Search

We demonstrate a working functional prototype of a cooperative perception system that maintains a real-time digital twin of the traffic environment, providing a more accurate and more reliable model than any of the participant subsystems—in this case, smart vehicles and infrastructure stations—would manage individually. The importance of such technology is that it can facilitate a spectrum of new derivative services, including cloud-assisted and cloud-controlled ADAS functions, dynamic map generation with analytics for traffic control and road infrastructure monitoring, a digital framework for operating vehicle testing grounds, logistics facilities, etc. In this paper, we constrain our discussion on the viability of the core concept and implement a system that provides a single service: the live visualization of our digital twin in a 3D simulation, which instantly and reliably matches the state of the real-world environment and showcases the advantages of real-time fusion of sensory data from various traffic participants. We envision this prototype system as part of a larger network of local information processing and integration nodes, i.e., the logically centralized digital twin is maintained in a physically distributed edge cloud. [ABSTRACT FROM AUTHOR]

The article provides information on how carbon costs and savings can be used when assessing intelligent transport systems (ITS) schemes in Great Britain. It considers the M25 junctions 15 to 16 variable mandatory speed limit (VMSL) scheme to determine both carbon dioxide costs and benefits. Moreover, the balance in carbon use between the building and operating of ITS-based traffic congestion management schemes and the outcome of emission reduction from traffic is discussed.

Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling. [ABSTRACT FROM AUTHOR]

There has been significant interest and progress in the field of vehicular ad hoc networks over the last several years. VANETs comprise vehicle-to-vehicle and vehicle-to-infrastructure communications based on wireless local area network technologies. The dinstinctive set of candidate applications (e.g., collision warning and local traffic information for drivers), resources (licensed spectrum, rechargeable power source), and the environment (e.g., vehicular traffic flow patterns, privacy concerns) make the VANET a unique area of wireless communication. This article gives an overview of the field, providing motivations, challenges, and a snapshot of proposed solutions. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems have been promoted as a means to improve both the efficiency and safety of the road network. The effectiveness of advanced technologies in improving road safety has been an area of research which has thus far yielded mixed results. In order to ensure that advanced technologies deliver on their intended outcomes, more research has to be devoted to understanding road users' perceptions and reactions to these systems. This study examines drivers' perceptions of the use of dynamic message signs and their self-reported reactions to the messages displayed. In general, drivers support the use of highway electronic boards for traffic incident reports and weather information which have an impact on traffic delays and level of service. They also think that it is a good idea to display road safety messages and to remind drivers to drive safely and be courteous on the roads. Moreover, most drivers reported that they do read and think about the messages displayed and react positively to some of the road safety messages. [ABSTRACT FROM AUTHOR]

The use of Intelligent Transportation Systems (ITS) in construction work zones to disseminate traffic information has increased significantly in recent years, mainly with the use of Variable Message Signs (VMS). VMS are used based on the assumption that informed drivers will make better travel decisions, thereby reducing congestion. However, the extent of change in driver behavior is difficult to predict prior to ITS deployment. This difficulty leads to the larger problem of justifying investment in ITS. This article proposes an ITS deployment decision support tool using micro-simulation. The approach determines the required diversion effectiveness of a work zone ITS deployment using VMS. The methodology was tested using the Glenmore Trail/Elbow Drive/5th Street interchange project (GE5) in Calgary, Canada. The results indicate that the proposed approach will assist agencies in justifying ITS investment by exhibiting the potential resultant societal benefits. [ABSTRACT FROM AUTHOR]

Traffic incidents are a major contributor to congestion on US freeways, resulting in millions of dollars in property damage and significant deaths and injuries each year. Because a single incident can cause traffic delays, equally devastating secondary incidents can result. Thus, the faster an incident is detected, verified, and cleared, the less significant the impact. Traffic cameras and other technologies are widely used for this purpose. This work examined the effectiveness of traffic cameras at five metropolitan freeway sites in South Carolina using the traffic simulation software PARAMICS to simulate various incident scenarios and traffic camera operations through application programming interfaces. The authors used these interfaces to produce random spatial and temporal occurrence of incidents, including the incident start times, durations and locations. A benefit-cost analysis based on the simulation results suggested traffic cameras returned $12 for every dollar spent under the prevailing conditions at the study sites. A sensitivity study, varying different benefit- and cost-related parameters, such as deployment costs, number of incidents, and discount rate, revealed benefit-cost values that were always above one. [ABSTRACT FROM AUTHOR]

Video detection has become an increasingly popular technology for vehicle detection at signalized intersections. Among the potential disadvantages of this technology is the tendency of video detectors to activate early at night due to headlight reflection on the pavement. This early activation results in a dramatic increase in the length of the effective vehicle detection zone. This observed variation in the effective length of the vehicle detection zone that varies by ambient lighting condition and camera placement presents a very serious impediment for traffic engineers to design vehicle extension intervals that operate consistently during day, night, and transition periods. Further, the stochastic variation in the length of the vehicle detection zone length has the potential to create driver expectancy issues. Tables are included that report the observed average and range of detection zone length variations for 16 observed video cameras that were extensively calibrated by the manufacturer at the test site. The paper concludes by recommending near-side placement of video detection devices to reduce the stochastic variation in detection zone length. [ABSTRACT FROM AUTHOR]

