2.1. Research Solutions
Pang et al. [
6] implemented an optimal routing system according to the preferences of the vehicle driver. In this system, a hybrid fuzzy–neural approach is used that combines the advantages of both fuzzy systems and neural systems, which further enhances the intelligence of the dynamic route guidance system modeling the decision-making process in route selection. This approach is general in the sense that fuzzy rules can handle any number of feasible paths when given the start–end path pair. In addition, the proposed approach can process multiple objectives for route selection, such as travel time, route distance, traffic congestion, route view, and so on. In this system, six main objectives for route selection are defined, which act as fundamental factors in the adaptive route selection algorithm.
Kanoh and Nakamura [
7] proposed the use of a genetic algorithm that adopts the viral infection model. The proposed method uses viruses as domain-specific knowledge. Thus, a segment of an arterial road is considered a virus, and a population of viruses is created in addition to a population of routes. The experiments used real road maps for navigation systems in Japan, and the results showed that contaminations are an effective solution to the problem of dynamic route selection. Furthermore, the proposed strategy is not problem-specific and can be effectively used in other optimization problems.
Eggenkamp and Rothkrantz explored the potential of artificial intelligence, and particularly expert systems, in path planning using dynamic data [
8]. They presented an expert system built to perform dynamic routing and a dynamic route planner using a traditional shortest path algorithm. Furthermore, the capabilities of an expert system for dynamic path planning were studied, and a comparison was made between Dijkstra’s shortest path algorithm and the expert system. The expert system showed great potential as it not only performs much better in terms of computation time, but the routes it returns are also as good as the routes calculated by the conventional shortest path algorithm. Additionally, the expert system shows great potential when real-time constraints are set. The expert system first generates all possible solutions and then calculates their travel time one by one. Once the travel time of a solution is calculated, the solution becomes available. The major disadvantage of the expert system approach is the construction of the rules, as it is a very intensive and time-consuming process.
In another work [
9], a practical method for dynamic route planning that uses a virus genetic algorithm is proposed, with the aim of finding the minimum time route from the starting point to the end of a trip. The proposed method includes the complexity of Dijkstra’s algorithm, applications in real road networks, and route guidance in dynamic environments. To evaluate the proposed method, experiments were carried out in both static and dynamic environments. The results of the experiments led to the conclusions that (i) the virus genetic algorithm has almost the same performance in terms of running time compared to the Dijkstra algorithm; (ii) the virus genetic algorithm is superior in terms of the computational time that is required to respond to changes in information coming from the road. Practically, this means that the virus genetic algorithm is a more efficient solution to real-time path-planning problems.
Kanoh and Hara [
10] formulate the path planning problem as a dynamic multiobjective problem and suggest that it can be solved using a genetic algorithm. There are three objective functions to be optimized simultaneously in this problem: (i) the length of the route, (ii) the travel time that changes rapidly with time, and (iii) the ease of driving. The proposed solution provides an optimal set of routes, using both the predicted movement and a hybrid genetic multiobjective algorithm (a combination of a genetic and the Dijkstra algorithms), so that the driver can choose a favorite route from a set of feasible routes.
The INSIGMA project [
11] is an advanced information system for tracking vehicles and detecting threats while they are moving. The first and main goal of this project is the analysis of traffic parameters based on dynamic maps. A dynamic map is defined as the representation of roadway infrastructure combined with up-to-date information on road traffic intensity and historical traffic data. Such a combination includes information stored in a database, as well as a representation of a map which can be presented to the end user through an interface. Algorithms for dynamic route optimization are implemented in the system, which assists traffic control systems and is particularly useful in urban environments. The second goal of the project is the development of automatic methods for observing moving objects (vehicles) and recording their parameters. The application of such systems aims to provide low-level data for the creation of dynamic maps. In addition, the search system will allow interfaces to define route optimizations, smart tracking, and other services.
