Do the Effects of ICT Use on Trip Generation Vary across Travel Modes? Evidence from Beijing

Yin, Chaoying; Wang, Xiaoquan; Shao, Chunfu

doi:10.1155/2021/6699674

Cited by 12 publications

(5 citation statements)

References 53 publications

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“…Therefore, when road segmentation is not required for pedestrian detection, we consider pedestrians as obstacles and segment the drivable area. The loss function of the joint detection algorithm can be defined as follows (1):…”

Section: Multitask Model and Network Structurementioning

confidence: 99%

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Research on multitask model of object detection and road segmentation in unstructured road scenes

Gao,

Zhao,

Zhang

et al. 2024

Meas. Sci. Technol.

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With the rapid development of artificial intelligence and computer vision technology, autonomous driving technology has become a hot area of concern. The driving scenarios of autonomous vehicles can be divided into structured scenarios and unstructured scenarios.Compared with structured scenes, unstructured road scenes lack the constraints of lane lines and traffic rules, and the safety awareness of traffic participants is weaker. Therefore, there are new and higher requirements for the environment perception tasks of autonomous vehicles in unstructured road scenes. The current research rarely integrates the target detection and road segmentation to achieve the simultaneous processing of target detection and road segmentation of autonomous vehicle in unstructured road scenes. Aiming at the above issues, a multitask model for object detection and road segmentation in unstructured road scenes is proposed. Through the sharing and fusion of the object detection model and road segmentation model, multitask model can complete the tasks of multi-object detection and road segmentation in unstructured road scenes while inputting a picture. Firstly, MobileNetV2 is used to replace the backbone network of YOLOv5, and multi-scale feature fusion is used to realize the information exchange layer between different features.Subsequently, a road segmentation model was designed based on the DeepLabV3+ algorithm.Its main feature is that it uses MobileNetV2 as the backbone network and combines the binary classification focus Loss function for network optimization. Then, we fused the object detection algorithm and road segmentation algorithm based on the shared MobileNetV2 network to obtain a multitask model and trained it on both the public dataset and the self-built dataset NJFU. The training results demonstrate that the multitask model significantly enhances the algorithm's execution speed by approximately 10 FPS while maintaining the accuracy of object detection and road segmentation. Finally, we conducted validation of the multitask model on an actual vehicle.

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Section: Multitask Model and Network Structurementioning

confidence: 99%

“…This, in turn, contributes to a reduction in traffic accidents. Consequently, autonomous vehicles have a pivotal role in enhancing traffic safety [1,2]. Vehicles equipped with autonomous driving technology will integrate into human life as a kind of robot [3].…”

Section: Introductionmentioning

confidence: 99%

Research on multitask model of object detection and road segmentation in unstructured road scenes

Gao,

Zhao,

Zhang

et al. 2024

Meas. Sci. Technol.

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“…The survey mainly included three parts. In the first part, the attitudes and perceptions of FFBS users before and after the pandemic were captured, using a five-point Likert scale from "strongly disagree" to "strongly agree" [34]. In the second part, respondents were asked about their travel attributes before and after the pandemic in the two periods separately.…”

Section: Data Source and Survey Designmentioning

confidence: 99%

Exploring Influential Factors of Free-Floating Bike-Sharing Usage Frequency before and after COVID-19

Xie

et al. 2023

Sustainability

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In order to better understand the impact of COVID-19 on the free-floating bike-sharing (FFBS) system and the potential role of FFBS played in the pandemic period, this study explores the impact mechanism of travel frequency of FFBS users before and after the pandemic. Using the online questionnaire collected in Nanjing, China, we first analyze the changes of travel frequency, travel distance, and travel duration in these two periods. Then, two ordered logit models are applied to explore the contributing factors of the weekly trip frequency of FFBS users before and after COVID-19. The results show that: (1) While the overall travel duration and travel distance of FFBS users decreased after the pandemic, the trip frequency of FFBS users increased as the travel duration increased. (2) Since COVID-19, attitude perception variables of the comfort level and the low travel price have had significantly positive impacts on the weekly trip frequency of FFBS users. (3) Respondents who use FFBS as a substitution for public transport are more likely to travel frequently in a week after the outbreak of COVID-19. (4) The travel time in off-peak hours of working days, weekends, and holidays has a significantly positive correlation with the trip frequency of FFBS users. Finally, several relevant policy recommendations and management strategies are proposed for the operation and development of FFBS during the similar disruptive public health crisis.

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“…Nanjing has formed a perfect three-dimensional transportation system. The information and communication technology has also changed the travel mode of residents [30]. The different structures of an urban road network have a deep impact on the mode of transportation [31].…”

Section: Situation Of Nanjing Metromentioning

confidence: 99%

Clustering Analysis of Multilayer Complex Network of Nanjing Metro Based on Traffic Line and Passenger Flow Big Data

Li,

Yu,

Zhang

2023

Sustainability

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Complex networks in reality are not just single-layer networks. The connection of nodes in an urban metro network includes two kinds of connections: line and passenger flow. In fact, it is a multilayer network. The line network constructed by the Space L model based on a complex network reflects the geographical proximity of stations, which is an undirected and weightless network. The passenger flow network constructed with smart card big data reflects the passenger flow relationship between stations, which is a directed weighted network. The construction of a line-flow multilayer network can reflect the actual situation of metro traffic passenger flow, and the node clustering coefficient can measure the passenger flow clustering effect of the station on adjacent stations. Combined with the situation of subway lines in Nanjing and card-swiping big data, this research constructs the line network with the Space L model and the passenger flow network with smart card big data, and uses these two networks to construct the multilayer network of line flow. This research improves the calculation method of the clustering coefficient of weighted networks, proposes the concept of node group, distinguishes the inflow and outflow, and successively calculates the clustering coefficient of nodes and the whole network in the multilayer network. The degree of passenger flow activity in the network thermal diagram is used to represent the passenger flow activity of the line-flow network. This method can be used to evaluate the clustering effect of metro stations and identify the business districts in the metro network, so as to improve the level of intelligent transportation management and provide a theoretical basis for transportation construction and business planning.

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Do the Effects of ICT Use on Trip Generation Vary across Travel Modes? Evidence from Beijing

Cited by 12 publications

References 53 publications

Research on multitask model of object detection and road segmentation in unstructured road scenes

Research on multitask model of object detection and road segmentation in unstructured road scenes

Exploring Influential Factors of Free-Floating Bike-Sharing Usage Frequency before and after COVID-19

Clustering Analysis of Multilayer Complex Network of Nanjing Metro Based on Traffic Line and Passenger Flow Big Data

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