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How Can Distance Matrix API Be Used to Optimize Container Routing for Global Logistics?

Published: September 5, 2025

Have you ever wondered why one late container can send a shockwave through the entire global supply chain, causing millions of dollars in losses? Efficiency may not be a foreign concept in the world of international logistics but it is a necessity in the high-stakes environment. Each additional mile, time spent idling in the traffic, and an inability to meet delivery windows adds to higher expenses and unsatisfied customers.

Here comes: Distance Matrix API, a revolutionary feature that allows logistics departments to calculate the most efficient routes and time spent in transit. It also helps readjust dynamically in case of a disruption in real time. Whether it is port-to-port or last-mile transport, the technology is changing the thought process and execution method of container movement all over the world.

Want to know how it works and how you can use it to cut costs, increase reliability, and enhance sustainability? Continue to read further to know how the Distance Matrix API can optimize container routing to a level never been seen in global logistics.

Did you know?

  • Every day of supply chain disruption can cost businesses approximately $1.5 million.

  • A 1% increase in port congestion in Asia can push shipping freight rates up by more than 1%.

  • In 2024, approximately 576 containers were lost at sea out of over 250 million transported, which is just 0.0002%, though it’s still higher than 2023’s record-low 221.

What is the Distance Matrix API?

distance matrix api

Distance Matrix API is a robust web resource which calculates the distance and approximate travelling duration between several origins and destinations. It is commonly used in freight management and freight optimization processes as well as e-commerce applications. It helps companies (through the application of real-time traffic, historical information, and multimodal transportation capabilities, including driving, walking, bicycle, or transit) to determine optimized routes.

Distance Matrix API also handles high-volume batch requests, with thousands of origin-destination pairs processed in an efficient manner, which makes it suitable in global logistics networks who have large-scale container routing to process. Outputs can be delivered in either a JSON or XML format making the Distance Matrix API easy to integrate with routing algorithms, TMS (Transportation Management Systems). It is also seamless to integrate with optimization engines which helps increase cost savings and accurate ETAs as well as sustainable gains. Distance Matrix API is the core behind the present optimization solutions and intermodal planning approaches in last-mile delivery networks because it provides data on granularity on travel time, distance, and congestion levels in real-time.

Example of Distance Matrix API in Practice

Suppose that a shipping firm has the task of transporting containers out of the Port of Los Angeles to two inland cities: Las Vegas, Nevada and Phoenix, Arizona. The operations team needs to know which delivery to fulfill first in terms of time and distance.

They apply the Distance Matrix API and insert the origin (Port of Los Angeles) and destinations (both addresses). The API promptly responds with:

  • Los Angeles Port to Las Vegas: approximately 270 miles in 4 hours 15 minutes.

  • Phoenix: approximately 373 miles and 5 hours 45 minutes to the Port of Los Angeles.

Upon this information, the company can make wiser decisions, which include:

  • Giving the Las Vegas route priority in time-sensitive cargo.

  • Accurate planning of fuel expenses and driver schedules.

  • Plugging this information into their optimization system to minimize empty vehicles and drive down costs.

How Distance Matrix API is the Most Efficient Container Routing Tool?

compute large metrics in seconds

The Distance Matrix API solves one of the most cumbersome tasks in our global logistics endeavor: knowing how to optimally move containers through a complex network of ports, terminals, depots and customer destinations. It helps to make better business decisions at each contact point in the supply chain by providing accurate, real-time, and historical travel data. Here’s how:

Optimum Route Plans

Using historical speed profiles, real-time traffic conditions and transit data provided by Distance Matrix API, logistics teams can dynamically pick the fastest, most cost-effective port-to-port and inland routes through an advanced routing application. This minimizes movement time fluctuation and improves reliability of the schedule. It furthermore facilitates vessel, truck, and rail asset allocations.

Configuration Changes DPR

Delays such as port congestion, labor strikes, customs delay or weather delays, etc., can disrupt planned schedules. Using the Distance Matrix API, systems can automatically recompute alternate routes and provide new ETAs in real time, allowing carriers to maintain delivery levels and reducing penalties for failure to meet delivery windows.