This article investigates the application of Kalman filter with discrete wavelet analysis in short-term traffic volume forecasting. Short-term traffic volume data are often corrupted by local noises, which may significantly affect the prediction accuracy of short-term traffic volumes. Discrete wavelet decomposition analysis is used to divide the original data into several approximate and detailed data such that the Kalman filter model can then be applied to the denoised data and the prediction accuracy can be improved. Two types of wavelet Kalman filter models based on Daubechies 4 and Haar mother wavelets are investigated. Traffic volume data collected from four different locations are used for comparison in this study. The test results show that both proposed wavelet Kalman filter models outperform the direct Kalman filter model in terms of mean absolute percentage error and root mean square error. [ABSTRACT FROM AUTHOR]

As transportation agencies seek to better manage and operate the transportation system, there is an increasing need for traffic condition data across the roadway network. Wireless location technology (WLT)-based traffic monitoring offers the opportunity to directly measure link travel times on a significantly larger portion of the roadway system than is currently monitored. In WLT-based monitoring, the locations of wireless devices (such as cellular phones) in vehicles are monitored. A series of these positions are then used to derive vehicle speeds on links, effectively making the wireless devices probes in the traffic stream. While this idea is conceptually appealing, previous field deployments have not been able to successfully implement this concept. This paper describes the results of a study that used simulation to examine the efficacy of WLT-based monitoring systems under various configurations. Two case studies were used to investigate how WLT system design and roadway network configuration impact the overall effectiveness of WLT-based monitoring systems. Conclusions on how these systems should be designed and where they are likely to function best are also included. The results of this paper show that while first-generation WLT-based monitoring systems can be used to monitor relatively simple networks, additional design refinements are needed before they can be used across a broad spectrum of roadway conditions. [ABSTRACT FROM AUTHOR]

Abstract: This paper evaluates strategies for operating buses on signal-controlled arterials using special lanes that are made intermittently available to general traffic. The advantage of special bus lanes, intermittent or dedicated, is that they free buses from traffic interference; the disadvantage is that they disrupt traffic. We find that bus lanes with intermittent priority (BLIPs), unlike dedicated ones, do not significantly reduce street capacity. Intermittence, however, increases the average traffic density at which the demand is served, and as a result increases traffic delay. These delays are more than offset by the benefits to bus passengers as long as traffic demand does not exceed by much the maximum flow possible on the non-special lanes; the smaller the excess the better. BLIPs are not intended for roadways nearing or in excess of capacity. The main factors determining whether an intermittent system saves time are: the traffic saturation level; the bus frequency; the improvement in bus travel time achieved by the special lane; and the ratio of bus and car occupant flows. In some scenarios where a dedicated bus lane could not be operated, a BLIP can save to bus and car occupants together as much as 20 persons-min of travel per bus-km. The required conditions for this to happen are quite particular. Typical savings are smaller. Formulae are given. [Copyright &y& Elsevier]

Abstract: New technologies in traffic can produce a range of unknown and unplanned deviations which require attention when assessing such technologies for market implementation. Current assessment methods focus on expected and usually desired effects and do not include identification and analyses of all kinds of other effects resulting from processes other than the desired processes. In this paper a method called HAZOP (Hazard and Operability analysis), originally developed for identifying unintended safety problems in chemical processes, is introduced and applied in order to analyse the added value of this method for large scale pilots with intelligent transport systems. The paper discusses the additional potential safety problems which the HAZOP identifies, that should be analysed before implementation of intelligent speed adaptation in daily traffic can be considered. [Copyright &y& Elsevier]

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent transportation systems. On the basis of many existing prediction models, each having good performance only in a particular period, an improved approach is to combine these single predictors together for prediction in a span of periods. In this paper, a neural network model is introduced that combines the prediction from single neural network predictors according to an adaptive and heuristic credit assignment algorithm based on the theory of conditional probability and Bayes’ rule. Two single predictors, i.e., the back propagation and the radial basis function neural networks are designed and combined linearly into a Bayesian combined neural network model. The credit value for each predictor in the combined model is calculated according to the proposed credit assignment algorithm and largely depends on the accumulative prediction performance of these predictors during the previous prediction intervals. For experimental test, two data sets comprising traffic flow rates in 15-min time intervals have been collected from Singapore’s Ayer Rajah Expressway. One data set is used to train the two single neural networks and the other to test and compare the performances between the combined and singular models. Three indices, i.e., the mean absolute percentage error, the variance of absolute percentage error, and the probability of percentage error, are employed to compare the forecasting performance. It is found that most of the time, the combined model outperforms the singular predictors. More importantly, for a given time period, it is the role of this newly proposed model to track the predictors’ performance online, so as to always select and combine the best-performing predictors for prediction. [ABSTRACT FROM AUTHOR]

