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A ramp metering (RM) or ramp signal is an effective method for traffic management, especially at merging sections in urban expressways. This research aims to assess the Asservissement Linéaire d’Entrée Autoroutière (ALINEA) method performance at different loop detector locations using field data and micro-simulation modeling via advanced interactive microscopic simulator for urban and non-urban networks (AIMSUN). The operation of RM was executed using the ALINEA method. The field data were collected from two locations in Manchester in the UK for calibration and validation in AIMSUN. Occupancy values at different loop detector locations were evaluated. The results indicated that the current simulation model could represent reality to acceptable limits. Increasing the number of detector locations at the merging entrance will decrease the occupancy value. The results showed that the effect of loop detector location is a significant factor in determining the critical occupancy of the merging section. Also, the downstream location is more appropriate for determining the congestion or critical status. The AIMSUN software showed significant consistency in representing field data of merging sections. The minimum value of GEH (Geoffrey E. Havers) that equal to 0.5 has been used to statistically and graphically demonstrate this behavior.

Traffic management and control is pivotal to maintaining orderliness in corporate organizations. The effective management and control of traffic will lead to improved security. As a result, investments in security take a good percentage of their expenses, which necessitates improved security solutions at reduced costs. Most corporate organizations adopt the use of a boom barrier, which is an improved version of the traditional or manual barrier system (MBS). However, the boom barrier uses a remote controller with limited capability in terms of operating range and security level. The running cost is also key in choosing the system to be adopted for security purposes. This study developed a techno-economic design and implementation of a vehicular access control system using the Bells University of Technology, Ota, Nigeria, as a case study. The study first designed a control system module by exploring the capabilities of the loop detector, Arduino, Raspberry pi, four-channel relay, radio frequency identification (RFID) card and scanner, and a camera. The control system module was then integrated into the boom barrier for seamless control to realize the boom barrier access control system (BACS). The paper developed economic models for the MBS and the BACS, which were simulated using MATLAB to justify the need for the adoption of the developed BACS. A sensitivity analysis was performed to demonstrate the robustness of the developed cost models. The functionality tests on the developed BACS showed that the system is characterized by an average response time, opening time, delay time, and closing time of 5.06 seconds, 6.12 seconds, 30.02 seconds, and 6.02 seconds, respectively. Results on costs showed that the BACS is cost-effective with an initial cost of $7,650 compared to $15,223 for the MBS. The BACS is also more resilient to annual increments than the MBS. A cost-benefit analysis revealed an annual increase in the cost-benefit over the entire system lifespan. The study has demonstrated the viability of BACS in vehicular access control considering both the ease of operation and systems costs.

In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data models that cannot fulfill the required accuracy. This deficiency is the main motivation to propose an enrichment imputation method for loop detectors namely EIM-LD, in which the imputation accuracy is increased for different missing patterns and ratios by introducing a data enrichment technique using statistical multi-class labeling. It first enriches the clean data by adding a statistical multi-class label, including C1...Cn classes. Then, the class of samples in the missed-volume data is labeled using the best data model constructed from the labeled clean data by five different classifiers. Experts of the traffic control department in Isfahan city determined classes of the statistical multi-class label for n = 5 (class labels), and we also developed subclass labels (n = 20) since the number of samples in the subclass labels was sufficient. Next, the enriched data are divided into n datasets, each of them is imputed independently using various imputation methods, and their results are finally merged. To evaluate the impact of using the proposed method, the original data, including missing volumes, are first imputed without our enrichment method. Then, the proposed method's accuracy is evaluated by considering two class labels and subclass labels. The experimental and statistical results prove that the proposed EIM-LD method can enrich the real data collected by loop detectors, by which the comparative imputation methods construct a more accurate data model. In addition, using subclass labels further enhances the imputation method's accuracy. [ABSTRACT FROM AUTHOR]

Models to predict ramp meter queue length from traffic detector data are potentially useful tools in improving traffic operations and safety. Existing research, however, has been based on microscopic simulation or relied on extensive calibration of Kalman filter and related models to produce reliable queue length estimates. This research seeks to develop methodologies for improving and simplifying the calibration process of existing queue length models by applying loop detector data including volume, occupancy, and the metering rate data for ramp meters along I-15 in Utah. A conservation model and several variations of a Kalman filter model generated estimated queues that were compared to observed queue lengths in 60 s bins. A modified Kalman filter model and a new heuristic model derived from cluster analysis—the models that yielded the best results—provided queue length estimates that were generally within approximately eight vehicles of the observed queue length. Using the ramp metering rate, the queue length estimates were converted into wait times that were generally within approximately 30 s of the actual wait time, producing a viable method to predict wait time from up-to-the-minute traffic detection information with relatively little required calibration. The implementation of the ramp meter queue length and wait time estimation algorithms presented in this research will allow departments of transportation to better assess freeway and ramp conditions, which can then aid in reducing congestion throughout the freeway network.

