2024 Selected Papers in English

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Rule-based Discriminative Identification and Travel Chain Characterization of Last-mile Delivery Stops

JIANG Xiaohong, CHEN Qingwei, YANYadan, HAN Bing, LI Jiawei

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (6): 232-241. DOI: 10.16097/j.cnki.1009-6744.2024.06.020

Abstract （155）

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Responding to demand of last-mile delivery programs can significantly improve customer satisfaction. Identifying and extracting characteristics of stopping points made by last-mile express tricycle deliveries is fundamental to analyze spatial-temporal distribution patterns and dynamic demand. This paper proposes a stopping point identification method by combining Point of Interest (POI) data with stopping time rules. The POI information and instantaneous speeds are utilized to screen express tricycle trajectory data. The stopping time threshold is introduced as secondary filtering criteria. The neighboring aggregation points are merged to create a complete set of stopping points. The accuracy of the identification results is verified through manual verification, and the entropy rate of the stop chain is calculated using the entropy rate method to quantitatively evaluate the accuracy of different identification methods. Taking trajectory data of express tricycles from Shun Feng Express courier outlets in Suzhou city as the empirical object, this paper compares the proposed method with the commonly used the density-based spatial clustering algorithm of applications with noise to identify stopping points of cargo trucks. The results show that the DBSCAN algorithm is prone to misidentifying traffic signal waiting as a delivery stop, while the proposed method effectively avoids this issue, achieving both precision and recall rates of up to 98%. Furthermore, the application of the entropy rate method further validates the effectiveness of the proposed method in terms of accuracy. On this basis, by expanding the research scope and identifying distribution stopping points, this paper analyzes the travel chains and spatial-temporal distribution characteristics of express tricycles. The results indicate that the number of delivery vehicles during the peak period around 8:00 am is significantly higher than that during the peak period around 4:00 pm. Residential areas are hotspots for distribution, with the highest concentration of vehicles, the longest travel distances, and the longest working hours. Hotel deliveries, on the other hand, exhibit shorter stopping times. Additionally, the spatial distribution of stopping points also reveals the delivery conditions to the remote locations.

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Urban Rail Transit Passenger Flow Induction Optimization Under Event Interference

ZHAOMingxi, MAChangxi, MACunrui

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (6): 76-85. DOI: 10.16097/j.cnki.1009-6744.2024.06.007

Abstract （290）

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Urban rail transit systems often experience operational disruptions or reduced service capacity during peak hours, major events, and adverse weather conditions. To effectively mitigate the negative impacts of these disruptions on passenger flow and enhance the resilience of urban rail transit systems, this paper proposes an optimization method for passenger flow guidance in response to disruptive events. First, considering the impact of disruptive events and the compliance rate of passenger guidance, this paper develops a rail transit passenger flow guidance model with the goal of minimizing the total travel time of passengers in the system. Then, a column generation-based exact algorithm is designed, and Gurobi is used to solve the restricted master problem. The A* algorithm is applied to solve the pricing subproblem, and the branch-and-bound algorithm is utilized to find integer solutions. Through actual case analysis, it is found that the acceleration strategies designed in this paper can improve the solving efficiency by 66%~89%, with performance significantly superior to using Gurobi alone. Simulations of scenarios ranging from minor to severe disruptions demonstrate that the proposed optimization method is applicable to urban rail transit passenger flows of varying scales, effectively guiding passenger travel paths under various disruption intensities.

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Suburban Railway Timetable Optimization Based on Through Operation Mode

LIANG Hui, JING Yun, SUN Guofeng, SONG Qi

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (6): 126-134. DOI: 10.16097/j.cnki.1009-6744.2024.06.011

Abstract （293）

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The through operation mode is an important part of the integrated regional rail transit system. This paper introduces the minute-dependent passenger flow as the input, takes into account the common benefit of multiple participants, and examines the relationship between the departure time of suburban and the through operation of subway. Considering the constraints of train departure interval and train service capacity, this paper develops an integer programming model from the perspectives of subway operation income and passenger travel quality to optimize subway through operation scheme and suburban timetable. Based on the characteristics of the model, two sets of logic variables are introduced to reconstruct the model. The constraint method is used to transform the model into a single objective optimization model, which is accurately solved by commercial optimization solver Gurobi. Several groups of numerical examples were analyzed based on two rail transit lines. The results show that the through operation of subway can not only save the travel time of passengers but also increase the income of operating enterprises. The proposed subway through operation scheme can increase the revenue of the subway operation company by 18000 RMB. The adjusted suburban timetable can save 8.3% of passenger waiting time, which can better match the spatial and temporal distribution of passenger demand.