Flow of traffic on freeways and limited access highways can be represented as a series of kinemetic waves. Solutions to these systems of equations become problematic under congested traffic flow conditions, and under complicated (real-world) networks. A simplified theory of kinematics waves (KWaves) was previously proposed. Simplifying elements includes translation of the problem to moving coordinate system, adoption of triangular speed-density relationships, and adoption of restrictive constraints at the on- and off-ramps. However, these simplifying assumptions preclude application of this technique to most practical situations. By directly addressing the limitations of the original theory, this article proposes a simplified Kwaves model for network traffic (N-KWaves). Several key constraints of the original theory are relaxed. For example, the original merge model, which gives full priority to on-ramp traffic, is relaxed and replaced with a capacity-based weighted queuing (CBWFQ) merge model. The original diverge model, which blocks upstream traffic as a whole when a downstream queue exceeds the diverge, is also relaxed and replaced with a contribution-based weighted splitting (CBWS) diverge model. Based on the above, the original theory is reformulated and extended to address network traffic. Central to the N-KWaves model is a five-step computational procedure based on a generic building block. It is assumed that a freeway network can be represented by the combination of some special cases of the generic building block. An empirical field study showed satisfactory results. The N-KWaves model is best suited for modeling traffic operation in a regional freeway network and has a strong connection to Intelligent Transportation Systems (ITS). [ABSTRACT FROM AUTHOR]

Video object tracking is an important method of traffic detection in Intelligent Transportation Systems. In video traffic tracking systems the matching method is often used to find the position of moving objects. In this article an improved algorithm of corner feature extraction is presented and corner points are tracked as the feature points of traffic objects. The tracking precision is mainly decided by matching algorithms. If the matching is not accurate, good tracking results cannot be achieved. In this article Kalman Filtering is used to track the moving traffic objects. In this system two kinds of data are used: One is from the general matching algorithm, which is the representation of the target's position; the other is detected by a spatial filtering velocimeter, containing the rough flow velocity of the targets. Though neither kind of data are highly accurate, Kalman Filtering is capable of integrating both position and velocity data to obtain better tracking results. [ABSTRACT FROM AUTHOR]

This article presents an overview of highway cooperative collision avoidance (CCA), which is an emerging vehicular safety application using the IEEE-and ASTM-adopted Dedicated Short Range Communication (DSRC) standard. Along with a description of the DSRC architecture, we introduce the concept of CCA and its implementation requirements in the context of a vehicle-to-vehicle wireless network, primarily at the Medium Access Control (MAC) and the routing layer. An overview is then provided to establish that the MAC and routing protocols from traditional Mobile Ad Hoc networks are not directly applicable for CCA and similar safety-critical applications. Specific constraints and future research directions are then identified for packet routing protocols used to support such applications in the DSRC environment. In order to further explain the interactions between CCA and its underlying networking protocols, we present an example of the safety performance of CCA using simulated vehicle crash experiments. The results from these experiments are also used to demonstrate the need for network data prioritization for safety-critical applications such as CCA. Finally, the performance sensitivity of CCA to unreliable wireless channels is discussed based on the experimental results. [ABSTRACT FROM AUTHOR]

Traffic engineering studies such as validating Highway Capacity Manual (HCM) models require complete and reliable field data. However, the wealth of intelligent transportation systems (ITS) data is sometimes rendered useless for these purposes because of missing values in the data. Many imputation techniques have been developed in the past with virtually all of them imputing a single value for a missing datum. While this provides somewhat simple and fast estimates, it does not eliminate the possibility of producing biased results and it also fails to account for the uncertainty brought about by missing data. To overcome these limitations, a multiple imputation scheme is developed which provides multiple estimates for a missing value, simulating multiple draws from a population to estimate the unknown parameter. This paper also develops a framework of imputation which gives a broad perspective so that one can relate imputation methods to each other. [ABSTRACT FROM AUTHOR]

Accurate and timely forecasting of traffic flow is of paramount importance for effective management of traffic congestion in intelligent transportation systems. In this paper, a novel nonparametric dynamic time-delay recurrent wavelet neural network model is presented for forecasting traffic flow. The model incorporates the self-similar, singular, and fractal properties discovered in the traffic flow. The concept of wavelet frame is introduced and exploited in the model to provide flexibility in the design of wavelets and to add extra features such as adaptable translation parameters desirable in traffic flow forecasting. The statistical autocorrelation function is used for selection of the optimum input dimension of traffic flow time series. The model incorporates both the time of the day and the day of the week of the prediction time. As such, it can be used for long-term traffic flow forecasting in addition to short-term forecasting. The model has been validated using actual freeway traffic flow data. The model can assist traffic engineers and highway agencies to create effective traffic management plans for alleviating freeway congestions. [ABSTRACT FROM AUTHOR]

