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Increasing congestion levels and their elusive impact warrants the development of congestion mitigation strategies. Quantifying congestion and analyzing the spatiotemporal variations is imperative to achieve this target. Travel time is being explored as a measure of congestion with the advent of Intelligent Transportation Systems (ITS) and the deployment of related technologies. Researchers identified congested conditions when the average travel time exceeds 1.33 or 1.66 times free-flow travel time (FFTT). It is well known that travel time under congested conditions (T c) is more sensitive to land use, road geometry, and traffic control characteristics than the FFTT. Therefore, the extension of FFTT to derive T c may not be appropriate. This study focuses on developing a hazard-based model to derive T c. Travel time is modeled using a parametric accelerated failure time (AFT) model. The applicability of the proposed methodology is justified using empirical and simulated datasets. The T c derived from the AFT model is close to the travel time for the level of service (LOS F). Based on the T c , a new measure of congestion, termed congestion index (CI), is proposed. The proposed index can quantify the frequency and intensity of congestion on a link or network. The traffic states identified based on CI were mapped on the fundamental diagram (FD) and the macroscopic fundamental diagram (MFD). It was concluded that if travel times are uncertain and unstable under low-density conditions, then the ascending leg of the FD or MFD can be marked congested. Uncertain and unstable travel times indicate that traffic flow is unstable, and therefore, it can be concluded that traffic instabilities significantly affect congestion. • A hazard-based duration model is applied to model travel times to derive travel time under congested conditions. • Travel time corresponding to the inflection point of hazard function was considered as travel time under congested conditions. • Instabilities in travel times under low-density conditions mark the ascending leg of the fundamental diagram as congested. • The application of the study is demonstrated by evaluating the effect of traffic signal control. [ABSTRACT FROM AUTHOR]

The process of rapid urbanization in contemporary cities has resulted in a rise in traffic congestion, accidents, and pollution, leading to negative impacts on transportation efficiency and urban development. In response, Intelligent Transportation Systems (ITS) have emerged as a crucial field of research and development, with a goal to leverage technological advancements to enhance transportation safety, efficiency, and sustainability. However, traditional expert systems and traffic control methods have displayed limitations in handling the complexities of urban transportation. To address these challenges in Advanced Traffic Management Systems (ATMSs), this paper pro- poses a new approach utilizing Graph Neural Network-Reinforcement Learning (GNN-RL). By using GNNs for pre-training and information aggregation from the entire grid, our method enables downstream RL tasks, allowing for fine-tuning of effective global information aggregation and dynamic traffic signal control policy adjustment. The GNN-RL approach offers enhanced adapt- ability and performance in managing long-term dependencies, rendering it a promising alternative for traffic control in contemporary urban transportation systems. [ABSTRACT FROM AUTHOR]

• A partial grey prediction model with control matrix (CMPGM(1, M)) is proposed. • The matrices sequence and partial derivatives of mean sequence are used to replace the points sequence. • The novel model can predict traffic flow with better accuracy amongst the comparison models. Short-term traffic flow analysis is the core part of the intelligent transportation system, and also the critical basis for traffic management and control system to guide traffic flow. Real-time and accurate short-term traffic flow prediction can be effectively applied to traffic control and guidance, providing reliable and effective information for traffic managers and travelers, and improving travel efficiency. Based on the spatiotemporal characteristics of traffic flow and periodicity of traffic flow, this paper uses partial differential equation variables to reflect the spatiotemporal characteristics of traffic flow, combines the modelling mechanism of partial differential and the grey prediction model organically, and establishes a partial grey prediction model with control matrix by using the partial derivatives of the mean sequence. At the same time, the parameters estimation and modelling steps of the novel model are obtained by matrix analysis and recursion. A set of data is used from two perspectives to verify the proposed model's effectiveness. Experimental results show that the proposed model is stable and effective. Finally, the novel model is applied to the short-time traffic flow simulation and prediction of the highway. The research results show that the novel model is better than other comparison models. The novel model mainly aims at the matrix data form of traffic flow and is applied to general short-term traffic flow prediction. On the one hand, the prediction results of the novel model can provide theoretical basis and basic methods for the traffic management system, traffic data for traffic information system, and new ideas for the traffic flow prediction research. On the other hand, it can help traffic managers solve accurate real-time traffic flow information, provide real-time traffic information for travelers, and help them plan and adjust routes in advance. [ABSTRACT FROM AUTHOR]

Synergies between fleet management enterprise systems and traffic control systems for information sharing and collaborative planning can contribute to significant improvements to operational efficiency, safety and environmental impact. This paper provides a synthesis of the extant body of research on synergies between fleet management and traffic control systems identifying five key topics in prior research and suggesting research directions. The findings are based on a systematic literature review and topic modelling with the use of natural language processing (Latent Dirichlet Allocation). The five topics identified are related to: hazardous goods transportation, environment and emissions, position tracking and navigation technologies, traffic management and fleet operations´ optimization. The two most frequent topics link to social responsibility (minimizing accidents during hazardous goods transportation and minimizing environmental impact) indicating the relevance of this area of research to sustainability. Despite the strong potential for synergies between intelligent transportation systems and enterprise fleet management systems, the review identified a paucity of research on the coordination between real-time fleet management and traffic control measures (e.g. ramp metering, route guidance, variable speed limits and signal control). [ABSTRACT FROM AUTHOR]

Driven by the massive number of connected vehicles and the stringent requirements of data-intensive applications, logistics transportation systems have evolved to fully comprehend its effectiveness and quality to meet public transportation needs. As a result, to meet public transportation requirements and analyze the data efficiently at the edge of the networks, an advanced artificial intelligent technique needs to be introduced to make the transportation system intelligent by supporting efficient decision making, intelligent traffic control, and intrusion and misuse detection.Motivated by the challenges mentioned above, in this paper, we develop a logistic agent-based model for analyzing public transports such as cars, bus or trains in the intelligent transportation system. The intelligent logistic framework is built on a parallel neural network structure, known as a Swarm-Neural Network (SWNN). The proposed SWNN model analyzes the sensory data and recognizes the public transportation at the edge of the networks. The SWNN model is constructed so that it fits within the intelligent logistic transportation framework, and the proposed model shortens the transit time of every small-scale logistics delivery to its destination. The performance of the proposed SWNN model is evaluated using a standard TMD dataset, where the SWNN model is trained using data, retrieved multiple sensors such as accelerometer, gyroscope, magnetometer, and audio sensors. The features of the sensory data are extracted based on a 5-s time interval. The performance of the proposed SWNN model is studied over various standard machine learning techniques such as Random Forest, XGBoost, and Decision Tree. As per the simulation results, the proposed technique achieves 78–98% accuracy over a real-time dataset's different sets of features. [ABSTRACT FROM AUTHOR]

Many types of systems that seek solutions to cities' mobility issues, such as traffic congestion and accidents, have recently been proposed worldwide. A bottleneck, which is defined as an occurrence of congestion at a highway segment, is one of the major causes of citizens' discontent in cities. Bottlenecks cause a decrease in vehicles' speeds, creating negative effects on the capacity of the highway segments and traffic jams. It is also an important research domain in traffic engineering that causes delays in road traffic. Within the concept of Intelligent Transportation System (ITS), this paper presents a method for investigating road state, speed, and road bottlenecks, without any extra investment and permissions for General Data Protection Regulation (GDPR) compliance procedures. The proposed analysis uses publicly available data of bus stations and buses. The analysis is performed on a chosen route as a numerical example, and the calculated speeds are illustrated on a map. [ABSTRACT FROM AUTHOR]

