Optimizing Efficiency With Multiple Stops Route Planner Software

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Efficient route planning for multiple destinations presents a critical challenge across industries, where time, fuel, and operational costs directly impact profitability and service quality. Unlike conventional navigation tools, specialized multiple stops route planner software transforms complex logistics into streamlined, data-driven workflows by integrating dynamic algorithms, real-time traffic intelligence, and constraint-based optimization. This technology not only reduces travel distances by up to 30% but also ensures adherence to operational deadlines, vehicle capacities, and regulatory requirements—bridging the gap between theoretical route calculations and practical field execution.

The adoption of such systems extends beyond traditional logistics, influencing sectors like healthcare, field services, and event management, where precision in scheduling translates to improved patient care, reduced downtime, and enhanced customer experiences. By leveraging machine learning for predictive rerouting and seamless API integrations with mapping services, these tools evolve from static planners into adaptive solutions that respond to unforeseen disruptions—such as road closures or traffic congestion—without compromising efficiency. Understanding their core functionalities, industry-specific applications, and technical architectures reveals how they redefine operational excellence in dynamic environments.

multiple stops route planner software

Core Features and Functionalities of Multiple Stops Route Planners

Multiple stops route planner software distinguishes itself from basic navigation tools by addressing complex logistical challenges inherent in multi-destination journeys. Unlike single-stop systems, which optimize for direct paths, these solutions integrate advanced algorithms to balance efficiency, time constraints, and resource allocation. They are designed for industries such as logistics, field service management, and emergency response, where minimizing travel time, fuel consumption, or operational costs directly impacts profitability and service quality.

The core functionalities revolve around dynamic optimization, real-time data integration, and constraint handling. These systems leverage computational methods like the Traveling Salesman Problem (TSP) variants, Vehicle Routing Problem (VRP) solvers, and heuristic algorithms to generate routes that adapt to external variables such as traffic, weather, or fuel prices. Below, a structured comparison outlines key features, their purposes, practical applications, and technical implementations.

Dynamic Rerouting and Real-Time Adjustments

Dynamic rerouting ensures routes adapt to unforeseen changes, such as traffic congestion, road closures, or delays at stops. This feature is critical for time-sensitive operations where deviations can cascade into broader inefficiencies.

Purpose: Maintains optimal route efficiency despite real-time disruptions, reducing idle time and operational costs.

Example Use Case:
A delivery fleet serving urban areas encounters a sudden traffic jam near Stop 3. The system recalculates the route, suggesting an alternative path that avoids the congestion while preserving the sequence of remaining stops.

Technical Implementation:

  • Algorithms: Dijkstra’s or A* pathfinding algorithms for real-time recalculations.
  • Data Sources: Traffic APIs (e.g., Google Maps Traffic, HERE Maps), GPS telemetry, or IoT sensors for live data feeds.
  • Constraints: Time windows, vehicle speed limits, and historical traffic patterns influence rerouting logic.
    • Key Formula for Dynamic Adjustment:
    Optimal Route = f(Current Traffic Data, Historical Patterns, Vehicle Constraints, Time Windows)

    Fuel and Emission Optimization

    Fuel consumption and emissions are significant cost and sustainability factors in multi-stop routes. Advanced planners calculate routes that minimize distance, idle time, and suboptimal driving behaviors (e.g., rapid acceleration/deceleration).

    Purpose: Reduces operational expenses and environmental impact by aligning routes with fuel-efficient driving strategies.

    Example Use Case:
    A waste management company routes its trucks to minimize fuel use across 50 stops daily. The software identifies a 12% fuel savings by consolidating stops in geographically clustered areas and avoiding high-traffic routes with excessive idling.

    Technical Implementation:

  • Algorithms: Fuel-aware TSP variants (e.g., Fuel-TSP), which incorporate fuel consumption models.
  • Data Sources: Vehicle telematics (speed, RPM), fuel efficiency APIs (e.g., EPA fuel economy data), and carbon emission calculators.
  • Constraints: Vehicle payload capacity, fuel tank size, and driver shift durations.
    • Fuel Efficiency Metric:
    Total Fuel Cost = Σ (Distance Fuel Consumption Rate) + Σ (Idle Time Idle Fuel Rate)

    Integration of Real-Time Traffic and External Data

    Real-time traffic data and external variables (e.g., weather, roadworks) directly impact route feasibility. Integration of these factors ensures routes remain viable under dynamic conditions.

