Transport data analytics can improve vehicle performance, reduce costs, improve processes, develop strategies, optimize routes and times, and foresee and identify problems, among others.
Transportation analytics makes it possible to make both commercial and personal travel more efficient and cost-effective. Examining data while in transit can enhance productivity, fuel efficiency, and overall efficiency, ultimately resulting in lower operational expenses.
Companies can anticipate daily volumes, optimize delivery routes, and allocate resources efficiently to improve user satisfaction. For example, teams can identify components most likely to break or underperform by combining transport data with preventive maintenance, enabling better maintenance planning.
“The global transportation analytics market size is projected to reach USD 27.4 billion by 2024”
Markets and Markets
In this article we will cover the following topics:
- Today's panorama for data analytics in transportation
- Where does transportation data come from?
- The impact of transport analytics on organizations
- The role of predictive analytics
- Leveraging Transportation Data Analytics for Truck Studies
- What insights can transportation data analytics provide for electric vehicle infrastructure and energy planning?
- Where to start?
Today’s panorama for data analytics in transportation
Decision-makers are paying more attention to using data from complex transactions to improve their distribution, logistics, and transportation processes through analytics.
According to KPMG, the transportation industry faces structural challenges like capacity constraints and driver shortages, making optimizing domestic and international moves crucial.
Furthermore, companies have been facing internal pressures. Mergers and acquisitions have highlighted the need for consolidating systems, networks, and freight spending to achieve expected synergies. Having visibility into the fundamental factors that drive cost and efficiency has become crucial for maintaining previous achievements.
That is why many organization leaders are transitioning from outdated optimization data techniques to forward-thinking simulation capabilities based on Big Data and AI. These capabilities can be integrated with existing technology platforms, allowing for the operationalization of models and long-lasting improvements.
In this context, the effectiveness of operating strategies is demonstrated through analytics and modeling. Along with traditional methods such as load management, driver availability, and carrier capacity, alternative data sources provide a more comprehensive analysis of:
- External partner cost models.
- Inventory strategies.
- Market patterns.
- External threats and opportunities.
Where does transportation data come from?
Transport data sources come from many different types of sensors and locations. Common sources include GPS data from vehicles, trucks, and transport units. Also, sensor data, route data, road traffic data, speed detectors, and even location data from mobile phone networks are helpful for transport analytics.
Geospatial data sources can also provide significant insights regarding vehicle and foot traffic in a specific city or country.
We also recommend you reading: How Does Location Intelligence Work & Why Do You Need It?
The impact of transport analytics on organizations
Transport flow analysis: By mapping out the physical flow of products between locations/places and identifying how it impacts operations, transportation insights can help improve costs and services.
Efficient strategies: In today’s global economy, supply chains are becoming more complex and reliant on partnerships. Transport analytics can give manufacturing and distribution areas the required visibility to make better decisions.
Learn more about our Supply Chain Mapping Tool
Route planning: Transportation strategies require route planning. Some routes involve quick deliveries, while others require complex work. Transport analytics can unveil traffic and mobility trends and patterns, improving efficiency.
Cost reduction: Transport analytics insights provide a precise and clear blueprint to understand which processes, strategies, and actions need correction to reduce operative and fleet costs.
Safety improvement: Companies often use transport data to improve their safety programs and schedule vehicle maintenance. Also, it is used to identify and avoid dangerous routes, high-risk areas, and roads where accidents often happen.
Improve customer experience: Transport data can help organizations improve their customer service processes. For example, transport data can provide valuable insights into improving response and delivery times.
The role of predictive analytics
In businesses, predictive analytics can answer inquiries like “What outcome would be the most favorable?” or “What is the most efficient distribution plan?
Also, the synergy between transport analytics and predictive models can help foresee events that may affect transportation, such as weather, road closures, strikes, maintenance, traffic, and risk zones, and estimate the impact of development projects to help identify an alternative project without obstructing mobility.
Leveraging Transportation Data Analytics for Truck Studies
Transportation data analytics can revolutionize truck studies by providing valuable insights and uncovering patterns that drive decision-making.
Key Applications:
- Real Estate Site Selection: Utilize location intelligence to determine optimal sites for warehouses and distribution centers. Informed by detailed transportation data, companies can identify areas with the best access for trucks, reducing delivery times and transportation costs.
- Portfolio Analysis: Analyze existing properties and transportation networks to improve operational efficiency. By understanding vehicle and truck travel patterns, companies can make data-driven decisions to enhance logistics and supply chain management.
- Due Diligence: Conduct thorough evaluations before acquisitions or developments. Detailed data on pedestrian, vehicle, bicycle, and transit travel trends helps in assessing the viability and potential challenges of new sites.
Key Metrics to Focus On:
- Vehicle and Truck Travel Patterns: Understand flow and congestion points to optimize routing.
- Pedestrian and Bicycle Traffic: Ensure safety and compliance with regulations in areas with mixed travel modes.
- Transit Usage: Evaluate how public transportation options integrate with truck logistics.
By integrating granular, validated transportation data, businesses can enhance their value proposition, making informed decisions that lead to increased efficiency and customer satisfaction.
What insights can transportation data analytics provide for electric vehicle infrastructure and energy planning?
Transportation data analytics offer a wealth of insights crucial for optimizing electric vehicle (EV) infrastructure and energy planning. By harnessing data-driven intelligence, stakeholders can make informed decisions to enhance efficiency and future-proof their investments.
Strategic Site Selection and Grid Planning:
Identify ideal locations for EV charging stations and align grid planning efforts to meet growing demand. Analytics pinpoint high-traffic areas and evaluate trip lengths, ensuring that infrastructure is placed where it’s most needed.
Traffic Volume and Travel Patterns:
Understand the flow of traffic and travel behaviors. Analyze where vehicles are coming from, where they’re going, and how long they’re traveling. This helps anticipate future needs and prevent bottlenecks.
Dwell Time and Charging Needs:
Calculate average dwell times at various locations to optimize charging station turnover rates. This ensures that drivers spend just enough time charging without causing long wait times for others.
Demographic Insights:
Leverage demographic data to tailor EV infrastructure to the specific needs of different communities. Recognize patterns in EV adoption based on age, income, and other demographic factors to ensure equitable access.
Comprehensive Metrics:
The metrics provided, such as traffic volumes, trip lengths, travel times, and EV travel patterns, all culminate in creating a comprehensive picture. This enables proactive planning, ensuring the infrastructure evolves alongside the growing EV market.
With these insights, transportation data analytics empower planners to make strategic, informed decisions, driving the advancement of smarter, more efficient EV infrastructure and energy solutions.
Where to start?
At PREDIK Data Driven we integrate data analysis from multiple sources, minimizing logistical problems and automating a predictive model to project future trends, identify business needs and build long-term strategies to maximize revenue.