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CTPATH: A Real World System to Enable Green Transportation by Optimizing Environmentaly Friendly Routing Paths
Abstract
Road transportation is becoming a major concern in modern cities. The growth of the number of vehicles is provoking an important increment of pollution and greenhouse gas emissions generated by road traffic. In this paper, we present CTPATH, an innovative smart mobility software system that offers efficient paths to drivers in terms of travel time and greenhouse gas emissions. In order to obtain accurate results, CTPATH computes these paths taking into account the layout and habits in the city and real-time road traffic data. It offers customized paths to drivers (including personal profiles) in a distributed and intelligent way so as to consider the whole city situation.
... Thus, Smart Mobility emphasize on providing the system of transport more sustainable to smoothen the commuting traffic. By applying the information timely with the help of communication technologies, smart mobility is gearing up the evolution in the future (Benevolo et al., 2016;Cintrano et al., 2016;Lin et al., 2017;Nesmachnow et al., 2018;Stolfi et al., 2016). Today to find out space for parking is considered an essential activity that affects negative flow for road traffic, and it is responsible for approximately 40 percent of the traffic jam in cities (Giuffrè et al., 2012). ...
Predicting the location of parking is a long-lasting problem that has ultimate importance in our daily life. In this paper, artificial neural networks are used to predict the parking location that will be helpful for drivers to settle on a reasonable area for stopping. This approach eventually adds to the familiarity and wellbeing of traffic on the roads which results in a decrease in turbulence. By using the approach of Deep Extreme Learning Machine (DELM), reliability can be achieved with a marginal error rate thus reducing the skeptical inclination. In this article, the Proposed Car Parking Space Prediction (CPSP) to elaborate on the dilemma of parking space for vehicles, we have used deep learning neural networks in contrast with feedforward and backward propagation. When the results were taken into consideration, it was unveiled that extreme deep machine learning neural network bears the highest accuracy rate with 60% of training
(21431 samples), 40% of test and validation (14287 examples). It has been observed that the proposed DELM has the highest precision rate of 91.25 %. Simulation results validate the prediction effectiveness of the proposed DELM strategy.
... Smart cities initiatives are here to address these issues (Neirotti et al., 2014). Whether it implies populating the city with sensors (Fernández-Ares et al., 2016) or developing several apps (Cintrano et al., 2016), the final goal is to take advantage of new technologies to improve our quality of life, encouraging citizens to not only use these new tools but also to participate actively in the development of the future smart city. ...
Finding an available parking space can be difficult in parts of most cities, especially in city centers. In this article, we address the study of parking occupancy data in Birmingham, Glasgow, Norfolk, and Nottingham in the United Kingdom. We test several prediction strategies, such as polynomial fitting, Fourier series, K-means clustering, and time series, and analyze their results. We use cross-validation to train the predictors and then test them with unseen occupancy data. Additionally, we develop a web service to visualize the current and historical parking data in a map, allowing users to consult the occupancy rate forecast up to one week in advance to satisfy their parking needs. We believe that the use of these accurate, intelligent techniques creates user services for citizens living in real smart cities as a way of improving their quality of life, decreasing wait times, and reducing fuel consumption.
... Traffic jams bother the daily life of the population, mainly because traffic congestion causes longer trip times and a larger associated pollution, not to mention the economic losses due to delays and other transport problems. Thus, great efforts are being made to develop along one of the dimensions of the Smart City initiative: Smart Mobility, which focuses on providing sustainable transport systems and logistics to allow a smooth urban traffic and commuting by mainly applying information and communication technologies [4,7,19,22,33]. ...
This study presents a new technique based on Deep Learning with Recurrent Neural Networks to address the prediction of car park occupancy rate. This is an interesting problem in smart mobility and we here approach it in an innovative way, consisting in automatically design a deep network that encapsulates the behavior of the car occupancy and then is able to make an informed guess on the number of free parking spaces near to the medium time horizon. We analyze a real world case study consisting of the occupancy values of 29 car parks in Birmingham, UK, during eleven weeks and compare our results to other predictors in the state-of-the-art. The results show that our approach is accurate to the point of being useful for being used by citizens in their daily lives, as well as it outperforms the existing competitors.