Models to predict ramp meter queue length from traffic detector data are potentially useful tools in improving traffic operations and safety. Existing research, however, has been based on microscopic simulation or relied on extensive calibration of Kalman filter and related models to produce reliable queue length estimates. This research seeks to develop methodologies for improving and simplifying the calibration process of existing queue length models by applying loop detector data including volume, occupancy, and the metering rate data for ramp meters along I-15 in Utah. A conservation model and several variations of a Kalman filter model generated estimated queues that were compared to observed queue lengths in 60 s bins. A modified Kalman filter model and a new heuristic model derived from cluster analysis—the models that yielded the best results—provided queue length estimates that were generally within approximately eight vehicles of the observed queue length. Using the ramp metering rate, the queue length estimates were converted into wait times that were generally within approximately 30 s of the actual wait time, producing a viable method to predict wait time from up-to-the-minute traffic detection information with relatively little required calibration. The implementation of the ramp meter queue length and wait time estimation algorithms presented in this research will allow departments of transportation to better assess freeway and ramp conditions, which can then aid in reducing congestion throughout the freeway network. [ABSTRACT FROM AUTHOR]

Abstract We study the problem of making algorithmic statistical inferences about the dynamics of city traffic. Our data is based on loop detector counts of observed vehicles in various roads in the city of Tampere, Finland. We show that meaningful correlations can be found between traffic asymmetries at different measurement locations. The traffic asymmetry is the difference of the traffic counts of the opposite directions of a road. The correlations can be further quantified by estimating how much they effect on the average values of the traffic asymmetries at the neighbouring locations. Conditional expectations, both sample and binormal model-based versions are useful tools for quantifying this effect. The uncertainty bounds of conditional expectations of the binormal model distribution are extremely useful for outlier detection. Furthermore, conditional expectations of the multinormal distribution model can be used to recover missing data with bounds to uncertainty.

The macroscopic fundamental diagram (MFD) is a graphical method used to characterize the traffic state in a road network and to monitor and evaluate the effect of traffic management. For the determination of an MFD, both traffic volumes and traffic densities are needed. This study introduces a methodology to determine an MFD using combined data from probe vehicles and loop detector counts. The probe vehicles in this study were taxis with GPS. The ratio of taxis in the total traffic was determined and used to convert taxi density to the density of all vehicles. This ratio changes over the day and between different links. We found evidence that the MFD was rather similar for days in the same year based on real data collected in Changsha, China. The difference between MFDs made of data from 2013 and 2015 reveals that the modification of traffic control can influence the MFD significantly. A macroscopic fundamental diagram could also be drawn for an area with incomplete data gained from a sample of loop detectors. An MFD based on incomplete data can also be used to monitor the emergence and disappearance of congestion, just as an MFD based on complete traffic data.

This report summarizes empirical research about the causes and impact of non-recurrent congestion. A method is presented to divide the total congestion delay in a freeway section into six components: the delay caused by incidents, special events, lane closures, and adverse weather; the potential reduction in delay at bottlenecks that ideal ramp metering can achieve; and the remaining delay, due mainly to excess demand. The method can be applied to any site with minimum calibration, but it requires data about traffic volume and speed; the time and location of incidents, special events and lane closures; and adverse weather. The method is illustrated by applying it to a 45-mile section of I-880 in the San Francisco Bay Area.Data limitations preclude applying the method statewide. A simpler method, which depends only on routine data collected in the PeMS system, has been implemented. A PeMS application now provides a ‘congestion pie’ for any district or freeway segment. The pie divides the total congestion delay into three categories: potential reduction, excess demand, and accidents; and an unexplained, ‘miscellaneous’, category.