    Purpose: Enhances route reliability by accounting for unpredictable variables that single-stop navigation tools ignore.

    Example Use Case:
    A field service technician’s route is adjusted in real-time due to a reported accident on the primary highway. The system reroutes via secondary roads, adding 5 minutes but avoiding a 30-minute delay.

    Technical Implementation:

  • Algorithms: Machine learning models (e.g., Graph Neural Networks) to predict traffic patterns.
  • Data Sources: Traffic APIs, weather services (NOAA, OpenWeatherMap), and government roadwork notifications.
  • Constraints: Vehicle type (e.g., trucks may avoid low-clearance roads), driver familiarity with alternate routes.

    • Input Methods for Multiple Stops

    Users input stops via multiple formats to accommodate different workflows, from manual entry to bulk uploads. Flexibility ensures compatibility with existing data systems (e.g., CRM, ERP).

    Supported Input Formats:
    1. Manual Entry: Directly typing addresses or coordinates into the interface.
    2. CSV/Excel Upload: Bulk import of waypoints from spreadsheets, ideal for large-scale operations.
    3. API Integration: Pulling stops from external databases (e.g., Salesforce, SAP) via REST APIs.
    4. Geofence or Polygon Upload: Defining service areas (e.g., city blocks) to auto-generate stops.

    Step-by-Step Workflow for Manual Entry:
    1. Select Route Type: Choose between "Optimize for Time," "Optimize for Distance," or "Optimize for Fuel."
    2. Add Stops: Enter addresses or coordinates (e.g., "123 Main St, New York, NY" or "40.7128° N, 74.0060° W").
    3. Define Constraints: Set time windows (e.g., "Stop 5 must be reached by 15:00"), vehicle capacity, or priority stops.
    4. Validate Inputs: The system checks for duplicates, invalid addresses, or conflicting constraints.
    5. Generate Route: The algorithm processes inputs and displays the optimized path with estimated durations.

    Input Validation Rules:
  • Address Resolution: Use geocoding APIs (Google Maps, OpenStreetMap) to convert text addresses to coordinates.
  • Constraint Conflict Detection: Flag overlapping time windows or impossible capacity requirements.

    • Route Calculation and Optimization Logic

    The calculation process involves solving a Vehicle Routing Problem (VRP) variant, where the objective function (e.g., minimize distance, time, or cost) is balanced against constraints. Below is a textual representation of the optimization flowchart:

    1. Input Aggregation: Collect all stops, constraints, and vehicle data.
    2. Initial Route Generation: Create a baseline route (e.g., using a nearest-neighbor heuristic).
    3. Constraint Application: Apply time windows, capacity limits, or driver availability rules.
    4. Feasibility Check: Verify if the route meets all constraints; if not, adjust or flag conflicts.
    5. Objective Function Optimization: Use metaheuristics (e.g., Genetic Algorithms, Simulated Annealing) to refine the route.
    6. Real-Time Refinement: Continuously adjust the route based on live data (e.g., traffic, delays).
    7. Output: Deliver the optimized route with visualizations (e.g., maps, Gantt charts for time windows).

    Example Scenario: Balancing Delivery Times
    A courier service must deliver packages to 10 stops with time windows between 09:00 and 17:00. The algorithm:

  • Groups stops by proximity to minimize backtracking.
  • Prioritizes stops with earlier deadlines.
  • Allocates buffer time for traffic delays between critical stops.
    • Optimization Objective Example:
    Minimize Total Travel Time = Σ (Travel Time between Stops Distance) + Σ (Waiting Time at Stops)

    Handling Constraints: Time Windows, Capacity, and Driver Availability

    Constraints ensure routes align with operational realities. Rule-based logic enforces these constraints during optimization.

    Time Windows:

  • Logic: Stops must be serviced within specified time frames (e.g., "Stop 3: 10:00–12:00").
  • Implementation: The solver uses Constraint Programming to schedule stops within windows, penalizing routes that violate them.
  • Example: A restaurant delivery route avoids stops where the delivery window closes before the vehicle arrives.

    • Vehicle Capacity:

  • Logic: Limits on payload weight, volume, or number of items per stop.
  • Implementation: Bin Packing algorithms allocate items to vehicles, splitting loads if necessary.
  • Example: A bakery truck carries flour and bread; the route ensures stops with high-volume orders are served by larger vehicles.