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Segmented Cooperative Control Method for Urban Road Traffic Flow in Connected Vehicle Environment

JIANG Xiancai, GUO Zihao, SONG Chengju

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (6): 47-62. DOI: 10.16097/j.cnki.1009-6744.2024.06.005

Abstract （664）

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The lane-changing behavior of vehicles approaching intersections will constrain the improvement of intersection traffic efficiency. Based on this, this paper proposes a Segmented Cooperative cOntrol Method for Urban Road Traffic Flow (SCOM-URTF) in a connected vehicle environment, which adopts a bi-level optimization model to achieve dynamic division of road section functional zones and collaborative optimization of traffic flow between road section and intersection. The upper-level model designs a Misaligned Lane-changing with Separated Lane Speed Guidance (ML-SLSG) to promote rapid lane changes for left and right turning vehicles through rearranging the vehicles entering from upstream intersection in longitudinal space, minimizing the vehicle lane-changing zone length, and balancing lane group traffic flow. The lower-level model uses dynamic programming to optimize the trajectory of connected vehicles and intersection signal timing parameters with the goal of minimizing average vehicle delay. The simulation results show that ML-SLSG can effectively shorten the total length of lane-changing. At the same time, the longitudinal trajectory optimization model proposed in this paper can reduce average vehicle delays at intersections by 5.9% ~8.0% under low, medium and high traffic demands. And after further collaborative optimization of vehicle trajectory and signal timing, the average vehicle delay can be further reduced by 3.7%~22.8%. Comparative studies with similar methods have shown that SCOM-URTF is more suitable for traffic environments where multiple driving behaviors are coordinated with each other. Sensitivity analysis shows that higher connected and automated vehicle penetration rates and road speed limits can help reduce average vehicle delays, and increasing the spacing between intersections can initially reduce average vehicle delay, but there may be a delay rebound after reaching the critical point. However, the coordinated optimization of trajectories and signals can effectively curb the rebound of delays.

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Hydrogen Fuel Cell Bus: A Literature Review and Prospects

LIU Tao, GUO Jiaxin, HAN Ying, TANG Chunyan

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (5): 1-13. DOI: 10.16097/j.cnki.1009-6744.2024.05.001

Abstract （648）

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The adoption of hydrogen fuel cell buses (HFCBs) contributes to reducing carbon emissions and promoting the sustainable development of transportation systems. This paper systematically reviews the research literature on HFCBs by searching relevant databases. The review covers five main areas: the feasibility and prospects of developing HFCBs, evaluation of HFCB systems and comparison with other transit modes, social acceptance of HFCBs, planning and operations management of HFCBs, and safety analysis of HFCBs. This study reveals that, as HFCBs are still in the exploratory development stage, there are relatively more studies on the feasibility, system evaluation, and social acceptance of HFCBs, whereas studies on the system planning, operations management, and safety analysis are relatively less. Although China's scientific research and practice in the field of HFCBs started later than other countries, it is currently among the world leaders. Driven by both policy support and market demand, HFCBs are rapidly developing in China. Based on the literature review, the paper further analyzes existing research limitations and proposes suggestions for future studies. The research indicates that further in-depth studies can be conducted in four areas: reducing the cost of HFCBs, enhancing infrastructure construction, increasing social acceptance, and strengthening safety management. Particularly, attention should be given to innovations in hydrogen fuel cell battery technology, supporting infrastructure development, and operational safety assurance. In the future, HFCBs have broad application prospects by providing transportation service in various transportation scenarios, such as in tourist attractions, large-scale sports events, urban transportation, or in intercity long-distance transport. Academia and industry should actively align with the relevant policy requirements and practical needs of the hydrogen energy industry and transportation development. Continuous in-depth research should be conducted on the key and challenging aspects of HFCBs development to jointly support its sustainable development.

