Urban Traffic Dynamics Prediction—A Continuous Spatial-temporal Meta-learning Approach

Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties. In this article, we solve the traffic dynamics prediction problem from Bayesian meta-learning perspective and propose a novel continuous spatial-temporal meta-learner (cST-ML), which is trained on a distribution of traffic prediction tasks segmented by historical traffic data with the goal of learning a strategy that can be quickly adapted to related but unseen traffic prediction tasks. cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: (1) cST-ML captures the dynamics of traffic prediction tasks using variational inference, and to better capture the temporal uncertainties within tasks, cST-ML performs as a rolling window within each task; (2) cST-ML has novel designs in architecture, where CNN and LSTM are embedded to capture the spatial-temporal dependencies between traffic status and traffic-related features; (3) novel training and testing algorithms for cST-ML are designed. We also conduct experiments on two real-world traffic datasets (taxi inflow and traffic speed) to evaluate our proposed cST-ML. The experimental results verify that cST-ML can significantly improve the urban traffic prediction performance and outperform all baseline models especially when obvious traffic dynamics and temporal uncertainties are presented.

Implementasi Peraturan Walikota Binjai Nomor 23 Tahun 2019 Tentang Pembentukan Unit Pelaksanaan Teknis Daerah Bus Rapid Transit

The purpose of this research is to find out and analyze, how the implementation of policies of the Mayor of Binjai in terms of improving the quality and quality of public services, especially transportation services to the people of Binjai City. The method used in this research is descriptive qualitative using the Merille S. Grindle theory where there are several variables that determine the effectiveness of policy implementation. From the results of the study it can be seen that after the implementation of the Mayor's Regulation at the Binjai City Transportation Agency, there was a change where previously the transportation management process, which had been in PD Angkutan Kota Binjai using transportation costs and other transportation, is now transferred to the Transportation Office to manage it with free of charge so that it can make it easier for people to carry out daily activities using Trans Binjai.

Optimasi Rute Distribusi Menggunakan Metode Tabu Search Perusahaan Daerah Air Minum (PDAM) Tirta Bangun Kulon Progo

Logistics management is known as distribution and transportation management, while others call it physical distribution management. Distribution activities are very important for companies to deliver a product to consumers across various regions. The Regional Drinking Water Company (PDAM) Tirta Binangun Kulon Progo is one of the companies that produce Bottled Drinking Water (AMDK) under the product name AirKu. The high demand for AMDK AirKu products in 240 ml cup packaging was causing limited vehicles and the absence of a clear standard distribution route, which caused the distribution process to be carried out randomly. Therefore, to overcome the company's problems, it is possible to determine the Vehicle Routing Problem (VRP) route with the CVRP approach, where the route determination is based on fleet capacity. The method used was tabu search using the MATLAB application to minimize the bottled product delivery route traveled. The results obtained were the total distance traveled by 284.9 km. The proposed route had a distance savings of 118.26 km, which is better than the company route.

Logistics and Freight Transportation Management: An NLP based Approach for Shipment Tracking

Tracking and tracing systems have become basic services for most logistics companies and are particularly essential for the shipping and logistics industry. Dynamic logistics management today need constant supervision and management of continuously-changing supply chains that motivate the necessity of goods-centric logistics monitoring and tracking, which guarantees a chance to improve transparency and control of a company’s multiple logistical activities. However, operational inefficiencies due to the conventional monitoring system for the supply chain management can also result in sales loss, higher cost, poor customer service–and eventually lower profits. Based on research literature, this paper aims to provide a novel approach for tracking and tracing shipment in a logistics organisation by implementing deep natural language processing concepts. The study aims to allow the stakeholders to think in new ways in their organisation and helping them to have a powerful influence on tracking and tracing to make the best decision possible at the right time. The proposed method is compared based on the accuracy of identifying the query, and results are significantly acceptable. This study is of related interest to researchers, academicians, and practitioners.

Predictive Model of Air Transportation Management Based on Intelligent Algorithms of Wireless Network Communication

Due to the numerous factors that affect the air passenger traffic in the air transportation market and the randomness of various factors, in addition, the relationship between it and the air passenger traffic is very complicated, so the air passenger traffic forecast in the air transportation market has always been difficult to solve problem. This research mainly discusses the prediction model of air transportation management based on the intelligent algorithm of wireless network communication. This article uses the wireless network communication intelligent algorithm, comprehensively considers the influence of the GDP growth rate, population growth rate, total import and export volume, and other factors on the air transportation market, and draws a relatively complete forecasting model of aviation business volume. In this paper, we use an equal-weight method, linear combination model method, and Bayesian combination model method when selecting the combination forecasting method (these three methods). Because of the parallelism, robustness, nonlinearity, and other characteristics of the Bayesian network method, it adapts to the complex and highly nonlinear characteristics between air passenger traffic and its influencing factors. In the comprehensive prediction of the single model, the different information contained in the single model is used to achieve different combined prediction effects. The economic information and forecasting angle of the system can reduce systematic forecasting errors and optimize the prognostic results, which can make us more intuitively understand the difference of forecasting results brought by different combination forecasting methods. The Theil inequality coefficient of the ARIMA model is 0.004874, and the average absolute percentage error is 0.005914. This research will play a certain guiding role in the development of China’s civil aviation industry.