Analysis on passenger flow evolution and service facility configuration for large-scale events in outer suburbs

Urban rail transit passenger flow distribution has a very important reference value for urban rail transit operation and management. Most of the current research focuses on static passenger flow distribution, and there are few studies on passenger flow allocation considering dynamic passenger flow demand changes. Therefore, this paper focuses on considering dynamic passenger flow. The problem of passenger flow distribution under the change of passenger flow demand. This paper first analyzes the time and space distribution characteristics of passenger travel demand, uses the 4-parameter Logistic function to fit the cumulative passenger flow curve segmentally, deduces the calculation formula of passenger flow demand at each station in any time period, and divides passenger travel into smooth state and the generalized travel cost of passengers is calculated based on the congestion state, combined with the logit passenger flow distribution model, the travel cost is dynamically revised and a solution algorithm is designed to achieve dynamic passenger flow distribution. Finally, the feasibility and effectiveness of the model and algorithm are analyzed and verified by constructing the urban rail transit network, and the passenger flow distribution under the dynamic passenger flow demand is realized.

To quantify the spatiotemporal distribution of passenger flow and the characteristics of an urban rail transit network, we introduce four radius fractal dimensions and two branch fractal dimensions by combining a fractal approach with passenger flow assignment model. These fractal dimensions can numerically describe the complexity of passenger flow in the urban rail transit network and its change characteristics. Based on it, we establish a fractal quantification method to measure the fractal characteristics of passenger follow in the rail transit network. Finally, we validate the reasonability of our proposed method by using the actual data of Beijing subway network. It has been shown that our proposed method can effectively measure the scale-free range of the urban rail transit network, network development and the fractal characteristics of time-varying passenger flow, which further provides a reference for network planning and analysis of passenger flow.

Based on analyzing the principle of BP neural network and time sequence characteristics of railway passenger flow, the forecast model of railway short-term passenger flow based on BP neural network was established. This paper mainly researches on fluctuation characteristics and short-time forecast of holiday passenger flow. Through analysis of passenger flow and then be used in passenger flow forecasting in order to guide the transport organization program especially the train plan of extra passenger train. And the result shows the forecast model based on BP neural network has a good effect on railway passenger flow prediction.

With the continuous of new rail transit line, the situation of large passenger flow impact is extremely severe. It is urgent to monitor the status of urban rail transit in real time and ensure the safe and efficient operation of rail transit. This paper analyzes various problems brought by the current large passenger flow impact on the rail transit network in Beijing. Based on the characteristics of the Internet of Things technology, such as sence, transmission, Intelligence, and application, the idea of building a IOT system for the prevention and pre-warning of large passenger flows in rail transit was proposed. This paper establishes a network passenger flow distribution model based on the improved logit model, which realizes accurate judgment and intelligent analysis of large passenger flow in rail transit, and uses passenger flow model data to automatically and intelligently display track events and risk prediction and intelligent pre-warning capabilities. Based on the Internet of Things technology, this paper builds a rail transit passenger flow prevention and pre-warning IOT system with rail operation, supervision and service. The construction and successful application of the platform can provide accurate data support for the daily operation of companies, government management department and the emergency evacuation command under the incident, which plays an exemplary role in the application of the Internet of Things technology in traffic safety prevention.

Passenger flow forecast is the basis of rail transit operation Department, and it is an important reference for rail transit cloud data fusion and optimal passenger capacity deployment. Due to the randomness and uncertainty of urban rail transit passenger flow, the traditional forecasting technology has been unable to meet the operational needs. Therefore, we should use more accurate methods to predict the short-term passenger flow. Therefore, this paper proposes a short-term passenger flow forecasting method for rail transit based on cloud data fusion, using cloud distributed and virtual technology Combined with big data technology for passenger flow data collection and analysis, line data classification from two dimensions of real-time judgment and real-time prediction, combined with cloud data fusion technology for the synthesis of various algorithms, to achieve the accurate prediction of rail transit short-term passenger flow. Finally, through experiments, it is confirmed that the rail transit short-term passenger flow prediction based on cloud data fusion has high practical application value and fully meets the research requirements requirement.

