Passenger Flow Prediction Based on Land Use around Metro Stations: A Case Study

Lin, Ciyun; Wang, Kang; Wu, Dayong; Gong, Bowen

doi:10.3390/su12176844

Cited by 31 publications

(13 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mutual support and comprehensive utilization of resources can achieve a reasonable distribution of vehicles on roads, improve travel efficiency, and reduce urban pollution and noise [4][5][6][7][8]. Urbanization leads to high-density development of land in urban areas for the increasing population and causes high-intensity traffic demand [9]. The population density will also generate a demand for travel by public transport.…”

Section: Introductionmentioning

confidence: 99%

Traffic Volumes as a Modal Split Parameter

et al. 2020

View full text Add to dashboard Cite

Traffic volume depends on several indicators. The most important are the degree of motorization, mobility, and especially the travel time and travel cost factor. The article presents an analysis of the possibility of using mobility surveys and traffic counts to achieve a balance between supply and demand. The frequency of congested traffic and over-capacity volumes are determined. By analyzing the trip information captured in the mobility surveys, we detected the strata causing the highest demand—economically active people with “job” as the purpose of their trip. The study area includes the Kysuce region and the city of Žilina in Slovakia. Three scenarios are processed in the article. Scenario 0 defines the current state, scenario 1 defines the situation with free-of-charge public transport, and scenario 2 represents a targeted modal split where saturation is not exceeded. The aim is to shift passengers to public transport and increase the share of public transport in the modal split. Scenario 2 is also presented in terms of saved emissions as an additional factor of relocating passengers from private to public transport. In terms of fare, we found a small change in the modal split with free-of-charge public transport.

show abstract

Section: Introductionmentioning

confidence: 99%

Traffic Volumes as a Modal Split Parameter

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Light Detection and Ranging (LiDAR) has been widely used for different transportation areas [18]. The typical LiDAR system is developed based on the Time of Flight (ToF) method.…”

Section: Related Workmentioning

confidence: 99%

Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network

Zhang

Ma³

et al. 2021

Electronics

View full text Add to dashboard Cite

Object classification is important information for different transportation areas. This research developed a probabilistic neural network (PNN) classifier for object classification using roadside Light Detection and Ranging (LiDAR). The objective was to classify the road user on the urban road into one of four classes: Pedestrian, bicycle, passenger car, and truck. Five features calculated from the point cloud generated from the roadside LiDAR were selected to represent the difference between different classes. A total of 2736 records (2062 records for training, and 674 records for testing) were manually marked for training and testing the PNN algorithm. The data were collected at three different sites representing different scenarios. The performance of the classification was evaluated by comparing the result of the PNN with those of the support vector machine (SVM) and the random forest (RF). The comparison results showed that the PNN can provide the results of classification with the highest accuracy among the three investigated methods. The overall accuracy of the PNN for object classification was 97.6% using the testing database. The errors in the classification results were also diagnosed. Discussions about the direction of future studies were also provided at the end of this paper.

show abstract

“…e development and prosperity of cultural tourism in the near future will be due to the dual role of industry consciousness and system design [1]. Promoting the consciousness, self-confidence, and self-improvement of regional cultural construction is not only the main tone of current cultural construction but also the fundamental driving force for the rapid development of cultural tourism [2]. Different cities may have the same mode and path of tourism development, but the connotation of urban cultural tourism is unique and competitive.…”

Section: Introductionmentioning

confidence: 99%

Design of a Cultural Tourism Passenger Flow Prediction Model in the Yangtze River Delta Based on Regression Analysis

2021

Scientific Programming

View full text Add to dashboard Cite

Cultural tourism has gained much attention in the last decade and has promoted the preservation of a variety of tangible and intangible assets of culture. In order to accurately predict the cultural tourism passenger flow in the Yangtze River Delta and improve its economic benefits, this paper designs the prediction model of cultural tourism passenger flow in the Yangtze River Delta based on regression analysis. Taking the competitiveness of passenger flow as the core, this paper selects 28 indexes from four aspects of cultural tourism brand resources, cultural tourism support and protection, and urban tourism market income to build the evaluation index system of influencing factors of passenger flow. The principal component analysis method is used to simplify many related factors into a few uncorrelated factors to eliminate the multicollinearity caused by too many dependent variables; on this basis, the principal component regression model is constructed, and the determination coefficient is used to test the model fitting. Taking 15 cultural tourism cities in the Yangtze River Delta as the research object, the results show that the designed model has a good fitting degree, and the average error is only 0.41%, which can meet the needs of the prediction of cultural tourism passenger flow in the Yangtze River Delta. After the application of the prediction model, the foreign exchange earning amount of each cultural tourism city can be increased by more than 12%. The study has revealed good results.

show abstract

Passenger Flow Prediction Based on Land Use around Metro Stations: A Case Study

Cited by 31 publications

References 48 publications

Traffic Volumes as a Modal Split Parameter

Traffic Volumes as a Modal Split Parameter

Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network

Design of a Cultural Tourism Passenger Flow Prediction Model in the Yangtze River Delta Based on Regression Analysis

Contact Info

Product

Resources

About