中国南水北调工程是迄今为止世界上规模最大的调水工程,是中国当代水利工程的伟大成就,也是中华文明和传统治水工程智慧的传承赓续.南水北调中线工程和东线工程沿线区域则是国家文旅融合发展规划布局的关键地带.南水北调沿线文旅融合发展有其长期的规划与研究基础,然而由于地域范围广阔、文旅资源量大面广、区域发展不平衡,如何认识区域文旅条件,进而推动南水北调沿线文旅融合的合理布局与科学适度开发是一个难题.对此,本文依托"中国文化遗产空间信息平台"及网络信息获取,阐明了南水北调沿线文旅资源基础及空间分布特征.面向系统呈现南水北调工程突出价值,提出南水北调工程沿线文旅融合发展战略目标,构建"龙头节点+关联节点+水上线路"的文旅融合体系.研发集成文旅大数据分析、GIS空间分析、文本分析的"旅游圈建构—竞争力评估—特色识别"一体化分析技术,实现了对区域文旅空间的识别、定量评估及信息可视化提取呈现.通过对资源基础、目标战略、项目体系和旅游圈布局进行系统研究,提出了南水北调工程沿线文旅融合发展规划建议.本文提出的创新性技术方法,填补了区域性文旅资源开发利用及区域文化遗产保护利用的技术空白.

2023年5月20日至6月9日,《用遗产的眼光看,从文明的角度论——陈同滨建筑遗产保护研究作品展》在中国建筑设计研究院举办(图1,2).5月24日,承蒙陈同滨老师悉心讲解,我们仔细观看了展览.陈先生数十年如一日,深耕建筑遗产的价值研究与整体保护规划技术,成果丰硕且成体系,学术研究之谨严与规划实践落地之成效兼具,令人叹为观止.

All-way stop-controlled (AWSC) intersections are a typical type of unsignalized intersection that has been commonly used when both intersecting roads have a relatively low vehicular traffic demand. At an AWSC intersection, vehicles proceed through the intersection under a “first come, first served” rule. Since AWSC intersections are often deployed in residential or business areas where pedestrian crossings tend to be inevitable. When a driver who has the right-of-way yields to the crossing pedestrians, the right-of-way at the intersection might be reallocated, which could result in capacity losses to the current approach. On the other hand, the conflicting vehicular traffic movement(s) to the current approach may use this gap and proceed through the intersection ahead of this vehicle. Nevertheless, the effects of pedestrian crossings at AWSCs have not been considered by the existing capacity modeling methods. This research develops a method to evaluate the capacity changes caused by pedestrian crossings at AWSC intersections. Field data collected at two representative sites were collected for model calibration and validation. A sensitivity analysis was conducted to reveal the combined effects of different pedestrian crossing movements and under various pedestrian crossing demands. Results indicated that pedestrian crossing movements could change the capacity at AWSC intersections, but the effects vary according to pedestrian activities and vehicle directions.

城市是人类文明的结晶,中国城市自诞生以来即表现出鲜明的体系特征.中国城市体系植根于我国幅员辽阔、区域特质多样的自然地理基础,先后经历了城邑起源与地域化、分封制与邦国化、郡县制与层级化等阶段.历经数千年的发展演进,中国城市体系在统一多民族国家之抟成、广域空间之治理中起到了枢纽作用,是中华文明的重要载体.面向未来,需要更加重视城市在国家治理中的重要作用,依托城市体系来规划国土空间,探索人类文明新形态.

中国国土空间辽阔,历史悠久,长期追求并维系着大一统的局面.从国土空间规划角度看,以都城为核心的城市体系(城邑天下)是国土空间结构的统率,城市体系与行政体系高度吻合,与交通网络相辅相成,与大国山河相得益彰,为统一多民族国家的塑造提供了基本的空间骨架,共同在广域国土空间控制与社会治理中发挥枢纽作用.在当前建立国土空间规划体系并监督实施的过程中,亟需传承大国山河规画传统,突出城市体系枢纽地位,合理组织"人居-生态-农业"空间,缔造美丽国土与美好人居.