We study the problem of making algorithmic statistical inferences about the dynamics of city traffic. Our data is based on loop detector counts of observed vehicles in various roads in the city of Tampere, Finland. We show that meaningful correlations can be found between traffic asymmetries at different measurement locations. The traffic asymmetry is the difference of the traffic counts of the opposite directions of a road. The correlations can be further quantified by estimating how much they effect on the average values of the traffic asymmetries at the neighbouring locations. Conditional expectations, both sample and binormal model-based versions are useful tools for quantifying this effect. The uncertainty bounds of conditional expectations of the binormal model distribution are extremely useful for outlier detection. Furthermore, conditional expectations of the multinormal distribution model can be used to recover missing data with bounds to uncertainty. [ABSTRACT FROM AUTHOR]

• Using the data this paper assesses what goes unseen by the perception sensors. • This work fuses microscopic vehicle trajectory and loop detector data. • Correcting for unknown spatial offset and slowly varying time offset. • Yielding a rich data set for studying traffic dynamics. • Anticipates synchronization issues that will arise with connected vehicles. It is becoming common to use instrumented probe vehicles (IPV) equipped with perception sensors (e.g., LIDAR or radar) to monitor ambient traffic for empirical studies of traffic dynamics and driver behavior, while the rapidly advancing field of autonomous vehicles (AV) uses perception sensors to identify and mitigate hazards. Yet the perception sensors are far from perfect, so targets of interest go undetected. The "unseen" targets might lead to inaccurate theories of traffic flow or worse, autonomous vehicle crashes. The challenge with the unseen targets is that it is difficult to detect the absence. There is little study of how vehicle-mounted perception sensors perform "in the wild" because often the perception sensors themselves usually provide the best measurements available. The focus of this work is IPV data for offline study, but the general findings are also relevant for perception sensors used in AV applications. While there is a growing number of IPV data sets, to date most suffer from the lack of independent validation of the perception sensors. This paper fuses individual vehicle actuations from loop detectors and concurrent ambient vehicle trajectories collected from an IPV to provide a rare opportunity to assess the perception sensor performance in situ, using the loop detector data to "see" what is missed by the perception sensors. While the specific results are unique to the IPV, one should expect similar biases from other perception sensor data; though without an independent measure like the loop detectors in this study, it will be difficult to quantify what goes unseen in those data sets. These findings also highlight the need to develop means to actively calibrate mobile perception sensors for AV's. It is important to capture the behavior of these systems in situ, as maintained by private owners, though we envision in the case of an AV the role of the loop detectors might be replaced with localization information from other AV's. The data fusion has added benefits for the study of traffic dynamics, with the loop detectors providing continuous monitoring at the detector locations and the IPV providing spatially rich information about the ambient traffic as it travels between detector stations. This fused data set will be available upon publication. [ABSTRACT FROM AUTHOR]

With the ability to accurately forecast road traffic conditions several hours, days and even months ahead of time, both travellers and network managers can take pro-active measures to minimise congestion, saving time, money and emissions. This study evaluates a previously developed random forest algorithm, RoadCast, which was designed to achieve this task. RoadCast incorporates contexts using machine learning to forecast more accurately contexts such as public holidays, sporting events and school term dates. This paper evaluates the potential of RoadCast as a traffic forecasting algorithm for use in Intelligent Transport System applications. Tests are undertaken using a number of different forecast horizons and varying amounts of training data, and an implementation procedure is recommended. [ABSTRACT FROM AUTHOR]

A novel framework is developed in this paper, to increase the real-time roadway traffic condition assessment accuracy, which integrates connected vehicle technology (CVT) with artificial intelligence (AI) paradigm forming a CVT-AI method. Traffic density is a major indicator of traffic conditions. In this paper, the traffic operational condition is assessed based on traffic density. A simulated network of Interstate 26 in South Carolina is developed to investigate the effectiveness of the method. The assumption is that the vehicle onboard units will forward the CV generated data to the edge devices (e.g., roadside units) for further processing. CV generated distance headway and number of stops, and speed data are used to estimate traffic density. This paper reveals that, with 20% and greater CV penetration levels, the accuracy of the density information with the AI-aided CVT is a minimum of 85%. Moreover, this paper demonstrates that the integrated CVT-AI method yields a higher accuracy with the increase of CV penetration levels. Level of service (LOS) is the indicator of traffic congestion level on highways and is described with traffic density in terms of passenger car/mile/lane for a specific free flow speed. LOS estimated using the CVT-AI density estimation method is compared with the density estimation algorithm used by the Caltrans Performance Measurement System (PeMS), which relies on the occupancy and flow data collected by the freeway inductive loop detectors. With a 10% or more CV penetration, higher accuracy is achieved using the CVT-AI algorithm compared with the PeMS density estimation algorithm. [ABSTRACT FROM PUBLISHER]