Empty Containers Repositioning

Empty repositioning adds between 5–12% to the operating cost of the carrier. The Distance Matrix API also reduces costly empty miles by finding the shortest repositioning routes with no congestion and reduces fuel use, carbon gas emissions and moving costs. This feature, when combined with network optimization models, generates big savings in operations.

Optimization of Sustainability

The decarbonization goals necessitate data in action. Logistics providers can model greener routes by integrating distance, speed and route data provided by the Distance Matrix API with CO2 emission factors in order to balance environment compliance demands with business needs.

Customer Visibility

Contemporary shippers are interested in predictability and transparency. The ability to integrate APIs into customer-facing systems/dashboards or TMS platforms presents accurate ETA predictions. It also assists with alternative route options and real-time status visibility, which promote trust and help to avoid costly service inquiries.

Global Scalability

The fact that the API can work on thousands of origin-destination combinations in batch mode is what makes it well suited to global operators. It is appropriate for operations such as Maersk or MSC, which have their own ocean legs at both ends, intermodal rail segments in between and last-mile delivery by truck. Its JSON/XML outputs further ensure seamless integration with TMS, ERP, and AI-driven optimization engines.

Challenges in Container Routing in Global Logistics

Although the benefits of container routing are numerous and relational to global trade efficiency. But, the logistics sector experiences a mixture of complicated interdependent issues. These issues need the right support of intelligent data and additional optimized toolsets and extended coordination amid the carriers, ports, and inland transport systems. 

The critical challenges are presented below.

1. Operational and Infrastructural Challenges

  • Port Congestion: Mega-hub ports like Rotterdam, Singapore, and Los Angeles often see a great deal of congestion, with vessels having to wait days to get a berthing slot. This ripple effect affects downstream delivery schedules inland and turnarounds.

  • Inland Bottlenecks: Narrow rail lines, overcrowded roadways, and undeveloped dry ports slow container movements between the seaports and destinations inland, causing chokepoints.

  • Empty Container Repositioning: It is estimated that 25-30 percent of the worldwide movement of containers is empty repositioning which incurs significant cost loss as well as asset utilization.

  • Limited Real-Time Visibility: Shipping lines, port operators, freight forwarders, and inland carriers maintain fragmented IT environments, which obstruct real-time visibility, which hampers proactive decision-making to modify routes.

2. Geopolitical and Regulatory Challenges

  • Trade Disruptions: Tariff wars, sanctions, and unpredictable trade agreements are compelling shipping lines to re-engineer routes, which leads to complicated planning.

  • Geopolitical Hotspots: Strategic areas of the Red Sea, Black Sea, and Strait of Hormuz have experienced unrest, which normally requires an extended detour, increasing the number of days a ship takes to move as well as the cost of fuel.

  • Customs & Border Delays: Varying compliance requirements and local customs clearance still present obstacles to delivering changes and ensuring predictability in routing schedules.

  • Cabotage Restrictions: National legislations prohibiting entry of foreign carriers to perform domestic legs make it difficult to design multimodal routes and also increase dependence on local subcontractors.

3. An Environmental and Public Challenge of Sustainability

  • Carbon Emission Targets: The green logistics IMO 2030 and 2050 GHG reduction targets will require low carbon route planning and low-carbon fuels.

  • Slow Steaming vs Transit Time: Sometimes, slow steaming is used by carriers to help save fuel costs and reduce their carbon emission. Although, it clashes with the desire of the customer to have shorter lead times.

  • Weather disturbances: An impact of hurricanes, typhoons and monsoon systems can create a significant risk, which requires the schedule buffer and rerouting in real time to ensure safety of the cargo.

4. Economic and Market Challenges

  • Unpredictable Spot Rates: Spot and contract rates are volatile because of fluctuations in demand-supply situations, port closures, or geopolitical conflicts among other reasons, making moment-to-moment routing a challenge.

  • Shortages of Containers and Chassis: The unavailability of containers and the chassis needed to move them derails optimized routing and forces suboptimal repositioning moves.

  • Fuel Expenses: Increased bunker prices and clean-fuel prices find their way into the choices of carrier companies to travel shorter or highly fuel-efficient routes.