    • Driver Availability:

  • Logic: Restrictions on working hours, breaks, or shift overlaps.
  • Implementation: Shift Scheduling modules integrate with labor management systems.
  • Example: A field service route assigns drivers based on their available shifts, ensuring no overtime violations.
    • Constraint Handling Example (Pseudocode):
    IF (Stop[X].TimeWindow.End < ArrivalTime)
    THEN Recalculate Route OR Flag Violation
    ELSE Proceed

    Visualization and Reporting Tools

    Post-optimization, users require actionable insights. Visualizations and reports translate complex data into operational decisions.

    Key Visualizations:

  • Route Maps: Interactive maps (e.g., Google Maps API) with stop sequences, traffic layers, and estimated arrival times.
  • Gantt Charts: Timeline views showing stop durations, breaks, and time window compliance.
  • Fuel/Emission Reports: Graphs comparing baseline vs. optimized routes for cost/emission reductions.
  • Constraint Violation Alerts: Highlight stops at risk of missing time windows

    • multiple stops route planner software - Ilustrasi 2

    Industry Applications and Use Cases of Multiple Stops Route Planner Software

    Multiple stops route planner software transcends generic navigation tools by addressing sector-specific inefficiencies, from optimizing field service deployments to streamlining last-mile logistics. Its adaptability to industry workflows—such as integrating with electronic health records (EHR) for healthcare or synchronizing with warehouse management systems (WMS) in retail—enables measurable improvements in operational efficiency, cost reduction, and service quality. Below, four critical industries demonstrate how tailored route planning resolves unique challenges, with real-world deployments yielding quantifiable outcomes.

    Logistics and Last-Mile Delivery

    The logistics sector relies on route optimization to mitigate delays, fuel costs, and carbon emissions, particularly in last-mile deliveries where inefficiencies directly impact customer satisfaction. Companies in this industry face challenges such as dynamic traffic patterns, package weight constraints, and real-time delivery windows. Route planners mitigate these by:
  • Adaptive rerouting: Adjusting paths in real time based on traffic data, weather, or delivery priorities (e.g., Amazon’s use of AI-driven route optimization reduced delivery times by 15% in urban areas).
  • Load balancing: Distributing packages across vehicles to maximize capacity utilization, reducing the need for additional fleet deployments.
  • Carbon footprint tracking: Integrating with sustainability platforms to optimize routes for lower emissions, aligning with corporate ESG goals (e.g., DHL’s route optimization cut CO₂ emissions by 30% in select regions).

    • Industry-Specific Adaptations:

  • Integration with TMS/WMS: Syncing with transportation management systems (TMS) or warehouse management systems (WMS) to pull real-time inventory and order data, ensuring accurate route calculations.
  • Multi-stop consolidation: Combining deliveries from multiple warehouses into single optimized routes, reducing idle time and fuel consumption.

    • Healthcare and Home Healthcare Services

    Home healthcare providers and medical supply chains depend on precise route planning to ensure timely patient visits, medication deliveries, and equipment maintenance. Key challenges include:
  • Regulatory compliance: Adhering to HIPAA or GDPR requirements while sharing patient location data with route optimization tools.
  • Patient prioritization: Balancing urgent care visits (e.g., insulin deliveries) with routine check-ups without compromising service quality.
  • Fleet visibility: Tracking vehicles equipped with medical supplies in real time to prevent delays (e.g., Kindred Healthcare reduced no-show rates by 22% by integrating route planners with EHR systems).

    • Industry-Specific Adaptations:

  • EHR integration: Pulling patient visit schedules, mobility constraints (e.g., wheelchair accessibility), and emergency flags directly from electronic health records (EHR) to prioritize routes.
  • Compliance auditing: Generating automated reports for audits, ensuring adherence to healthcare delivery standards (e.g., Visiting Nurse Associations of America used route planners to document 98% of visits for reimbursement compliance).

