Forecasting the Accident Frequency and Risk Factors: A Case Study of Erzurum, Turkey

Nowadays, life is intimately associated with transportation, generating several issues on it. Numerous works are available concerning accident prediction techniques depending on independent road and traffic features, while the mix parameters including time, geometry, traffic flow, and weather conditions are still rarely ever taken into consideration. This study aims to predict future accident frequency and the risk factors of traffic accidents. It utilizes the Generalized Linear Model (GLM) and Artificial Neural Networks (ANN) approaches to process and predict traffic data efficiently based on 21500 records of traffic accidents that occurred in Erzurum in Turkey from 2005 to 2019. The results of the comparative evaluation demonstrated that the ANN model outperformed the GLM model. The study revealed that the most effective variable was the number of horizontal curves. The annual average growth rates of accident occurrences based on the ANNꞌs method are predicted to be 11.22% until 2030

The urban real-time traffic control (URTC) system : a study of designing the controller and its simulation

The growth of the number of automobiles on the roads in China has put higher demands on the traffic control system that needs to efficiently reduce the level of congestion occurrence, which increases travel delay, fuel consumption, and air pollution. The traffic control system, urban real-time traffic control system based on multi-agent (MA-URTC) is presented in this thesis. According to the present situation and the traffic's future development in China, the researches on intelligent traffic control strategy and simulation based on agent lays a foundation for the realization of the system. The thesis is organized as follows: The first part focuses on the intersection' real-time signal control strategy. It contains the limitations of current traffic control systems, application of artificial intelligence in the research, how to bring the dynamic traffic flow forecast into effect by combining the neural network with the genetic arithmetic, and traffic signal real-time control strategy based on fuzzy control. The author uses sorne simple simulation results to testify its superiority. We adopt the latest agent technology in designing the logical structure of the MA-URTC system. By exchanging traffic flows information among the relative agents, MA-URTC provides a new concept in urban traffic control. With a global coordination and cooperation on autonomy-based view of the traffic in cities, MA-URTC anticipates the congestion and control traffic flows. It is designed to support the real-time dynamic selection of intelligent traffic control strategy and the real-time communication requirements, together with a sufficient level of fault-tolerance. Due to the complexity and levity of urban traffic, none strategy can be universally applicable. The agent can independently choose the best scheme according to the real-time situation. To develop an advanced traffic simulation system it can be helpful for us to find the best scheme and the best switch-point of different schemes. Thus we can better deal with the different real-time traffic situations. The second part discusses the architecture and function of the intelligent traffic control simulation based on agent. Meanwhile the author discusses the design model of the vehicle-agent, road agent in traffic network and the intersection-agent so that we can better simulate the real-time environment. The vehicle-agent carries out the intelligent simulation based on the characteristics of the drivers in the actual traffic condition to avoid the disadvantage of the traditional traffic simulation system, simple-functioned algorithm of the vehicles model and unfeasible forecasting hypothesis. It improves the practicability of the whole simulation system greatly. The road agent's significance lies in its guidance of the traffic participants. It avoids the urban traffic control that depends on only the traffic signal control at intersection. It gives the traffic participants the most comfortable and direct guidance in traveling. It can also make a real-time and dynamic adjustment on the urban traffic flow, thus greatly lighten the pressure of signal control in intersection area. To sorne extent, the road agent is equal to the pre-caution mechanism. In the future, the construction of urban roads tends to be more intelligent. Therefore, the research on road agent is very important. All kinds of agents in MA-URTC are interconnected through a computer network. In the end, the author discusses the direction of future research. As the whole system is a multi-agent system, the intersection, the road and the vehicle belongs to multi-agent system respectively. So the emphasis should be put on the structure design and communication of all kinds of traffic agents in the system. Meanwhile, as an open and flexible real-time traffic control system, it is also concerned with how to collaborate with other related systems effectively, how to conform the resources and how to make the traffic participants anywhere throughout the city be in the best traffic guidance at all times and places. To actualize the genuine ITS will be our final goal. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Artificial Intelligence, Computer simulation, Fuzzy control, Genetic Algorithm, Intelligent traffic control, ITS, Multi-agent, Neural Network, Real-time