In this article a nonlinear model predictive control approach to the problem of coordinated ramp metering is presented. The previously designed optimal control tool Advanced Motorway Optimal Control (AMOC) is used within the framework of a hierarchical control structure which consists of three basic layers: the estimation/prediction layer, the optimization layer, and the direct control layer. More emphasis is given to the last two layers where the control actions on a network-wide and on a local level, respectively, are decided. The hierarchical control strategy combines AMOC's coordinated ramp metering control with local feedback Asservissement LInéaire d'Entré Autoroutière (ALINEA) control in an efficient way. Simulation investigations for the Amsterdam ring-road are reported whereby the results are compared with those obtained by applying ALINEA as a stand-alone strategy. It is shown that the proposed control scheme is efficient, fair, and real-time feasible. [ABSTRACT FROM AUTHOR]

This article presents a discrete-event stochastic microscopic simulation model specifically developed to evaluate the operational performance of toll plazas. The model has been calibrated, validated, and applied to toll plazas equipped with Electronic Toll Collection (ETC) in Orlando, Florida. Traffic behavior is represented using a set of mathematical and logic algorithms that control the conflicts among vehicles within the toll plaza area. Modified versions of Car-Following and Lane-Changing Algorithms and a new Toll-Lane Selection Algorithm are integrated into this new model to simulate traffic operation at toll plazas. The model output includes Measures of Effectiveness that can be used to evaluate the performance of existing and future individual toll lanes and the entire toll plaza system. Real-life data collected at the busiest toll plaza in the Orlando-Orange County Expressway Authority (OOCEA) system was compared with the model output to validate the developed model. Statistical tests indicated that there is no significant difference at the 95% confidence level between Measures of Effectiveness obtained from the model and those collected in the real world. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITS) are playing increasingly important roles in addressing traffic congestion, safety, and environmental concerns. Archiving and “reusing” the vast amounts of data initially collected by ITS for real-time operations holds the potential to significantly improve a wide range of transportation analyses. Effectively archiving and deriving information from ITS data requires the application of technology and algorithms recently developed and proven in research. This paper describes and summarizes this research, particularly in areas such as data warehousing, complex systems development, traffic data aggregation, traffic data imputation, and traffic data characterization. The paper concludes with a list of key future research needs required to allow expanded use of ITS data archives. [ABSTRACT FROM AUTHOR]

Active warning systems are traffic control devices consisting of variable signs or flashing beacons with conventional warning signs, which are activated by sensors detecting real-time roadway, environmental, and operational hazards. Currently most active warning systems detect and warn drivers of variable weather or pavement conditions, high-risk vehicle operating speeds, or the presence of intermittent hazards such as other vehicles, pedestrians, or wild animals. Within the Intelligent Transportation Systems framework, active warning systems applications detect these and other variable hazards and communicate these warnings directly to the vehicle or operator. Because most active warning systems are relatively recent, limited long-term system performance data are available. Testing and evaluation of active warning signs should continue to more firmly establish their benefits to highway safety and traffic operations. This paper provides an overview of available active warning systems and discusses their implementation requirements and potential benefits based on results and recommendations from evaluation studies. [ABSTRACT FROM AUTHOR]

An emerging technology known as wireless location technology has been developed to allow mobile wireless devices (the most common of which are cellular telephones) to be geolocated. Over the past decade, a number of research studies and operational tests have attempted to develop probe-based traffic monitoring systems using wireless location technology, but there still exist significant misperceptions concerning its capabilities. To address this problem, in this paper we seek to provide an assessment of wireless location technology-based traffic monitoring. The specific purposes of this paper are to (1) describe the concept of wireless location technology-based traffic monitoring, (2) present a critical assessment of past studies of this technology, (3) document the evaluation of one of the most recent operational tests—the 2001 Virginia Department of Transportation, Maryland State Highway Administration, and U.S. Wireless Corporation effort in the Washington, D.C., region, and (4) discuss the challenges that these systems pose to traffic engineering. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic vehicle routing and scheduling model that incorporates real time information using variable travel times. Dynamic traffic simulation was used to update travel times. The model was applied to a test road network. Results indicated that the total cost decreased by implementing the dynamic vehicle routing and scheduling model with the real time information based on variable travel times compared with that of the forecast model. As well, in many cases total running times of vehicles were also decreased. Therefore, the dynamic vehicle routing and scheduling model will be beneficial for both carriers in reducing total costs and society at large by alleviating traffic congestion. [Copyright &y& Elsevier]

The Simple Platoon Advancement (SPA) model describes a conceptual Intelligent Transportation System in which vehicles are progressed through signalised intersections under the automatic control of roadside and in-vehicle infrastructure. The objective is to increase the throughput of vehicles by moving the queue as a closely-spaced platoon on the start-of-green. Comparisons of stopline throughput are made between the SPA model and a valid representation of current road traffic behaviour. The paper contains a description of a basic model—the homogeneous SPA model with constant acceleration—where all vehicles behave the same in terms of their acceleration characteristics, the time headway they desire and the target speed each is trying to reach after passing through the intersection. The acceleration characteristics in question are the rate and the style: that of this model has no smoothing effect and is instantaneously wrought on a vehicle. Though this is somewhat unrealistic for a working system, the constant acceleration model serves as a yardstick for which to check and compare the development of future models. The framework for this model is defined to enable calculations of measurable goals such as stopline throughput for comparison with the EQD model of current road traffic behaviour. In terms of throughput, the homogeneous SPA model shows that even with a very cautious approach using conservative parameter values, the number of vehicles that would pass the stopline of a SPA-equipped intersection is a little more than 150% compared with the throughput found in today’s road systems. Reductions in jam spacing increase the throughput still further. [Copyright &y& Elsevier]