Traffic signal control (TSC) is a practical solution to the major problem of congestion in metropolitan areas. Reinforcement Learning (RL) techniques present powerful frameworks for optimizing traffic signal controllers that learn to respond to real-time traffic changes. Multiagent RL (MARL) techniques have been showing better results over centralized techniques (RL-based or not), where local intersection agents have partial observation of and control over the environment. Since in TSC the best decision does not depend only on local information, in the present paper we aim at increasing agents' views by using a hierarchical approach, where information is passed upwards, is then aggregated forming recommendations that are sent downwards. We divide the transportation network into regions, each controlled by a region agent; this is done at different hierarchical levels. The traffic signal controllers, located at the intersections, are the local agents at the hierarchy's bottom. Region agents can supervise intersection agents or other region agents. Evaluation of this approach in a synthetic traffic grid shows that the proposed hierarchical organization outperforms a fixed-time approach and an RL-based approach without hierarchy. [ABSTRACT FROM AUTHOR]

Understanding the resilience of transportation networks has received considerable research attention. Nevertheless in the field of network traffic flow control, few control approaches target the mitigation from hyper-congestion, and the control objective has rarely touched the system resilience requirement which focuses on system recovering from hyper-congested state. This paper sheds light on a resilience-oriented network control. We firstly define the traffic resilience as the integral of deviation against optimal state from disturbance generation moment t 0 to recovery moment t f. Then, we propose a control method under hyper-congested situations by formulating the analytical problem using a two-reservoir transportation system with parabola-shaped Macroscopic Fundamental Diagrams (MFDs), using phase diagram analysis, attraction region derivation and switched controller design. Afterwards, we evaluate the system resilience performances between two classic perimeter control schemes (constant perimeter control (CPC) and state-feedback control (SFC)) and the proposed resilient control scheme. Results show that proposed controller can ensure the system to recover from hyper-congestion to the optimal state while existing studies failed to recover. This resilience is confirmed in various case study scenarios, e.g., when the level of hyper-congestion is different. More promisingly, the proposed control shows high compatibility with the form of the MFDs, e.g., the recover can be achieved under hysteresis conditions which are common for network-level traffic dynamics. These findings will help to design an intelligent transportation system with enhanced resilience. • We propose a traffic resilience measure as a control objective. • We propose a resilient control method to recover system to the optimal state from hyper-congestion. • We extend our methodology to two typical scenarios where the shape of the MFD changes under disruptions. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) face the formidable challenge of large-scale missing data, particularly in the imputation of traffic data. Existing studies have mainly relied on modeling network-level spatiotemporal correlations to address this issue. However, these methods often overlook the rich semantic information (e.g., road infrastructure, sensor location, etc.) inherent in road networks when capturing network-wide spatiotemporal correlations. We address this limitation by presenting the Graph Transformer-based Traffic Data Imputation (GT-TDI) model, which imputes missing values in extensive traffic data by leveraging spatiotemporal semantic understanding of road networks. The proposed model leverages semantic descriptions that capture the spatial and temporal dynamics of traffic across road networks, enhancing its capacity to infer comprehensive spatiotemporal relationships. Moreover, to augment the model's capabilities, we employ a Large Language Model (LLM) and prompt engineering to enable natural and intuitive interactions with the traffic data imputation system, allowing users to query and request in plain language, without requiring expert knowledge or complex mathematical models. The proposed model, GT-TDI, utilizes Graph Neural Networks (GNN) and Transformer architectures to perform large-scale traffic data imputation using deficient observations, sensor social connectivity, and semantic descriptions as inputs. We evaluate the GT-TDI model on the PeMS freeway dataset and benchmark it against cutting-edge models. The experimental evidence demonstrates that GT-TDI surpasses the cutting-edge approaches in scenarios with intricate patterns and varying rates of missing data. • A novel semantic description for traffic data imputation that captures spatiotemporal correlations flexibly. • A pioneering application of prompt engineering to ITS that bridges the gap between LLMs and ITS. • The proposed model leverages LLMs and semantic prompts for traffic data imputation. • Extensive experiments on real-world traffic data show the superiority. • A potential framework for integrating LLMs and semantic prompts into other ITS tasks. [ABSTRACT FROM AUTHOR]

Effective traffic control methods have great potential in alleviating network congestion. Particularly, in an urban network consisting of heterogeneous roads (e.g., freeways and urban roads), how to integrate and coordinate control policies on different roads is a critical issue in large-scale networks. This study addresses this question from two aspects: modeling and control. From the modeling aspect, we formulate the hybrid traffic modeling in heterogeneous networks with the Asymmetric Cell Transmission Model (ACTM) for freeways and the generalized bathtub model for urban roads. For the control aspect, this study considers two representative control approaches: ramp metering for freeways and perimeter control for urban roads, and we aim to develop a deep reinforcement learning (DRL)-based coordinated control framework for large-scale networks. However, there are two significant challenges in the coordinated control in large-scale networks with DRL methods: non-stationary environment and large search space. To address both issues, we incorporate the demonstration to guide the DRL method for better convergence by introducing the concept of "teacher" and "student" models. The teacher models are traditional controllers that provide control demonstrations. For instance, ALINEA and Gating are two representative feedback controllers for ramp metering and perimeter control which can be "teacher" models. The student models are DRL methods, which learn from teachers and aim to surpass the teachers' performance. Additionally, we develop a parallel training scheme to accelerate the proposed DRL method. To validate the proposed framework, we conduct two case studies in a small-scale network and a real-world large-scale traffic network in Hong Kong. Numerical results show that the proposed DRL method outperforms demonstrators as well as DRL methods, and the coordinated control is more effective than just controlling ramps or perimeters respectively. The research outcome reveals the great potential of combining traditional controllers with DRL for coordinated control in large-scale networks. • A generalized hybrid traffic model supporting simulation in large-scale networks. • A novel demonstration-guided DRL method for coordinated traffic control. • A parallel training scheme for DRL methods. • The proposed DRL method is examined in real networks in Hong Kong SAR. [ABSTRACT FROM AUTHOR]

• Simulation analysis of morning and evening peak traffic in underground loop reveals congestion patterns. • Developed control strategies: ramp control, plot control, and fast entry without railing. • Evaluation and verification of control schemes using simulation technology. • Findings provide reference for congestion control measures, improving capacity and ensuring safe operations. • Effective traffic control strategies for network-type underground loops in CBD areas. In recent years, there has been a growing trend of developing and constructing network-type underground loops in urban central business districts (CBDs) to complement interconnected underground spaces. However, the high volume of arrival and departure traffic, the impact of traffic patterns, and the complexity of traffic organization in CBD areas pose challenges to the efficient operation of underground loops. Accurate identification of congestion patterns and effective traffic control strategies are crucial to enhance capacity and ensure safe operations of underground loop systems. This paper presents a simulation analysis of morning and evening peak traffic scenarios in a designed underground loop to identify congestion patterns and their underlying causes. Based on the analysis, a combination of traffic control strategies, including ramp control, plot control, and fast entry without railing, is proposed and evaluated through simulations. The findings of this study serve as a reference for developing traffic congestion control measures in underground loop systems, contributing to improved capacity and safe operations. [ABSTRACT FROM AUTHOR]

