A Fast and Scalable Re-routing Algorithm based on Shortest Path and Genetic Algorithms J. Lee, J. Yang Jungkyu Lee

This paper presents a fast and scalable re-routing algorithm that adapts to dynamically changing networks. The proposed algorithm, DGA, integrates Dijkstra’s shortest path algorithm with the genetic algorithm. Dijkstra’s algorithm is used to define the predecessor array that facilitates the initialization process of the genetic algorithm. Then the genetic algorithm keeps finding the best routes with appropriate genetic operators under dynamic traffic situations. Experimental results demonstrate that DGA produces routes with less traveling time and computational overhead than pure genetic algorithm-based approaches as well as Dijkstra’s algorithm in largescale routing problems

Lean emergency medical operations

The fundamental responsibilities of Emergency Medical Service (EMS) systems are to provide urgent medical care, such as pre-hospital care, and to transport the patient to the hospital if needed. The agility and efficiency of EMS systems are major public concerns. Since the EMS process involved is fundamentally a transportation process, the Lean Transportation approach is a viable option for improving Emergency Medical Service (EMS) operations performance. The identification of waste during the ambulance cycle time is the basis for designing an operations improvement strategy. The scheme utilized uses modified versions of the Transportation Value Stream Map (TVSM) and The Operational Vehicle Effectiveness (TOVE) Index. Thus, availability, performance and quality wastes are identified to define the improvement strategy. Results of the application for the Monterrey metropolitan operations of the Mexican Red Cross are provided.N/

Vehicle telematics for safer, cleaner and more sustainable urban transport:a review

Urban transport contributes more than a quarter of the global greenhouse gas emissionns that drive climate change; it also produces significant air pollution emissions. Furthermore, vehicle collisions kill and seriously injure 1.35 and 60 million people worldwide, respectively, each year. This paper reviews how vehicle telematics can contribute towards safer, cleaner and more sustainable urban transport. Collection methods are reviewed with a focus on technical challenges, including data processing, storage and privacy concerns. We review how vehicle telematics can be used to estimate transport variables, such as traffic flow speed, driving characteristics, fuel consumption and exhaustive and non-exhaustive emissions. The roles of telematics in the development of intelligent transportation systems (ITSs), optimised routing services, safer road networks and fairer insurance premia estimation are highlighted. Finally, we outline the potential for telematics to facilitate new-to-market urban mobility technologies, signalised intersections, vehicle-to-vehicle (V2V) communication networks and other internet-of-things (IoT) and internet-of-vehicles (IoV) technologies

Using Ontologies and Intelligent Systems for Traffic Accident Assistance in Vehicular Environments

A pesar de que las medidas de seguridad en los sistemas de transporte cada vez son mayores, el aumento progresivo del número de vehículos que circulan por las ciudades y carreteras en todo el mundo aumenta, sin duda, la probabilidad de que ocurra un accidente. En este tipo de situaciones, el tiempo de respuesta de los servicios de emergencia es crucial, ya que está demostrado que cuanto menor sea el tiempo transcurrido entre el accidente y la atención hospitalaria de los heridos, mayores son sus probabilidades de supervivencia. Las redes vehiculares permiten la comunicación entre los vehículos, así como la comunicación entre los vehículos y la infraestructura [4], lo que da lugar a una plétora de nuevas aplicaciones y servicios en el entorno vehicular. Centrándonos en las aplicaciones relacionadas con la seguridad vial, mediante este tipo de comunicaciones, los vehículos podrían informar en caso de accidente al resto de vehículos (evitando así colisiones en cadena) y a los servicios de emergencia (dando información precisa y rápida, lo que sin duda facilitaría las tareas de rescate). Uno de los aspectos importantes a determinar sería saber qué información se debe enviar, quién será capaz de recibirla, y cómo actuar una vez recibida. Actualmente los vehículos disponen de una serie de sensores que les permiten obtener información sobre ellos mismos (velocidad, posición, estado de los sistemas de seguridad, número de ocupantes del vehículo, etc.), y sobre su entorno (información meteorológica, estado de la calzada, luminosidad, etc.). En caso de accidente, toda esa información puede ser estructurada y enviada a los servicios de emergencia para que éstos adecúen el rescate a las características específicas y la gravedad del accidente, actuando en consecuencia. Por otro lado, para que la información enviada por los vehículos accidentados pueda llegar correctamente a los servicios de emergencias, es necesario disponer de una infraestructura capaz de dar cobertura a todos los vehículos que circulan por una determinada área. Puesto que la instalación y el mantenimiento de dicha infraestructura conllevan un elevado coste, sería conveniente proponer, implementar y evaluar técnicas consistentes en dar cobertura a todos los vehículos, reduciendo el coste total de la infraestructura. Finalmente, una vez que la información ha sido recibida por las autoridades, es necesario elaborar un plan de actuación eficaz, que permita el rápido rescate de los heridos. Hay que tener en cuenta que, cuando ocurre un accidente de tráfico, el tiempo de personación de los servicios de emergencia en el lugar del accidente puede suponer la diferencia entre que los heridos sobrevivan o fallezcan. Además, es importante conocer si la calle o carretera por la que circulaban los vehículos accidentados ha dejado de ser transitable para el resto de vehículos, y en ese caso, activar los mecanismos necesarios que permitan evitar los atascos asociados. En esta Tesis, se pretende gestionar adecuadamente estas situaciones adversas, distribuyendo el tráfico de manera inteligente para reducir el tiempo de llegada de los servicios de emergencia al lugar del accidente, evitando además posibles atascos.Barrachina Villalba, J. (2014). Using Ontologies and Intelligent Systems for Traffic Accident Assistance in Vehicular Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/39004TESI