... Indeed, the speed at which new challenges in mobility in cities is larger than the services given by city councils and collaborating companies. With the new concept of smart cities [1][2][3][4][5][6], countless new problems have been appearing in mobility [7][8][9], including: parking, optimized routes, car sharing, smart systems in buses, private models of mobility, signaling, lane decisions, social implications of mobility, energy consumption, and environmental implications. ...
This article develops the design, installation, exploitation, and final utilization of intelligent techniques, hardware, and software for understanding mobility in a modern city. We focus on a smart-campus initiative in the University of Malaga as the scenario for building this cyber–physical system at a low cost, and then present the details of a new proposed evolutionary algorithm used for better training machine-learning techniques: BiPred. We model and solve the task of reducing the size of the dataset used for learning about campus mobility. Our conclusions show an important reduction of the required data to learn mobility patterns by more than 90%, while improving (at the same time) the precision of the predictions of theapplied machine-learning method (up to 15%). All this was done along with the construction of a real system in a city, which hopefully resulted in a very comprehensive work in smart cities using sensors.
... Fuel, modern environmental concerns, or reduced traffic jams are also new goals to be taken into account for smart mobility in cities. A good point to start with new formulations is to consider the bi-objective shortest path (BSP) problem, that has received special attention in recent years both in academy and industry [3]. ...
Road journeys are one of our most frequent daily tasks. Despite we need them, these trips have some associated costs: time, money, pollution, etc. One of the usual ways of modeling the road network is as a graph. The shortest path problem consists in finding the path in a graph that minimizes a certain cost function. However, in real world applications, more than one objective must be optimized simultaneously (e.g. time and pollution) and the data used in the optimization is not precise: it contains errors. In this paper we propose a new mathematical model for the robust bi-objective shortest path problem. In addition, some empirical studies are included to illustrate the utility of our formulation.
This book constitutes the proceedings of the second International Conference on Smart Cities, Smart-CT 2017, held in Málaga, Spain, in June 2017.
The 16 papers presented in this volume were carefully reviewed and selected from 21 submissions. The topics covered include studies and tools to improve road traffic, energy consumption, logistics, frameworks to provide new services and take decisions in a holistic way, driving assistance, electric vehicles, public transport, and surveys on smart city concepts.
This paper aims at solving the stochastic shortest path problem in vehicle routing, the objective of which is to determine an optimal path that maximizes the probability of arriving at the destination before a given deadline. To solve this problem, we propose a data-driven approach, which directly explores the big data generated in traffic. Specifically, we first reformulate the original shortest path problem as a cardinality minimization problem directly based on samples of travel time on each road link, which can be obtained from the GPS trajectory of vehicles. Then, we apply an l1-norm minimization technique and its variants to solve the cardinality problem. Finally, we transform this problem into a mixed-integer linear programming problem, which can be solved using standard solvers. The proposed approach has three advantages over traditional methods. First, it can handle various or even unknown travel time probability distributions, while traditional stochastic routing methods can only work on specified probability distributions. Second, it does not rely on the assumption that travel time on different road segments is independent of each other, which is usually the case in traditional stochastic routing methods. Third, unlike other existing methods which require that deadlines must be larger than certain values, the proposed approach supports more flexible deadlines. We further analyze the influence of important parameters to the performances, i.e., accuracy and time complexity. Finally, we implement the proposed approach and evaluate its performance based on a real road network of Munich city. With real traffic data, the results show that it outperforms traditional methods.
SUMO is an open source traffic simulation package including the simulation application itself as well as supporting tools, mainly for network import and demand modeling. SUMO helps to investigate a large variety of research topics, mainly in the context of traffic management and vehicular communications. We describe the current state of the package, its major applications, both by research topic and by example, as well as future developments and extensions.
Currently most car drivers use static routing devices based on the shortest distance between start and end position. But the shortest route can differ from the shortest route in time. To compute alternative routes it is necessary to have good prediction models of expected congestions and a fast algorithm to compute the shortest path while being able to react to dynamic changes in the network caused by special incidents. In this paper we present a dynamic routing system based on Ant Based Control (ABC). Starting from historical traffic data, ants are used to compute and predict the travel times along the road segments. They are finding the fastest routes not only looking to the past and present traffic conditions but also trying to anticipate and avoid future congestions.