An intelligent trip modeling system is presented in [
12], which was developed using Machine Learning (ML) to predict the travel speed profile for a selected route, based on traffic information that is available at the start of the trip. The system contains neural networks to predict short-term traffic speed based on the day of travel, congestion levels, and the distances between individual sensor locations along the route. The proposed intelligent travel modeling system was trained and evaluated using 10 months of traffic data from the California freeway performance measurement system on a 26-mile route containing 52 sensors along its length. The experimental results showed that the proposed system could provide accurate predictions of dynamic traffic changes and trip speed at the start of the trip.
A new model for a path planning system based on multi-agent reinforcement learning algorithms is presented in [
13]. A combination of Q-value-based dynamic programming and Boltzmann distribution was used to solve vehicle delay problems by studying the weights of various parameters of the road network, such as weather, traffic, road safety, and the fuel capacity of the vehicle, to create a priority route for the vehicles. The most important part of this study is the use of a multi-agent system with learning capabilities in order to make decisions about vehicle routing between cities. The evaluation of this system was carried out using a series of case studies focused on Malaysian road networks and showed that agent technology is suitable for solving communication problems in a distributed rapid transit network. The innovation of this study is the use of multi-agent reinforcement learning for route planning systems, which can be used to provide access to data resources of rapid transit networks.
A dynamic urban route planning algorithm for real-time traffic density distribution in a city is proposed in [
14]. The proposed framework tries to minimize the number of congested traffic nodes by even and real-time traffic distribution using traffic diversion hints, thereby achieving the reduction in dense air pollution at traffic nodes. The proposed framework is based on an Internet of Things (IoT) architecture for real-time traffic and geographic data to reduce the amount of air pollution and reduce the exposure time of vehicle occupants to it. The proposed approach is based on the cost function for pollution levels, nodes connectivity, and route type as key parameters, without, however, ignoring the possible measurement of the shortest distance. Thus, this proposed framework provides suggestions for alternative routes by compensating for the additional distance with the least exposure of commuters to pollution, thus minimizing the total cost function of time, distance, and pollution.
In [
15], a hybrid dynamic routing system was developed that interacts with the current traffic information of the city, and its traffic history, while predicting the future traffic flow of the city. The algorithm of the present system is implemented using the dynamic Dijkstra algorithm in a three-dimensional graph (3D graph), in which the x and y axes map the Dutch highway network, while the z-axis represents time. This dynamic routing system is provided as a website by the Knowledge-Based System Group (KBS) of the Delf University of Technology, as well as a mobile phone application. The graphical environment of the system numbers all the roads and arterial roads of the Netherlands. After the user enters the departure time, the starting point, and the destination of the route, the system calculates the shortest route in time and colors it in red on the representation map of the road network.
Kong et al. [
16] proposed a two-phase approach for dynamic route planning for shared metro buses, namely SubBus. First, based on a passenger behavior analysis, they identify the multidimensional properties to predict passenger travel requirements, such as the distribution and volume of passengers at different time intervals. Then, they designed a dynamic programming algorithm to obtain the optimal routes based on the prediction results. The proposed approach consists of three main components: (i) data preprocessing, (ii) travel demand prediction, and (iii) dynamic route planning. Based on actual shared metro bus data, extensive experiments were conducted to prove that the proposed approach could effectively generate operating routes to optimize the operating status of shared buses and promote their deployment. A resident travel behavior analysis was performed to extract multiple important features and predict passenger flow using an ML method. Although the data are very erratic, the forecast accuracy at many stations can reach 80%. Based on the set of candidate origin points and the set of candidate routes generated, the optimal routes for the shared buses were obtained from the designed dynamic programming algorithm. The results of the experiments revealed that the planned routes have a shorter operating distance and more passengers than the actual routes. Thus, the proposed SubBus approach can generate routes for the shared metro buses to optimize their operating status.