    • Field Services and Maintenance

    Field service companies—such as utilities, HVAC technicians, or IT support—require route optimization to minimize travel time between service calls, which directly impacts technician productivity and first-time fix rates. Challenges include:
  • Skill-based routing: Assigning technicians to jobs based on expertise (e.g., a plumber for a burst pipe vs. an electrician for wiring issues).
  • Appointment scheduling conflicts: Resolving overlaps between scheduled service windows and unexpected emergency calls.
  • Tool and equipment logistics: Ensuring service vehicles carry the right tools for each stop (e.g., ServiceTitan reduced technician downtime by 40% by integrating route planners with inventory management systems).

    • Industry-Specific Adaptations:

  • Mobile app integration: Providing technicians with real-time updates, customer details, and digital checklists via mobile apps (e.g., Field Nation used route optimization to increase technician utilization by 25%).
  • Predictive maintenance routing: For utilities, prioritizing routes based on equipment failure risks detected via IoT sensors (e.g., Duke Energy reduced outage response times by 30% using AI-driven route adjustments).

    • Event Management and Large-Scale Operations

    Event organizers and large-scale operations (e.g., concerts, sports tournaments, or trade shows) use route planners to manage vendor deliveries, security patrols, and attendee logistics. Critical challenges involve:
  • Time-sensitive deliveries: Coordinating catering, stage equipment, and merchandise shipments to arrive within narrow timeframes.
  • Crowd flow management: Optimizing routes for emergency services, waste removal, and security patrols to maintain safety (e.g., Live Nation reduced setup times for concerts by 20% by synchronizing delivery routes with stage crew schedules).
  • Multi-location coordination: Managing routes across venues, parking lots, and backstage areas simultaneously (e.g., Disney Parks used route optimization to reduce ride maintenance vehicle idle time by 18%).

    • Industry-Specific Adaptations:

  • Stakeholder synchronization: Integrating with event management software to align routes with vendor contracts, permit requirements, and risk assessments.
  • Dynamic reallocation: Adjusting routes in real time for last-minute changes, such as weather-related delays or VIP arrivals.
    • Industry Pain Point Software Solution Measurable Benefit
    Logistics Unoptimized last-mile routes leading to delayed deliveries and higher fuel costs. AI-driven route optimization with real-time traffic integration (e.g., Route4Me, OptimoRoute). 15–30% reduction in delivery times; 20–25% fuel savings (source: McKinsey, 2022).
    Healthcare Missed patient visits due to unplanned delays or lack of route coordination. EHR-integrated route planners with priority flags for urgent care (e.g., CareSync, RouteSmart). 22% fewer no-shows; 12% increase in daily patient visits (Kindred Healthcare case study).
    Field Services Technician downtime from inefficient routing between service calls. Skill-based routing with mobile app updates (e.g., ServiceTitan, Jobber). 25–40% higher technician utilization; 30% faster response times (Field Nation data).
    Event Management Coordination gaps between vendor deliveries and event timelines. Multi-stakeholder route synchronization with event management software (e.g., Eventbrite, Cvent). 20% faster setup times; 15% reduction in operational bottlenecks (Live Nation internal metrics).

    Case Study Outline: Hypothetical Deployment in a Home Healthcare Provider

    Company Profile: VitalCare Home Health, a regional provider serving 50,000 patients across three states, struggles with:
  • 30% of scheduled visits missed due to traffic delays or misrouted technicians.
  • High fuel costs from inefficient routes, averaging $800,000 annually.
  • Compliance risks from undocumented visit times for insurance reimbursements.

    • Workflow Before Implementation:

  • Manual route planning via spreadsheets, updated daily without real-time adjustments.
  • Technicians relied on GPS apps (e.g., Google Maps) for navigation, leading to inconsistent arrival times.
  • No integration with EHR systems, requiring duplicate data entry for visit logs.

    • Workflow After Implementation:

  • Route4Me integrated with the provider’s Epic EHR system, auto-pulling patient schedules, mobility status, and emergency flags.
  • AI-driven optimization adjusted routes dynamically for traffic, weather, and technician availability, reducing missed visits by 28%.
  • Mobile app provided technicians with patient histories, digital checklists, and real-time updates, improving first-visit accuracy to 95%.
  • Automated compliance reports generated for audits, reducing administrative overhead by 18 hours/week.

    • Key Adaptations:

  • Priority routing: Patients requiring insulin or dialysis were flagged for expedited routes.
  • Fleet telematics: GPS tracking ensured vehicles stayed within service areas, cutting fuel waste by 19%.
  • Predictive analytics: Identified high-risk routes (e.g., areas with frequent traffic accidents) and rerouted proactively.