Urban traffic congestion and crashes have been considered by city planners as critical challenges to the economic development of the city. Traffic signal coordination, which connects a series of signals along an arterial by various coordination methodologies, has been proved as one of the most cost-effective means of reducing traffic congestion. In this regard, Metropolitan Planning Organizations (MPO) or Transportation Management Centers (TMC) have included signal timing coordination in their strategic plans. Nevertheless, concerns on the safety effects of traffic signal coordination have been continuously raised by both transportation agencies and the public. This is mainly because signal coordination may increase the travel speed along an arterial, which increases the risk and severity of traffic collisions. To date, there is neither solid evidence from the field to support the concern, nor theoretical-level models to analyze this issue. This research aims to investigate the effects of traffic signal coordination on the safety performance of urban arterials through microsimulation modeling of two traffic operational conditions: free signal operation and coordinated signals, respectively. Three urban arterials in Reno, Nevada were selected as the simulation testbed and were coded in the PTV VISSIM software. The simulated trajectory data were analyzed by the Surrogate Safety Assessment Model (SSAM) to estimate the number of traffic conflicts. Sensitivity analyses were conducted for various traffic demand levels. Results show that under unsaturated conditions, traffic signal coordination could reduce the number of conflicts in comparison with the free signal operation condition. However, under oversaturated conditions, no significant difference was found between coordinated and free signal operations. Findings from this research indicate that traffic signal coordination has the potential to reduce the risk of crashes on urban arterials under unsaturated conditions.

Traffic signal coordination, which connects a series of signals along an arterial by various coordination methodologies, has been proven as one of the most cost-effective means for alleviating traffic congestion. Various metropolitan planning organizations (MPO) or transportation management centers (TMC) have included signal timing updates in their strategic plans. However, in practice, signal coordination is usually implemented when traffic volume is heavy (i.e., during peak hours). For the rest of the day, the free operation strategy is usually used to reduce the waiting time of uncoordinated phases. However, this free operation strategy may result in the loss of operational efficiency on the major street. Currently, implementing signal coordination during off-peak hours is rare in the U.S. since there is lack of an efficient method that considers traffic operations for both the major and the minor streets. Therefore, this research provides a novel method that balances the control delays between the major street and the minor street. The procedure is to optimize the splits of the major street while also using the reservice strategy in the signal controller for the minor street. Microsimulation modeling was employed to assess the performance of traffic signal coordination during off-peak periods. Results show that, under reasonable splits, the coordination effect on the major street can be achieved and protected with an acceptable delay to minor street traffic. The strategy can be immediately implemented to reduce travel time for major street traffic.

Two-way stop-controlled (TWSC) intersections have been used extensively in the United States and other parts of the world when traffic signal control is not warranted. However, it was found that when a major road vehicle yields to crossing pedestrians, minor road traffic could use this extra gap, which tends to improve the capacity of some minor vehicle movements. The current capacity modeling methods did not take into account the effects of the pedestrian crossing on minor road capacity. This paper proposes an analytical model to quantify the increased capacity of minor street movements contributed by minor street pedestrian crossings and validates the model using both field data collected at a three-leg TWSC intersection and through the stochastic simulation method. A sensitivity analysis was performed to reveal the impacts of various factors on minor road capacity. In general, it was found that minor road left-turn capacity at the study intersection was positively correlated to pedestrian crossing volume, yielding rate, and pedestrian crossing time. Besides, modeling results showed that under relatively heavier conflict traffic volume conditions, the effect of the pedestrian crossing on increased capacity on the minor road was more significant.

Land use reflects human activities on land. Urban land use is the highest level human alteration on Earth, and it is rapidly changing due to population increase and urbanization. Urban areas have widespread effects on local hydrology, climate, biodiversity, and food production. However, maps, that contain knowledge on the distribution, pattern and composition of various land use types in urban areas, are limited to city level. The mapping standard on data sources, methods, land use classification schemes varies from city to city, due to differences in financial input and skills of mapping personnel. To address various national and global environmental challenges caused by urbanization, it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping methods. This is because, only with urban land use maps produced with similar criteria, consistent environmental policies can be made, and action efforts can be compared and assessed for large scale environmental administration. However, despite of the fact that a number of urban-extent maps exist at global scales [3,4], more detailed urban land use maps do not exist at the same scale. Even at big country or regional levels such as for the United States, China and European Union, consistent land use mapping efforts are rare.

The operation cost of signalized intersections is usually higher than unsignalized intersections, not only because of the expenses on hardware devices but also due to the device maintenance as well as software upgrading. Moreover, at a signalized corridor, the coordination between signals is also considered to be necessary, which requires additional labor and maintenance costs. With these considerations, this paper aimed to investigate the feasibility of using a single signal controller to control two or multiple adjacent intersections. This study developed a procedure to decide when to use one-controller strategy and evaluated the operation of two real-world cases in Reno, Nevada, where the previous two-controller strategy has just replaced by a one-controller strategy. Based on microsimulation study, it was concluded that the Level-of-Service (LOS), delay, and the average number of stops under one-controller strategy maintained a similar condition in comparison with the previous two-controller strategy, indicating that the proposed one-controller strategy would be a feasible alternative to reduce the operation costs of adjacent intersections. It is expected that reducing the number of signal controllers will not only reduce the infrastructure cost, but also lead to notable operation benefits, such as it facilitates the development of signal coordination plans, and the implementation of future adaptive signal control in a connected vehicle environment, since it reduced the number of communication nodes within the arterial system.