Hybrid-VITS [
17] is a hybrid system consisting of vehicular ad hoc networks (VANETs), traffic servers, and a 5G-based cellular public transport system. The system, considering the traffic congestion in urban areas, calculates the optimal route to re-plan the routes based on the shortest path algorithm, and a load balancing technique is adopted to avoid further congestion. The Hybrid-VITS architecture includes vehicles, roadside units, vehicular traffic servers, and a 5G-based cellular base station. Vehicles are equipped with onboard units, which are responsible for the communication between vehicles and road units. Vehicles perceive and send the information (speed, density, and location) periodically to other vehicles and to the nearest road unit. In addition, the cellular public transport system based on 5G that was implemented consists of mobile remote monitoring and taxi management. As soon as the vehicles perceive the congestion, a warning message is generated and sent to the road units. If a taxi (hypernode) receives the warning message, it immediately uploads the warning to the database, which forwards it to the vehicle traffic servers. Road units share the warning message, which contains segment ID and congestion removal time, with other road units through the proposed common base mechanism and vehicle traffic servers.
In [
18], a Recurrent Neural Network (RNN) based on the default logic for route planning is proposed and consists of three parts. The first part is the default path design theory, which does not impose loops on the generated paths. The second part is the RNN, based on default reasoning, to suggest the default rules. The RNN outputs the probability distribution of the defaults used afterward, so the proposed method is a probabilistic method. The third part is the map model update algorithm to improve the accuracy of default reasoning in dynamic environments. The training of the proposed RNN is simple, as no statistical computation is required for its training. The time complexity of the proposed method during testing is O(
2), where
is the length of the optimal path for two given nodes. To evaluate the proposed method, a new map model named BJ was constructed, which simulates the complex urban environments of the city of Beijing in China. Extensive experiments on the BJ map model in both static and dynamic environments have shown the effectiveness and accuracy of the proposed method.
Abdelrahman et al. [
19] presented a dynamic, personalized route planning framework based on crowd-sensed vehicle data. The proposed system utilizes the data coming from the vehicles and from various smart devices and creates a database that contains properties of different parts of the road networks. Additionally, driver behavioral information, along with the environmental context of those behaviors, is used to build a probabilistic safety database. Thus, road information and driver profiles are used to provide route navigation options that are now based on personalized levels of safety and road comfort within a large number of environmental attributes. A case study for a real-life system operation scenario from point A (origin) to point B (destination) was carried out in the city of Kingston, Ontario. This case study highlights the difference between the conventional best route suggested by Google Maps and the best route based on route quality and hazard level provided by the proposed system. In this case study, in a driving scenario, Google Maps suggested three possible routes, according to the estimated travel time and distance of points A and B, taking into account the real-time road traffic.
2.2. Commercial Solutions
The company FarEye [
20] has created an intelligent delivery management platform that is efficient, self-sustaining, and profitable and can be leveraged by various types of delivery businesses, helping them to operate faster, implement changes in the delivery process, and keep the delivery ecosystem flexible. This platform is a customizable delivery management platform based on Software-as-a-Service (SaaS), and is a complete delivery solution that consists of the following three parts: (i) the Intelligent Delivery Orchestration, which enables businesses to meet ever-increasing expectations and achieve operational efficiency based on routing with ML-based methods; (ii) the Intelligent Delivery Visibility, which achieves an enhanced visibility of deliveries in real-time, across all modes of transportation, enables customers to have reliable information, and achieves the timely delivery of shipments in the first, intermediate, and last mile of delivery; and (iii) the Intelligent Delivery Experience, which provides personalized experiences to customers/receivers of deliverables.
Another commercial solution that provides both route planning solutions and rescheduled optimization of the delivery process is the OptimoRoute service [
21]. OptimoRoute takes into account all the needs of each route automatically and provides the most efficient routes in a user-friendly way. This service has various features for planning each route. It can automatically redesign and reschedule routes if factors change during route execution, as OptimoRoute’s route management software sends real-time updates and exact arrival times to dispatchers, drivers, and recipients of a deliverable. The solution offered by the OptimoRoute service is already used by various businesses of all sizes, as well as various categories, such as distribution, retail, healthcare, laundry, cleaning and maintenance services, waste collection, transport companies, sales services, pharmacies, etc.
