Dynamic route scheduling for vehicle-based GPS navigation

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May 26, 2023


FEATURED INSIGHTS / Synthetic Biology / technology


Dynamic route scheduling for vehicle-based GPS navigation

Advancements in Robust Routing Algorithms for Vehicle Navigation


Dynamic route scheduling – Rapid transportation and swift logistics have been on the rise with improved road connectivity and network availability in the most remote locations imaginable. In order to bridge this gap between the latest advancements and the growing need of more accurate navigation systems, a lot of research and development is heading towards making robust routing algorithms for vehicle navigation.

Regular commuters going to work or ambulances carrying critical patients can leverage mobile apps based on state-of-the-art algorithms to ensure efficient navigation and timely arrival. Available real-time data has made it possible to replace random assumptions with proof-based information, making dynamic route scheduling an area of constant evolution and consumerism.


With the volume of incoming data, which is flooding the datacenters around the world, we have been successful in developing uber-computing infrastructures not only for our datacenters but even for our mobile phones. Big data when combined with the Internet of things can provide meaningful insights to drive everyday decisions of a user for e.g., taking the best predicted route to reach their destination. Users rely more on the latest technology to show them the best possible path to reach their destination than on traditional ways where they had no idea about the road or traffic conditions. Emphasis on saving time on commuting has never been so high and there is a pressing need to enhance existing navigation algorithms. 


Latest discoveries


Many of the latest GPS based navigation systems like google maps, waze, etc. are using static route planning algorithms such as Dijkstra (DA) and its bidirectional version along with some crowdfunded data from various other users to provide the optimal routing schemes. Dynamic update and optimization of the aforementioned algorithms is not possible using real-time data which is always moving and changing with time. Data related to short-term highway work-zones, traffic density or road accidents need to be continuously taken into account while calculating the optimum route for the user and should sporadically be used to update the algorithm along with their results which may vary from the initial proposed route. Such a dynamic framework would require a very low communication latency from the device to the cloud which might pose some challenges in developing a highly accurate and dynamic routing software that works in real-time.

The fog computing paradigm proposed by Cisco can be utilized to eliminate latency issues by introducing an additional layer between the edge and the cloud. This approach enables the provision of time-sensitive information on the go. Heuristic based methods such as ant-colony optimization, Tabu search, genetic algorithms (GA), adaptive memory (AM) techniques and variable neighborhood search (VNS), have been provided to reduce the computation time of the shortest path while having the flexibility to be dynamic unlike other traditional static methods. Continuous real-time data polling for variables such as road conditions, drivers’ schedules, employee breaks, hours on the road, geographical locations, work-zones, traffic density, number of lanes, traffic signals and weather forecasts would make the dynamic methods more robust for estimating the optimal route for vehicle navigation systems. 

Challenges in Scalability and Fault Tolerance for Dynamic Route Scheduling Systems

The scalability of the system being developed for navigation can pose a significant challenge in the domain of dynamic route scheduling. The problem has two sides. One, as the number of variables increases in the dataset (e.g., a road blockage), the algorithm must perform equally well or better in terms of accurately estimating the optimum route with almost similar latency. Second, as the number of nodes increases in the network, in other words, the vehicular traffic expands, the system should be able to scale up to the increased number with a similar latency score as well as accuracy. Although developers often develop a system in isolation, it is necessary to test it for these scenarios.

The system needs to ensure that not only the latency but also the throughput of the network, which represents the number of transactions made per second, remains intact as the number of variables and vehicular density increase in real-world scenarios. Another challenge might be how fault-tolerant the developed system might be against the failure of a particular node or the lack of network receptibility in remote areas. The developers need to find ways to interpolate or restart the node that has gone out of the coverage area of the network in order to make sure that the system is still capable of generating optimum routes for navigation. Lastly, the system inside the vehicle needs to minimize power consumption as it runs on platforms with limited power resources.

Use cases for route scheduling


The dynamic routing system that has been discussed not only benefits the average user but also serves as a solid pillar for a plethora of industries worldwide. In addition to improving their logistics, such systems have proven to be highly profitable to a number of different manufacturers. During the covid lockdowns, overwhelming demand had shaken up the e-commerce industry, which highly benefited the dynamic routing systems. These systems enabled small businesses to save on fuel costs and time and provided dynamic delivery time to the customers. Trucking companies have been using these systems for a long time to monitor driver behavior, compliance and construct an optimized freight network.

In manufacturing and heavy equipment transportation, such technology can improve logistics visibility and prevent theft or tampering by helping drivers avoid dangerous routes. In healthcare, timely delivery of devices, tools, and medicines is very crucial. Whether it is a third-party logistics company or a healthcare provider, route optimization and planning can assist in serving clinics and patients rapidly and efficiently with minimal delay possible. Planning a delivery route using state-of-the-art algorithms can help to prevent delayed and repeated parcel delivery attempts. In addition to increasing customer trust, real-time optimization can also enable the same fleet of couriers to make more deliveries at the neighboring locations. 


Industry Players – Dynamic route scheduling


Paragon from Aptean, is a US-based company which provides routing and scheduling software services for fleet operation and logistics. Their optimization algorithms provide a transportation plan that optimizes truck and driver utilization for the user based on their delivery data.


Upper Route Planner is another business outfit that offers route planning software integrated with the GPS vehicle tracking system, which may already be installed in the vehicle. They provide features such as real-time locations, live ETAs, and the ability to replay the routes taken by drivers to ensure duty compliance.


LogiNext offers highly viable solutions to various businesses operating at different industrial standards. To supplement the regular route planning functionality, the user can get separate apps for their last mile delivery, field staff management, optimization of express deliveries, and automatic real-time fleet monitoring. The company also provides a reverse logistics solution for enterprises that need to collect items from customers rather than deliver them.


What’s next?  


Despite numerous cutting-edge advancements in dynamically scheduling routes for vehicular navigation, there are still some challenges that researchers and developers need to address in this domain to push the envelope of the latest developed technologies in this area. Implementing these well-researched facets of dynamic route scheduling would need rigorous testing not only for one isolated system but for the entire scalable network of vehicles.           




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