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Joint Optimization of Dynamic Pricing and Pre-sale Period Division for High speed Trains

XU Jing, DENG Lianbo, LIU Huaru, HU Xinlei

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (5): 259-267. DOI: 10.16097/j.cnki.1009-6744.2024.05.024

Abstract （367）

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Based on the need to enhance high-speed rail revenue and implement a flexible market ticket pricing system, this paper focuses on the joint optimization of dynamic pricing and pre-sale period division considering the demand fluctuations and differences on each day during the booking horizon, as well as the impact of the pre-sale period division on railway revenue. Separate elastic demand functions are constructed for each day. A large-scale nonlinear model is developed to optimize the dynamic pricing and pre-sale period division for high-speed trains in consideration of the train capacity constraints, demand constraints, and price-related constraints. To solve the optimization problem, a bi-level genetic-simulated annealing algorithm is designed according to the model's properties. The optimization problem is divided into an outer-level pre-sale period division problem and an inner-level dynamic pricing and seat allocation problem, which are solved by genetic algorithm and simulated annealing algorithm, respectively. At last, a numerical instance is provided to evaluate the effectiveness of the optimization model and solution algorithm, and the results for different numbers of pre-sale period are discussed. The results indicate that as the number of period increases, the division of the booking horizon primarily concentrates on the latter half. For a case with five periods, the optimized revenue increased by approximately 1.21%.

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Research on Energy Consumption and Carbon Emissions in the Whole Life Cycle of Beijing-Xiong'an Intercity Railway

CAO Meng, YUAN Zhenzhou, YANG Yang, NIE Yingjie, NA Yanling, SUN Yunchao, CHEN Jinjie

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (5): 37-44. DOI: 10.16097/j.cnki.1009-6744.2024.05.004

Abstract （764）

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As the backbone of green transportation, intercity railways play a supporting role in reducing energy consumption and achieving the goal of "carbon peak and carbon neutrality". This paper analyzes the development trajectory and energy consumption patterns of intercity railways in China, with a particular focus on the entire lifecycle of planning, design, construction, and operation within the context of the "dual carbon" initiative. Utilizing the Beijing-Xiong'an intercity railway as a prototypical case, the paper examines energy consumption and carbon emissions across its lifecycle, incorporating the influence of energy-saving, emission-reduction strategies, and green carbon sink measures. The findings reveal that carbon emissions during the planning and design phases are negligible, whereas the energy consumption during the operation stage dominates, accounting for approximately 74.9% of the total annual lifecycle energy consumption. Additionally, the energy consumption attributed to building materials production (scaled to 100 years) constitutes roughly 22.4% of the total. Notably, the implementation of energy conservation, emission reduction, and green carbon sequestration measures has yielded substantial outcomes, achieving an average annual energy savings of approximately 12%. When compared with similar railway energy consumption indicators globally, the Beijing- Xiong'an intercity railway's unit transportation traction energy consumption of 6.42 tce per million person-kilometer aligns with expectations. Furthermore, its carbon reduction impact is significant in diverting highway passenger traffic, savings approximately 612.67 million yuan in carbon sink transactions and generating substantial societal benefits. This comprehensive analysis offers valuable insights and reference for the establishment of a green and low-carbon intercity railway carbon emission dual control indicator system.

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Optimization of Electric Bus Scheduling Considering Time-of-use Electricity Pricing Policy and Multiple Vehicle Types

XIONGJie, LIANG Jingjing, LI Xiangnan, DOU Xueping, LI Tongfei

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (4): 188-199. DOI: 10.16097/j.cnki.1009-6744.2024.04.018

Abstract （242）

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This paper proposes an optimization model for electric bus scheduling and charging scheduling with the consideration of time-of-use electricity pricing policy and multiple vehicle types, aiming to minimize the total operation cost of electric bus system. The practical operational constraints of bus schedule chain formulation, charging time window, and limited number of chargers are considered in the model. An adaptive large neighborhood search (ALNS) algorithm is proposed to solve the bus schedule optimization problem. This algorithm incorporates diverse destruction and repair operators tailored to the characteristics of the problem, such as the trip-to-vehicle allocation and the feasibility of the bus schedule chain under multiple vehicle types. For the feasible bus schedule chain combinations generated by ALNS, the charging schedule optimization subproblem under time-of-use electricity price is constructed and mapped into a dedicated network. An algorithm based on the minimum-cost-flow is designed to solve for the charging duration, which leads to an optimal decision on charging start time. The model and algorithm are validated using three bus routes in Beijing. The results show that compared with the current situation, the fleet size is reduced from 30 to 24 vehicles, resulting in a decrease in electricity cost and total operation cost by 25.84% and 20.63%, respectively. Comparative experiments are conducted to explore the impact of different weights of repair indicators and combinations of vehicle types on the optimization results.