The aim of this paper is to develop insight into the potential of reinforcement learning (RL) agents and distributed reinforcement learning agents in the domain of transportation and traffic engineering and specifically in Intelligent Transport Systems (ITS). This paper provides a crystallized, comprehensive overview of the concept of RL and presents related successful applications in the field of traffic control and transportation engineering. It is divided into two parts: the first part provides a thorough overview of RL and its related methods and the second part reviews most recent applications of RL algorithms to the field of transportation engineering. Finally, it identifies many open research subjects in transportation in which the use of RL seems to be promising. [ABSTRACT FROM AUTHOR]

Estimation of freeway travel time with reasonable accuracy is essential for successful implementation of an advanced traveler information system (ATIS) for use in an intelligent transportation system (ITS). An ATIS consists of a route guiding system that recommends the most suitable route based on the traveler''s requirements using the information gathered from various sources such as loop detectors and probe vehicles. This information can be disseminated through mass media or on on-board satellite-based navigational system. Based on the estimated travel times for various routes, the traveler can make a route choice. In this article, a neural network model is presented for forecasting the freeway link travel time using the counter propagation neural (CPN) network. The performance of the model is compared with a recently reported freeway link travel forecasting model using the backpropagation (BP) neural network algorithm. It is shown that the new model based on the CPN network, and the learning coefficients proposed by Adeli and Park, is nearly two orders of magnitude faster than the BP network. As such, the proposed freeway link travel-forecasting model is particularly suitable for real-time advanced travel information and management systems. [Copyright &y& Elsevier]

This paper summarizes a standardized verification process for network traffic simulation models. After the general introduction of philosophy of verification, we explain detailed processes of the verification and its application to several well-known simulation models. 'Verification' here means several examination tests of simulation models using virtual data on a simple network so as to confirm their fundamental functions. In the course of model development, the developers have to examine whether the model performance is consistent with the specifications that they intend and also with the well-authorized traffic engineering theory. Because of several constraints in putting the model specifications into the computer programming such as discretizing of time and space and simplifying vehicle behaviors to some degree, the intended model specifications may not be fully achieved in a computer. Therefore, we strongly recommend the verification before applying the models to a real network. [ABSTRACT FROM AUTHOR]

Area traffic control is an important element in Intelligent Transportation System (ITS). This paper extends the lane-based optimization method to a traffic equilibrium network, which improves the operational performance of signal-controlled network. We formulate a decomposition approach to simultaneously optimize the lane markings and signal settings for a signal-controlled network that comprises two levels of optimization. At the junction level, the lane markings, control sequence, and other aspects of the signal settings are optimized for individual junctions, whereas at the network level, the group-based signal settings are optimized to take into account the re-routing characteristics of travelers and signal coordination effects that are based on a TRANSYT traffic model, which is a well-known procedure for evaluating the performance of signal-controlled networks. We use a numerical example to demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

In-vehicle information has an important social role to play in improving the efficiency and safety of travel by all modes. In this review, three generations of system are identified. The first generation consists of simple in-vehicle units relying heavily on external data. The second generation has more sophisticated in-vehicle units with colour TFT screens and DVD players for maps and entertainment. The third generation again makes use of external data, using the mobile phone network to download map sections and other data as and when required, thereby obviating the need for beacons and map CDs. For locationing, GPS (and/or Galileo, the European version of GPS) remains the favoured technology. Portable devices offering multi-modal information could improve inter-modal transport efficiency. [ABSTRACT FROM AUTHOR]

Recent advances in traffic control methods have led to flexible control strategies for use in an adaptive traffic control system (ATCS). ATCS aims at controlling the imminent traffic, which is yet to arrive and hence not known perfectly. Therefore, volume prediction is an essential part. Associated with the prediction are two aspects: resolution and accuracy. Recent studies indicate a tradeoff between prediction resolution and accuracy: finer resolutions, larger errors. It is imperative to study the relationship and tradeoff between the control strategy, prediction resolution, and its associated error, which are crucial to the development of ATCS. This study investigates this relationship through an extensive simulation of scenarios in Hong Kong with a recently developed dynamic traffic control model, DISCO. Based on the Hong Kong scenarios conducted with DISCO, the major findings include: (i) the importance of resolution outweighs that of error; (ii) dynamic timing plans generally outperform time-invariant timing plans; (iii) up to a certain extent, overestimated predictions lead to better results than underestimated predictions. [ABSTRACT FROM AUTHOR]

Presents an a real-time intelligent decision making system for urban traffic control applications. Background and motivations that influence intelligent highway vehicle systems; Architecture of the proposed system; Mapping of the application to the system.

Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real-time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin-destination (O-D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O-D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O-D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding-off, and, 2) low ratio of total number of trips to total number of O-D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O-D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed. [ABSTRACT FROM AUTHOR]

Dynamic traffic assignment (DTA) is at the heart of much ITS research. Assigning traffic, whether for planning purposes or for real time route guidance, is a difficult problem. Recent advances in user-optimal dynamic traffic assignment have built on the methods developed for static user-optimal assignments. Since assignment models are complex, they will not use many of the network analysis functions found in commercial GIS packages. Custom software will have to be developed. In this paper we explore the problems faced in solving static and dynamic assignments and relate those problems to information that is likely to be based in a traffic control center’s GIS database. Because of the size of the problem and the need for faster-than-real-time analysis, how and when data is transferred between a GIS to analysis modules is important. Further, many approaches for GIS software design and spatial data handling, such as OOP and dynamic segmentation, may impose too much overhead to be of much use in time-sensitive ITS applications. [ABSTRACT FROM AUTHOR]

The emergence of Connected and Automated Vehicles (CAVs) promises better traffic mobility for future transportation systems. Existing research mostly focused on fully-autonomous scenarios, while the potential of CAV control at a mixed traffic intersection where human-driven vehicles (HDVs) also exist has been less explored. This paper proposes a notion of " 1 + n " mixed platoon, consisting of one leading CAV and n following HDVs, and formulates a platoon-based optimal control framework for CAV control at a signalized intersection. Based on the linearized dynamics model of the " 1 + n " mixed platoon, fundamental properties including stability and controllability are under rigorous theoretical analysis. Then, a constrained optimal control framework is established, aiming at improving the global traffic efficiency and fuel consumption at the intersection via direct control of the CAV. A hierarchical event-triggered algorithm is also designed for practical implementation of the optimal control method between adjacent mixed platoons when approaching the intersection. Extensive numerical simulations at multiple traffic volumes and market penetration rates validate the greater benefits of the mixed platoon based method, compared with traditional trajectory optimization methods for one single CAV. • Connected and Automated Vehicle (CAV) control in mixed traffic environment. • Mixed platoon control in signalized intersection. • System stability and controllability analysis. • System dynamical modeling and optimal control framework. • Market Penetration Rate (MPR) analysis. [ABSTRACT FROM AUTHOR]

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches. [ABSTRACT FROM AUTHOR]

The use of real-time vehicle detection and classification systems is essential for the accurate management of traffic and road infrastructure. Over time, diverse systems have been proposed for it, such as the widely known magnetic loops or microwave radars. However, these types of sensors do not offer all the information currently required for exhaustive and comprehensive traffic control. Thus, this paper presents the design, implementation, and configuration of laser systems to obtain 3D profiles of vehicles, which collect more precise information about the state of the roads. Nevertheless, to obtain reliable information on vehicle traffic by means of these systems, it is fundamental to correctly carry out a series of preliminary steps: choose the most suitable type of laser, select its configuration properly, determine the optimal location, and process the information provided accurately. Therefore, this paper details a series of criteria to help make these crucial and difficult decisions. Furthermore, following these guidelines, a complete laser system implemented for vehicle detection and classification is presented as result, which is characterized by its versatility and the ability to control up to four lanes in real time. [ABSTRACT FROM AUTHOR]

In smart cities, it is expected that transport, communication as well as the movement of people and goods will take place in the shortest possible time while maintaining a high level of safety. In recent years, due to the significant increase in the number of passengers and vehicles on the road and the capacity limitations of transport networks, it has become necessary to use new technologies for intelligent control and traffic management. Intelligent transport systems use advanced technologies in the field of data gathering, information processing, and traffic control to meet current transport needs. To be able to effectively control and manage road traffic, it is necessary to have reliable mathematical models that allow for a faithful representation of the real traffic conditions. Models of this type are usually the basis of complex algorithms used in practice in road traffic control. The application of appropriate models reflecting the behavior of road users contributes to the reduction of congestion, the vehicles travel time on the transport network, fuel consumption and the emissions, which in turn support broadly understood energy savings. The article proposes a model that allows for the estimation of the maximum queue size at the signal-controlled intersection approach (so-called: maximum back-of-queue). This model takes into account the most important traffic characteristics of the vehicles forming this queue. The verification allowed for the conclusion that the proposed model is characterized by high compliance with the actual traffic and road conditions at the intersections with signal controllers located in built-up areas in Poland. The obtained compliance confirms the possibility of using the model for practical applications in calculating the maximum back-of-queue at signal-controlled intersections located in built-up areas in Poland. [ABSTRACT FROM AUTHOR]

