2024 Selected Papers in English

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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 （513）

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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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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 （308）

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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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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 （510）

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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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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 （205）

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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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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 （593）

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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 （236）

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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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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 （410）

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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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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 （237）

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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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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 （291）

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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 （272）

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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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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 （101）

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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 （200）

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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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