International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 07 | July 2023
p-ISSN: 2395-0072
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Urban Bus Route Planning Using Reverse Labeling Dijkstra Algorithm for Temporal Network Akanksha Sachan1, Dr Kumar Gaurav2 1M.tech Scholar, Dept. Of Electronics Engineering, HBTU, Uttar Pradesh, India
2Assistant Professor, Dept. Of Electronics Engineering, HBTU, Uttar Pradesh, India
---------------------------------------------------------------------***----------------------------------------------------------------current state of urban traffic for people, ensuring travel Abstract— One of the significant challenges in path safety for people. [2, 3].
optimization is unpredictability of traffic patterns. Route Planning is the process of finding the best possible path to reach the destination while considering various factors such as distance, traffic, road condition, and other constraints. Effective route planning is critical for optimizing travel time, reducing fuel consumption, and improving overall transportation efficiency. The existing algorithms mostly considers waiting time at particular node and distance between two node to find the route. They generally do not consider the demand or traffic congestion toward next node. The demand or traffic congestion value differs for both the incoming and outgoing road. To resolve this issue we have applied Reverse Labeling Dijkstra Algorithm, which find the optimum route between source destination pair considering traffic congestion or demand.
DRGS (Dynamic Route Guidance System), which utilizes the latest technologies such as GPS (Global Positioning System), computers, and telecommunications, to obtain more time and traffic information on the network and provide the driver with an optimal driving route, is an important component. This way of smart transportation is used to improve urban traffic conditions. The feature of this system is based on the road data and includes elements all of which are traffic, people, vehicle, roads. It leads to increase in the profits of the drivers, and proper distribution of city traffic. In the current path optimization research, time spent in the arc is usually calculated and the time spent on the node is typically ascribed to or ignored for the related arc. There are numerous intersections and road segments within the city's road network. The time taken at the junction will also be longer than the time to cross the street due to the effects of traffic lights and regulations, as well as the fact that vehicles travelling straight, sideways, turning right, turning left, or turning from a U-intersection have different driving patterns. Therefore when planning routes for city traffic, one must take into account the time to cross road and intersection at the same time.
Keywords—RLDA, Arc Attribute, Edge Attribute, Route Planning
1. INTRODUCTION Route Planning in urban cities is a crucial aspect of modern transportation planning. With the growth of urbanization, there has been a significant increase in traffic volume, leading to traffic congestion, safety concerns, and environmental pollution. Route planning aims to address these challenges by providing most efficient and safe routes for vehicles and pedestrian to travel in urban areas. To improve the road's traffic conditions, it is important to perform research on the Intelligent Transportation System (ITS).
2. LITERATURE REVIEW The shortest path method and intelligent optimization algorithm are the two most commonly utilized optimization algorithms in DRGS. [4]. The shortest path algorithm: Dijkstra Algorithm [5,6], A* Algorithm[7], Flyod algorithm and other heuristic search algorithm. Intelligent Optimisation algorithm includes: Particle Swarm Optimisation Algorithm, Ant Colony Optimisation algorithm, Genetic Algorithm, neural network algorithm, etc.The shortest path problem was initially addressed by Dijkstra's method in 1959. This strategy is typically used to resolve the single source shortest path problem. The Dijkstra algorithm has a temporal complexity of O(n2) [8].
ITS refers to the comprehensive system designed to achieve greater traffic control, modern information integration, electronic technology for communication and other high-tech goods [1]. ITS uses real-time data and communication networks to collect, process, and disseminate information about traffic conditions, weather, road infrastructure, and other relevant factors that affect transportation. The application of contemporary communication technologies, management skills, and analytical abilities like big data, cloud computing, and artificial intelligence is the most crucial integration of the transportation industry to realise the real-time traffic condition. It improves the
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Liu et al. further enhanced the Dijkstra algorithm to address the issue of poor availability by carefully
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