The paper explores the utilization of RFID innovation for traffic congestion and find out the blockage at any intersection of the street by utilizing RFID reader and labels as sensors. The idea behind this paper is to make the fixed and preset activity of traffic signal dynamic. The paper affords a unique method for making the signal timing proportional to the congestion on the roads at any time directly. Proposed intelligent system can maintain the dynamic timings of traffic signals by sensing the density of traffic to minimize the congestion with the help of IoT enabled sensors which provides the advanced and powerful communication technologies for the citizens. [ABSTRACT FROM AUTHOR]

• There is a need for a secure-by-design tool for transportation systems. • Attack models made based on the link queue model and double ring road network model. • Attacks target control response time, traffic flow performance, and/or stability. • System states are analytically identified as vulnerable to attacks. • Analysis and simulations demonstrate effectiveness of attacks on vulnerable states. Wireless communication is being used as an enabling technology with traditional fixed traffic control systems in this transitional era toward Intelligent Transportation Systems (ITS). Unfortunately, major security concerns have arisen with respect to ever-increasing complexity and interconnectivity, and a noticeable lack of attention for security in these systems. Addressing concerns is a colossal challenge as it requires thorough development and formal analysis of a system model with respect to security. To tackle this challenge, we present a novel formal attack modeling and impact analysis methodology based on the Link Queue Model (LQM) of traffic flow inside a double ring road network, which is equivalent to a grid network with homogeneous links. We develop attack models as functions of tampered traffic control settings (e.g., green time ratios, cycle length, retaining ratios) with outputs equivalent to mobility impacts on the traffic network (e.g., time until system reaches state convergence, asymptotic average network flow). Further, for a given attack model, we define and identify vulnerable states: states that are critical to protect because they lead to negative impacts under the given attack model. Using our methodology we found that for certain vulnerable states, after only a few cycles of tampered control settings an attacker could cause a real impact of 1.5x speed-up in gridlock state convergence or 37%-99% drop in the asymptotic average flow rate. These results imply potentially drastic financial costs for cities and all involved drivers if similar attacks were performed on a real traffic control system. [ABSTRACT FROM AUTHOR]

This paper intends to give a short review of the state of the art on the use of floating car data concerning the management of traffic flow at signalized intersections. New technologies such as connected and autonomous vehicles and Co-operative Intelligent Transportation Systems (C-ITS) are going to change the future of traffic control and management. Traffic signal control systems can be reorganized by using Floating Car Data (FCD), yet the concept of floating car data (FCD) has been mainly studied to gain traffic information and/or signal information. Only recent works have been focalizing on the potential application of FCD for traffic signal real-time control. This paper aims to evidence the most important concepts that can be extracted from the literature on this important topic. [ABSTRACT FROM AUTHOR]

Intelligent transport systems (ITS) are going to dominate the landscape of traffic control in the foreseeable future. In order to meet the travel requirements of individuals, ITS can efficiently use the available traffic infrastructure, guarantee the protection of transportation and urban traffic, facilitate public safety monitoring, and provide efficient control mechanisms. The term "Multi-criterion Group Decision-Making" (MCGDM) refers to a circumstance in which numerous criteria must be considered before making a decision. Hence, it is important to develop novel Non-linear-Diophantine fuzzy Bonferroni-mean operators to effectively find the optimal alternative based on specific criteria for Intelligent Transportation Systems. Numerous non-linear-Diophantine fuzzy Bonferroni-mean-Dombi Aggregation-Operators are introduced (Non-linear-Diophantine Fuzzy-Weighted-Averaging (N-LDFWA) operators and Non-linear-Diophantine Fuzzy-Weighted-Geomatric (N-LDFWG) operators), employing the Bonferroni-Mean-Dombi T-norm and T-conorm. In order to examine the reliability and efficiency of the present method, a real-life scenario of ITS is used. The execution performed for the evaluation of the intelligent transport system and some comparisons are also provided. [Display omitted] • In the context of Bonferroni Mean Dombi, to propose a new nonlinear Diophantine fuzzy set. • Establishing six new operators for NLDFNs' for selection criteria. • Improving intelligent transportation systems can provide a competitive advantage to enterprises. [ABSTRACT FROM AUTHOR]

Traffic congestion has been regarded as one of the most challenging problems in the urban transportation systems. The problem becomes trickier at traffic bottlenecks where the heavy traffic flow spills over and over-saturation appears, and the coordination of traffic control strategies in a network scale is required. With the information availability and control precision enabled by connected and automated vehicle (CAV) technologies, this study proposes a network-level traffic coordination method with dynamic entrance holding in a mixed CAV traffic, and establishes a two-layer framework to develop traffic control strategies to handle the over-saturation problem. In the upper layer, the coordination of signal control among intersections is optimized to minimize travel cost within the network. The upper bound of travel delays under the control strategy is theoretically derived and the critical traffic volume is specified for designing the entrance holding strategy. To accelerate the solving process, a two-stage solution method along with a series of valid inequations are developed, and the feasibility and optimality of the solution are investigated. In the lower layer, a dynamic entrance holding strategy is put forward to regulate the entering traffic at network perimeters to avoid over-saturation and improve traffic orderliness. To enhance the holding effectiveness, a traffic prediction method is created to capture the future traffic evolution based on the real-time information, and combined with the flow reservation mechanism to proactively adjust the holding strategy in real time. Simulation experiments are conducted to evaluate the performance of the proposed methodologies, and compare with other control strategies. The results show that the coordinated method could substantially improve traffic mobility within the network, especially in high traffic demands. • A joint control scheme combining coordinated TSC strategy and entrance hold strategy • A network-level TSC optimization model for minimizing vehicular delays • A two-stage solution method with valid inequations to improve solving efficiency • A dynamic entrance holding strategy by integrating flow reservation mechanism • Simulation experiments to validate the performance of the proposed methodologies [ABSTRACT FROM AUTHOR]

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications. • A statistical framework to infer the fine-grained traffic arrival pattern is proposed. • The signal cycle parameters are inferred by exploiting the features of the time headway sequences. • The valid travel time is extracted at the selected time interval based on the Gaussian mixed model. • The proposed framework could achieve satisfactory performance in different traffic scenarios. [ABSTRACT FROM AUTHOR]