Loop detectors are devices that are most commonly used for obtaining data at intersections. Multiple detectors are usually required to monitor a location, and this reduces the accuracy of detectors for collecting traffic volumes. The purpose of this paper is to increase the accuracy of loop detector counts using Adaptive Neural Fuzzy Inference System (ANFIS) and Genetic Programming (GP) based on detector volume and occupancy. These methods do not need microscopic analysis and are easy to employ. Four approaches for one intersection are used in a case study. Results show that the models can improve intersection detector counts significantly. Results also show that ANFIS produces more accurate counts compared to regression and GP. [ABSTRACT FROM AUTHOR]

Red-light-running (RLR) emerges as a major cause that may lead to intersection-related crashes and endanger intersection safety. To reduce RLR violations, it’s critical to identify the influential factors associated with RLR and estimate RLR frequency. Without resorting to video camera recordings, this study investigates this important issue by utilizing high-resolution traffic and signal event data collected from loop detectors at five intersections on Trunk Highway 55, Minneapolis, MN. First, a simple method is proposed to identify RLR by fully utilizing the information obtained from stop bar detectors, downstream entrance detectors and advance detectors. Using 12 months of event data, a total of 6550 RLR cases were identified. According to a definition of RLR frequency as the conditional probability of RLR on a certain traffic or signal condition (veh/1000veh), the relationships between RLR frequency and some influential factors including arriving time at advance detector, approaching speed, headway, gap to the preceding vehicle on adjacent lane, cycle length, geometric characteristics and even snowing weather were empirically investigated. Statistical analysis shows good agreement with the traffic engineering practice, e.g., RLR is most likely to occur on weekdays during peak periods under large traffic demands and longer signal cycles, and a total of 95.24% RLR events occurred within the first 1.5 s after the onset of red phase. The findings confirmed that vehicles tend to run the red light when they are close to intersection during phase transition, and the vehicles following the leading vehicle with short headways also likely run the red light. Last, a simplified nonlinear regression model is proposed to estimate RLR frequency based on the data from advance detector. The study is expected to helpbetter understand RLR occurrence and further contribute to the future improvement of intersection safety. [ABSTRACT FROM AUTHOR]

Floating car- and loop detector-based methods are two different types of methods frequently used to collect travel time delay information across a freeway network. Sometimes, it is necessary to use them jointly to achieve the necessary freeway network coverage, due to the high labor costs for the floating car-based method and the indispensability of sufficient network instrumentation for the loop detector-based method. For example, both floating car- and loop detector-based methods were once used in the Highway Congestion Monitoring Program in the California Department of Transportation. It is therefore necessary to evaluate whether these two types of methods estimate similarly in terms of total travel time delay. To this end, corresponding delay information estimated using both types of methods from 37 freeway segments in the Greater Sacramento Area were collected and compared. It was found that these two types of methods do not estimate similarly in terms of total segment travel time delay. The mean absolute relative difference (MARD) can be as high as 78%, especially when delay is defined using a lower reference speed, such as 56 km/h. However, in terms of total segment travel time, the loop detector and the modified floating car method estimated similarly. The MARD is 19%. It was also found that the estimation from the different methods did correlate fairly well, which provides a means of conversion when different methods are used to monitor the total delay across a freeway network. As a spin-off, it was also found that a 1.5 km spacing of loop detectors is sufficient to achieve the 19% MARD as compared with the modified floating car method in terms of total travel time estimation.