  • Imbalance of Trade Flows: East-West lanes (e.g., Asian-European, Asian-US) experience much stronger demand than the reverse directions, creating an imbalance (inefficiency of use) and empty back-hauls.

5. Customer & Service-Level Challenges

  • Consistent ETAs: Shippers are expecting the same reliability on tracking and delivery that they get with Amazon, but all the factors that cause disruptions throughout the supply chain make it hard to predict or provide reliable ETAs.

  • Cost-Flexibility: Multiple route alternatives increases customer satisfaction, but also escalates operation complexity and costs.

  • E-commerce Expectations: Globally, Ecommerce is driving demand on B2C and D2C shipping with compressed delivery times, even between continents, which is impacting long-established systems in ocean freighting.

A Possible Solution to All Container Routing Challenges? Advanced Routing Optimization

high order volumes

To find the best route rather than the shortest between point A and point B is more than routing optimization, it is the dynamics of the sequence of multi leg point to point solution with complexity in the network. That translates to the organisation of intermodal nexuses, navigation of port-to-inland transfers and the juggling of (time, cost and compliances).

Optimization of routing works as a computerized map-maker with smart algorithms. It takes into account issues like traffic jams, road conditions, weather disturbances, port dwell times, regulatory restrictions, and so on. Optimization makes every route as fast, cost-efficient, and reliable as possible.

But even the most cutting-edge route optimization software will still require precise, real time information in order to construct the most effective paths, and that is where the Distance Matrix API at NextBillion.ai becomes necessary. Without reliable travel time data, traffic patterns and flexible mode choice, an optimization engine will not be able to deliver the guaranteed ROI.

NextBillion.ai’s Distance Matrix API acts as the foundation for seamless routing optimization:

  • Planning Your Route: By integrating NextBillion.ai’s API into your routing workflows, you unlock highly precise route planning for thousands of origin-destination pairs at scale.

  • No More Traffic Surprises: Real-time traffic intelligence ensures that your system proactively reroutes vehicles to avoid congestion and delays, keeping ETAs on track.

  • Travel Your Way: From trucking to intermodal transfers, the API supports multiple transportation modes, enabling customized routing for diverse supply chain needs.

  • On Time, Every Time: Accurate travel time predictions improve schedule reliability and help you consistently meet delivery windows, boosting customer trust and reducing penalty costs.

  • Built for Global Scalability: Handle complex, multi-region routing challenges across ocean, rail, and last-mile delivery without sacrificing performance.

If you’re ready to eliminate inefficiencies, reduce costs, and scale sustainable container logistics, it’s time to explore NextBillion.ai’s routing solutions.
Read more about how NextBillion.ai’s Distance Matrix API can transform your global logistics strategy.

Use Cases of Distance Matrix API in Container Routing

containers

Here are the most prominent use cases of Distance Matrix API in the realm of container routing:

1. Port to Port Route Optimization

The complicated nature of port-to-port routing is the need to consider berth availability, recent congestion, draft restrictions, and access to the hinterland, as well as nautical mileage. Use of Distance Matrix API allows the comparison of transit distance and time windows among a larger set of ports.

Example: When freighting into Northern Europe, cost-effective port = Rotterdam vs. Hamburg vs. Antwerp with real-time vessel traffic data and connected inland trucking fleet would mean an optimal rotational blend eliminating idle vessel arrival times and onward trucking delays.

2. Truck/Rail Haulage Planning (Inland)

The interior leg that can determine the overall reliability of delivery is prevalent. The API can incorporate multi-modal parameters, and thus TMS systems can be able to calculate dynamically:

  • ETA variability of peak-traffic direct trucking

  • First/last-mile drayage restrictions on rail schedules

  • Restrictions of routes to allow hazardous freight

When introducing this into routing engines, logistic teams can contrast the economics of trade-offs: cost, transportation time and sustainability of inland tamper.

3. Empty Container Repositioning

Repositioning inefficiency is the result of skewed unbalanced trade flows and an upper limit of equipment pooling. Using the API makes it easy to compare nodes at a number of different depots and terminals, including:

  • The delays due to congestion that are route specific

  • Custom prioritisation measures (time vs. cost)

  • Triangulation with useful models of optimization

This facilitates cost minimized and carbon-reduced empty repositioning in multi-country networks.

