Tao Ji

The growing tension between heritage protection and transportation demands in historic urban districts poses significant planning challenges. This study addresses this issue by developing a multi-modal transportation coordination optimization model from a dual protection–renewal perspective. The model integrates three objectives—minimizing comprehensive impacts, maximizing traffic utility, and minimizing travel costs—encompassing both time and monetary expenditure. A linear weighting method is used to consolidate these into a single objective function, which is then solved with a particle swarm optimization algorithm. Applied to the historic urban districts of Yangzhou, China, the model evaluates and compares three typical policy-oriented transport development strategies: the protection strategy, the balanced strategy, and the renewal strategy. The protection strategy minimizes the comprehensive impact on the historic environment and total social costs, making it suitable for core areas with high conservation pressure. The balanced strategy achieves the optimal trade-off among comprehensive impact, traffic utility, and total cost, serving as a baseline for most historic districts. The renewal strategy, while improving traffic utility, significantly undermines historical integrity and increases total costs, and is therefore applicable only to peripheral areas with low protection pressure. The study concludes that strategy selection is inherently context-dependent, considering local heritage values, spatial constraints, and travel demand patterns. This method provides a transferable decision-support framework for sustainable transportation planning in historic urban districts.

Various approaches have been explored to address the path planning problem for mobile robots. However, it remains a significant challenge, particularly in environments where a multi-tasking mobile robot operates alongside stochastically moving humans. This paper focuses on path planning for a mobile robot executing multiple pickup and delivery tasks in an environment shared with humans. To plan a safe path and achieve high task success rate, a Reinforcement Learning (RL)-based double layer controller is proposed in which a double-layer learning algorithm is developed. The high-level layer integrates a Finite-State Automaton (FSA) with RL to perform global strategy learning and task-level decision-making. The low-level layer handles local path planning by incorporating a Markov Decision Process (MDP) that accounts for environmental uncertainties. We verify the proposed double layer algorithm under different configurations and evaluate its performance based on several metrics, including task success rate, reward, etc. The proposed method outperforms conventional RL in terms of reward (+63.1%) and task success rate (+113.0%). The simulation results demonstrate the effectiveness of the proposed algorithm in solving path planning problem with stochastic human uncertainties.

Public transit has been continuously developing because of advocacy for low-carbon living, and concerns about its safety have gained prominence. The various factors that constitute the bus operating environment are extremely complex. Although existing research on operational security is crucial, previous studies often fail to fully represent this complexity. In this study, a novel method was proposed to identify the risk factors for bus operations based on a Bayesian network. Our research was based on monitoring data from the public transit system. First, the Tabu Search algorithm was applied to identify the optimal structure of the Bayesian network with the Bayesian Information Criterion. Second, the network parameters were calculated using bus monitoring data based on Bayesian Parameter Estimation. Finally, reasoning was conducted through prediction and diagnosis in the network. Additionally, the most probable explanation of bus operation spatial risk was identified. The results indicated that factors such as speed, traffic volume, isolation measures, intersections, bus stops, and lanes had a significant effect on the spatial risk of bus operation. In conclusion, the study findings can help avert dangers and support decision-making for the operation and management of public transit in metropolitan areas to enhance daily public transit safety.

This study focuses on the main urban area of Yangzhou City and conducts a quantitative comparative analysis of traffic accessibility during normal weather and extreme precipitation conditions (typhoon) based on GPS trajectories of buses. From both temporal and spatial dimensions, it comprehensively examines the impact of extreme precipitation on bus travel speed, travel time, and the commuting range of residents in the main urban area of Yangzhou City. (1) Through the mining and analysis of multi-source heterogeneous big data (bus GPS trajectory data, bus network data, rainfall remote sensing data, and road network data), it is found that the rainstorm weather greatly affects the average speed and travel time of buses. In addition, when the intensity of heavy rainfall increases (decreases), the average bus speed and travel time exhibit varying degrees of spatio-temporal change. During the morning and evening rush hour commuting period of rainstorm weather, there are obvious differences in the accessibility change in each typical traffic community in the main urban area of Yangzhou city. In total, 90% of the overall accessibility change value is concentrated around −5 min~5 min, and the change range is concentrated around −25~10%. (2) To extract the four primary traffic districts (Lotus Pond, Slender West Lake, Jinghua City, and Wanda Plaza), we collected Points of Interest (POI) data from Amap and Baidu heat map, and a combination analysis of the employment–residence ratio model and proximity methods was employed. The result show that the rainstorm weather superimposed on the morning peak hour has different degrees of impact on the average speed of the above-mentioned traffic zones, with the most obvious impact on the Lotus Pond and the smallest impact on Wanda Plaza. Under the rainstorm weather, the traffic commute in the main urban area of Yangzhou in the morning and evening peak hour is basically normal. The results of this paper can help to quantify the impact of typhoon-rainstorm weather events on traffic commuting in order to provide a scientific basis for the traffic management department to effectively prevent traffic jams, ensure the reliability of the road network, and allow the traffic management department to more effectively manage urban traffic.