This research presented a new approach for vehicle classification using roadside LiDAR sensor. Six features (one feature, object height profile, contains 10 sub-features) extracted from the vehicle trajectories were applied to distinguish different classes of vehicles. The vehicle classification aims to assign the objects into ten different types defined by FHWA. A database containing 1,056 manually marked samples and their corresponding pictures was provided for analysis. Those samples were collected at different scenarios (roads and intersections, different speed limits, day and night, different distance to LiDAR, etc.). Naïve Bayes, K-nearest neighbor classification, random forest (RF), and support vector machine were applied for vehicle classification. The results showed that the performance of different methods varied by class. RF has the highest overall accuracy among those investigated methods. Some types were merged together to serve different types of users, which can also improve the accuracy of vehicle classification. The validation indicated that the distance between the object and the roadside LiDAR can influence the accuracy. This research also provided the distribution of the overall accuracy of RF along the distance to LiDAR. For the VLP-16 LiDAR, to achieve an accuracy of 91.98%, the distance between the object and LiDAR should be less than 30 ft. Users can set up the location of the roadside LiDAR based on their own requirements of the classification accuracy.

Connected-vehicle system is an important component of smart cities. The complete benefits of connected-vehicle technologies need the real-time information of all vehicles and other road users. However, the existing connected-vehicle deployments obtain the real-time status of connected vehicles, but without knowing the unconnected traffic since there are still many unconnected vehicles and pedestrians on the roads. Therefore, it is urgent to find an approach to collect the high-resolution real-time status of unconnected road users. When it is difficult for all vehicles, pedestrians, and bicyclists to broadcast their real-time status in the near future, enhancing the traffic infrastructures to actively sense and broadcast each road user's status is an intuitive solution to fill the data gap. This paper introduces a new-generation LiDAR-enhanced connected infrastructures that can actively sense the high-resolution status of surrounding traffic participants with roadside LiDAR sensors and broadcast connected-vehicle messages through DSRC roadside units. The system architecture, the LiDAR data processing procedure, the data communication, and the first pilot implementation at an intersection in Reno, Nevada are included in this paper. This research is the start of the new-generation connected infrastructures serving connected/autonomous vehicles with the roadside LiDAR sensors. It will accelerate the deployment of the connected network for the smart cities to improve traffic safety, mobility, and fuel efficiency.

Trajectory tracking and crossing intention prediction of pedestrians at intersections are important to intersection safety. Recently, on-board video sensors have been developed for detection of pedestrians. However, both the detection range and operating environment of video-based systems seem to be constrained by the advancement of image-processing technologies. Additionally, on-board systems cannot alarm pedestrians to take evasive actions when at risk, a feature which is critical to saving lives. This paper summarises the authors' practice on using roadside LiDAR sensors to monitor and predict pedestrians' crossing intention, as part of an ongoing effort to develop a pioneering LiDAR-based system to systematically reduce pedestrian and vehicle collisions at intersections. The LiDAR sensors were installed at intersections to collect pedestrian data such as presence, location, velocity, and direction. A new method based on deep autoencoder - artificial neural network (DA-ANN) was used to process data and predict pedestrian crossing intention. The case study shows the proposed model is about 95% prediction accuracy and computational efficiency for real-time systems. The roadside LiDAR system has great potential to significantly reduce vehicle-to-pedestrian crashes both at intersections and non-intersection areas, either used as a stand-alone system or in conjunction with the connected V2I and I2V technologies.

Light Detection and Ranging (LiDAR) is a remote sensing technology widely used in many areas ranging from making precise medical equipment to creating accurate elevation maps of farmlands. In transportation, although it has been used to assist some design and planning works, the application has been predominantly focused on autonomous vehicles, regardless of its great potential in precise detection and tracking of all road users if implemented in the field. This paper explores fundamental concepts, solution algorithms, and application guidance associated with using infrastructure-based LiDAR sensors to accurately detect and track pedestrians and vehicles at intersections. Based on LiDAR data collected in the field, investigations were conducted in the order of background filtering, object clustering, pedestrian and vehicle classification, and tracking. The results of the analysis include accurate and real-time information of the presence, position, velocity, and direction of pedestrians and vehicles. By studying the data from infrastructure-mounted LiDAR sensors at intersections, this paper offers insights into some critical techniques that are valuable to both researchers and practitioners toward field implementation of LiDAR sensors.