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Influence of Built Environment on Integrated Use of Bike Sharing and Metro

GUANHaotian, JI Xiaofeng, LI Wu, CHEN Fang, DENG Ruofan

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (4): 200-211. DOI: 10.16097/j.cnki.1009-6744.2024.04.019

Abstract （419）

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This study investigates the impact of the built environment on the demand for dockless bike-sharing (DBS) and integrated metro use. A number of 120,000 DBS trip records were utilized, and spatial confidence ellipse technology was employed to illustrate the clustering characteristics of DBS near metro stations. Subsequently, a quantitative method for delineating bicycle-metro catchment areas was developed, through which the built environment surrounding metro stations was evaluated through five dimensions: density, transportation facilities, land use, destination accessibility, and metro ridership. Finally, a gradient boosting decision trees (GBDT) model is employed to map the complex and non-linear interactions between the built environment and the necessity for integrated use modalities. The results indicated that metro ridership and workplace locations emerged as significant factors influencing the integrated use, exhibiting a distinct threshold effect. An increase in commercial activities initially elevates the integrated travel demand, but excessive density subsequently triggers adverse effects due to traffic congestion. An uptick in bus stop density indicates a competitive dynamic between shared bikes and public transit, underscoring the intricate interactions within urban transportation systems. Furthermore, the nonlinear effects of land use diversity and population density underscore the profound relationship between urban planning and residents' commuting behaviors.

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Robust Model Predictive Control of Connected and Automated Vehicle Trajectories on Urban Roads

LIU Meiqi, JIN Kairan, LI Yalan, GUO Ge

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (4): 31-40. DOI: 10.16097/j.cnki.1009-6744.2024.04.004

Abstract （499）

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To solve the problem of the actuator delay and uncertainties which may cause platoon instability or even destabilization, this paper proposes a robust model predictive control approach for vehicle trajectory optimization on urban roads. A third-order vehicle dynamics model was developed to optimize ride comfort, safety, platoon stability, fuel efficiency, and traffic delay. The behaviors of the red-light violations and the unsafe inter-vehicle distances were penalized, and the speed and acceleration were bounded. The signal changes were treated as system feedback. The proposed vehicle trajectory controller aims to improve the operational efficiency of controlled vehicles. The vehicle trajectory controller was formulated as a Min-Max model predictive control problem to enhance platoon stability by determining the control inputs in the worst case of actuator delays and uncertainties. Then, the iterative Pontryagin's maximum principle was used to solve the control problem, which discretized the control problem and divided the uncertain parameters into multiple intervals. To improve the computational efficiency, the proposed solution approach identified the worst case, iteratively computed the state variables forward in time, and solved the costate variables backward in time. The numerical simulation results demonstrate that the proposed controller performs well on the lane sections with and without signal controllers. The robust model predictive control approach can effectively response to random actuator delays and external vehicle disturbances, such as signal changes, abrupt speed changes, and small trajectory deviations caused by human drivers. The proposed robust Min-Max model predictive controller (MM-MPC) manifests better stability and superiority than the normal MPC controller in riding comfort (improved by 75.7%) and fuel consumption (reduced by 18.4%).

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Car-following Model Construction and Behavior Analysis of Connected Vehicles in Foggy Conditions

HUANGYan, LI Haijun, YAN Xuedong, DUAN Ke

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (4): 41-49. DOI: 10.16097/j.cnki.1009-6744.2024.04.005