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility

Mobility mining for time-dependent urban network modeling

170 p.Mobility planning, monitoring and analysis in such a complex ecosystem as a city are very challenging.Our contributions are expected to be a small step forward towards a more integrated vision of mobilitymanagement. The main hypothesis behind this thesis is that the transportation offer and the mobilitydemand are greatly coupled, and thus, both need to be thoroughly and consistently represented in a digitalmanner so as to enable good quality data-driven advanced analysis. Data-driven analytics solutions relyon measurements. However, sensors do only provide a measure of movements that have already occurred(and associated magnitudes, such as vehicles per hour). For a movement to happen there are two mainrequirements: i) the demand (the need or interest) and ii) the offer (the feasibility and resources). Inaddition, for good measurement, the sensor needs to be located at an adequate location and be able tocollect data at the right moment. All this information needs to be digitalised accordingly in order to applyadvanced data analytic methods and take advantage of good digital transportation resource representation.Our main contributions, focused on mobility data mining over urban transportation networks, can besummarised in three groups. The first group consists of a comprehensive description of a digitalmultimodal transport infrastructure representation from global and local perspectives. The second groupis oriented towards matching diverse sensor data onto the transportation network representation,including a quantitative analysis of map-matching algorithms. The final group of contributions covers theprediction of short-term demand based on various measures of urban mobility

A review of the internet of floods : near real-time detection of a flood event and its impact

Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future

Reducing non-recurrent urban traffic congestion using vehicle re-routing

Recently, with the trend of world-wide urbanization, some of the accompanying problems are getting serious, including road traffic congestion. To deal with this problem, city planners now resort to the application of the latest information and communications technologies. One example is the adaptive traffic signal control system (e.g. SCATS, SCOOT). To increase the throughput of each main intersection, it dynamically adjusts the traffic light phases according to real-time traffic conditions collected by widely deployed induction loops and sensors. Another typical application is the on-board vehicle navigation system. It can provide drivers with a personalized route according to their preferences (e.g. shortest/fastest/easiest), utilizing comprehensive geo-map data and floating car data. Dynamic traffic assignment is also one of the key proposed methodologies, as it not only benefits the individual driver, but can also provide a route assignment solution for all vehicles with guaranteed minimum average travel time. However, the non-recurrent road traffic congestion problem is still not addressed properly. Unlike the recurrent traffic congestion, which is predictable by capturing the daily traffic pattern, unexpected road traffic congestion caused by unexpected en-route events (e.g. road maintenance, an unplanned parade, car crashes, etc.), often propagates to larger areas in very short time. Consequently, the congestion level of areas around the event location will be significantly degraded. Unfortunately, the three aforementioned methods cannot reduce this unexpected congestion in real time. The contribution of this thesis firstly lies in emphasizing the importance of the dynamic time constraint for vehicle rerouting. Secondly, a framework for evaluating the performance of vehicle route planning algorithms is proposed along with a case study on the simulated scenario of Cologne city. Thirdly, based on the multi-agent architecture of SCATS, the next road rerouting (NRR) system is introduced. Each agent in NRR can use the locally available information to provide the most promising next road guidance in the face of the unexpected urban traffic congestion. In the last contribution of this thesis, further performance improvement of NRR is achieved by the provision of high-resolution, high update frequency traffic information using vehicular ad hoc networks. Moreover, NRR includes an adaptation mechanism to dynamically determine the algorithmic (i.e. factors in the heuristic routing cost function) and operational (i.e. group of agents which must be enabled) parameters. The simulation results show that in the realistic urban scenario, compared to the existing solutions, NRR can significantly reduce the average travel time and improve the travel time reliability. The results also indicate that for both rerouted and non-rerouted vehicles, NRR does not bring any obvious unfairness issue where some vehicles overwhelmingly sacrifice their own travel time to obtain global benefits for other vehicles