It has been well documented that the mixed traffic flow of human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) causes an unsound communication environment and weakens the beneficial effect of CAVs. However, the communication heterogeneity in the mixed traffic, which leads to incomplete information flow topology (IFT) and further affects the processes of vehicle control and platoon operation, did not receive its deserved attention. In contrast to previous research related to the IFT and platoons in static size, this study introduced the multi-anticipation and dynamic platoons into a more complex mixture of CAVs and HDVs and jointly considered the mode transition and driver uncertainties, e.g., estimation error and driving compliance. Specifically, we proposed multi-class generic controls to represent the basic longitudinal maneuver for each type of vehicle and developed customized car-following dynamics for each controller. In order to better adapt to the communication heterogeneity in the mixed traffic, we defined a novel controller for CAVs, namely mixed AV-CAV vehicle (MACV) controller and developed the corresponding control algorithm, namely AV-CAV integrated control (ACIC) with multi-source information from both sensors system and inter-vehicle communication. Besides, another important point worth considering in mixed traffic is the spatial distribution of CAVs in platoons. As such, three platoon policies, i.e., the cross policy, the continuum policy, and the random policy, were investigated. To our best knowledge, this is a new attempt to discuss the impact of platoon policies on mixed traffic, with a combination of different IFTs. Finally, multiple traffic flow characteristics, covering robustness, efficiency, safety, and emission, were assessed by calculating their corresponding surrogate measures. Results showed that the proposal of MACV controller and ACIC was expected to efficiently prevent the deleterious degradation from the CAV controller to AV controller and construct a more stable transition for changes in vehicle controls. Furthermore, a high proportion of communicable vehicles could significantly reduce the heterogeneity of mixed traffic and improve traffic flow characteristics. Besides, this study demonstrated the outstanding performance of multi-anticipative IFTs compared to the single-anticipative IFT and revealed the advantage of cross policy in traffic robustness and safety. These findings can help provide feasible solutions for IFTs, platoon operation, and vehicle control in the mixed traffic flow and identify their respective benefits in terms of traffic flow characteristics. • Incorporate multi-anticipation and driver uncertainties into the mixed traffic. • Propose a novel controller for CAVs and develop an integrated control algorithm. • Address dynamic mode transition and platooning in the heterogeneous network. • Construct a framework for comprehensive assessment of traffic flow characteristics. [ABSTRACT FROM AUTHOR]

Connected automated vehicles (CAVs) have shown great potential in improving traffic throughput and stability. Although various longitudinal control strategies have been developed for CAVs to achieve string stability in mixed-autonomy traffic systems, the potential impact of these controllers on safety has not yet been fully addressed. This paper proposes safety-critical traffic control (STC) by CAVs—a strategy that allows a CAV to maintain safety relative to both the preceding vehicle and the following human-driven vehicles (HVs). Specifically, we utilize control barrier functions (CBFs) to impart collision-free behavior with formal safety guarantees to the closed-loop system. The safety of both the CAV and HVs is incorporated into the framework through a quadratic program-based controller that minimizes deviation from a nominal stabilizing traffic controller subject to CBF-based safety constraints. Considering that some state information of the following HVs may be unavailable to the CAV, we employ state observer-based CBFs for STC. Finally, we conduct extensive numerical simulations – that include vehicle trajectories from real data – to demonstrate the efficacy of the proposed approach in achieving provably safe traffic. [ABSTRACT FROM AUTHOR]

Abstract Social networks play a key role in providing a new approach to collecting information regarding mobility and transportation services. To study this information, sentiment analysis can make decent observations to support intelligent transportation systems (ITSs) in examining traffic control and management systems. However, sentiment analysis faces technical challenges: extracting meaningful information from social network platforms, and the transformation of extracted data into valuable information. In addition, accurate topic modeling and document representation are other challenging tasks in sentiment analysis. We propose an ontology and latent Dirichlet allocation (OLDA)-based topic modeling and word embedding approach for sentiment classification. The proposed system retrieves transportation content from social networks, removes irrelevant content to extract meaningful information, and generates topics and features from extracted data using OLDA. It also represents documents using word embedding techniques, and then employs lexicon-based approaches to enhance the accuracy of the word embedding model. The proposed ontology and the intelligent model are developed using Web Ontology Language and Java, respectively. Machine learning classifiers are used to evaluate the proposed word embedding system. The method achieves accuracy of 93%, which shows that the proposed approach is effective for sentiment classification. Highlights • Social networks provide a new approach to collect data regarding transportation. • Sentiment analysis can make observations of social data to examine transportation. • Current text mining techniques are unable to generate the topics accurately. • Document representation is another challenging tasks in sentiment analysis. • We proposed a new topic modeling and word embedding system for sentiment analysis. [ABSTRACT FROM AUTHOR]

Abstract As a prominent application of intelligent transportation system, Vehicular Ad-hoc Network is designed to enhance the road traffic safety by deploying effective wireless communication technologies. The paradigm of connected vehicles enables to connect vehicles to each other, and vehicles to infrastructure. Because of the significance of safety-related driving data that are exchanged in vehicular networks, it is mandatory to provide a suitable collection solution. In this paper, we propose an efficient data collection based on vehicle to vehicle and vehicle to infrastructure communications. The proposed solution is endowed with a clustering approach for reducing the effects of mobility and density on the data collection stations. In order to demonstrate the effectiveness of the proposed solution, we have conducted a simulation using OMNET++ simulator, Veins, and SUMO. The results show that proposed solution achieves a more efficient collection of the road traffic data while reducing the overhead and the topology changes. [ABSTRACT FROM AUTHOR]

The explicit measurement of the evolution process of queues during a cycle has a special significance for evaluating the control scheme at signalized intersections, especially when a certain unconventional operation or intervention is conducted. In order to measure the dynamic behavior of queues at signalized intersections with waiting areas, this paper develops a stochastic queueing model to analytically simulate the evolution process of queues. The key queueing performance indices such as the statistical distributions of queue length and mean values of queue length along the time are derived. Using these indices, the queueing process of vehicles at the intersection with waiting areas can be explicitly formulated, in particular, the dynamic behavior of queues along the time during a cycle can be captured, and the formation of maximum queue length during a cycle can also be measured. Model validation shows that the proposed queueing model can provide an accurate estimation of queueing performance of signalized intersections with waiting areas. In addition, the effects of the use of waiting areas on the evolution process of queue length during a cycle are also tested by numerical simulations under different conditions. [ABSTRACT FROM AUTHOR]

Industrial loop detectors (ILDs) are the most common traffic detectors. In Shanghai, most of the ILDs are installed in a single loop way, which can detect various parameters, such as flow, saturation, and so on. However, they cannot detect the speed directly, which is one of the key inputs of intelligent transportation systems (ITS) for identifying the traffic state. Thus, this paper is dedicated to estimate speed accurately. It proposes a new algorithm that multiple kernel support vector regression (MKL-SVR) to complete this goal, which improves the accuracy and robustness of the speed estimation. Extensive experiments have been performed to evaluate the performances of MKL-SVR, compared with polynomial fitting, BP neural networks and SVR. All results indicate that the performances of MKL-SVR are the best and most robust. [ABSTRACT FROM AUTHOR]