Abstract （512）

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Connected vehicle (CV) has been proven to effectively improve traffic safety under fog weather conditions in microscopic driving behavior analysis. A microscopic car-following model is important for simulating the trajectory of CV in fog weather. Based on the traffic information perception mode and car-following behavior characteristics of CV in fog weather, this paper proposes a fog-related intelligent driver model of connected vehicle (FIDMCV) considering factors such as time headway, weighting, and compliance, based on the fog-related intelligent driver model. To evaluate the effectiveness of the FIDMCV model and assess the traffic impact of CV in fog weather, the cumulative reciprocal of Time-to-collision (1/TTC) and throughput were selected as analysis indicators, and numerical simulation scenarios with different CV penetration rates and decelerations of the leading vehicle were established. Before conducting numerical simulations, sensitivity analyses were performed on key parameters of time headway and compliance. The simulation results show that with the increase in the penetration rate of CV, mixed traffic flow more effectively improved traffic safety in fog weather. However, it also led to an increase in car-following distances of vehicles, thereby reducing road throughput and decreasing traffic efficiency. The proportion of reduction in cumulative 1/TTC values for CV in a high risk scenario (deceleration of 6 m⋅s²) is 14.3%, and in medium-low risk scenarios (decelerations of 4 m ⋅ s² and 2 m ⋅ s²) is 5.6% and 6.3%, respectively, indicating that the improvement of traffic safety for CV is more significant in the high risk scenario. The proposed FIDMCV model can effectively reflect the traffic safety improvement effect and car-following distance increase characteristics of CV in fog weather conditions, and can be used as a microscopic simulation tool for CV.

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Joint Optimization of Train Timetabling and Rolling Stock Circulation Planning in Urban Rail Transit Line with Multiple Train Compositions

RAN Xinchen, CHEN Jian, CHEN Shaokuan, LIU Gehui, ZOU Qingru

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (3): 184-193. DOI: 10.16097/j.cnki.1009-6744.2024.03.018

Abstract （608）

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To address the issues of peak-hour congestions and off-peak underutilization of transportation capacity on an urban rail line, a joint optimization method of train timetabling and rolling stock circulation planning with multiple train compositions is proposed. Based on dynamically changing OD passenger demand and multiple types of line resource, a two-objective optimization model is constructed to minimize the total passenger waiting time and the train operating cost. The total number of operating trains, the timetable, the train types, the entry and exit of trains from depots, and the train succession relationship are taken as decision variables. Timetable-related constraints, rolling stock circulation- related constraints, fleet size constraints, turnaround constraints, and train capacity constraints are considered in this model. Since the total number of trains is not determined, a NSGA-II (Non-dominated Sorting Genetic Algorithm-II) with variable-length chromosomes is designed to solve for the Pareto optimal solution of the twoobjective optimization model. A case study conducted on a subway line demonstrates the effectiveness of this modelling and solution approach. The results show that the optimized multi-train composition strategy simultaneously reduces the total passenger waiting time by 26.16% and the train operating costs by 25.75%. Moreover, the optimized average load factor of trains is increased by 1.3% ~9.6% , further improving the matching between transportation capacity and passenger flow demand.

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A Train Group Control Method Based on Car Following Model Under Virtual Coupling

SHUAI Bina, LUO Jianan, FENG Xinyan, HUANG Wencheng

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (3): 1-11. DOI: 10.16097/j.cnki.1009-6744.2024.03.001

Abstract （586）

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There remains a gap between transportation capacity and demand under the high-speed railway moving block mode, prompting the exploration of new approaches such as virtual coupling to enhance transportation capacity. With the concept of virtual coupling and inspired by car-following models utilized in road traffic, we propose a novel acceleration adjustment strategy by train dynamics and multi-agent methods for tracking trains based on the speed and distance relationship between adjacent trains, with the goal of ensuring train safety and passenger comfort while enabling virtual coupling within the train group. A corresponding virtual coupling acceleration adjustment model is established for train groups, aiming to achieve equal speed and distance between all trains in the group. The proposed model is validated using the CRH380A high-speed train as a case study. Simulation results demonstrate that the proposed acceleration adjustment strategies effectively realize the virtual coupling of train groups. Compared to the moving block method, adopting virtual coupling reduces the time required for train collaboration by 9.7% and decreases the distance between trains by 10.1% , thereby improving efficiency. Furthermore, the time required to achieve virtual coupling is shorter when considering the train group as a whole compared to when the group is separated into multiple groups.

