Real Road Network Research Articles

Optimal location query based on k nearest neighbours

Optimal location query in road networks is a basic operation in the location intelligence applications. Given a set of clients and servers on a road network, the purpose of optimal location query is to obtain a location for a new server, so that a certain objective function calculated based on the locations of clients and servers is optimal. Existing works assume no labels for servers and that a client only visits the nearest server. These assumptions are not realistic and it renders the existing work not useful in many cases. In this paper, we relax these assumptions and consider the k nearest neighbours (KNN) of clients. We introduce the problem of KNN-based optimal location query (KOLQ) which considers the k nearest servers of clients and labeled servers. We also introduce a variant problem called relocation KOLQ (RKOLQ) which aims at relocating an existing server to an optimal location. Two main analysis algorithms are proposed for these problems. Extensive experiments on the real road networks illustrate the efficiency of our proposed solutions.

The Steiner bi-objective shortest path problem

• we introduce the Steiner Bi-objective Shortest Path Problem. • We propose a solution method based on a labeling approach with a multi-objective. • Test instances are generated from real road networks. • Computational results show the efficiency of the proposed algorithm compared to state-of-art approaches. In this paper, we introduce the Steiner Bi-objective Shortest Path Problem. This problem is defined on a directed graph G = ( V , A ) , with a subset T ⊂ V of terminals. Arcs are labeled with travel time and cost. The goal is to find a complete set of efficient paths between every pair of nodes in T . The motivation behind this problem stems from data preprocessing for vehicle routing problems. We propose a solution method based on a labeling approach with a multi-objective A* search strategy guiding the search towards the terminals. Computational results based on instances generated from real road networks show the efficiency of the proposed algorithm compared to state-of-art approaches.

Parking Lots Assignment Algorithm for Vehicles Requiring Specific Parking Conditions in Vehicle Routing Problem

Based on the analysis of the current literature, mathematical modelling of the studied phenomenon was carried out. It was conducted according to the graph theory for vehicles transporting dangerous, oversized, and valuable cargo, moving within a transport network. The mapping of the parking areas included organisational, technical, and security aspects. The main algorithm determines the driving routes based on the parkings that meet the requirements specified by the carrier. These routes can ultimately be analysed and evaluated based on the parameters resulting from the formulated criterion functions. To verify the performance of the algorithm, its implementation was carried out in a computer environment using the Neo4j graph database. The transport network, consisting of 462 transport nodes and 602 transport links, was mapped based on the real national road network of the province in Poland. Data from 113 parking locations in the study area were included in the analysis. The research covered a total of three case studies, one for each vehicle type requiring specific parking conditions, for various input data. The results obtained allowed us to assess the validity of selecting particular routes and evaluating the scalability of the solution. The result of this work is a method, which, on the one hand, fits the current transportation requirements. On the other hand, it lends itself to scaling, extension by additional logical constraints, and is compatible with modern parking systems.

Finding the most navigable path in road networks

Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and “navigability score” values; (b) a source and a destination and; (c) a budget value which denotes the maximum permissible cost of the solution. Given the input, MNP aims to determine a path between the source and the destination which maximizes the navigability score while constraining its cost to be within the given budget value. The problem can be modeled as the arc orienteering problem which is known to be NP-hard. The current state-of-the-art for this problem may generate paths having loops, and its adaptation for MNP that yields simple paths, was found to be inefficient. In this paper, we propose five novel algorithms for the MNP problem. Our algorithms first compute a seed path from the source to the destination, and then modify the seed path to improve its navigability. We explore two approaches to compute the seed path. For modification of the seed path, we explore different Dynamic Programming based approaches. We also propose an indexing structure for the MNP problem which helps in reducing the running time of some of our algorithms. Our experimental results indicate that the proposed solutions yield comparable or better solutions while being orders of magnitude faster than the current state-of-the-art for large real road networks.