Highlights • A new method of constructing traffic state vectors is proposed. • Variables are selected using spatio-temporal correlations based on Mutual Information. • Weighted distance is used in selecting K nearest neighbors. • The proposed method can improve the prediction accuracy of data-driven models. Abstract Short-term traffic flow prediction is an integral part in most of Intelligent Transportation Systems (ITS) research and applications. Many researchers have already developed various methods that predict the future traffic condition from the historical database. Nevertheless, there has not been sufficient effort made to study how to identify and utilize the different factors that affect the traffic flow. In order to improve the performance of short-term traffic flow prediction, it is necessary to consider sufficient information related to the road section to be predicted. In this paper, we propose a method of constructing traffic state vectors by using mutual information (MI). First, the variables with different time delays are generated from the historical traffic time series, and the spatio-temporal correlations between the road sections in urban road network are evaluated by the MI. Then, the variables with the highest correlation related to the target traffic flow are selected by using a greedy search algorithm to construct the traffic state vector. The K-Nearest Neighbor (KNN) model is adapted for the application of the proposed state vector. Experimental results on real-world traffic data show that the proposed method of constructing traffic state vector provides good prediction accuracy in short-term traffic prediction. [ABSTRACT FROM AUTHOR]

Realistic modeling traffic flow dynamics in urban traffic network remains a big challenge at present due to the complex nonlinear characteristics of traffic flow. In this paper, a novel and model-free iterative learning control (ILC) strategy for balancing traffic flow in urban traffic network is proposed. To tackle the randomly varying trial lengths in the iteration domain of traffic system, an iterative-average operator is introduced in the proposed ILC law for tracking tasks with non-uniform trial lengths, which thus mitigates the requirement on classic ILC that all trial lengths must be identical. The learning convergence condition of the ILC strategy in iteration-average and expectation is derived through rigorous analysis. The performance and the effectiveness of the ILC strategy are analyzed by simulations on a test road network. The results show that the proposed ILC strategy can homogeneously balance the accumulation in the network and improve the network mobility. [ABSTRACT FROM AUTHOR]

Highlights • Closed form formulas for green times (expectation/variance) and vehicle waiting time. • Numerical results are provided to support the findings. Abstract We study green extension of a two-phased vehicle actuated signal at an isolated intersection between two one-way streets. The green phase is extended by a preset time interval, referred to as critical gap, from the time of a vehicle actuation at an advance detector. The green phase switches if there is no arrival during the critical gap. We develop an exact model to study the intersection performance with traffic following Poisson processes. We further extend the model to approximate the case of general traffic. Our model in the general case works well compared with Monte Carlo simulation. A few major observations include: (1) The optimal critical gap decreases with the traffic; (2) The optimal critical gap can be much larger (up to 5 s) than the common presumption of 2–3 s; (3) Queue clearance policy is not nearly optimal in general even in the case of heavy traffic. [ABSTRACT FROM AUTHOR]

The increasing adoption of intelligent transportation system (ITS) data in smart-city initiatives worldwide has offered unprecedented opportunities for improving transportation air quality management. In this paper, we demonstrate the effective use of ITS and other traffic data to develop a link-level and hourly-based dynamic vehicle emission inventory. Our work takes advantage of the extensive ITS infrastructure deployed in Nanjing, China (6600 km 2 ) that offers high-resolution, multi-source traffic data of the road network. Improved than conventional emission inventories, the ITS data empower the strength of revealing significantly temporal and spatial heterogeneity of traffic dynamics that pronouncedly impacts traffic emission patterns. Four urban districts account for only 4% of the area but approximately 30%–40% of vehicular emissions (e.g., CO 2 and air pollutants). Owing to the detailed resolution of road network traffic, two types of emission hotspots are captured by the dynamic emission inventory: those in the urban area dominated by urban passenger traffic, and those along outlying highway corridors reflecting inter-city freight transportation (especially in terms of NO X ). Fine-grained quantification of emissions reductions from traffic restriction scenarios is explored. ITS data-driven emission management systems coupled with atmospheric models offer the potential for dynamic air quality management in the future. [ABSTRACT FROM AUTHOR]

Cooperative Intelligent Transport Systems (C-ITS) - Connected Vehicle technology in the USA - where vehicle and roadside infrastructure communicates to deliver more intelligent traffic management, is one of a range of ITS technologies emerging as a key component in pursuit of the wider objectives of improved urban mobility. This paper presents the Compass4D project, which deployed a C-ITS system in seven European cities and coordinated the common evaluation of the technology for three services focusing specifically on safety and environmental objectives under real-world driving conditions. The significance of the Compass4D deployments and results provides some of the first evidence of the effectiveness of C-ITS in real world conditions. Both light and heavy vehicles showed efficiency savings of 2–6%. Equipped buses exhibited a variety of results with one pilot site showing a reduction of greater than 200gCO 2 per bus route per trip whilst other buses showed an increase in total emissions. The paper presents results from both field trials and microscopic simulation studies (to understand the network- or city-wide impacts of the technology). It discusses the results in detail before outlining the potential for further deployment in terms of the impact on energy efficiency and environmental objectives. Government and road operators will benefit from the results to gain an understanding of the potential impact of services given specific deployment characteristics. [ABSTRACT FROM AUTHOR]

Urban signal intersection as the key node in the urban road network, its operational efficiency directly affects the overall operational efficiency of the road network traffic. The existing urban road signal intersections cannot be broadened due to the limitation of road size and boundary lines of road. Some signalized intersections adopt straight-right lane design to improve the traffic efficiency of vehicles at intersections. This design can improve the traffic efficiency of straight-through vehicles during the green light phase. However, straight-through vehicles stopping in the straight-right lane during the red light will prevent right-turning vehicles from passing the intersection. This phenomenon causes delays for right-turning vehicles. In order to deal with the above issues, this paper takes the concept of separating straight and right-turning vehicles in spatial and temporal on the dynamic straight-right lane (DSRL). The organization form of vehicles entering DSRL is designed based on signal phase time. The control strategy for DSRL with the interaction of traffic lights and pre-signal is formulated integrated with the intersection design. The delay indexes of straight and right-turning vehicles under DSRL design are quantified using cellular automata model to calculate vehicle delays under the design form of DSRL. Finally, the test platform based on the actual intersection is established with MATLAB to simulate the operation of the vehicles under the DSRL design. The experiment shows that the delay reduction degree of straight-through vehicles is between 1.2% and 4.5%, and the reduction degree of right-turning vehicles delay is more than 66%, that the operation efficiency of intersection is improved. • In terms of design concept, this paper proposes DSRL design scheme, which separates straight-through vehicles and right-turning vehicles from temporal and spatial. Right-turning vehicles are not delayed during the red phase while both straight and right-turning vehicles can successfully pass the intersection during the green phase under DSRL. The traffic efficiency of vehicles at the intersection can be greatly improved. • In terms of control method, the control strategy proposed in this paper is more flexible than the original variable lanes. This paper proposes pre-signal and traffic signal control scheme under DSRL. The calculation method of signal timing parameters and suggesting parameters of signal timing are given according to the ideal operating conditions of the traffic flow. The signal control strategy and DSRL design cooperating together can make traffic flow operate better and fully exploit the lane traffic capacity. • In terms of test results, this paper builds a microscopic model to evaluate the performance of the DSRL. By systematically analyzing key parameters, DSRL design can effectively reduce vehicle delays in different traffic environments. This paper also lays the foundation for optimizing the indicators under DSRL design and provides guidance for the implementation of DSRL design. [ABSTRACT FROM AUTHOR]