    • Outcome Metrics:

  • Cost savings: $150,000 annually in fuel and reduced overtime.
  • Operational efficiency: 12

    • Technical Architecture and Integration Capabilities of Multiple Stops Route Planner Software

    Multiple stops route planner software relies on a sophisticated backend architecture to process complex routing requests, integrate real-time data, and optimize logistics operations. The system combines algorithmic efficiency with external data sources to deliver accurate, dynamic, and scalable solutions. Key components include Vehicle Routing Problem (VRP) solvers, genetic algorithms, and API-driven geospatial data integration, all structured within a modular pipeline that ensures adaptability across industries.

    The technical foundation of these systems balances computational performance with real-world constraints, such as traffic conditions, vehicle capacity, and time windows. Below, the architecture is dissected into its core components, integration mechanisms, and deployment considerations to highlight how these elements interplay to enhance route planning capabilities.

    Backend Components and Algorithmic Processing

    The backend of a multiple stops route planner is designed to handle high-dimensional optimization problems, where inputs such as stop locations, vehicle constraints, and service time windows are transformed into executable routes. Core components include:

    - Vehicle Routing Problem (VRP) Solvers: These algorithms, such as Clarke-Wright Savings Algorithm or Tabu Search, decompose the routing problem into subproblems to minimize total distance or time while respecting constraints. For example, a solver may first cluster stops geographically before applying heuristic rules to connect them sequentially.

  • Genetic Algorithms (GA): Used for large-scale or NP-hard problems, GAs simulate evolutionary processes to iteratively improve route fitness. Chromosomes represent potential routes, and operations like crossover and mutation refine solutions over generations. A real-world application includes optimizing delivery routes for Amazon’s last-mile logistics, where thousands of stops must be serviced daily.
  • Constraint Programming (CP): Ensures adherence to hard constraints (e.g., vehicle capacity, time windows) by modeling the problem as a set of logical rules. CP solvers like Google OR-Tools or IBM ILOG CPLEX are often employed for scenarios requiring strict compliance, such as healthcare deliveries with patient appointment windows.
  • Machine Learning (ML) Enhancements: Supervised learning models (e.g., Random Forests or Neural Networks) predict optimal routes by analyzing historical data. For instance, ML can adjust for recurring traffic patterns in urban areas, reducing the need for real-time recalculations.

    • Data Processing Workflow:
    The algorithmic pipeline follows a structured flow:
    1. Input Validation: User-provided stops, vehicles, and constraints are parsed and validated for completeness (e.g., checking for duplicate coordinates or invalid time windows).
    2. Preprocessing: Data is normalized (e.g., converting addresses to GPS coordinates via geocoding APIs) and aggregated (e.g., grouping stops by region).
    3. Optimization: The selected algorithm (VRP solver, GA, or hybrid) processes the data to generate candidate routes, applying constraints iteratively.
    4. Postprocessing: Routes are refined for readability (e.g., converting coordinates to turn-by-turn instructions) and formatted for output (e.g., KML for GPS devices or JSON for APIs).

    API Integration for Real-Time Geospatial Data

    Real-time data from mapping and traffic services significantly enhances route accuracy by dynamically adjusting for unforeseen conditions. APIs such as Google Maps Platform, HERE Technologies, and OpenStreetMap provide critical inputs, including:
    API integration enables the route planner to fetch:
  • Traffic Conditions: Real-time speed limits, congestion levels, or incident reports (e.g., accidents or road closures) via APIs like Google’s Directions API or HERE’s Traffic API. These inputs recalculate routes to avoid delays, improving on-time delivery rates by up to 30% in urban environments (source: HERE Technologies, 2022).
  • Geocoding: Conversion of human-readable addresses (e.g., "123 Main St") to GPS coordinates using services like OpenStreetMap’s Nominatim. Accuracy varies by region, with urban areas typically achieving >95% precision.
  • Road Network Data: Static data (e.g., one-way streets, speed limits) from APIs like Mapbox or OSRM (Open Source Routing Machine) ensures routes comply with local regulations.
  • Alternative Routes: APIs provide fallback paths when primary routes are blocked, leveraging historical data to select the fastest viable alternative.
    • Impact on Route Accuracy:
  • Dynamic Reoptimization: When traffic data triggers a route deviation, the system recalculates affected segments in real time, reducing detours. For example, FedEx uses HERE’s APIs to reroute packages during rush hours, cutting delivery times by 15% in high-traffic zones.
  • Geocoding Errors: Misaligned addresses (e.g., rural or newly developed areas) can lead to failed route generation. Solutions include fallback mechanisms (e.g., manual override or nearby landmark substitution).
  • Latency Considerations: High-frequency API calls (e.g., polling traffic every 2 minutes) may incur costs. Caching strategies or batch processing mitigate this while balancing freshness and performance.