The 15-minute community life circle (15min-CLC) strategy is one of Shanghai's important methods for building a global city and facing a society with a more diverse population structure in the future. In the existing research, the balance between the construction of the life circle and the needs of the people in the life circle still needs to be further fulfilled. This paper is based on the city's multi-source large data set including 2018 AutoNavi POI (Point of Interests), OSM (OpenStreetMap) road network data and LandScan population data set, and evaluates the current status of Shanghai's 15min-CLC through the fusion of kernel density estimation, service area analysis and other statistical models and proposes relevant optimization suggestions. The results show that there are the following shortcomings: (1) From the perspective of different types of infrastructure service facilities, the spatial construction of Shanghai's overall life service facilities and shopping service facilities needs to be optimized. (2) From the perspective of comprehensive evaluation, the comprehensive service convenience of infrastructure service facilities in the downtown area is relatively high, while the comprehensive service convenience of urban infrastructure service facilities in the suburbs and outer suburbs is relatively low; The diversity of basic service facilities in the 15min-CLC in the downtown area is more consistent with the population distribution; However, in the peripheral areas of the urban area, too many infrastructure service facilities have been constructed. Based on the above shortcomings and the perspective of supply and demand matching, relevant optimization strategies are proposed in different regions and different types of infrastructure service facilities: (1) focus on the construction of basic service facilities in the urban fringe and urban-rural areas, improve the full coverage of the basic service facilities, and appropriately reduce the number of basic service facilities in the downtown area. (2) The development of community business models can be used to promote the development of new life service facilities and shopping service facilities. (3) Improve community medical institutions through facility function conversion, merger and reconstruction, etc. (4) Optimize the hierarchical basic service facility system and improve the population supporting facilities of basic service facilities in the 15min-CLC. This paper incorporates people's needs and concerns on the living environment into the 15min-CLC evaluation model, and uses Shanghai as an example to conduct research, summarizes the existing shortcomings, and proposes corresponding optimization strategies based on the matching of supply and demand. This article attempts to explore a replicable 15min-CLC planning model, so that it can be extended to the Yangtze River Delta urban agglomeration, to provide reference for further research on the 15min-CLC, and to promote urban construction under the concept of sustainable development.

Reduction of high speed railway speed has an influence on not only high speed railway passenger flow but also passenger flow of the public transport system within the scope of relevant transportation corridor. Calculate method of percentage of passenger transport with a variety of public transport mode using Logit model is presented. And analysis of Wuhan Guangzhou high speed railway passenger flow changes before and after reducing speed is introduced. Modify Logit model making it can be applied to the passenger flow partaking rates to meet the changing trend of passenger flow. Bring in new ideas of using Logit to calculate the distribution rate of passenger flow and provide reliable and effective solutions. To provide reference for a more scientific way of express passenger flow partaking rates for development of passenger flow law and analyzing of China's current influence factors.

In order to get the size and characteristics of passenger flow in the future in Guangzhou-Shenzhen(Hongkong) high speed railway, the paper studies the forecast question. Firstly, the Gray Forecast Model is applied on calculation of GDP, population and employee number in traffic district along the railway. Secondly, after carefully analysed the four step traffic forecast method, the paper calculate number of passenger flow generation and attraction, passenger flow distribution and passenger flow proportion of high speed railway. At last, the forecast result of passenger flow in 2020 and 2030 is worked out.

Establishing simulation models is a widely used and effective approach for analysing passenger flow distribution in urban rail transit systems. Recently, multi-agent and discrete event-based simulation models have shown exceptional performance in studying passenger flow information within urban rail transit systems. While simulations of passengers and trains often yield satisfactory results, few models capture the overall operational status of urban rail transit systems. The complex interactions among stations, trains and passengers make it challenging to integrate these elements into a unified system framework. In this paper, we introduce a triple simulation framework that integrates stations, trains and passengers as foundational elements to comprehensively simulate the entire urban rail transit system and observe overall passenger flow distribution. Experimental results demonstrate that our system surpasses existing advanced simulation models, achieving an accuracy rate of 88.44% with a tolerance for a 30% deviation. To further illustrate the effectiveness of our framework in analysing passenger flows, we conducted experiments using the Nanjing Metro AFC dataset, analysing passenger flow distributions at stations and on trains.