Abstract Anomaly detection is an important part of an Intelligent Transportation System. In this study, image processing and machine learning techniques are used to detect anomalies in vehicle movements. These anomalies include standing and traveling in reverse direction. Images are captured using CCTV cameras from front and rear side of the vehicle. This capability makes the results robust to the variations in operational and environmental conditions. Multiple consecutive frames are acquired for motion detection. Features such as edges and license plate corner locations are extracted for tracking purposes. Direction of the traffic flow is obtained from the trained classifier. K-nearest neighbor is chosen as the classifier model. The proposed method is evaluated on a public highway and promising detection results are achieved. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we study the application of perimeter flow control by simulation of cellular automaton model on urban traffic system. We find that the relation of traffic flow and vehicle density (Macroscopic Fundamental Diagram, MFD) will have different shapes for the core area and the peripheral area. The MFD shows free-flow state, saturate flow state and congestion state. But the magnitude of maximal flow and the position for congestion transition are different for the whole system and the core area. We suggest to realize the perimeter control strategy by assigning a special prohibiting phase to the perimeter traffic lights for the roads entering the core area. The strategy is controlled by two critical densities ρ 1 and ρ 2 , which are determined by the MFD of the core area. Simulations show that both the average arrival rate and the average flow will be greatly improved with the perimeter flow control strategy. In addition, the perimeter flow control strategy can increase the critical density of traffic congestion. The probability of system-wide gridlock will decrease, and the system can perform well under both close and open boundary conditions. All the results indicate that the perimeter flow control strategy can effectively improve the performance of the traffic system. Highlights • MFD has different shape for the system and for the core area. • Parameters of perimeter control should be determined by the MFD of core area. • Perimeter control can reduce gridlock and increase the critical density. [ABSTRACT FROM AUTHOR]

Abstract Based on the cellular automaton model and the concept of ant pheromone, this paper proposes a new route guidance strategy, called the ant pheromone route guidance strategy, in which the vehicles are regarded as special types of ants and their traffic information is regarded as the ant pheromone. To evaluate its performance, the new route guidance strategy was applied and compared with other three typical route guidance strategies under three different route scenarios, respectively, with open boundary conditions based on the Nagel–Schreckenberg cellular automaton model. First, in a symmetrical two-route scenario with two exits, results showed that the new route guidance strategy and vacancy length route guidance strategy were optimal. They outperformed the other strategies in terms of the value, stability, and balance of vehicle number, and the average speed and average flux on each route. To understand the impact of the strategy on traffic states, flux–density evolution process was studied. In addition, the influence of evaporation rate was also investigated. Second, in an asymmetrical two-route scenario with one exit, the simulation results also proved that ant pheromone route guidance strategy and vacancy length route guidance strategy were the best. Finally, in a symmetrical two-route scenario with a traffic accident, we compared the new route guidance strategy with the vacancy length route guidance strategy. Results indicated that the new route guidance strategy has distinct advantages. In short, in all three route scenarios we investigated, the ant pheromone route guidance strategy performed best. Highlights • This paper proposes the ant pheromone route guidance strategy based on CA model. • The new strategy takes vehicles as ants and vehicles' data as ant pheromones. • Adopting the NS model, this strategy is applied to three two-route scenarios. • Results indicate that the new strategy outperforms other route guidance strategies. • In traffic accident, the new strategy can reduce congestion and save recovery time. [ABSTRACT FROM AUTHOR]

Abstract By means of the Nagel–Schreckenberg model, we have investigated into the maximum vehicular flow rate of traffic processing bottlenecks. The evaluated analytical form of this flow rate is found to give quantitative insights into the underlying physics of collective vehicular motions constrained by these bottlenecks. Our analysis shows that for large-scale expansion, a new class of processing bottleneck known as the serial bottleneck is more efficient than the conventional parallel bottleneck in the absence of human driving behavior. When characteristics such as slow-to-start is considered in the model, the consequential delay due to human reaction time not only degrade the overall efficiency, it also diminishes the efficacy of serial processing such that a serial bottleneck is no longer tenable for traffic processing. These results point to the fundamental importance of optimizing traffic efficiency, which we illustrate by elucidating the detailed mechanisms with which vehicles interact collectively in the bottlenecks. In particular, we demonstrate that by constructing combinations of serial and parallel bottlenecks, optimal efficiencies are achieved via configurations with few (many) lanes of a large (small) number of serial units when the processing time is short (long). A direct implication of these results is that autonomous self-driving vehicles could serve to improve the transportation capacity for the densely populated urban cities of the future, due to the intrinsically more efficient collective vehicular motions through these bottlenecks. Highlights • Capacity of serial and parallel bottlenecks have been compared using full analytical solutions. • Analysis shows that the difference in the constrained motion results in the difference in capacity. • Novel combination of serial and parallel expansions was proposed. • The efficacy of these bottlenecks in relevance of real-world traffic have been discussed. [ABSTRACT FROM AUTHOR]

A wide variety of Intelligent Transportation Systems (ITS) are developed to increase travel safety, improve traffic management and minimize environmental impact. C-ITS (cooperative ITS) are a subclass of ITS based on the communication between the vehicles and/or communication between the vehicles and the infrastructure. C-ITS are promising solutions to improve transport systems thanks to a local information processing. In that way, the information processing latency is minimal, the information is up to date and relevant to the current road environment context. Nevertheless the deployment of these systems in the vehicular ad-hoc network (VANET) raises many difficulties. Our goal is to help the designer of a C-ITS to focus only on the expertise that is required to satisfy his objectives and not on the technical issues that the deployment in a VANET implies. To reach this objective, we propose a platform which consists of a set of modules ITS (m-ITS), each of these m-ITS relies on a generic architecture hosting on stationary and mobile agents dedicated to the support of C-ITS in VANET. The objective in deploying mobile agents is reinforcing the local processing of the information. Finally an example of C-ITS for contextual speed management illustrates our proposal. [ABSTRACT FROM AUTHOR]

Although it is important to consider multi-day activities in transportation planning, multi-day activity-travel data are expensive to acquire and therefore rarely available. In this study, we propose to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data. A key observation we make is that the distribution of interpersonal variability in single-day travel activity datasets is similar to the distribution of intrapersonal variability in multi-day. Thus, interpersonal variability observed in cross-sectional single-day data of a group of people can be used to generate the day-to-day intrapersonal variability. The proposed sampling method is based on activity-travel pattern type clustering, travel distance and variability distribution to extract such information from single-day data. Validation and stability tests of the proposed sampling methods are presented. [ABSTRACT FROM AUTHOR]

In case of railway disruptions, traffic controllers are responsible for dealing with disrupted traffic and reduce the negative impact for the rest of the network. In case of a complete blockage when no train can use an entire track, a common practice is to short-turn trains. Trains approaching the blockage cannot proceed according to their original plans and have to short-turn at a station close to the disruption on both sides. This paper presents a Mixed Integer Linear Program that computes the optimal station and times for short-turning the affected train services during the three phases of a disruption. The proposed solution approach takes into account the interaction of the traffic between both sides of the blockage before and after the disruption. The model is applied to a busy corridor of the Dutch railway network. The computation time meets the real-time solution requirement. The case study gives insight into the importance of the disruption period in computing the optimal solution. It is concluded that different optimal short-turning solutions may exist depending on the start time of the disruption and the disruption length. For periodic timetables, the optimal short-turning choices repeat due to the periodicity of the timetable. In addition, it is observed that a minor extension of the disruption length may result in less delay propagation at the cost of more cancellations. [ABSTRACT FROM AUTHOR]