Nextmv [
22] is a startup company that offers a Software Development Kit (SDK) and Application Programming Interface (API) and provides decision automation and optimization for vehicle routing, staff scheduling, distribution of expenses, etc. Nextmv’s decision automation stack is easy to use either as is or with appropriate customizations to account for various solution paradigms, such as decision diagrams, as well as unique constraints and business Key Performance Indicators (KPIs). Additionally, it provides the ability to visualize routes in various ways, such as through Open-Source Routing Machine, Google Maps, MapBox, OpenStreetMap, geojson.io, or even something that a developer may have developed in-house.
Another smart delivery solution is provided by the Sqilline company with its Smart Delivery service [
23]. Sqilline’s Smart Delivery service is a mobile phone solution to make the delivery, loading, and unloading of goods faster, safer, and more transparent. This service significantly reduces delivery time, increases driver accountability, and ensures customer satisfaction, while giving real-time feedback to the customers on their order’s status. This service also works offline, so that the driver can work with a delivery document without the need for an Internet connection. In addition, it reduces the cost of filing, processing, and handling paper documents.
The Mendix company [
24] offers its own smart delivery management platform. The proposed solution of the Mendix company provides the possibility to the companies that use it to plan their routes, to fulfill the deliveries of their orders, and to assign their data, for the execution of their deliveries, to the various infrastructures that the company has and offers. The features of this platform are (i) real-time data; (ii) route optimization; (iii) QR scanning; and (iv) integration with human capital management.
The Milkman Home-Delivery Platform for last-mile operations [
25] is another service that offers businesses that require product delivery various optimizations for it. The optimizations offered by Milkman are (i) real-time capacity analysis; (ii) route optimization; (iii) optimization of operations throughout the supply chain, from retailers to order delivery drivers; and (iv) last-mile delivery service.
Pickpack [
26] provides a robust real-time optimization algorithm for delivery routes that increase order efficiency and ensure that orders and drivers arrive accurately. The service algorithm takes into account deliveries with pickups and deliveries for local delivery services, one pickup and multiple deliveries for logistics companies and retail, delivery times, vehicle capacity, on-demand deliveries, priority stops, traffic on roads, the speed of the respective drivers, and the shift times, the breaks, and the remaining service time of the respective drivers, etc. The Pickpack service provides a powerful real-time route optimization algorithm based on artificial intelligence for deliveries, which increases the efficiency of deliveries and ensures the best possible driver and vehicle for each delivery. Finally, this service provides two applications for mobile phones, one application for drivers and one application for customers. The driver application provides the user with real-time optimization of driver tasks and routes to save labor time, fuel and vehicle costs, detailed job information, estimated delivery time, distance and status update, easy communication with the shipper and customers, help to navigate pick-up/drop-off points, and support working offline. Additionally, the customer application provides the user with real-time tracking of both the order and the driver, the real-time estimated delivery time and distance, the details of the shipment distributor, as well as the ability to contact him, and automatic status updates and proof of delivery.
The examination of the aforementioned routing services led to the identification of a number of critical considerations for the design of a contemporary VRP system. Several of the examined systems provide a route without managing the first steps of the procedure, which are the management of vehicle capacity and the dynamic out-of-order batching of geographic locations based on vehicle capacity. Creating batches is a crucial step in the approach suggested in this paper. In addition, the vast majority of systems use a single algorithm to determine the ideal route. As will be further discussed in the following paragraphs, Smart Delivery is built on an ensemble scheme of algorithms to present users with the optimal path. Moreover, given that the translation of data in order to gain valuable insights is currently one of the most important processes, it appears to be optimal to provide businesses with an all-in-one solution consisting of a routing system and an analytics system.