Traffic congestion propagation identification method in smart cities

Managing the frequent traffic congestion (traffic jams) of the road networks of large cities is a major challenge for municipal traffic management organizations. In order to manage these situations, it is crucial to understand the processes that lead to congestion and propagation, because the occurrence of a traffic jam does not merely paralyze one street or road, but could spill over onto the whole vicinity (even an entire neighborhood). Solutions can be found in professional literature, but they either oversimplify the problem, or fail to provide a scalable solution. In this article, we describe a new method that not only provides an accurate road network model, but is also a scalable solution for identifying the direction of traffic congestion propagation. Our method was subjected to a detailed performance analysis, which was based on real road network data. According to testing, our method outperforms the ones that have been used to date.

Agent based simulation for the design of a mobility service in the Paris-Saclay area

Autonomous vehicles promise an important opportunity for a complete paradigm shift for the design of transportation systems. These systems will become more flexible and efficient by adapting to the travelers’ demand. On the other hand, multi agent systems offer an intuitive and powerful way to design complex distributed systems and agent based simulation has proved to be well suited for studying the performance of mobility services and their impact on the users and the traffic in general.In this study, we use an Agent-Based modeling approach to design a mobility system based on a fleet of autonomous vehicles (robo-taxis and shuttles) and connected road side units, each of these elements represented by intelligent collaborative agents. In our model, the control is distributed among the agents and the global behaviour of the system emerges from local decisions. We then used a microsimulation tool to test the model on the real road network of the Paris-Saclay area. Since this area is planned to become a center for technology, innovation and education and is currently under constant development, it offers an opportunity for the design and the implementation of new models of transportation services.We designed our mobility system in an incremental manner by introducing more components and more intelligent behaviour and testing its performance at each step.Our results suggest that for our area of study, a mobility service that relies on autonomous vehicles aided by connected road side units that allow to retrieve information about the traffic would perform better than a regular service.

Analysis of an Optimal Planning Model for Electric Vehicle Fast-Charging Stations in Al Ain City, United Arab Emirates

For the large-scale promotion of electric vehicles (EV), reliable fast-charging stations (FCS) demand high priority among EV users. However, unplanned locations of charging stations (CSs) and station capacity determination have adverse effects on the operation and the performance of power-distribution network. In this study, we developed an optimal FCS-planning model considering the aspects of EV users’ convenience, station economic benefits, the impact on distribution systems and the effect on environment. A queuing-theory-based CS sizing algorithm that benefits EV users as well as improves CS capacity utilization was proposed. The proposed planning model was verified through a case study using real road network data by employing multi-objective binary and non-dominated sorting genetic algorithm. In addition, to evaluate the efficiency of the proposed sizing algorithm, sensitivity analyses for different EV penetration levels and station utilization were conducted. The simulation results show that the proposed CS-allocation model is beneficial in terms of achieving the satisfaction of EV users, cost savings, better station utilization, and less impact on power grids and the environment. Finally, to validate the effectiveness of the proposed planning model, a comparative study with one of the previous work on CS planning is also performed. The results demonstrate that the proposed charging station sizing method is highly efficient in optimizing EV users’ satisfaction and for better station utilization.

Shortest Path Algorithm in Dynamic Restricted Area Based on Unidirectional Road Network Model.

Accurate and fast path calculation is essential for applications such as vehicle navigation systems and transportation network routing. Although many shortest path algorithms for restricted search areas have been developed in the past ten years to speed up the efficiency of path query, the performance including the practicability still needs to be improved. To settle this problem, this paper proposes a new method of calculating statistical parameters based on a unidirectional road network model that is more in line with the real world and a path planning algorithm for dynamically restricted search areas that constructs virtual boundaries at a lower confidence level. We conducted a detailed experiment on the proposed algorithm with the real road network in Zhengzhou. As the experiment shows, compared with the existing algorithms, the proposed algorithm improves the search performance significantly in the condition of optimal path under the premise of ensuring the optimal path solution.

Vehicle routing on road networks: How good is Euclidean approximation?

Suppose that one is given a Vehicle Routing Problem (VRP) on a road network, but does not have access to detailed information about that network. One could obtain a heuristic solution by solving a modified version of the problem, in which true road distances are replaced with planar Euclidean distances. We test this heuristic, on two different types of VRP, using real road network data for twelve cities across the world. We also give guidelines on the kind of VRP for which this heuristic can be expected to give good results.

An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data.