Effective path finding has been identified as an important requirement for dynamic route guidance in Intelligent Transportation Systems (ITS). Path finding is most efficient if the all-pair (shortest) paths are precomputed because path search requires only simple lookups of the precomputed path views. Such an approach however incurs path view maintenance (computation and update) and storage costs which can be unrealistically high for large ITS networks. To lower these costs, we propose a Hierarchical Path View Model (HPVM) that partitions an ITS road map, and then creates a hierarchical structure based on the road type classification. HPVM includes a map partition algorithm for creating the hierarchy, path view maintenance algorithms, and a heuristic hierarchical path finding algorithm that searches paths by traversing the hierarchy. HPVM captures the dynamicity of traffic change patterns better than the ITS path finding systems that use the hierarchical$A$ * approach because: (1) during path search, HPVM traverses the hierarchy by dynamically selecting the connection points between two levels based on up-to-date traffic, and (2) HPVM can reroute the high-speed road traffic through local streets if needed. In this paper, we also present experimental results used to benchmark HPVM and to compare HPVM with alternative ITS path finding approaches, using both synthetic and real ITS maps that include a large Detroit map (> 28,000 nodes). The results show that the HPVM incurs much lower costs in path view maintenance and storage than the non-hierarchical path precomputation approach, and is more efficient in path search than the traditional ITS path finding using $A$* or hierarchical $A$* algorithms. [ABSTRACT FROM AUTHOR]

An efficient and credible approach to road traffic management and prediction is a crucial aspect in the Intelligent Transportation Systems (ITS). It can strongly influence the development of road structures and projects. It is also essential for route planning and traffic regulations. In this paper, we propose a hybrid model that combines extreme learning machine (ELM) and ensemble-based techniques to predict the future hourly traffic of a road section in Tangier, a city in the north of Morocco. The model was applied to a real-world historical data set extracted from fixed sensors over a 5-years period. Our approach is based on a type of Single hidden Layer Feed-forward Neural Network (SLFN) known for being a high-speed machine learning algorithm. The model was, then, compared to other well-known algorithms in the prediction literature. Experimental results demonstrated that, according to the most commonly used criteria of error measurements (RMSE, MAE, and MAPE), our model is performing better in terms of prediction accuracy. The use of Akaike's Information Criterion technique (AIC) has also shown that the proposed model has a higher performance. [ABSTRACT FROM AUTHOR]

This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introduced to decide if positions reported by particular vehicles are reliable and should be taken into account for controlling traffic signals. Extensive simulation experiments were conducted to verify effectiveness of the proposed approach in realistic scenarios. The experimental results show that the proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods. The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on the performance of traffic signal control. [ABSTRACT FROM AUTHOR]

License plate recognition is an emerging topic for real-time applications in smart city development because of automatic systems for toll fee-paying, traffic controlling, and vehicle detection. Speed of vehicles, unpredictable weather conditions such as night/low light/limited light images, and capturing images at different angles make recognition harder. This paper presents a new Fractal Series Expansion (FSE) model for license plate image enhancement. The proposed FSE model is justified because it estimates the high probability for the pixels which represent the license plate compared to the pixels which represent background irrespective of the above challenges, resulting in enhanced image. Besides, since the FSE model considers local information for estimating probability, the model has the ability to tackle non-uniform degradations as well as distortion affected multiple adverse factors. In addition to qualitative results, to validate the effectiveness of the proposed enhancement, quantitatively, recognition rates of the different methods before and after enhancement are computed. For this purpose, we have considered different datasets like dataset of Night License Plate Images (NLPI), which consists of images captured in the night and low lights environment, the UCSD benchmark dataset which provides poor and high quality day license plate images, etc. It is noted that recognition results after enhancement is higher than that of before enhancement, and hence our enhancement is useful and effective. • A new Fractal Series Expansion model for license plate image enhancement. • It tackles non-uniform degradations and distortions. • Different datasets including night license images are validated. [ABSTRACT FROM AUTHOR]

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on Y O L O V 3 , (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Introduction: Due to the menacing increase in the number of vehicles on a daily basis, abating road congestion is becoming a key challenge these years. To cope-up with the prevailing traffic scenarios and to meet the ever-increasing demand for traffic, the urban transportation system needs effective solution methodologies. Changes made in the urban infrastructure will take years, sometimes may not even be feasible. For this reason, traffic signal timing (TST) optimization is one of the fastest and most economical ways to curtail congestion at the intersections and improve traffic flow in the urban network. Purpose: Researchers have been working on using a variety of approaches along with the exploitation of technology to improve TST. This article is intended to analyze the recent literature published between January 2015 and January 2020 for the computational intelligence (CI) based simulation approaches and CI-based approaches for optimizing TST and Traffic Signal Control (TSC) systems, provide insights, research gaps and possible directions for future work for researchers interested in the field. Methods: In analyzing the complex dynamic behavior of traffic streams, simulation tools have a prominent place. Nowadays, microsimulation tools are frequently used in TST related researches. For this reason, a critical review of some of the widely used microsimulation packages is provided in this paper. Conclusion: Our review also shows that approximately 77% of the papers included, utilizes a microsimulation tool in some form. Therefore, it seems useful to include a review, categorization, and comparison of the most commonly used microsimulation tools for future work. We conclude by providing insights into the future of research in these areas. [ABSTRACT FROM AUTHOR]