Dynamic Vehicular Routing in Urban Environments

Traffic congestion is a persistent issue that most of the people living in a city have to face every day. Traffic density is constantly increasing and, in many metropolitan areas, the road network has reached its limits and cannot easily be extended to meet the growing traffic demand. Intelligent Transportation System (ITS) is a world wide trend in traffic monitoring that uses technology and infrastructure improvements in advanced communication and sensors to tackle transportation issues such as mobility efficiency, safety, and traffic congestion. The purpose of ITS is to take advantage of all available technologies to improve every aspect of mobility and traffic. Our focus in this thesis is to use these advancements in technology and infrastructure to mitigate traffic congestion. We discuss the state of the art in traffic flow optimization methods, their limitations, and the benefits of a new point of view. The traffic monitoring mechanism that we propose uses vehicular telecommunication to gather the traffic information that is fundamental to the creation of a consistent overview of the traffic situation, to provision real-time information to drivers, and to optimizing their routes. In order to study the impact of dynamic rerouting on the traffic congestion experienced in the urban environment, we need a reliable representation of the traffic situation. In this thesis, traffic flow theory, together with mobility models and propagation models, are the basis to providing a simulation environment capable of providing a realistic and interactive urban mobility, which is used to test and validate our solution for mitigating traffic congestion. The topology of the urban environment plays a fundamental role in traffic optimization, not only in terms of mobility patterns, but also in the connectivity and infrastructure available. Given the complexity of the problem, we start by defining the main parameters we want to optimize, and the user interaction required, in order to achieve the goal. We aim to optimize the travel time from origin to destination with a selfish approach, focusing on each driver. We then evaluated constraints and added values of the proposed optimization, providing a preliminary study on its impact on a simple scenario. Our evaluation is made in a best-case scenario using complete information, then in a more realistic scenario with partial information on the global traffic situation, where connectivity and coverage play a major role. The lack of a general-purpose, freely-available, realistic and dependable scenario for Vehicular Ad Hoc Networks (VANETs) creates many problems in the research community in providing and comparing realistic results. To address these issues, we implemented a synthetic traffic scenario, based on a real city, to evaluate dynamic routing in a realistic urban environment. The Luxembourg SUMO Traffic (LuST) Scenario is based on the mobility derived from the City of Luxembourg. The scenario is built for the Simulator of Urban MObiltiy (SUMO) and it is compatible with Vehicles in Network Simulation (VEINS) and Objective Modular Network Testbed in C++ (OMNet++), allowing it to be used in VANET simulations. In this thesis we present a selfish traffic optimization approach based on dynamic rerouting, able to mitigate the impact of traffic congestion in urban environments on a global scale. The general-purpose traffic scenario built to validate our results is already being used by the research community, and is freely-available under the MIT licence, and is hosted on GitHub

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Vehicle trajectory prediction for safe navigation of autonomous vehicles

Trajectory prediction of the other road users in the vicinity of an autonomous vehicle is important for safe navigation in dense traffic. Once an autonomous vehicle anticipates how the other road actors will react in the near future, path planning is a lot more simpler and safer. Moreover, the knowledge of future movement of other road actors allows control of sudden jerks in the planned ego vehicle’s path and thus makes travel smoother. This trajectory prediction stage can be used at any level, from restricted driver assistance to full vehicle autonomy. In this thesis two novel trajectory prediction models have been developed. In the first model, the spatio-temporal features that form the basis of behaviour prediction were captured using a Convolutional Long Short Term Memory (Conv-LSTM) neural network architecture consisting of three modules: 1) Interaction Learning to capture the motion of and interaction with surrounding cars, 2) Temporal Learning to identify the dependency on past movements and 3) Motion Learning to convert the extracted features from these two modules into future positions. In addition, a novel feedback scheme was introduced in which the current predicted positions of each car are leveraged to update future motion, encapsulating the effect of the surrounding cars. In the second model a conventional Long Short Term Memory (LSTM) cell based encoder-decoder architecture was developed which uses not only the historical observations but also the associated map features. Moreover, unlike existing architectures, the proposed method incorporates and updates the surrounding vehicle information in both the encoder and decoder, making use of dynamically predicted new data for accurate prediction in longer time horizons. This seamlessly performs four tasks: first, it encodes a feature given the past observations, second, it estimates future maneuvers given the encoded state, third, it predicts the future motion given the estimated maneuvers and the initially encoded states, and fourth, it estimates future trajectory given the encoded state and the predicted maneuvers and motions. Both the developed models were evaluated extensively on two publicly available datasets which include both multi-lane highway and signalled intersections, to benchmark the prediction accuracy with the state-of-the-art models. Later, the conventional encoder-decoder model was also evaluated with a newly collected “Radiate” dataset which includes two intersections, the Kingussie T-junction and the Edinburgh four-way junction, both without traffic signals. The accuracy of the predicted trajectories on the benchmark datasets are comparable with state-of-the-art methods. Moreover, evaluation on the latter dataset (“Radiate”) made it possible to understand better the effect of inter-vehicle interactions on future motion without any influence from mandatory traffic signals.Engineering and Physical Sciences Research Council (EPSRC) funding