With the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted on routes using LBS data, the specific routes are deficient in dynamic scalability and the correlations between environmental constraints and personal choices have not been investigated. This paper proposes an improved HMM-based (hidden Markov model) method for planning personalized routes with crowd sourcing spatiotemporal data. It tries to integrate the dynamic public preferences, the individual interests and the physical road network space in the same spatiotemporal framework, ensuring that reasonable routes will be generated. A novel dual-layer mapping structure has been proposed to bridge the gap from brief individual preferences to specific entries of POIs (points-of-interest) inside realistic road networks. A case study on Changsha city has proven that the proposed method can not only flexibly plan people’s travel routes under different spatiotemporal backgrounds but also is close to people’s natural selection by the perception of the group.

Directionality‐centric bus transit network segmentation for on‐demand public transit

The recent growth in real-time, high-capacity ride-sharing has made on-demand public transit (ODPT) a reality. ODPT systems serving passengers using a vehicle fleet that operates with flexible routes, strive to minimise fleet travel distance. Heuristic routing algorithms have been integrated in ODPT systems in order to improve responsiveness. However, route computation time in such algorithms depends on problem complexity and hence increases for large scale problems. Thus, network segmentation techniques that exploit parallel computing have been proposed in order to reduce route computation time. Even though computation time can be reduced using segmentation in existing techniques, it comes at the cost of degradation of route quality due to static demarcation of boundaries and disregarding real road network distances. Thus, this work proposes, a directionality-centric bus transit network segmentation technique that exploits parallel computation capable of computing routes in near real-time while providing high scalability. Additionally, a dynamic fleet allocation algorithm that exploits proximity and flexibility to minimise vehicle detours while maximising fleet utilisation is proposed. Experimental evaluations on a real road network confirm that the proposed method achieves notable speed-up in flexible route computation without compromising route quality compared to a widely used unsupervised learning technique.

\u03b1-Probabilistic flexible aggregate nearest neighbor search in road networks using landmark multidimensional scaling

In spatial database and road network applications, the search for the nearest neighbor (NN) from a given query object q is the most fundamental and important problem. Aggregate nearest neighbor (ANN) search is an extension of the NN search with a set of query objects Q = { q_0, dots , q_{M-1} } and finds the object p^* that minimizes g { d(p^*, q_i), q_i in Q }, where g (max or sum) is an aggregate function and d() is a distance function between two objects. Flexible aggregate nearest neighbor (FANN) search is an extension of the ANN search with the introduction of a flexibility factor phi , (0 < phi le 1) and finds the object p^* and the set of query objects Q^*_phi that minimize g { d(p^*, q_i), q_i in Q^*_phi }, where Q^*_phi can be any subset of Q of size phi |Q|. This study proposes an efficient alpha -probabilistic FANN search algorithm in road networks. The state-of-the-art FANN search algorithm in road networks, which is known as IER-khbox {NN}, used the Euclidean distance based on the two-dimensional coordinates of objects when choosing an R-tree node that most potentially contains p^*. However, since the Euclidean distance is significantly different from the actual shortest-path distance between objects, IER-khbox {NN} looks up many unnecessary nodes, thereby incurring many calculations of ‘expensive’ shortest-path distances and eventually performance degradation. The proposed algorithm transforms road network objects into k-dimensional Euclidean space objects while preserving the distances between them as much as possible using landmark multidimensional scaling (LMDS). Since the Euclidean distance after LMDS transformation is very close to the shortest-path distance, the lookup of unnecessary R-tree nodes and the calculation of expensive shortest-path distances are reduced significantly, thereby greatly improving the search performance. As a result of performance comparison experiments conducted for various real road networks and parameters, the proposed algorithm always achieved higher performance than IER-khbox {NN}; the performance (execution time) of the proposed algorithm was improved by up to 10.87 times without loss of accuracy.