4. Dynamic Re-Routing in the face of Disruptions

Such uncertainties as unexpected delays and weather patterns, labour strikes and blockages of canals require real-time re-balancing of sophisticated routes. The API enables systems to:

  • Create alternative maritime legs on Can fly now

  • Inland intermodal links flexibly

  • Distribute updates to downstream schedule tools and dashboard displays to customers

Example: Cape of Good Hope routing in lieu of the Suez Canal is accompanied with readjusted rail and trucking ETAs to maintain commitments.

5. TA Prediction & Customer Visibility

Precise repetition of ETA is no longer a fixed calculation. The API is supported by:

  • Dynamic ETA adjustment with a live traffic input

  • Integration with disruption forecasting predictive models

  • The API also has hooks into the client-facing apps to provide a continuous view

This renders blind spots in first mile, last mile, and transshipment processes, allowing automated alerts that a schedule is deviating.

6. Green Routing (Sustainability)

Environmental compliance is number-oriented. The fine-grained API distance and time information may be used alongside:

  • Road v. rail emission coefficients

  • Eco-routing algorithms that prefer weighting CO₂ versus cost versus time

  • Integration with carbon reporting dashboards to satisfy ESG compliance

This enables the companies to switch to green-first routing without any disruption to operations.

7. Multi-Leg / Multimodal Route Optimization

It is seldom that global logistics reveals one mode. The API will facilitate:

  • Parallel comparison of sea-rail-road pairs

  • Dynamic updating of modal splits due to disruptions

  • Multi-layered scenario-based cost modeling on each interchange node

Example: A journey from Shanghai, Rotterdam, Duisburg, to Munich, with a continuous time, cost and emission-optimized journey possible en route.

8. Scenario Planning and Cost Simulations

Strategic planning involves the modeling of possible lane arrangements prior to implementation. The API offers:

  • Historical and predictive transit standards

  • New lane viability studies by batch processing

  • Testing of the effects of disruption on cascading inland moves

Example: Contrast economies of Asia-US diversion through Canadian Pacific gateways and West Coast U.S. ports under peak congestion states.

How to Use Google Distance Matrix API for Container Routing?

google distance matrix api

Google Distance Matrix API provides an effective place to begin implementing distance intelligence into container logistics operations. This is how to go about implementing it:

  • Create Your Google Cloud Project: Create a project in Google cloud console, assign your project name and ID, and specify billing account, and enable service use on the Distance Matrix API.

  • Create your own API Key: Obtain secure key to authorize all API requests.

  • Start with an Uncomplicated Request: Test the API in your browser one-origin/one-destination query so that you can comprehend how returned data (distance, duration, traffic- adjusted values) are structured.

  • Scale to Complex Queries:
    Extend to multiple origins and destinations. Leverage optional parameters:

  1. Departure time for traffic-aware predictions

  2. Mode selection (driving, transit, biking)

  3. Traffic model (best guess, pessimistic, optimistic)

  4. Route restrictions (e.g., avoid tolls, ferries)

  • Interpret Responses for Analytics: Extract distance in meters and time in seconds, along with congestion factors, for decision-making in routing algorithms.

  • Integrate Into Core Systems:
    Embed the API into Transportation Management Systems (TMS), ERP platforms, or custom optimization engines to power:

  1. Real-time ETA updates

  2. Multi-modal route scoring

  3. Contingency planning logic

Shortcomings of Google Distance Matrix API

While Google’s Distance Matrix API is widely adopted, it is primarily designed for consumer applications rather than large-scale, enterprise-grade logistics operations. This introduces critical limitations when applied to global container routing:

1. Less flexibility to customize in order to use enterprise use cases

Enterprise logistics applications involve APIs that must be able to include bespoke business data items, including:

  • Proprietary-Past Speed Profiles

  • Locations of the operations in the form of lat-long pairs

  • Vehicle-specific restrictions (Axle weighting, carrying hazardous goods)

  • Trip times per lane

The API does not feature in-depth customization thus businesses are left with standardized calculations that cannot take into consideration real-life operations peculiarities. This may lead to inefficient communication, budget overages and miss-matched SLAs.