In modern, rapidly developing cities of the world, building an urban transport model requires traffic data. The lack of those data does not allow making timely management decisions on distribution of passenger flows, namely within transport flows. Currently, there are various methods and systems for counting passenger flows, such as the manual staff counts, survey and counted ticketed entries methods, and various automated technology-based systems. However, those well-known methods have their drawbacks.For this reason, the task to search for alternative methods and data sources for the study of passenger flows remains relevant.This article is based on the updated results of the study recently conducted by the author during preparation of his master’s thesis. During the study and developing previous author’s papers, data on connections of passengers to Wi-Fi routers were chosen as a data source. Since this phase of the study was conducted on the territory of Moscow transport hub, in metro and on Moscow Central Diameters (MCD), where the cars are equipped with great number of Wi-Fi routers, with free connection and Internet access, it has increased the sample Wi-Fi data array significantly.The objective of the study was to study the possibility of processing Wi-Fi data obtained from Wi-Fi scanners as a passenger flow analysis tool.The study has revealed that, on average, up to 40% of passengers in metro and MCD cars on the studied lines use the WI-FI module turned on in their mobile devices.The results of the study have confirmed that Wi-Fi data can be used as a tool for passenger traffic analysis, but at the same time revealed the necessity to integrate them with other data sources, as well as the strong dependence of the result of Wi-Fi data processing on the technical features of the Wi-Fi scanner and its location in the vehicle during experiments. You can find the first part of the article in the issue.

As the passenger flow distribution center cooperating with various modes of transportation, the comprehensive passenger transport hub brings convenience to passengers. With the diversification of passenger travel modes, the passenger flow scale gradually increases, which brings significant challenges to the integrated passenger hub. Therefore, it is urgent to solve the problem of early warning and response to the future passenger flow to avoid congestion accidents. In this paper, the passenger flow GRNN prediction model is proposed, based on the K-means cluster algorithm, and an improved index named BWPs (Between-Within Proportion-Similarity) is proposed to improve the clustering effect of K-means so that the clustering effect of the new index is verified. In addition, the passenger flow data are studied and trained by combining with the GRNN neural network model based on parameter optimization (GA); the passenger flow prediction model is obtained. Finally, the passenger flow of Chengdu East Railway Station has been taken as an example, which is divided into 16 models, and each type of passenger flow is predicted, respectively. Compared with the traditional method, the results show that the model can predict the passenger flow with high accuracy.

Real-time anomaly detection of passenger flows in the metro system is very important to maintain the URT system’s normal operation and ensure passengers’ safety. This paper proposes a novel abnormal passenger flow detection method based on smart card data. The method constructs a graphic model whose topological structure can capture the spatial distribution of anomalous passenger flow. It further incorporates external information (e.g. geographical information) to depict the latent passenger flow’s spatial dependence embedded in URT system. Considering abnormal flows may only exist in local regions of the metro system, a detection statistic is constructed by using graph community detection. The statistic also incorporates an adaptive sampling strategy for further signal selection and noise filter. It can be efficiently solved via a Min-Cut-based algorithm and can provide real-time solutions to anomaly detection and diagnosis. Preliminary experimental results demonstrate the efficiency of our method.

The on-building Shanghai Metro Line 16 is a typical regional metro line which connects downtown area and a satellite city located in outer suburb. It is long and has an uneven and ever-changing passenger flow distribution as the city develops. Three kinds of trains including non-stop, part-stop and regular trains have been planned mixed running in it, so the design and efficiency analysis of operation schemes is more complex compared with urban metro lines. In this paper, Train Plan Maker (TPM) has been used as simulation tool to analyze train and station use efficiency problems including train and vehicle use quantities, the optimal takeover station and the use of switchback lanes under the condition of those three kinds of trains mixed running in different ratios. In view of the geography feature and future passenger flow changing trend, the optimal dynamic operation organization graph for this line during different city developing periods have been drawn according to the simulation results.

Due to various constraints (such as geological structures, ground facilities etc.), there are some rather long, narrow, and snake-shaped transfer channels, most of which are facilitated with stairways and escalators, in the current subway network in Beijing. Such transfer channels are often congested during peak hours, thus increasing accident probability. The approach proposed in this paper attempts to promote passenger rational behavior by providing the information reflecting the passenger flow states of the channel to be entered. First, the passenger flow distribution was determined based on the statistical data. Second, the suitable representation form was developed for each state of passenger flow in transfer channels on the basis of a survey conducted with a well-designed questionnaire.

The Optimize Management of Passenger Organization in Transfer Station based on Dynamic Passenger Flow Analysis