Modeling and Engineering Constrained Shortest Path Algorithms for Battery Electric Vehicles

We study the problem of computing constrained shortest paths for battery electric vehicles. Because battery capacities are limited, fastest routes are often infeasible. Instead, users are interested in fast routes on which the energy consumption does not exceed the battery capacity. For that, drivers can deliberately reduce speed to save energy. Hence, route planning should provide both path and speed recommendations. To tackle the resulting [Formula: see text]-hard optimization problem, previous work trades correctness or accuracy of the underlying model for practical running times. We present a novel framework to compute optimal constrained shortest paths (without charging stops) for electric vehicles that uses more realistic physical models, while taking speed adaptation into account. Careful algorithm engineering makes the approach practical even on large, realistic road networks: We compute optimal solutions in less than a second for typical battery capacities, matching the performance of previous inexact methods. For even faster query times, the approach can easily be extended with heuristics that provide high quality solutions within milliseconds.

Cracking urban mobility

Assessing the resilience of a road network is instrumental to improve existing infrastructures and design new ones. Here we apply the optimal path crack model (OPC) to investigate the mobility of road networks and propose a new proxy for resilience of urban mobility. In contrast to static approaches, the OPC accounts for the dynamics of rerouting as a response to traffic jams. Precisely, one simulates a sequence of failures (cracks) at the most vulnerable segments of the optimal origin-destination paths that are capable to collapse the system. Our results with synthetic and real road networks reveal that their levels of disorder, fractions of unidirectional segments and spatial correlations can drastically affect the vulnerability to traffic congestion. By applying the OPC to downtown Boston and Manhattan, we found that Boston is significantly more vulnerable than Manhattan. This is compatible with the fact that Boston heads the list of American metropolitan areas with the highest average time waste in traffic. Moreover, our analysis discloses that the origin of this difference comes from the intrinsic spatial correlations of each road network. Finally, we argue that, due to their global influence, the most important cracks identified with OPC can be used to pinpoint potential small rerouting and structural changes in road networks that are capable to substantially improve urban mobility.

An Electric Vehicle Routing Problem With Intermediate Nodes for Shuttle Fleets

In this article, we propose a variant of the electric vehicle routing problem considering explicitly the intermediate nodes. Ultimately aiming to provide an optimal routing plan for the shuttle fleet that serves to a university settlement internally, we consider a real road network by explicitly taking into account in the formulation the entire intersections existing and the time-varying passenger demand at shuttle stops, as well as the vehicle dynamics, battery, and recharging features. On purpose, a mathematical program to obtain the joint minimization of a number of objectives in terms of cost, i.e., vehicle operating, battery recharging, and recharging station purchasing, is formulated. Solutions employing an exact method are sought using models of mixed integer program within scenarios involving a number of features including the campus-wide passenger demand, seat capacity of shuttles, battery capacity of shuttles, and recharging station location. Further solutions to our model formulation have been obtained using a benchmark set of instances designed for a large-scale real network. Our findings show that considering a real road network as it is, is significant in exact routing solutions despite the fact that the level of network complexity is an issue. We suggest that a trade-off among the actuality of the network topology and the consequent computational load should be carefully made in order to obtain solutions using exact methods. As is shown, there is room to investigate further in details the dynamics of routing considering especially the effects of changes in flow conditions at intermediate nodes using our formulation.

Space-time routing in dedicated automated vehicle zones

With the fast development of automated vehicle (AV) technologies, scholars have proposed various innovative local traffic control schemes for more effective management of AV traffic, especially at intersections. However, due to computational intractability, the investigation of network-level AV control is still at the initial stage. This study proposes a space-time routing framework applicable in dedicated AV zones. To relieve the computational load, we establish a node-based conflict point network to model realistic road networks, and at each conflict point, we record the space-time occupations of AVs in continuous timelines. Then, based on the conflict point network, we develop two space-time routing algorithms for each AV once it enters the dedicated AV zone to minimize its trip travel time while maintaining the non-collision insurances; these two algorithms can trade-off between solution quality and computational load. Furthermore, to enhance the network throughput for handling heavy traffic, we develop a ”platoon strategy” that forces AVs to pass through conflict points in platoons, and we adopt Deep Q-learning (DQN) to optimize the platoon sizes at different spots dynamically. Numerical tests show that both proposed algorithms perform well in that they can execute the routing tasks with very limited computational time, and the average vehicle delay approaches zero when the traffic is relatively mild. Meanwhile, compared with the FCFS policy and the optimization-based approach, the platoon strategy can greatly reduce the average vehicle delay under congested scenarios and give a better balance between the optimality and real-time performance.