Unsafe driver behavior has been a major concern for traffic safety. Limited studies were conducted for driver behavior analysis on ramp-related areas due to limited data. The advanced Intelligent Transportation Systems (ITS) technologies, such as connected-vehicles, are helpful to reduce driver behavior fault. However, considering the numerous ramps in the road network in the United States, priority of ITS deployment needs to be given to those sites with a high risk of having driver behavior fault. This paper analyzed the role of driver behavior fault in crashes/near-crashes on ramp-related areas with the Strategic Highway Research Program 2 Naturalistic Driving Study (NDS) data. A total of 428 crash/near-crash records and 100 baseline events (normal driving) were extracted from the database. It is found that driver behavior fault is the most important factor in the occurrence of crashes/near-crashes on ramp-related areas. Driver distraction is the most common types of driver behavior faults leading to crash/near-crash. The influence of different factors on driver behavior fault was also examined. Gender and Level of Service (LOS) were found highly related to unsafe driver behavior. Female drivers were more likely to have unsafe driver behavior at ramp-related areas. The LOS did not impact driver behavior significantly until the traffic was congested. It is recommended to initially deploy connected-vehicle devices at the sites with heavy traffic volume.

Safety evaluation based on historical crashes usually has a lot of limitations. In previous studies, near-crashes are considered as surrogate data for safety evaluation. One challenge for the use of near-crashes data is the difficulty of data collection. The driving simulators and naturalistic driving data may not be suitable for safety evaluation at specific sites. The observational site-based methods such as human observers and video analysis also suffer from some limitations such as long time data processing or reduced performance influenced by weather or light condition. The roadside Light Detection and Ranging (LiDAR)-enhanced infrastructure provides a new solution for real-time data collection without the impact from weather or light. The high-resolution trajectories of all road users can be obtained from roadside LiDAR data. This paper aims to fill these gaps by presenting a method for near-crash identification based on the trajectories of road users extracted from roadside LiDAR data. This paper focused on vehicle-pedestrian near-crash identification particularly considering the increased risk of vehicle pedestrian conflicts. Three parameters: Time Difference to the Point of Intersection (TDPI); Distance between Stop Position and Pedestrian (DSPP); Vehicle-pedestrian speed-distance profile, were developed for vehicle pedestrian near-crash identification. The authors also recommended the thresholds for risk assessment of pedestrian safety. This method was coded into an automatic procedure for near-crash identification. This method is expected to significantly improve the current evaluation of pedestrian safety.

The Cellular Automaton (CA) is a kind of discrete system whose dynamic performance depends on simple reactions among single cells. It has been widely applied in many fields nowadays because it can mimic some intricate situations. The present designs of cellular automaton, however, are mostly such systems with stationary cell size. In particular cases, there could be some huge differences between simulation and real statistics. The CA model with variable cell size (CA-VCS) then becomes significant, and the design for extended CA based on the existing model is proposed to simulate the movement of passengers in subway. In most cases, the cell size is referred to psychological size not actually physical body size. With the consideration of high density in subway in rush hours, Cells could alter their cell size based on the normal CA models to describe passengers much accurately according to the condition around. When meeting highly dense population, cells will be compressed and transformed into small size until they could find enough space to enlarge their size. The simulation based on the defined movement rules of cells shows the necessary processes for the cell transformation. The relative research and its results provide the proof to apply the proposed model in analysis of subway.

To simulate the passenger behavior in subway system, a Dynamic Parameters Cellular Automaton (DPCA) model is put forward in this paper. Pedestrian traffic flows during waiting, getting on or off, and traveling can be simulated. The typical scenario in Beijing Subway Line 13 is modeled to analyze the passenger behavior in subway system. By comparing simulation results with statistical ones, the correctness and practicality of the DPCA model are verified. At last, the additional results made by DPCA model can make contribution to passenger comfort analysis and pedestrian facility planning and guidance.

With rapid urbanization, subway systems are widely acknowledged as one of the best solutions to urban transportation problems. The operators or managers of subway systems should pay more attention to passenger’s perceptions of service quality to maintain its competitive position. Taking the traffic state, efficiency,and environmental impact into consideration, the concept of generalized comfort is proposed in this paper. Based on a nested logit model, the selection probability for each factor in a generalized comfort function can be estimated using a nested structure. A certain factor is considered to be more significant in a generalized comfort function than others, when the corresponding probability of this factor is higher in value. Using stated preference and revealed preference data about passenger travel behavior obtained from the Beijing subway, the parameters of generalized comfort function are estimated by maximum likelihood techniques.

At present, different viewpoints and theories are put forward on divisions and transitions of traffic states in highway, for which decide the fundamental properties of traffic flow. Coherent-moving state, among these states, was firstly proposed in Helbing's paper [1] to discuss the hypothetical steady states in mixed traffic flow in highway traffic. Since then, possible explanations on existence, stability and transition of coherent-moving state have been proposed in other research. In the paper, fast simulation and scenario reproduction in cellular automaton model are taken to test and verify those assumptions. A two-lane highway scenario with variety of vehicle parameters is built to reproduce the situation where coherent-moving phenomenon is likely to occur. At last, according to the simulation results, we bring forward our explanations on coherent-moving state which may be helpful on further study.