The route planning problem is one of the core functions of the car navigation system, it can help users planning out an optimal route. The ant colony algorithm is a novel evolutionary algorithm, it has some advantages in solving complex optimization problems. Considering the road length, steering and congestion information, this paper proposes an improved ant colony algorithm to find a route which costs the shortest travel time and uses firefly algorithm to optimize the parameters in the improved ant colony algorithm. Experimental results demonstrate that this method can gain a satisfying route and provide some reference value to related researchers.
The past decades witnessed a big effort in solving road-network congestion problem through routing optimization approaches. With a multi-objective optimization perspective, this paper proposed a new method which solved the road-network congestion problem by combining two objectives of shortest routing and congestion avoidance. Especially, we applied the approach of Brownian agents to find the next intersection of road network to avoid congestion. Vehicles were simulated as Brownian agents with automatic movements in the road-network, and the entire network congestion distribution were optimized at the same time. We tried to find out the relationship between the moving strategies of the vehicles and the network congestion. By means of computer simulation, we implemented our proposed method with a predefined road-network topological structure. We tested the parameters sensitivity by scaling the proportion of agent with two moving strategies: the shortest path strategy and a mix strategy combining two objectives of shortest routing and congestion avoidance. Furthermore, we analyzed the various network congestions under a mix strategy by changing the weights to represent different focus on two moving strategies. The simulation results proved the applicability and efficiency of our proposed method for alleviating the network congestion distribution, and the intersections within a higher vehicle density were observed decreased.
Travel demand necessitates a big expenditure in spending, as has been proven by the National Audit Department (NAD). Every year the auditing process is carried out throughout the country involving official travel claims. This study focuses on the use of the Spanning Tree model to determine the shortest path to minimize the cost of the NAD's official travel claims. The objective is to study the possibility of running a network based in the Kluang District Health Office to eight Rural Clinics in Johor state using the Spanning Tree model applications for optimizing travelling distances and make recommendations to the senior management of the Audit Department to analyze travelling details before an audit is conducted. Result of this study reveals that there were claims of savings of up to 47.4% of the original claims, over the course of the travel distance.
Vehicles equipped with wireless communication technology, Dedicated Short Range Communication are a promising field for fuel optimization navigation applications. This paper presents a vehicle routing methodology modeled as a Petri Net (PN) for optimizing travel time and vehicle emission in a connected roadway network with minimal total traffic capacity to route vehicle in a dynamically changing traffic environment, and in an optimal and predictive manner. The novel unfolded PN model presented in this paper incorporates the essential features in Dynamic Programming (DP) to solve the stochastic traffic routing problem. The effectiveness of the proposed eco-friendly navigation methodology is validated by comparing the performance with conventional travel time based navigation methods.
We present a practical parallel algorithm for finding shortest paths in the presence of two objective functions. The algorithm builds on a recent theoretical result that on the first glance looks impractical. We address the problem of significant constant factor overheads due to numerous prefix sum computations by carefully re-engineering the algorithm for moderate parallelism. In addition, we develop a parallel weight-balanced B-tree data structure that cache efficiently supports bulk updates. This result might be of independent interest and closes the gap between the full-blown search tree data structure required by the theoretical result over the simple priority queue for the sequential algorithm. Comparing our implementation against a highly tuned sequential bi-objective search, we achieve speedups of 8 on 16 cores.
This book focuses on emerging technologies in the field of Intelligent Transportation Systems (ITSs) namely efficient information dissemination between vehicles, infrastructures, pedestrians and public transportation systems. It covers the state-of-the-art of Vehicular Ad-hoc Networks (VANETs), with centralized and decentralized (Peer-to-Peer) communication architectures, considering several application scenarios. With a detailed treatment of emerging communication paradigms, including cross networking and distributed algorithms. Unlike most of the existing books, this book presents a multi-layer overview of information dissemination systems, from lower layers (MAC) to high layers (applications). All those aspects are investigated considering the use of mobile devices, such as smartphones/tablets and embedded systems, i.e. technologies that during last years completely changed the current market, the user expectations, and communication networks. The presented networking paradigms are supported and validated by means of extensive simulative analysis and real field deployments in different application scenarios.