A time-based model and GIS framework for assessing hazardous materials transportation risk in urban areas

Every day, trucks carrying hazardous materials (hazmat) in a large and densely populated city expose public health risks to the residents of the city as well as risks to the economic assets in the area. In this paper, we introduce a new risk model that considers population exposure along a route and the duration of such exposure, the latter being variable due to the congested nature of road transportation in urban areas. We have developed a spatial decision support system (SDSS) for quantitative risk analysis and the calculation of minimum-risk paths as well as their visualization on digital maps. We illustrate the usage of our proposed model and SDSS via a case study using the real road network of Istanbul, Turkey, a large metropolitan area with more than 15 million residents. We analyze Istanbul's hazmat transportation risk profile using risk analysis techniques via our interactive Geographical Information Systems (GIS)-based decision support system. We then produce a risk map of the city and run several routing scenarios between selected hazmat shipment origins and destinations. Results suggest that the hazmat routes under the new model may not always match with how hazmat is usually transported, with an economic decision-making perspective, in an urban area. Our proposed approach provides decision makers with new insights into urban hazmat transportation and is likely to reduce the consequences of incidents with large impact on public health.

An Enhanced Network-Consistent Travel Speed Generation Scheme on Time-Dependent Shortest Path and Routing Problems

The time-dependent shortest path and vehicle routing literature depends on realistic and reasonable test data for demonstration and performance evaluation. Despite the advancements in GPS and tracking technologies there is still lack and inaccessibility of publicly available real-world road networks with time-dependent arc costs and speeds. Since most of the time-dependent travel time layer generation models proposed for vehicle routing problems (VRPs) are mainly developed for synthetic networks, they cannot capture some realistic features of the real road networks and cannot be used in time-dependent shortest path problems (TDSPPs). In this paper, we first exploit spatial and temporal behavior of travel times using real life road network and speed data, and discuss the cases where the existing methods in the literature are not applicable. Then, we propose an enhanced method that is best fitted for TDSPP and time-dependent VRP (TDVRP). The proposed method can be implemented on both synthetic and real road networks. Finally, we apply our method to generate realistic speed data on Istanbul road network and demonstrate the applicability in TDSPP and TDVRP.

Route intersection reduction with connected autonomous vehicles

A common cause of traffic congestions is the concentration of intersecting vehicle routes. It can be difficult to reduce the intersecting routes in existing traffic systems where the routes are decided independently from vehicle to vehicle. The development of connected autonomous vehicles provides the opportunity to address the intersecting route problem as the route of vehicles can be coordinated globally. We prototype a traffic management system for optimizing traffic with connected autonomous vehicles. The system allocates routes to the vehicles based on streaming traffic data. We develop two route assignment algorithms for the system. The algorithms can help to mitigate traffic congestions by reducing intersecting routes. Extensive experiments are conducted to compare the proposed algorithms and two state-of-the-art route assignment algorithms with both synthetic and real road networks in a simulated traffic management system. The experimental results show that the proposed algorithms outperform the competitors in terms of the travel time of the vehicles.

Toward equity in large-scale network-level pavement maintenance and rehabilitation scheduling using water cycle and genetic algorithms

ABSTRACT Appropriate pavement maintenance is of great importance due to the increasing deterioration of pavements and limited resources. Nowadays, highway agencies face large-scale networks. The management of large-scale networks is a challenge concerning the computational complexity, which typically increases exponentially with the dimension of the network. Water cycle and genetic algorithms are utilised in this paper to find the optimal maintenance schedule of large-scale pavement networks. A new practical constraint is introduced in the mathematical formulation that the total annual costs should not fluctuate more than a predefined limit over the planning horizon. Moreover, a novel index is developed to calculate the equity level in pavement maintenance scheduling, and the outcomes of the algorithms are compared based on this equation. A real road network with 103 pavement sections is the case study of this paper. The results show that ‘Equity index’ is reduced by 94% and 48% during the analysis period by WCA and GA, respectively. Drawing on WCA and GA optimal solutions, the average international roughness index of the network is decreased by 35% and 31% respectively in a 5-year horizon. Moreover, the variance of the maximum and minimum allocated budget in the analysis period is less than 15%.