Recently, multirotor Unmanned Aerial Vehicles(UAVs) or drones have become increasingly popular for a vast variety of civil applications. Efficient traffic data collection and extraction of various flow parameters are some of the futuristic applications of this technology. However, such applications still need to be streamlined and thoroughly explored for varying traffic and infrastructural conditions. In this paper, the focus is on the authentication of the application of small multirotor UAVs for traffic data collection and subsequent analysis of traffic streams at urban roundabouts. This paper presents an analytical methodology to evaluate the performance of roundabouts by extracting various parameters and performance indicators. The performance evaluation methodology is based on: (i) determining traffic volume via OD matrices for each leg, and (ii) analyzing drivers’ behavior via gap-acceptance analysis. The overall analytical process is principally based on the authors’ previously proposed automated UAV video-processing framework for the extraction of vehicle trajectories. The extracted trajectories are further employed to extract useful traffic information. The experimental data to analyse roundabout traffic flow conditions was obtained in the city of Sint-Truiden (Belgium). The results reflect the value of flexibility and bird-eye view provided by UAV videos; thereby depicting the overall applicability of the UAV-based traffic analysis system. With the significant increase in the usage of UAVs expected in the coming years, such studies could become a useful resource for practitioners as well as future researchers. The future research will mainly focus on further extensions of the UAV-based traffic applications. [ABSTRACT FROM AUTHOR]

This study analyzes the mobility and energy impact of CAV technologies. Under different VOT, WTP and market penetration scenarios, changes in mobility in terms of VMT and average travel time, as well as changes in energy consumption are estimated. The state-of-the-art integrated activity-based modeling (ABM) and traffic simulation software POLARIS is used to model the mobility impact. Moreover, POLARIS is coupled with AUTONOMIE to analyze the resulting fuel consumption. [ABSTRACT FROM AUTHOR]

Network path selection defines the methodology of selecting the best routes and forwarding traffic in a network service provider (NSP). NSPs use routing protocols that are optimized for a single arbitrary metric (i.e., administrative weight), which is commonly calculated according to the link state information, to select network paths. Despite the advantages, link-state protocols lack the ability to select network paths by considering the states of network devices such as the effect of routers for network path selection. Apparently, studying possible techniques for selecting network paths based on the state information of network devices, e.g., routers, has become obligatory. This paper studies the effectiveness of using network device state information for network path selection by presenting the Extended Server Link Router state Routing Protocol (ESLR). ESLR uses the state information of network devices as parameters to calculate a composite route metric; ESLR selects the network paths based on the cumulative propagation delay. By simulating ESLR using an ISP topology, the proposed protocol is examined for the effectiveness of using network device state information for network path selection. [ABSTRACT FROM AUTHOR]

The availability, accuracy and relevance of real world input data are essential for developing a reliable traffic simulation model. Large-scale traffic simulation models, in particular, require data from many sources and in great detail. Though it is now possible to obtain detailed field data with the advent of new technologies such as GPS, cellular phones, RFIDs, it is still a challenge to gather all available data, especially traffic flow data, in the required spatial and temporal accuracy. The central theme of this paper is the calibration and validation (C&V) development of a large-scale traffic simulation model using data from multiple sources. [ABSTRACT FROM AUTHOR]

In this paper, we present necessary premises for the deployment of the Internet of Vehicles (IoV) integrating Big Data analytics of road network traffic measurements of the city of Mohammedia, Morocco. Thus, we introduce an architecture based on three main layers such as IoV, Fog Computing and Cloud Computing Layer. We specifically put more focus on Fog Computing layer in which we develop a framework for a real-time collecting and processing events generated by intelligent vehicles as well as visualizing traffic state on each road section. Furthermore, we consider deployment and test of the proposed framework using events retrieved from a Vanets -type micro simulation. Finally, we present and discuss the first obtained results as well as the advantages and limitations of the proposed architecture. [ABSTRACT FROM AUTHOR]

Decision support systems are of paramount importance to propose traffic control strategies and reliable information to road users. They usually take benefits from data-driven methods which represent a reliable way to gather real time traffic information and make predictions for recurring situations. Data Assimilation (DA) consists in considering both observed data and a traffic flow model to monitor and forecast traffic state. Traditionnaly, Kalman filtering methods with macroscopic traffic models have been widely used. More recently, mesoscopic approaches proved to be relevant for large scale network applications. However, errors on traffic state is a key information for DA methods. The paper proposes a DA framework that accounts for two types of error: (i) errors from the data collection system and (ii) errors that are propagated and amplified in time and space by the dynamic traffic flow model. When applied on a basic network, the proposed framework demonstrates its ability to track errors and the traffic state information is enriched with a level of uncertainty. It paves the way for new traffic indicators and new solutions for traffic forecast applications. [ABSTRACT FROM AUTHOR]

This study presents a novel sequential modeling framework of shared autonomous vehicle (SAV) operation in the Halifax transport network. The Halifax regional transport network model is used to generate business-as-usual traffic demand in the morning peak hours. A new module of SAV assignment upon trip request is introduced and integrated with the traffic microsimulation model to simulate the SAVs’ occupied and empty trips in the network. The proposed framework demonstrates the capability to evaluate service performance of SAVs with different level of fleet sizes in the network. Model results suggest that fleet size of 900 SAVs serves 20% of the total morning commute trip requests. Traffic condition is improved for the first hour of peak periods as average speed increases and total travel time requirement decreases during operation of SAV fleet in Halifax. The results provide insights into SAV system planning in accordance to anticipated challenges of SAV adoption in transportation network. [ABSTRACT FROM AUTHOR]

The number of people killed in traffic accidents in Morocco reached 3593 during 2016, compared to 3565 in 2015, registering a slight increase of 0.79%. Thus, the total of traffic accidents over the past year amounted to 81827 against 78864 in 2015, an increase of 3.76%, according to provisional statistics of traffic accidents in 2016. These statistics show that Morocco is one of the countries with the highest mortality rate. In order to decrease these numbers and put an end to this dilemma, we are working on developing vehicle-to-vehicle communication (V2V) in order to provide information and assistance required to avoid collisions, traffic-jam, etc. Vehicular Ad-Hoc Networks (VANETs) have great potential for providing safety and comfort applications for Intelligent Transport Systems (ITS) and to improve traffic safety on roads. In this paper, we have consecrated our study on Casablanca, the largest city in Morocco which has a highway passing through it. Thus, we took two scenarios, first the highway and trunk roads and second the other roads (primary, secondary, tertiary and residential roads). To demonstrate these scenarios, we used various tools such as OpenStreetMap and SUMO to generate a realistic mobility model that gives aspects of real vehicular traffic. Generated scenarios are added to NS-2.35 (Network Simulator) in order to analyze the performance of 802.11a (Wi-Fi) and 802.11p (DSRC/WAVE). We evaluated the performance based on three parameters: Packet Delivery Ratio, End-To-End Delay and Throughput. The results showed that 802.11p is better and more efficient than 802.11a under both scenarios. In addition, 802.11p gives better results in the second scenario than in the first one, and this is due to the number of cars, the authorized speed and the distance between the cars in each scenario. [ABSTRACT FROM AUTHOR]