2. 25×25 Cap Static Matrix Size Limits (25×25)

Along with support of different kinds of matrix API, one of the most important characteristics of any distance matrix API is the ability to work with large matrices.

Google has limited API calls (25 origins 25 destinations 625 route calculations per request). This limitation is problematic in container routing problems with hundreds or thousands of depots, ports and drop-off points, because it:

  • Makes numerous API requests

  • Increases latency

  • Creates high API expense

The repositioning of empty containers across multiple countries is an example of large-scale many-to-many scenarios, which require significantly larger matrices in order to be efficient.

3. Latency and Throughput issues

Container logistics optimization includes:

  • Real-time recalculation in the case of interference

  • ETA updates using high-frequency API calls

The plan of Google to be cloud exclusive brings in:

  • Greater latency (through internet work-based round trip)

  • Low peak request throughput

Comparatively, on-premise deployment model can do:

  • 2-83X lower latency

  • 10-20X higher throughput

  • Predicted expenses with no API limitations

Such aspects are essential to the time-crucial tasks of shipping, like vessel scheduling, drayage plans, and mutual synchronization of multimodal routes.

4. Insufficient Industry Specific Controls

Google APIs do not offer native support for:

  • Hazmat routing prohibitions

  • Custom toll avoidance logics

  • Local regulation requirements (e.g., cabotage rules and regulations, weight limitations of axles)

This renders it incompatible with specialised markets such as chemical deliveries, heavy goods and containerised bulk transportation.

How to Use NextBillion.ai’s Distance Matrix API?

distance matrix api

The answer to all the above stated shortcomings is NextBillion.ai’s Distance Matrix API. It is finely engineered for enterprise-grade logistics, bridging the gaps left by generic solutions like Google. Key capabilities include:

  • Massive Matrix Size Support: Process thousands of origin-destination pairs in one batch, enabling many-to-many routing for large container fleets.

  • Deep Customization: Incorporate historical traffic data, custom speed profiles, and business-specific constraints to generate realistic ETAs.

  • Flexible Deployment: Choose on-premise, private cloud, or hybrid setups for:
    • Low-latency performance

    • Unlimited API calls at fixed costs

    • Enterprise-grade security and compliance

  • Advanced Multimodal Optimization: Support ocean, rail, and last-mile trucking under one unified routing logic.

  • Integration-Ready: Native compatibility with TMS, ERP, and AI-driven optimization engines, delivering predictive routing and dynamic re-routing during disruptions.

For implementation details, see NextBillion.ai Distance Matrix API documentation.

The Bottom Line

Generic APIs like Google’s can handle basic routing needs, but global-scale container logistics demands:

  • Scalability for high-volume route computation

  • Customization for business-specific variables

  • Low-latency performance and SLA compliance

NextBillion.ai provides an enterprise-grade alternative designed for precision, scalability, and sustainability in container routing. Connect with our team to know more.

FAQs

In order to access the Google Maps Distance Matrix API, a project must be created in the Google Cloud Console and the API enabled. Once you have created an API key, you can then use HTTP to create requests containing origin and destination coordinates. The API issues JSON or XML responses with distances and travel times and traffic-adjusted estimates. In more advanced scenarios, you may want to add parameters like traffic models, time of departure and means of transport to get more accurate results.

In an Android application, start by adding Google Play Services as a dependency in your build.gradle file. Then, store your API key securely in the strings.xml or use an encrypted configuration file. The key is passed as part of the request when invoking the Distance Matrix API through the Google Maps Platform SDK. For security, always restrict your API key in the Google Cloud Console to specific apps and IP addresses.

Yes, the Google distance matrix API is a paid product that occupies the Google maps platform pricing model. The charges are calculated on the basis of the number of elements processed (origin-destination combinations) and usage of traffic data and the total API call volume per month. Although Google offers a modicum of monthly free usage, large container routing operations often result in depleting this limit and therefore cost becomes a key consideration in utilizing this in an enterprise context.

About Author

Bhavisha Bhatia

Bhavisha Bhatia is a Computer Science graduate with a passion for writing technical blogs that make complex technical concepts engaging and easy to understand. She is intrigued by the technological developments shaping the course of the world and the beautiful nature around us.

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