Global warming, sea-level rise, and rapid urbanization all increase the risk of compound extreme weather events, presenting challenges for the operation of urban-related infrastructure, including transportation infrastructure. In this context, some questions become important. For example, what are the temporal and spatial distribution and development trends of transportation resilience when considering the impact of multilpe extreme weather events on the urban transportation system? What is the degree of loss of urban transportation resilience (UT resilience) under different extreme event intensities, and how long will it take for the entire system to restore balance? In the future, if extreme weather events become more frequent and intense, what trends will urban transportation resilience show? Considering these problems, the current monitoring methods for transportation resilience under the influence of extreme events are lacking, especially the monitoring of the temporal and spatial dynamic changes of transportation resilience under the influence of compined extreme events. The development of big data mining technology and deep learning methods for spatiotemporal predictions made the construction of spatiotemporal data sets for evaluating and predicting UT resilience-intensity indicators possible. Such data sets reveal the temporal and spatial features and evolution of UT resilience intensity under the influence of compound extreme weather events, as well as the related future change trends. They indicate the key research areas that should be focused on, namely, the transportation resilience under climate warming. This work is especially important in planning efforts to adapt to climate change and rising sea levels and is relevant to policymakers, traffic managers, civil protection managers, and the general public.

Suburban tourist railway is an emerging transportation mode for tourism. Knowing the travel demand and trip distribution patterns of tourists is an important prerequisite to the planning and construction of suburban tourist railways. However, this issue has attracted very little research attention so far. Therefore, this paper proposes a forecasting model focused on the trip distribution of tourists who travel with the suburban tourist railway. Based on the analysis of the characteristics of tourists’ trips and the use of the trip chain method, the frequency, order, distance, and visiting volume of stay points of the trips of tourists have been intensively studied. Then, a tourist trip distribution forecasting model was built in this paper. It uses the Entropy-Maximizing theory to predict trip chain distribution probability and obtain the distribution of tourists within the city. A case study that takes the H city as an example was conducted to test the proposed model. The results of this case show that the output of the model can reflect the real trip distribution characteristics of tourists very well, which demonstrates the applicability and effectiveness of the proposed model.

The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists.

The entrance and exit area of parking facilities has the characteristics of high concentration of urban traffic and prominent traffic intertwining phenomenon, which easily induces rapid congestion of mixed heterogeneous traffic at specific times and local locations and quickly spreads to the entire road section or even a larger area. In order to better understand the congestion distribution characteristics and propagation effects of access section of the parking entrance and exit from the mid and microperspective, a 5 m lane width pixel grid is used to divide the frontage road research. It also proposes a spatially robust autoregressive model and complex network tools suitable for analysis of local traffic flow to analyze it. The results show that as spatial scale increases, the congestion propagation decreases sharply and spatial adjacency within the fourth order can account for more than 90% of the propagation; the frontage road to the entrance and exit is the place where the congestion first happens, and the congestion gradually attenuates as it propagates to the inner lane and the upstream of the road segments; the lateral congestion propagation attenuates faster, so the area affected by congestion is mainly distributed in the outermost lane. This paper can provide theoretical guidance for alleviating traffic congestion in the entrance and exit areas of parking facilities and has theoretical and empirical significance.

The present study investigates the temporal and spatial variation of storm surge activity at different temporal scales using reconstructed daily maximum storm surge levels in the southeastern coastal area of China for the past 60 years. Considering the surge height and the duration of storm surge, the comprehensive intensity of storm surge is proposed, namely Accumulated Storm Surge Potential Impact. The results show that with the intensification of climate warming, the intensity has increased rapidly, although the overall the frequency and duration of storm surges have not changed significantly. In relation to the spatial distribution of storm surge intensity, storm surge at different time scales (interannual and decadal) was mainly located in the areas of concentration of historical tropical cyclones landing and in subconcentration areas. In terms of climate change response, it is clear that the frequency, duration, and intensity of storm surges have increased significantly since the end of the 1980s. From the perspective of the spatial distribution of the climate tendency rate (it reflects the change trend of regional climatic elements) of storm surge intensity, the areas of increased intensity are mainly located in several concentrated areas where storm surge events occur frequently, while the weakened areas are also other areas where storm surges have rarely occurred. El Niño‐Southern Oscillation events have a certain effect on the frequency, duration, and intensity of storm surge activity, although the effect on intensity is more significant. The impact of the Pacific Decadal Oscillation on the frequency, duration, and intensity of storm surge activity at different phases was also different, with clear regional distribution features for each phase. In this way, we can make targeted preparations for disaster prevention and mitigation, and avoid further socio‐economic losses.