions from train traction, ventilation and air conditioning, lighting, and signal systems, and incorporating the carbon emissions of the stop plans of express and local trains with carbon trading, the impact on operating costs is determined. A bi-objective nonlinear optimization model is established to minimize both passenger travel costs and enterprise operating costs. A two-stage solution algorithm is designed, which involves determining overtaking stations and partition- based calculation using Gurobi. Finally, a case study is provided to validate the model and algorithm. Through the case analysis, it is evident that different positions of overtaking stations yield different results. Compared with the all-stop mode, the operation mode of express and local trains considering carbon trading reduces passenger travel time by 5.8%, carbon emissions by 17.4%, and enterprise operating costs by 5.3%. The research findings indicate that the organization of express and local trains considering carbon trading has remarkable effects in reducing passenger travel time, carbon emissions, and enterprise operating costs. With the approaching of carbon peaking and the gradual rise in carbon prices, the carbon trading mechanism will effectively lower enterprise operating costs and encourage enterprises to actively reduce carbon emissions, thus promoting the sustainable development of urban rail transit.

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Integrated Optimization of Grain Loading Strategies and Transportation Routes Considering Losses

WAN Min, KUANG Haibo, JIA Peng, YU Fangping, MA Qianli, ZHANG Yige, ZHAO Sue

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (1): 15-23. DOI: 10.16097/j.cnki.1009-6744.2025.01.002

Abstract （704）

PDF （1877KB）（228）

A high-quality grain distribution system is critical to ensure the balance of grain supply and demand and food security. This study considers the perishable nature of grain types and aims to minimize the total costs of transportation, carbon emissions, and loss. An integrated optimization model is proposed to consider different loading methods (bagged-bulk-container) and various transportation modes (road-rail-sea). A case study was performed using the heuristic genetic algorithm in the "grain transport from North to South China" scenario in Northeast China. The results indicate that compared to bagged grain and bulk grain transport, multimodal transport of grain containers by rail, road, and water has clear advantages in terms of lower total cost and reduced loss. The proportion of grain loss cost in container transport, bagged grain transport, and bulk grain transport is 9.86%, 42.29%, and 29.82%, respectively. In the "grain transport from North to South China" process, roads are primarily used for local collection and distribution, while railways and waterways handle long- distance trunk transportation. When the delivery time requirements increase, the proportion of railway transportation would gradually increase, and the proportion of waterway transportation would decrease. When the total delivery time reaches 71.5 hours, the optimal transportation scheme would shift from container multimodal transport via road, rail, and sea to container multimodal transport via road and rail only. In the composition of total costs, the transportation costs and carbon emission costs of the optimal routes for the three loading methods are essentially the same. The study result also serves as a reference for the government regulatory agencies and logistics service providers that reducing grain transportation losses is an effective way to lower the overall logistics transportation costs.

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Multi-objective Path Optimization of Container Road-rail Intermodal Transportation Considering Hub Delays

DUANLiwei, YANG Hang, CHEN Jian

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (6): 275-285. DOI: 10.16097/j.cnki.1009-6744.2024.06.024

Abstract （547）

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To address the path optimization problem in container road-rail intermodal transportation while considering hub delays, triangular fuzzy numbers are employed to characterize the uncertainty associated with such delays. This uncertainty takes into account the dual uncertainty in the number of containers awaiting transshipment and the remaining rail capacity in transport, as well as the limitations of hub transshipment capacity and rail departure requirements on hub delays. Therefore, the resulting delay time, along with the associated carbon emissions and storage costs, is quantified from the perspectives of waiting for transshipment and waiting for departure schedules. A multi objective path optimization model is then developed to minimize the total cost and total carbon emissions associated with the path scheme. The model is de-fuzzified using the expected value method and fuzzy chance-constrained programming. A self-adaptive fast nondominated sorting genetic algorithm (ANSGA-II) is designed to solve the model. Additionally, the simulation methods are used to assess the reliability of the path scheme and to identify the optimal combination of confidence levels. The case study was conducted on a specific road-rail intermodal transportation network, and the results indicate that, compared to the NSGA-II algorithm, the proposed method exhibits a faster convergence speed. The total cost and total carbon emissions were improved by 2.03% and 5.87%, respectively, and the reliability of the frontier solutions was greater than 0.95. Further sensitivity analysis indicates that when the number of containers awaiting transfer reaches a specific threshold, the preferred mode of transport tends to shift towards single road transport. This adjustment aims to minimize hub delays caused by waiting for transfer. At the same time, hub delays also have different effects on the path selection of goods with different time sensitivity.

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Multi-objective Routing Optimization Model and Algorithm for Multimodal Transportation with Uncertain Time

ZHOUJinlong, ZHANGYinggui, XIAOYang, WANG Juan

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (6): 193-205. DOI: 10.16097/j.cnki.1009-6744.2024.06.017

Abstract （533）

PDF （1827KB）（206）

Multimodal transportation leverages the advantages of various transport modes, contributing to cost reduction and efficiency improvements in freight logistics, with route decision-making being a critical factor. The organization of multimodal operations and external environmental changes can cause fluctuations in transportation times. This study considers the impact of stochastic transportation times and transfer times on route optimization in multimodal transportation by introducing trapezoidal fuzzy numbers to represent time uncertainty. A time-window constrained multimodal transportation route optimization model is constructed with the objectives of minimizing transportation costs, carbon emissions, and transportation time. Based on fuzzy chance-constrained programming theory, the uncertainty model is transformed into a more tractable mixed-integer programming model. The evolutionary process is divided into two stages based on the real-time state of the population: the first stage focuses on optimizing the objective function, while the second stage objective optimization with constraint satisfaction. On this basis, a multi stage multi-objective evolutionary algorithm is designed to solve the model. Finally, a case study of a multimodal transportation network demonstrates that the proposed method effectively generates a set of route optimization solutions under uncertain transportation times, with chance constraint satisfaction probabilities exceeding 90% . Compared to the state-of-the-art constrained multi-objective evolutionary algorithms, the hypervolume indicator improves by 2.11% to 41.95%, showing significant performance gains and providing effective route decision-making support for multimodal transportation operators.

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