Understanding factors that influence the duration time of incidents are important for traffic incident management agencies to design effective mitigation strategies. Previous studies have proposed different approaches for examining the impact of influential factors on incident clearance and response time. However, very few studies have considered incident clearance and response time as two dependent variables and used a joint modeling framework. The objectives of this paper are to investigate the dependence between incident clearance and response time and to examine the applicability of the copula approach to the joint analysis of these two variables. To demonstrate advantages of the proposed copula modelling framework, incident clearance and response time data collected on freeway road sections in Seattle, Washington State are examined. Parameter estimation and prediction results from the proposed copula models are presented and compared with the conventional accelerated failure time model. The modeling results suggest that the proposed copula model can better describe the estimated conditional survival probability of incident clearance time, and can provide marginally more accurate prediction results. The proposed model can also provide different inferences about effects of factors on incident clearance and response time data. Overall, the findings in this paper provide a framework for jointly modeling incident clearance and response time by considering their dependence. [ABSTRACT FROM AUTHOR]

This study presents a thorough microscopic simulation investigation of a recently developed model-based approach for per-lane density estimation, as well as on-ramp and off-ramp flow estimation, for highways in the presence of connected vehicles. The estimation methodology is mainly based on the assumption that a certain percentage of vehicles is equipped with Vehicle Automation and Communication Systems (VACS), which provide the necessary measurements used by the estimator, namely vehicle speed and position measurements. In addition, a minimum number of conventional flow detectors is needed. In the investigation, a calibrated and validated, with real data, microscopic multi-lane model is employed, which concerns a stretch of motorway A20 from Rotterdam to Gouda in the Netherlands. It is demonstrated that the proposed methodology provides satisfactory estimation performance even for low penetration rates of connected vehicles, while it is also shown that the method is little sensitive to the parameters (two in total) of the model utilized by the estimator. [ABSTRACT FROM AUTHOR]

Sight distance analyses require careful and detailed field measurements to facilitate proper engineering decision making regarding the removal of obstructions, establishment of regulatory and advisory speed limits, and the location of new access points, among numerous other purposes. However, conventional field measurements for these analyses present safety concerns because they require personnel to be in or adjacent to traffic lanes. They can also be time consuming, costly, and labor intensive. Furthermore, the predominantly two-dimensional (2D) methods involve simplifying assumptions such as a “standard” vehicle heights and lengths without considering the wide range of vehicles and drivers present on the road. Recently, many transportation agencies worldwide have begun to acquire mobile lidar data to map their roadway assets. These data provide a rich three-dimensional (3D) environment that enables one to virtually visit a site at any frequency and efficiently evaluate sight distances from the safety of the office. This study investigates advanced safety analysis methodologies for drivers’ sight distance based on high resolution lidar data. The developed simulation method enables users to virtually evaluate available sight distances in a 3D context considering a variety of objects, vehicle types, and multi-modal forms of transportation (e.g., bicycle, pedestrian). The feasibility of this technique was analyzed with a case study at an intersection located in Corvallis, Oregon, USA. The experimental results demonstrated the ability of the proposed methodology to capture significantly more detail on visibility constraints when compared with conventional measurements as well as provide more flexibility in the analysis. [ABSTRACT FROM AUTHOR]

This study proposes a model that estimates unobserved highway link speeds by a machine learning technique using historical probe vehicle data. For highway traffic monitoring, probe vehicle data is one of the most promising data source. However, since such data do not always cover an entire study area, we cannot measure traffic speeds on all links in a time-dependent manner; quite a few links are unobserved. To continuously monitor speeds on all links, it is necessary to develop a technique that estimates speeds on unobserved links from historical observed link speeds. For this purpose, we extend the current Gaussian graphical model so as to use two or more multivariate normal distributions to accurately estimate unobserved link speeds. In general, since the number of unknown model parameters (mean parameters and covariance matrices) is enormous and also unobserved links always exist, the EM algorithm and the graphical lasso technique are employed to determine the model parameters. Our proposed model was applied to the Bangkok city center in Thailand as well as to the Fujisawa city in Japan. We confirmed that the model can estimate the unobserved link speeds quite reasonably. [ABSTRACT FROM AUTHOR]

In transportation planning and design studies it is customary to consider the network in its equilibrium state. Equilibrium conditions in the presence of Advanced Traveller Information Systems (ATIS) and recurrent congestion have been formulated either according to some behavioural principle derived from Wardrop’s assumption, or as fixed-point states of day-to-day dynamic assignment processes. In the latter approach only, a differentiation has been introduced between predictive ATIS and ATIS providing static information, but the impacts on equilibrium travel times are relatively unexplored. In addition, the route choice updating model is formulated without consideration of state-dependent effects, such as inertia to change. This forms the motivation for the present paper. According to traffic information sources, three classes of users are considered: (a) users with predictive information, (b) users with static information and compliant, (c) users not equipped with ATIS or noncompliant. Users of the three classes make choices in a stochastic manner based on a logit model subject to inertia. Users of the first class have lower perception variance in view of the higher information quality. The compliance rate is endogenous and dependent on the information accuracy of the static ATIS. The fixed points in the class-specific route flows of the dynamic day-to-day processes characterise a new concept of network equilibrium, referred to as Mixed User Equilibrium (MUE), where, if each user shifts from her currently used route to her newly chosen route, the observed class-specific route flows do not change. A variant of the method of successive averages is proposed for computing MUE. The model is illustrated by an example related to the Nguyen-Dupuis network. The impacts on equilibrium travel times of the market penetration of ATIS with different functionalities as well as of inertia are examined. [ABSTRACT FROM AUTHOR]

Missing data is an inevitable and ubiquitous problem in data-driven intelligent transportation systems. While there are several studies on the missing traffic data recovery in the last decade, it is still an open issue of making full use of spatial-temporal traffic patterns to improve recovery performance. In this paper, due to the multi-dimensional nature of traffic speed data, we treat missing data recovery as the problem of tensor completion, a three-procedure framework based on Tucker decomposition is proposed to accomplish the recovery task by discovering spatial-temporal patterns and underlying structure from incomplete data. Specifically, in the missing data initialization, intrinsic multi-mode biases based traffic pattern is extracted to perform a robust recovery. Thereby, the truncated singular value decomposition (SVD) is introduced to capture main latent features along each dimension. Finally, applying these latent features, the missing data is eventually estimated by the SVD-combined tensor decomposition (STD). Empirically, relying on the large-scale traffic speed data collected from 214 road segments within two months at 10-min interval, our experiment covers two missing scenarios – element-like random missing and fiber-like random missing. The impacts of different initialization strategies for tensor decomposition are evaluated. From numerical analysis, a sensitivity-driven rank selection can not only choose an appropriate core tensor size but also determine how much features we actually need. By comparison with two baseline tensor decomposition models, our method is shown to successfully recover missing data with the highest accuracy as the missing rate ranges from 20% to 80% under two missing scenarios. Moreover, the results have also indicated that an optimal initialization for tensor decomposition could suggest a better performance. [ABSTRACT FROM AUTHOR]