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Impact of Charging and Incentive Strategies on Commuting Mode Choice

WANGDianhai, LIYiwen, CAI Zhengyi

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (2): 1-12. DOI: 10.16097/j.cnki.1009-6744.2024.02.001

Abstract （791）

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This paper investigates the regulatory impact of two traffic demand management strategies, tolls and rewards, on travel mode choices, using the main urban area of Hangzhou as a case study. The stated preference (SP) and revealed preference (RP) surveys were performed to understand the intention of private car commuters' mode choice under parking charge and travel reward scenarios. The disaggregate theory was used to establish Nested Logit (NL) models for commuting mode selection under separate and joint implementation of parking fees and travel rewards. The results indicate that both parking fees and travel incentives can reduce private car travel demand and promote public transportation. Only when the parking price reaches a certain level can private car trips be effectively reduced, and appropriate incentives can actively encourage travelers to switch to other modes of travel. If charging and incentive strategies are implemented simultaneously, it will manifest a joint effect of charging as the main approach and incentive as a supplement. In all three scenarios, income is a significant factor influencing travel mode choices. The higher the income, the more likely the continuation of private car usage. In the scenario with only a parking fee, the elasticity of parking fees increases with the rate; there are limited elasticity when the rate is low. The elasticity of travel rewards initially raises and then drops with the increase in the reward amount; Small rewards also show elasticity.

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Formation and Evolution Mechanism of Connected and Autonomous Fleet Based on Fish Streaming Effect

WEILiying, WU Runze

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (2): 76-85. DOI: 10.16097/j.cnki.1009-6744.2024.02.008

Abstract （380）

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With the rapid development of connected and autonomous vehicles (CAV), the research on traffic characteristics and cooperative control of the intelligent mixed traffic that is composed of CAVs and human-driven vehicles, has become a research focus. In this paper, a multi-lane cellular automata model for the mixed traffic is established to simulate the formation and evolution process of a CAV fleet. Firstly, the fish streaming effect is introduced to describe the formation process of four kinds of CAV fleets based on their networked characteristics. Secondly, the Markov property is used to calculate the fleet scale transfer probability from the perspective of the fleet, and the evolution process of the CAV fleet state is described. Thirdly, the rule of Gipps safety distance is introduced to improve the NaSch model, and CAV vehicles and fleet are subjected to the speed guidance. Finally, this paper carries out simulation experiments on the established mixed traffic flow cellular automata model based on fish streaming according to the measured vehicle arrival rate. The results show that the CAV fleet can effectively improve the operating state of mixed traffic and alleviate traffic congestion; Under the condition of a 60% penetration rate, the congestion rate can be reduced by 43.9% when the CAV fleet scale is 3 compared with the non-fleet, the traffic flow speed can be increased about 43%, and the average speed tends to be stable with the increase of fleet scale.

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Collaborative Optimization of Rail-mounted Gantry Crane and Container Truck Based on Actual Transportation Capacity in Railway Container Terminals

CHANG Yimei, WANG Yang, ZHU Xiaoning

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (1): 188-198. DOI: 10.16097/j.cnki.1009-6744.2024.01.019

Abstract （378）

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To address the problem posed by limited equipment resources in railway container terminals, this study focuses on the collaborative optimization problem of rail mounted gantry crane and container truck based on the actual transportation capacity of container trucks within the mode of one-truck-two-containers. An integer programming model is formulated with the objective of minimizing truck completion times of the trucks. The model considers task allocations of containers within the one-truck-two-containers mode and incorporates safety constraints relevant to rail mounted gantry cranes. To solve the model, a genetic simulated annealing algorithm considering the distribution strategy of the rail crane is designed. Three sets of experiments with different scales are conducted to verify the feasibility and effectiveness of the proposed model and algorithm. The experimental results show that the one-truck-two-containers mode results in an average completion time reduction of 13.3% compared with the traditional collaborative operation under the one-truck-one-container mode. As the scale of examples increases, the time reduction become more significant. Furthermore, the one-truck-two-containers mode exhibits enhanced flexibility in rail mounted gantry crane operations, ensuring a more balanced workload distribution. Importantly, when the resources of container truck are limited, the one-truck-two-containers also improves the utilization rate of equipment.