The measurements and analysis of traffic intensity were performed in the capital city – Vilnius, the largest urban area in Lithuania. Vilnius is a centre of business, industry and tourism, and therefore traffic intensity remains the highest in this part of the country. The intensity of vehicle traffic is not only generally calculated but also simultaneously classified which means is divided predefining vehicles into beforehand established categories. Data on traffic flows are used in a road maintenance program for calculating and assessing air pollution, ensuring traffic safety, regulating traffic flows etc. The article presents the methods for measuring traffic intensity which are and were used for calculating traffic intensity not only in the streets of Vilnius but also across Lithuania. Data on vehicle intensity and classification are collected either using technologies (loop and tube detectors, counters and video detectors) or expressing them visually. The article presents the dynamics of changes in the traffic volume on the roads of Lithuania for the period 2000–2009. Also, this article examines traffic intensity of all transport means, including trucks in the permanent traffic volume measuring stations that were installed near the roads in Vilnius zone (data on traffic for the period 2005–2009) and the streets of Vilnius city (data on traffic for the period 2007–2009). Data on traffic intensity were obtained by the Road Research Laboratory of the Road Department of Vilnius Gediminas Technical University in cooperation with the State Enterprise Transport and Road Research Institute (TRRI).

Because of many advantages, loop detectors are the most common practice for obtaining data to control intersections. However, they have some drawbacks, including the fact that multiple detectors are usually required to monitor a location. The current practice in many cities is to install four consecutive loop detectors per lane, or two at the stop bar and one as an advanced detector. In some cities, there are also departure detectors. All these configurations have some practical problems and do not produce accurate counts especially in shared lanes. In this paper, a new placement configuration for departure detectors is proposed and named the mid-intersection detector (MID). In this configuration, departure detectors are moved back to the middle of the intersection in such a way that they can be activated by more than one movement at different times. In some cases, departure detectors lack equations for calculating turning movements, a problem solved by MIDs because each movement passes more detectors along its path (without increasing the number of loops), and therefore they can produce more accurate and reliable data for obtaining turning movement counts. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Travel time estimation and its variation for urban expressways are vital to both the information provision to road users, and the system evaluation and management for traffic administrators. Fruitful research efforts have been made to develop methodologies of reconstructing spatiotemporal traffic states mainly for freeways based on one or multiple data sources. However, few studies specifically focused on urban expressways. There are more intensive merging and diverging traffic due to short distances between ramps, for example, 300–500 m. Based on the empirical analysis of traffic data collected on a typical segment of a congested urban expressway, this study proposes an extended generalized filter algorithm for the urban expressway traffic state estimation based on heterogeneous data. More specifically, the multiple sources of data include both fixed sensor data (e.g., inductive loops or radar data) and global positioning system (GPS) probe vehicle data. This study compares the proposed algorithm and the traditional algorithm for freeways using data collected on the segment of expressway in Beijing, China. Results demonstrate the advantage of the proposed method, as well as its feasibility and effectiveness. [ABSTRACT FROM PUBLISHER]

In order to improve traffic safety on expressways, it is important to develop proactive safety management strategies with consideration for segment types and traffic flow states because crash mechanisms have some differences by each condition. The primary objective of this study is to develop real-time crash risk prediction models for different segment types and traffic flow states on expressways. The mainline of expressways is divided into basic segment and ramp vicinity, and the traffic flow states are classified into uncongested and congested conditions. Also, Korean expressways have irregular intervals between loop detector stations. Therefore, we investigated on the effect and application of the detector stations at irregular intervals for the crash risk prediction on expressways. The most significant traffic variables were selected by conditional logistic regression analysis which could control confounding factors. Based on the selected traffic variables, separate models to predict crash risk were developed using genetic programming technique. The model estimation results showed that the traffic flow characteristics leading to crashes are differed by segment type and traffic flow state. Especially, the variables related to the intervals between detector stations had a significant influence on crash risk prediction under the uncongested condition. Finally, compared with the single model for all crashes and the logistic models used in previous studies, the proposed models showed higher prediction performance. The results of this study can be applied to develop more effective proactive safety management strategies for different segment types and traffic flow states on expressways with loop detector stations at irregular intervals. [ABSTRACT FROM AUTHOR]

Highlights: [•] Only Edie’s definitions applied on full trajectory data guaranty a proper estimation of the NFD. [•] Mean network speed (or density) estimated from loop detectors can be significantly biased. [•] Probe vehicles provide accurate estimate for mean network speed even for low sampling rate. [•] A simple correction method can be applied to improve mean network density estimation from loop detectors. [Copyright &y& Elsevier]