An Exploration of the Relationship between Street Patterns and Floodplains in The Woodlands, Texas

The objective of this thesis is to explore the relationship between street patterns and floodplains. Although some researchers have written about the relationship between land use and floodplains in The Woodlands, few have discussed how the city form was designed around the hydrological system. This thesis will focus on one aspect of the city form, the street pattern, to determine the effectiveness of street designs' response to floodplains. Unlike the grid-like pattern advocated by the New Urbanists, street patterns in The Woodlands are loops and cul-de-sacs -- a typical suburban pattern at the time it was developed; however, street patterns adapt to the boundaries of floodplains and protect them very well. Using a GIS tool to overlay 100-year floodplains on the street layer, it is clear to see that there are low percentages of streets in the 100-year floodplains. Thus, The Woodlands employed nonstructural techniques to mitigate flood hazard, which minimize the development in floodplains. Flood control in The Woodlands is much better than other places in the Houston area. From flood control and the protection of the natural environment standpoints, the nonstructural techniques are advocated more than structural techniques for floodplains in the development management. Therefore, the design of street patterns in an area is determined by both the aim of convenient transportation and the aim of hazard mitigation

A Comprehensive Analysis of Multi-level Factors Affecting Individuals Walking to Transit Stations in the City of Los Angeles, California

To decrease auto use and encourage public transit usage, transit-oriented development has been growing in importance. However, a few existing studies have examined the travel modes to transit stations. This research addresses this gap of knowledge by examining multi-level factors, including socio-demographic factors of individuals, socioeconomic characteristics, built environment attributes, and safety factors influencing walking to transit stations in the city of Los Angeles, California. This study primarily relies on travel survey data from the Post-Census Regional Household Travel Survey conducted from 2001 to 2003 by the Southern California Association of Governments. In the first phase, this research uses bivariate linear regression models to examine the disparities of the built environment across the station areas. The results indicate that the street light density and sidewalk completeness are lower in neighborhoods with higher percentages of Blacks or Hispanics. The density of tree coverage is higher in neighborhoods with higher median household income. The second phase of this study employs four binary logistic regression models to predict the odds of walking to transit stations. The results indicate that the distance to transit stations and the availability of transit parking have significant negative impacts on the likelihood of walking to transit stations. Pedestrian amenities, such as street lights, tree shade, and sidewalk completeness increase the odds of walking to stations. Land use mixture is a positive factor for predicting walking to transit stations. The greater diversity of land uses increase the chances of walking to transit stations. In summary, for promotion of walking to transit stations, this study suggests the strategies, such as increasing sidewalk completeness, street light density, street tree density, and land use mixture. Decreasing the parking lots around stations would discourage driving to stations. Meanwhile, more public attention is necessary to improve the pedestrian facilities in the minority or poor neighborhoods

Network Traffic Classification Based on External Attention by IP Packet Header

As the emerging services have increasingly strict requirements on quality of service (QoS), such as millisecond network service latency ect., network traffic classification technology is required to assist more advanced network management and monitoring capabilities. So far as we know, the delays of flow-granularity classification methods are difficult to meet the real-time requirements for too long packet-waiting time, whereas the present packet-granularity classification methods may have problems related to privacy protection due to using excessive user payloads. To solve the above problems, we proposed a network traffic classification method only by the IP packet header, which satisfies the requirements of both user's privacy protection and classification performances. We opted to remove the IP address from the header information of the network layer and utilized the remaining 12-byte IP packet header information as input for the model. Additionally, we examined the variations in header value distributions among different categories of network traffic samples. And, the external attention is also introduced to form the online classification framework, which performs well for its low time complexity and strong ability to enhance high-dimensional classification features. The experiments on three open-source datasets show that our average accuracy can reach upon 94.57%, and the classification time is shortened to meet the real-time requirements (0.35ms for a single packet).Comment: 12 pages, 5 figure

TransMUSE: Transferable Traffic Prediction in MUlti-Service Edge Networks

The Covid-19 pandemic has forced the workforce to switch to working from home, which has put significant burdens on the management of broadband networks and called for intelligent service-by-service resource optimization at the network edge. In this context, network traffic prediction is crucial for operators to provide reliable connectivity across large geographic regions. Although recent advances in neural network design have demonstrated potential to effectively tackle forecasting, in this work we reveal based on real-world measurements that network traffic across different regions differs widely. As a result, models trained on historical traffic data observed in one region can hardly serve in making accurate predictions in other areas. Training bespoke models for different regions is tempting, but that approach bears significant measurement overhead, is computationally expensive, and does not scale. Therefore, in this paper we propose TransMUSE, a novel deep learning framework that clusters similar services, groups edge-nodes into cohorts by traffic feature similarity, and employs a Transformer-based Multi-service Traffic Prediction Network (TMTPN), which can be directly transferred within a cohort without any customization. We demonstrate that TransMUSE exhibits imperceptible performance degradation in terms of mean absolute error (MAE) when forecasting traffic, compared with settings where a model is trained for each individual edge node. Moreover, our proposed TMTPN architecture outperforms the state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic prediction task. To the best of our knowledge, this is the first work that jointly employs model transfer and multi-service traffic prediction to reduce measurement overhead, while providing fine-grained accurate demand forecasts for edge services provisioning