• A hybrid spatiotemporal approach for traffic missing data imputation. • Tackling multiple missing patterns, including randomly, clustered non-completely and clustered completely missing. • Extensive numerical tests with real-world dataset. • Applicable even when the missing rate approaches 90%. With the advent of intelligent transportation systems (ITS), spatiotemporal traffic data has gained growing importance in real-time monitoring, prediction, and control of traffic. However, in practical implementations, data collection devices are often faced with malfunctions caused by various unpredictable disruptions, thereby resulting in the so-called "missing value problems." In realistic cases, the disruptions to the data collection devices are often associated with some key events (e.g., power cut and natural disasters), in addition, along with other disruptions the missing value problem could be in a complicated manner with both randomly and completely missing patterns. To perform the imputation task with such complicated missing patterns, we propose a hybrid spatiotemporal method which utilizes the time series properties by "prophet" model and captures the spatial residuals information by iterative random forest model. The spatiotemporal method first applies the temporal part to fill the missing value and then adopts the spatial part to acquire the residual component of the missing values. The results of the two components are integrated into the final imputations. Based on the PeMS freeway dataset (PeMS, 2019) and an urban road dataset under extensive artificially designed scenarios like randomly, clustered non-completely and completely missing patterns, we test our proposed approach with some existing techniques such as K-Nearest Neighbor (KNN), Seasonal-Trend decomposition using Loess (STL), Bayesian tensor decomposition, Denoising AutoEncoder (DAE). The test results indicate that the hybrid method achieves the best imputation quality for most missing patterns, particularly for those with completely or hybrid missing patterns. Furthermore, the hybrid model still performs well under extreme missing rates as high as 0.9, which validates the robustness of the model in extreme situations. [ABSTRACT FROM AUTHOR]

In recent years, people have witnessed numerous Internet of Things (IoT)-based attacks with the exponential increase in the number of IoT devices. Alongside this, the means to secure IoT-based applications are maturing slower than our budding dependence on them. Moreover, the vulnerabilities in an IoT system are exploited in chains to penetrate deep into the network and yield more adverse aftereffects. To mitigate these issues, this paper gives unique insights for handling the growing vulnerabilities in common IoT devices and proposes a threat architecture for IoT, addressing threats in the context of a three-layer IoT reference architecture. Furthermore, the vulnerabilities exploited at the several IoT attack surfaces and the challenges they exert are explored. Thereafter, the challenges in quantifying the IoT vulnerabilities with the existing framework are also analyzed. The study also covers a case study on the Intelligent Transportation System, covering road transport and traffic control specifically in terms of threats and vulnerabilities. Another case study on secure energy management in the Smart Grid is also presented. This case study covers the applications of Internet of Vulnerable Things (IoVT) in Smart energy Grid solutions, as there will be tremendous use of IoT in future Smart Grids to save energy and improve overall distribution. The analysis shows that the integration of the proposed architecture in existing applications alarms the developers about the embedded threats in the system. [ABSTRACT FROM AUTHOR]

With the contemporary improvements in intelligent transportation systems, automatic car License Plate Detection and Recognition (LPDR) has involved extensive research interests. It has a variety of potential applications in security and traffic control, and much work has been completed on the subject of LPDR. But, majority of prevailing procedures work well either at measured circumstances or with sophisticated image capture systems. It is still a challenging task to read license plates precisely in an open environment. The problem exists in extreme assortment in pattern of characters, such as dissimilar sizes, letterings, misrepresentation, obstruction or blurring, as well as high intricate backgrounds, similar to the common fonts in boards of shops, windows, handrails or construction blocks. In this study, a hybrid methodology has been established in recognizing license plates of candidates in a specific area. This research proposes a technique called kernel density function along with the binary technique that has been utilized for pre-processing. By means of the filtered binary value of the image, the position of car plate can be found by multiplying the binary value and original value of the image. The proposed method outperforms advanced techniques by a huge margin concerning about accuracy in detection as well as efficiency of run-time. The proposed method achieved 98.1 % accuracy to detect the License Plate (LP) and the computation cost is about 0.452 s. [ABSTRACT FROM AUTHOR]

With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment. [ABSTRACT FROM AUTHOR]

The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic. [ABSTRACT FROM AUTHOR]

• Applying Bayesian network theory to intelligent decision-making of traffic lights. • Using K2 algorithm to obtain network structure and carry out structure learning. • A forward backward algorithm based on time window is proposed. Intelligent traffic lights are an important part of intelligent transportation systems. In this paper, the Bayesian network theory is used to establish a traffic light independent intelligent decision model based on dynamic Bayesian network. According to the real-time dynamic information of traffic conditions, the proposed dynamic Bayesian network approximate reasoning algorithm is used to realize online reasoning and determine the best traffic light time. The algorithm combines the time window with the improved forward-backward algorithm. By adjusting the time window width of the algorithm, the evidence information can be used to maximize online reasoning. Compared with the existing time window based on interface algorithm, it's proved that the reasoning algorithm proposed is more efficient. The research results of this paper have important practical significance in solving the traffic congestion problem and reducing the waiting time of people at the intersection of traffic lights. Image, graphical abstract According to the real-time dynamic information of traffic conditions, the proposed dynamic Bayesian network approximate reasoning algorithm is used to realize online reasoning and determine the best traffic light time. The algorithm combines the time window with the improved forward-backward algorithm. [ABSTRACT FROM AUTHOR]