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Signalized Intersection Eco-driving Strategy Based on Deep Reinforcement Learning

LI Chuanyao, ZHANG Fan, WANG Tao, HUANG Dexin, TANG Tieqiao

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (1): 81-92. DOI: 10.16097/j.cnki.1009-6744.2024.01.008

Abstract （797）

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Eco-driving in a connected and autonomous driving environment has great potential to improve traffic efficiency, energy saving, and emission reduction. This paper proposes a prosocial eco-driving strategy based on the deep reinforcement learning algorithm that optimizes the longitudinal manipulation and lateral decision-making of the connected and automated vehicle (CAV). The state space is divided into the local variables related to dynamic vehicle characteristics and the global variables associated with signalized intersection to ensure adequate interaction between the CAV and the roadway environment. The designed reward function integrates the vehicle driving requirements, synergy with signals and global energy saving incentives. In addition, this study developed a typical urban road intersection scenario to train the model. The results show that the proposed strategy can achieve eco-driving of the CAV in collaboration with the signal and output lateral control to ensure the vehicle travels to the target lane. In addition, simulations in a dynamic traffic environment reveal that the proposed method can improve the capacity at the intersection by about 17.90% and reduce the traffic system's fuel consumption and pollutant emissions by approximately 8.76% through the control of multiple CAVs to guide the human-driven vehicles.

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Spatiotemporal Characteristics of Eco-transport Efficiency in Transport Hub Cities of China

WANG Ling, WANG Qi, TANG Lei

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (1): 14-23. DOI: 10.16097/j.cnki.1009-6744.2024.01.002

Abstract （436）

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This study aims to explore the development stages and spatiotemporal characteristics of eco-transport efficiency within Chinese transport hub cities and to identify effective pathways for fostering green and low-carbon comprehensive transportation systems. A selection of 20 international integrated transport hub cities in China serves as the research subjects. The paper employs the super-efficiency EBM (Epsilon-Based Measure) model to calculate eco-transport efficiency and applies the kernel density estimation method, standard deviation ellipse method, and Dagum Gini coefficient to explore their characteristics of spatiotemporal evolution and regional difference. The findings reveal that, between 2011 and 2021, the overall eco-transport efficiency within the 20 hub cities showcased significant development but failed to reach an effective level. Comparison among hub cities based on their transport functions indicated a hierarchy of efficiency: seaport hub cities > railway hub cities > aviation hub cities. With the accelerated construction of various transport infrastructures in China's early stages and the gradual implementation of the green and low-carbon transport development strategy in the later stage, there is a fluctuation in overall eco-transport efficiency, initially decreasing and then increasing. Meanwhile, the polarization phenomenon among cities exhibited instability, but the number of cities with high-efficiency values was increasing. The spatial distribution presented a "Southwest-Northeast" pattern, agglomerating from the southwest toward the northeast. Coastal hub cities exhibited higher average efficiency compared to their inland hub cities, and the overall regional difference and inter-regional difference showed the same trend of first expanding and then narrowing.

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Prediction of Transportation Industry Carbon Peak in China

LI Ninghai, CHEN Shuo, LIANG Xiao, TIAN Peining

Journal of Transportation Systems Engineering and Information Technology 2024, 24 (1): 2-13. DOI: 10.16097/j.cnki.1009-6744.2024.01.001

Abstract （974）

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Transportation industry faces a series of challenges under the strategy of "carbon peak" due to the high carbon emissions. This paper analyzes the current situation of the carbon emissions in passenger and freight transportation in China. Based on the statistical data and relevant research results, this study simulates the carbon emissions of the transportation industry including private cars. The carbon emission factors of each transportation mode are calculated. The trend of passenger and freight turnover in 2019 to 2040 is predicted based on the experience of some developed countries. Taking 2040 as the target year, the scenarios of future transportation structure and carbon emission factors were designed, and the time and value of carbon peak for transportation in China are estimated. The results show that the transportation carbon emission, including private cars, is 1.11 billion tons in 2020. It is predicted that the passenger transportation demand will be 8.2 to 8.7 trillion person-kilometers, and the freight transportation demand will be 27.3 to 28.7 trillion tonnage kilometers in 2040. It is verified that it would be difficult to achieve the carbon peak before 2040 only through improving the transportation structure, and it is also significantly important to promote the upgrading of clean transportation technology. The scenario analysis shows that the transportation industry is expected to achieve the carbon peak in 2031 to 2034 by encouraging the transformation of transportation structure such as "road to rail" and "road to water", and promoting the cleanliness of roadway transportation.

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