This book represents a reference for professional technologist, postgraduates and researchers in the area of Intelligent Transportation Systems (ITSs), wireless communication and distributed systems.
We take the reader on a very brief guided tour of MultiObjective Combinatorial Optimization (MOCO). We point out the increasing importance of consideration of multiple objectives in real world applications of combinatorial optimization, survey the problem context and the main characteristics of (MOCO) problems. Our main stops on the tour are for overviews of exact and heuristic solution methods for MOCO. We conclude the presentation with an outlook on promising destinations for future expeditions into the field.
he Biobjective Shortest Path Problem (BSP) is the problem of finding (one-to-one) paths from a start node to an end node, while simultaneously minimizing two (conflicting) objective functions. We present an exact recursive method based on implicit enumeration that aggressively prunes dominated solutions. Our approach compares favorably against a top-performer algorithm on two large testbeds from the literature and efficiently solves the BSP on large-scale networks with up to 1.2 million nodes and 2.8 million arcs. Additionally, we describe how the algorithm can be extended to handle more than two objectives and prove the concept on networks with up to 10 objectives.
Bicriterion path problems in directed graphs are systematically studied. A series of ten problems are defined and their computational complexity examined. Algorithms are provided for some of them, including polynomial algorithms for the MAXMIN-MAXMIN problem and the MINSUM-MAXMIN problem, and a pseudo-polynomial exact algorithm as well as a fully polynomial approximation scheme for the MINSUM-MINSUM problem.
The classical 0–1 knapsack problem is considered with two objectives. Two methods of the “two–phases” type are developed to generate the set of efficient solutions. In the first phase, the set of supported efficient solutions is determined by optimizing a parameterized single-objective knapsack problem. Two versions are proposed for a second phase, determining the non-supported efficient solutions: both versions are Branch and Bound approaches, but one is “breadth first”, while the other is “depth first”. Extensive numerical experiments have been realized to compare the results of both methods.
Our paper on the use of heuristic information in graph searching defined a path-finding algorithm, A*, and proved that it had two important properties. In the notation of the paper, we proved that if the heuristic function ñ (n) is a lower bound on the true minimal cost from node n to a goal node, then A* is admissible; i.e., it would find a minimal cost path if any path to a goal node existed. Further, we proved that if the heuristic function also satisfied something called the consistency assumption, then A* was optimal; i.e., it expanded no more nodes than any other admissible algorithm A no more informed than A*. These results were summarized in a book by one of us.
In the field of vector (or multi objective) optimization there has been a relatively little interest in solving combinatorial or discrete problems. During the 70’s just a few papers have been published on multi objective (m.o.) integer linear programming. However no special emphasis was put on the important aspect of computational complexity. This can be certainly ascribed to the fact that the theory of NP—completeness was developing at a fast pace in those same years.
We consider a graph with n vertices, all pairs of which are connected by an edge; each edge is of given positive length. The following two basic problems are solved. Problem 1: construct the tree of minimal total length between the n vertices. (A tree is a graph with one and only one path between any two vertices.) Problem 2: find the path of minimal total length between two given vertices.
Multi-objective shortest path problem (MOSP) is an extension of a traditional single objective shortest path problem that seeks for the efficient paths satisfying several conflicting objectives between two nodes of a network. MOSP is one of the most important problems in network optimization with wide applications in telecommunication industries, transportation and project management. This research presents an algorithm based on multi-objective ant colony optimization (ACO) to solve the bi-objective shortest path problem. To analyze the efficiency of the algorithm and check for the quality of solutions, experimental analyses are conducted. Two sets of small and large sized problems that generated randomly are solved. Results on the set problems are compared with those of label correcting solutions that is the most known efficient algorithm for solving MOSP. To compare the Pareto optimal frontiers produced by the suggested ACO algorithm and the label correcting algorithm, some performance measures are employed that consider and compare the distance, uniformity distribution and extension of the Pareto frontiers. The results on the set of instance problems show that the suggested algorithm produces good quality non-dominated solutions and time saving in computation of large-scale bi-objective shortest path problems.
Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.
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