2026· International Journal of Advanced Computer Science and Applications· Vol 17· 0 citations· 36 references
TL;DR
This study investigates the applicability of three well-established graph-based algorithms, namely Dijkstra, Bellman-Ford, and A*, within an emergency transportation framework inspired by the urban road network of Skopje, and results indicate that all investigated algorithms successfully identify shortest feasible routes when transportation paths exist.
Abstract
Shortest path algorithms play an essential role in intelligent transportation systems, emergency response planning, and numerous Smart City services. This study investigates the applicability of three well-established graph-based algorithms, namely Dijkstra, Bellman-Ford, and A*, within an emergency transportation framework inspired by the urban road network of Skopje. A two-stage evaluation methodology was adopted. First, scalability experiments were performed on weighted graphs containing 50 to 500 nodes to examine computational efficiency and algorithm behavior as network size increases. Subsequently, a Smart City transportation model was developed and implemented using Java and JavaFX, enabling the simulation and visualization of several emergency scenarios, including ambulance dispatching, road disruptions, and route unavailability situations. The experimental results indicate that all investigated algorithms successfully identify shortest feasible routes when transportation paths exist, whereas the heuristic guidance employed by A* reduces the number of explored nodes and improves execution times, particularly in larger graph instances. The proposed framework illustrates how algorithmic analysis can be complemented by simulation-based case studies to support intelligent transportation applications and emergency management decision-making. Future research will focus on incorporating real-time traffic information, stochastic travel times, and larger transportation networks to provide more realistic emergency routing environments.
Comparative analysis against A* search Algorithm, ALT, ALT (A* search Landmarks and Triangle inequality), and Arc Flags algorithms demonstrated that Dijkstra consistently achieved faster computation times, lower memory overhead, and higher accuracy in path selection, resulting in significant reductions in delivery and planning time.
O. Uchechi, Dennis Mary Chinonye, Oparauwah Nnaemeka Macdonald· International journal of res...· 0 citations
Map navigation and graph-based path planning are the foundations of intelligent transportation systems today, supporting all kinds of applications in daily life, logistics and emergency rescue. A spatially weighted graph of the real road network is built in the system, and then the best path is selected based on the weights of these factors. This paper will introduce the main methods of the study and first present road-network models that combine static data from sources such as OpenStreetMap with dynamic Global Positioning System (GPS) trajectory data to construct and update weighted directed graphs. Next, this paper will introduce the basic and advanced shortest-path algorithms for static and dynamic urban environments, such as Dijkstra's algorithm, A* algorithm, etc., and compare their strengths and weaknesses. The following are the steps of the whole navigation workflow, as well as data preprocessing, multi-source fusion and execution of multi-objective path search. Lastly, this paper will also discuss the current deficiencies and problems in the above methods, such as missing data, algorithmic complexity of large-scale networks, trade-offs in multi-objective optimisation, GPS errors and user privacy, etc. Graph theory, spatial data science and optimisation algorithms have been combined to provide the theoretical basis for intelligent path planning in this paper and offer a reference for future research on lightweight, high-efficiency dynamic navigation.
Unknown authors· Theoretical and Natural Scie...· 0 citations
This paper presents a comprehensive comparative study of classical and heuristic shortest path algorithms, with a specific focus on their integration into the classical Transportation Problem (TP). In the conventional TP, the unit transportation cost between a supply node and a demand node is treated as a fixed constant, independent of the underlying network. In real logistics systems, however, this cost arises from the shortest route through a network of intersections and links. To bridge this gap, the present work models the transportation infrastructure as a directed weighted graph and redefines each TP cost coefficient, c_ij’ as the cost of a shortest path between node ‘i’ and node ‘j’. Dijkstra’s algorithm is implemented as the primary engine for computing these network-derived costs and is compared against the Bellman–Ford algorithm and the A* search algorithm under varying network sizes and densities. Simulation experiments on random and grid-based networks with 100–5000 nodes provide a mathematically grounded and empirically validated analysis of time complexity, scalability, and runtime variability. The results show that Dijkstra’s algorithm remains the benchmark for non-negative edge-weight networks, while A* can be faster when an admissible spatial heuristic is available. Bellman–Ford offers flexibility in handling negative weights but becomes computationally prohibitive for large-scale cost-matrix generation. The study establishes a route-aware TP modelling framework and provides practical guidance on algorithm selection for large-scale logistics and transportation planning.
Mrs.Vijayalaxmi, Tanaji S. Pawar· International Research Journ...· 0 citations
Navigating large university campuses is challenging, especially for newcomers, due to complex multi-level indoor spaces connected by walkways. This study models a university campus as a weighted graph and employs an extended A* search algorithm to generate multiple candidate routes between locations. Unlike conventional approaches that compute a single optimal path, the proposed method returns several feasible routes. These routes are then evaluated using multi-criteria analysis considering distance, accessibility, and path simplicity, allowing users to select routes based on their preferences. A Pareto dominance-based filtering mechanism identifies nondominated paths, offering meaningful alternatives. The system follows a modular architecture comprising routing, spatial data processing, and visualization components. Experimental evaluation demonstrates that route computation occurs in microseconds, confirming real-time usability. The system reliably generates multiple valid paths while maintaining computational efficiency even as graph size increases. Results show that integrating multicriteria assessment into graph-based routing provides flexible, user-adaptive navigation without significant computational overhead.
Nidhi Vinod, K. D. Reddy, K. H. Priya et al.· 2026 International Conferenc...· 0 citations
This article proposes a new approach to routing, termed RACER (Real-time Adaptive Congestion-aware Emergency Routing), which dynamically responds to changing traffic conditions without requiring additional traffic-signal-control infrastructure, relying instead on congestion information obtained through standard vehicle-to-infrastructure telemetry.
Harinath Ankarboina, Jasmini Kumari, Amit Kumar Singh et al.· IEEE Open Journal of the Com...· 0 citations
With the continued electrification and digitalization of urban logistics, electric freight routing increasingly requires the coordinated consideration of customer time windows, vehicle capacity, limited battery range, and en-route charging. This study formulates an electric vehicle routing problem with time windows (EVRPTW) for smart-city electric freight and develops a multi-strategy improved ant colony optimization algorithm (IACO). The proposed model integrates customer service, route continuity, time windows, vehicle capacity, battery-energy propagation, and en-route charging. IACO combines a route–charging-state representation with feasibility-guided sweep-insertion initialization, max–min pheromone control, multi-representative guidance, reachable charging-station insertion, greedy feasibility repair, and 2-opt local search, forming a multi-stage search process that integrates global exploration, feasibility restoration, and local intensification. Computational experiments on an R-C benchmark scenario with 51 customers and 9 charging stations compare IACO with ACO, GA, TS, LNS, SA, PSO, and WOA over 100 independent runs under a common 300-iteration limit. Under the current experimental protocol, IACO records a representative generalized cost of 570.88, with reductions of 7.75–44.65% relative to the seven comparison methods, while its median CPU time is 28.42 s. These results demonstrate a clear solution-quality–computation trade-off and indicate the potential of IACO for plan-level electric freight routing and en-route charging coordination.
Li-Ping Gao, Zhao-Lei He, Cong Lin et al.· Energies· 0 citations
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