Knowing the estimated time of arrival is important for everyone: logistics service providers, fleet managers, small delivery companies, big chains, warehouses, etc. Accurate and predictable ETAs lead to increased customer satisfaction. But to predict ETAs accurately you need to consider a lot of factors. One way to improve the accuracy of ETA calculations is with route optimization.<\/p>\n For predictable ETAs, you need to build optimal routes for customers, fleets, suppliers, and manufacturers: routes that are cost-efficient, flexible, and fast. Route optimization software plays a critical role in this.<\/p>\n Route optimization software<\/a> can cope with much more than planning the way from point A to point B. Route planning optimization solutions based on advanced technologies like mapping, GPS tracking, artificial intelligence (AI)<\/a>, and data analytics<\/a> allow companies to manage fleets, plan drivers\u2019 loads, track assets, adjust routes to accommodate multiple stops, find charging stations for EVs<\/a>, etc.<\/p>\n Apart from the usual route planning functionality incorporated within vehicle navigation systems, fleet owners can design custom truck routing optimization software. Custom routing software can build resilient and efficient maps with custom layers depending on fleet needs. Logistics companies and fleet managers already use GPS tracking<\/a> to identify vehicle speeds. Today, truck route optimization software can also be used to alternate routes in order to send trucks in the optimal directions. Fleet route optimization software<\/a> can avoid congestion, geofenced driving areas, tolls, and more.<\/p>\n Route planning software for truck drivers<\/a> can replay a vehicle\u2019s route history for specific dates and road stretches to identify reasons for delays or incidents. This helps logistics providers and fleet managers identify how specific events, such as speeding or stops, impact ETAs, why these events occur, and most importantly, how to address them.<\/p>\n Fleet managers and logistics providers can identify KPIs and insights critical for the business using advanced truck routing. Companies can build comprehensive historical reports on fleet performance to find insightful data and prepare reliable forecasts.<\/p>\n One widely adapted technology for route planning is GPS tracking. But GPS is just one tool for predicting ETAs using maps. By applying more advanced technologies that use data from vehicle sensors, road infrastructure, and traffic monitoring systems<\/a> along with crowdsourced data, you can align routes with ETAs considering many more factors along the way.<\/p>\n Learn how we applied GIS services to accelerate the compilation of maps for optimizing logistics routes<\/p>\n A machine learning model applied to route scheduling software can calculate ETAs taking into account contextual data and the amount of time spent at each delivery stage. For training these models, data pre-processing<\/a> and feature engineering are vital. Route scheduling and optimization software can rely on additional variables to train a machine learning model. These could be vehicle type, customer segment, time period, pickup and destination locations, and traffic speed at a particular hour. Contextual data on things like weather conditions, public holidays, and scheduled vehicle maintenance<\/a> can also make ETA predictions more accurate.<\/p>\n The machine learning workflow for a route optimization engine is built on these steps:<\/p>\n Fleet Management Software Development Services<\/p>\n When it comes to the accuracy of ETAs, route planning optimization makes a real difference. There are two major approaches to planning delivery routes with several additional routing scenarios<\/a> that use the benefits of these two. The first approach is static routing, when the route is pre-planned and taken by a driver without any changes to the destination. The second is dynamic routing, when the driver can adjust the route based on factors influencing changes in the ETA.<\/p>\n Standard or static routing works best when you have a set of established routes for long-term customers who want exactly the same route each time you deliver their goods.<\/p>\n Pros<\/b><\/p>\n Cons<\/b><\/p>\n Emphasizing the customer, dynamic routing adjusts routes to specific requirements and changes that may appear just before sending cargo or even in real time during delivery. This method requires accurate data about customers, their preferences, and their order histories along with contextual data related to products. In this method, routes are built automatically considering all constrains, while customers can make changes on the way.<\/p>\n Pros<\/b><\/p>\n Cons <\/b><\/p>\n This is a combination of static and dynamic routing. With fragmented routing, you build a standard route while adding new customers dynamically who are located along the way. You can add new points before, during, and in between planned deliveries.<\/p>\n Pros<\/b><\/p>\n Cons<\/b><\/p>\n Each route taken can be a base layer on which to add new destination points with accurate calculations of projected ETAs. Fleet managers can assign customers to already built routes.<\/p>\n Pros<\/b><\/p>\n Cons<\/b><\/p>\n Geographical zoning of customers can combine the benefits of static template-based routing and dynamic routing. By grouping areas, drivers can operate within areas and cross boundaries in some cases to improve ETAs.<\/p>\n Pros<\/b><\/p>\n Cons<\/b><\/p>\n![\"How](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/09\/logistics-and-delivery-insights.jpg\")
\nSource: Bizzrate Survey \u2013 Five consumer insights shaping logistics and delivery<\/a><\/em><\/p>\n![\"Digitally](\"https:\/\/intellias.com\/wp-content\/uploads\/2022\/07\/Digitally-Mature-Logistics.jpg\")
<\/div>\n\t\t\tMain factors to improve ETA calculations and predictions<\/h2>\n
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Why route planning and optimization software is so important for ETAs<\/h2>\n
Advanced features of route optimization system<\/h3>\n
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What to consider when developing route planning and optimization software<\/h3>\n
![\"How](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/09\/route-planning-and-optimization-software.jpg\")
<\/p>\nThe place of machine learning and data in route optimization<\/h3>\n
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Optimizing truck routes for accurate ETAs<\/h2>\n
Standard\/static routing
\n![\"How](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/09\/static-routing.jpg\")
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Dynamic\/on-demand routes
\n![\"How](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/09\/Dynamic-routing.jpg\")
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Fragmented routes<\/h3>\n
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Preferred routing ID<\/h3>\n
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Grouping areas<\/h3>\n
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Conclusion<\/h2>\n