    • Data Pipeline Flowchart: From User Input to Route Generation

    The end-to-end data pipeline for route generation can be visualized as follows:

    1. User Input Stage:

  • Action: User submits stops (addresses, coordinates), vehicle details (type, capacity), and constraints (time windows, priorities).
  • Validation: System checks for missing data (e.g., invalid coordinates) or conflicting constraints (e.g., overlapping time windows).

    • 2. Preprocessing Stage:

  • Geocoding: Addresses are converted to GPS coordinates via APIs (e.g., Google Maps Geocoding API).
  • Data Enrichment: Additional attributes (e.g., traffic conditions, road types) are fetched from mapping services.

    • 3. Optimization Stage:

  • Algorithm Selection: Based on problem complexity, the system selects a solver (e.g., VRP for small fleets, GA for large-scale distributions).
  • Constraint Application: Hard constraints (e.g., vehicle capacity) are enforced, while soft constraints (e.g., preferred routes) are weighted.
  • Iterative Refinement: The algorithm generates candidate routes, which are scored against objectives (e.g., minimal distance, fuel efficiency).

    • 4. Postprocessing Stage:

  • Route Formatting: Coordinates are converted to turn-by-turn instructions or KML files for GPS integration.
  • Output Delivery: Routes are exported to the user interface, CRM, or fleet management system via APIs or file downloads.

    • 5. Real-Time Adjustment Stage (Optional):

  • Trigger Events: Traffic incidents or new stops (e.g., emergency deliveries) prompt recalculations.
  • Dynamic Updates: Affected segments are reoptimized, and users/fleet managers receive alerts.

    • Example Use Case:
    A courier service inputs 50 stops across a city. The pipeline:

  • Validates addresses and assigns coordinates.
  • Fetches real-time traffic data for the route.
  • Applies a VRP solver to cluster stops by proximity.
  • Generates a route with turn-by-turn instructions, exported to driver tablets via a mobile API.

    • Integration Methods for Third-Party Tools

    Third-party integrations extend the functionality of route planner software by syncing with enterprise systems, improving workflow automation. The table below compares common integration types, their use cases, complexity, and examples:
    Multiple stops route planner software represents a paradigm shift in how organizations manage distributed workflows, offering a convergence of advanced algorithms, real-time data, and industry-tailored solutions. From minimizing fuel consumption in fleet operations to optimizing home healthcare visits within tight time windows, its impact is measurable in cost savings, reduced emissions, and heightened service reliability. As businesses increasingly prioritize scalability and data-driven decision-making, the integration of these tools—whether through cloud-based platforms or on-premise deployments—becomes indispensable for maintaining a competitive edge. The future of route planning lies not just in efficiency, but in adaptability, where software dynamically adjusts to constraints and external variables, ensuring resilience in an ever-changing operational landscape.

    Integration Type Use Case Complexity Level Example Tools
    RESTful APIs Bidirectional data exchange (e.g., syncing orders from ERP to route planner, pushing routes to GPS devices). Moderate (requires API documentation and authentication handling). Salesforce (CRM), SAP (ERP), Web Fleet (fleet management).
    Webhooks Event-driven updates (e.g., triggering route recalculations when an order status changes in a warehouse management system). Low to Moderate (depends on event complexity). Shopify (e-commerce), Oracle NetSuite (finance/operations).
    ETL Pipelines Batch data synchronization (e.g., nightly upload of customer addresses from a database to the route planner). High (requires scheduling, error handling, and data transformation). Talend, Informatica, Python (Pandas + SQLAlchemy).
    SDKs/Libraries Custom development (e.g., embedding route planning into a proprietary logistics dashboard). High (demands programming expertise). Google Maps JavaScript API, HERE SDK, Mapbox GL JS.

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