- "Transportation forecasting is the attempt of estimating the number of vehicles or people that will use a specific transportation facility in the future."
The use of big data analytics and management techniques to collect and analyze transportation data, including traffic patterns, accidents and weather conditions.
Data Storage and Retrieval: Exploring different methods to store, retrieve and access voluminous data, with a particular focus on relational databases and structured query language (SQL).
Statistical Analysis: Applying statistical methods and tools to help identify patterns and relationships hidden in data.
Data Visualization: Creating compelling visual representations of data using diagrams, charts, and heatmaps.
Data Mining: Using Machine Learning algorithms to identify hidden patterns in large volumes of data.
Data Modeling: Creating data models to help identify key variables and relationships between them.
Geographic Information System (GIS): Understanding spatial data and how to analyze it using geographic information systems (GIS).
Time Series Analysis: Understanding how data changes over time and how to apply time-series analysis.
Data Warehousing: Building and maintaining a robust data warehouse to store and organize large volumes of data.
Data Cleansing and Quality Assurance: Ensuring that data is clean, complete, accurate and standardized to be usable and valuable.
Data Ethics: Understanding and abiding by ethical and legal considerations in data management and analysis, including privacy, security, and intellectual property.
Data Governance: Managing data as a strategic asset, defining standards and guidelines to ensure the quality and consistency of data.
Machine Learning: Learning about machine learning models and techniques to analyze large data sets.
Data Integration: Integrating disparate data sources to provide a complete view of the data.
Big Data: Data sets too large for traditional data processing tools and how to manage and analyze them.
Business Intelligence: Using data analysis to make informed business decisions.
Data Security: Protecting data from theft, loss, and unauthorized access.
Data Strategy: Developing a data-driven strategy to achieve business goals and objectives.
Data Architecture: Designing and implementing data architectures to support data storage, management, and analysis.
Predictive Analytics: Using data models to make predictions about future outcomes based on historical data.
Agile Project Management: Methodologies in project management that prioritize continuous delivery, data-driven decision making, and customer experience.
Traffic flow analysis: Examining the movement of vehicles and people through a transportation system to identify areas of congestion and potential optimization.
Route planning and optimization: Identifying the best routes and modes of transportation for individuals or groups based on time, cost, and other factors.
Demand forecasting: Predicting the expected usage of transportation systems to help plan for future needs and allocate resources.
Incident management: Reacting to and resolving incidents that affect the safety and efficiency of transportation systems, such as accidents or road closures.
Real-time data collection and visualization: Gathering and presenting real-time data on traffic patterns, weather events, and other factors that affect transportation systems.
Environmental analysis: Examining the environmental impacts of transportation systems, such as emissions and noise pollution.
Freight management: Optimizing the movement of goods and resources to reduce transportation costs and improve supply chain efficiency.
Maintenance planning: Scheduling and executing maintenance and repair activities to ensure the reliability and safety of transportation infrastructure.
Safety analysis: Identifying and addressing safety risks in transportation systems through data analysis and targeted interventions.
Public transit optimization: Improving the effectiveness and efficiency of public transit systems, such as buses and trains, through data analysis and management.
- "For instance, a forecast may estimate the number of vehicles on a planned road or bridge, the ridership on a railway line, the number of passengers visiting an airport, or the number of ships calling on a seaport."
- "Traffic forecasting begins with the collection of data on current traffic."
- "This traffic data is combined with other known data, such as population, employment, trip rates, travel costs, etc., to develop a traffic demand model for the current situation."
- "Feeding it with predicted data for population, employment, etc. results in estimates of future traffic."
- "typically estimated for each segment of the transportation infrastructure in question, e.g., for each roadway segment or railway station."
- "The current technologies facilitate the access to dynamic data, big data, etc., providing the opportunity to develop new algorithms to improve greatly the predictability and accuracy of the current estimations."
- "Traffic forecasts are used for several key purposes in transportation policy, planning, and engineering."
- "to calculate the capacity of infrastructure, e.g., how many lanes a bridge should have."
- "using cost–benefit analysis and social impact assessment."
- "e.g., air pollution and noise."
- "Feeding it with predicted data for population, employment, etc."
- "providing the opportunity to develop new algorithms to improve greatly the predictability and accuracy of the current estimations."
- "Traffic forecasts are used for several key purposes in transportation policy, planning, and engineering."
- "Traffic forecasts are used for several key purposes in transportation policy, planning, and engineering."
- "to estimate the financial and social viability of projects, e.g., using cost–benefit analysis and social impact assessment."
- "to calculate the capacity of infrastructure, e.g., how many lanes a bridge should have."
- "to calculate environmental impacts, e.g., air pollution and noise."
- "Traffic forecasting begins with the collection of data on current traffic."
- "This traffic data is combined with other known data, such as population, employment, trip rates, travel costs, etc., to develop a traffic demand model for the current situation."