Vehicle and Traffic Safety

The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered

Performing Short-Term Travel Time Prediction on Arterials

As urban centers become larger and more densely developed, their roadway networks tend to experience more severe congestion for longer periods of the day and increasingly unreliable travel times. Proactive traffic management (PTM) strategies such as proactive traffic signal control systems and advanced traveler information systems provide the potential to cost effectively improve road network operations. However, these proactive management strategies require an ability to accurately predict near-future traffic conditions. Traffic conditions can be described using a variety of measures of performance and travel time is one of the most valued by both travelers and transportation system managers. Consequently, there exists a large body of literature dedicated to methods for performing travel time prediction. The majority of the existing body of research on travel time prediction has focused on freeway travel time prediction using fixed point sensor data. Predicting travel times on signalized arterials is more challenging than on freeways mainly as a result of the higher variation of travel times in these environments. For both freeways and arterial environments, making predictions in real-time is more challenging than performing off-line predictions, mainly because of data availability issues that arise for real-time applications. Recently, Bluetooth detectors have been utilized for collecting both spatial (i.e. travel time) and fixed point (e.g. number of detections) data. Bluetooth detectors have surpassed most of the conventional travel time measuring techniques in three main capacities: (i) direct measurement of travel time, (ii) continuous collection of travel times provides large samples, and (iii) anonymous detection. Beside these advantages, there are also caveats when using these detectors: (i) the Bluetooth obtained data include different sources of outliers and measurement errors that should be filtered out before the data are used in any travel time analysis and (ii) there is an inherent time lag in acquiring Bluetooth travel times (due to the matching of the detections at the upstream and downstream sensors) that should be carefully handled in real-time applications. In this thesis, (1) the magnitude of Bluetooth travel time measurement error has been examined through a simulation framework; (2) a real-time proactive outlier detection algorithm, which is suitable for filtering out data anomalies in Bluetooth obtained travel times, has been proposed; (3) the performance of the existing real-time outlier detection algorithms has been evaluated using both field data and simulation data; and (4) two different data-driven methodologies, that are appropriate for real-time applications, have been developed to predict near future travel times on arterials using data obtained from Bluetooth detectors. The results of this research demonstrate that (1) although the mean Bluetooth travel time measurement error is sufficiently close to zero across all the examined traffic conditions, for some situations the 95% confidence interval of the mentioned error approaches 35% of the true mean travel time; (2) the proposed proactive filtering algorithm appropriately detects the Bluetooth travel time outliers in real time and outperforms the existing data-driven filtering techniques; (3) the performance of different outlier detection algorithms can be objectively quantified under different conditions using the developed simulation framework; (4) the proposed prediction approaches significantly improved the accuracy of travel time predictions for 5-minutre prediction horizon. The daily mean absolute relative errors are improved by 18% to 24% for the proposed k-NN model and 8% to 14% for the proposed Markov model; (5) prevailing arterial traffic state and its transition through the course of the day can be adequately modeled using data obtained from Bluetooth technology

Proceedings of the 8th International Conference on Civil Engineering

This open access book is a collection of accepted papers from the 8th International Conference on Civil Engineering (ICCE2021). Researchers and engineers have discussed and presented around three major topics, i.e., construction and structural mechanics, building materials, and transportation and traffic. The content provide new ideas and practical experiences for both scientists and professionals