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Traffic congestion is a phenomenon that has been extensively explored by researchers due to its impact on reliability and safety. This research is focused on proactively detecting and mitigating congestion on freeways by fuzing conventional traffic data obtained from radar and loop detectors with newer sources, such as Bluetooth and connected vehicles (CV). Data-driven and signal-processing techniques are explored to develop algorithms that use near- or real-time traffic measurements to predict the onset and intensity level of traffic congestion. The developed algorithm can be applied to both conventional and low penetration CV-based datasets to identify four types of congestion, that is, normal, recurring, other non-recurring, and incident. This research also demonstrates the advantage of using CV-based travel time estimates to calibrate microsimulation models over fixed point-based derivations of travel time from spot speeds. Finally, a set of mitigation strategies consisting of speed harmonization and dynamic rerouting are implemented in the calibrated simulation network to demonstrate their effectiveness in proactively reducing recurring and non-recurring congestion. The final derived algorithm is effective in proactively predicting the onset of congestion and its intensity level, with an overall mean prediction error of 30.2%. A limitation to the algorithm's methodology is that it cannot disentangle the type of congestion when two or more are occurring simultaneously and only predicts/classifies the anticipated highest level. However, this does not impair the user's ability to readily deploy appropriate mitigation strategies to alleviate the predicted intensity of congestion. [ABSTRACT FROM AUTHOR]

For the dynamic takeout delivery vehicle routing problem, which faces fluctuating order demand and time-varying speeds, this study presents a novel approach. We analyze the time distribution of takeout orders and apply a Receding Horizon Control (RHC) strategy to convert the dynamic challenge into a static one. The driving speed of delivery vehicles on different roads at different times is determined based on the subdivision criteria of the urban road network and a traffic congestion measurement method. We propose a dynamic takeout delivery vehicle routing optimization model and a time-varying subdivision road network is established to minimize the total delivery cost. We validated the model through simulation examples. The optimization results show that the total distribution cost is reduced after considering the time-varying subdivision road network, with the penalty cost decreasing by 39%. It is evident that considering the subdivision of the road network can enhance order delivery efficiency and optimize the overall dining experience. The sensitivity analysis of various parameters reveals that the delivery platform must appropriately determine the time domain and allocate the number of delivery personnel based on order scale to avoid escalating delivery costs. These findings provide theoretical guidance for vehicle routing planning in the context of delivery platforms. [ABSTRACT FROM AUTHOR]

In urban areas, efficient parking management is crucial for reducing traffic congestion and environmental impact. This research introduces a new view for making the parking management system that leverages the capabilities of the NVidia Jetson Nano Single Board Computer (SBC) and OpenCV for real-time detection and classification of parking slot occupancy. Unlike traditional systems that rely on intrusive sensors, our proposed solution employs non-intrusive Oriented Fast and Rotated Brief (ORB) key point detection techniques using video feeds. The system architecture integrates video stream processing, ORB via OpenCV, cloud-based data storage, and a Flask server for user notifications. The methodology prioritizes traditional computer vision methods optimized for the Jetson Nano's CUDA cores, offering a computationally efficient alternative to deep learning approaches. Python's versatility and MongoDB's document-based storage are employed for backend development. Our system's performance, evaluated using open datasets, demonstrates high accuracy, precision, recall, and F1 scores, underlining its effectiveness in real-world urban parking scenarios. This study not only presents a robust solution for parking management but also opens avenues for similar applications in traffic measurement and urban planning. [ABSTRACT FROM AUTHOR]

Millions of commuters face congestion as a part of their daily routines. Mitigating traffic congestion requires effective transportation planning, design, and management. Accurate traffic data are needed for informed decision making. As such, operating agencies deploy fixed-location and often temporary detectors on public roads to count passing vehicles. This traffic flow measurement is key to estimating demand throughout the network. However, fixed-location detectors are spatially sparse and do not cover the entirety of the road network, and temporary detectors are temporally sparse, providing often only a few days of measurements every few years. Against this backdrop, previous studies proposed that public transit bus fleets could be used as surveillance agents if additional sensors were installed, and the viability and accuracy of this methodology was established by manually processing video imagery recorded by cameras mounted on transit buses. In this paper, we propose to operationalize this traffic surveillance methodology for practical applications, leveraging the perception and localization sensors already deployed on these vehicles. We present an automatic, vision-based vehicle counting method applied to the video imagery recorded by cameras mounted on transit buses. First, a state-of-the-art 2D deep learning model detects objects frame by frame. Then, detected objects are tracked with the commonly used SORT method. The proposed counting logic converts tracking results to vehicle counts and real-world bird's-eye-view trajectories. Using multiple hours of real-world video imagery obtained from in-service transit buses, we demonstrate that the proposed system can detect and track vehicles, distinguish parked vehicles from traffic participants, and count vehicles bidirectionally. Through an exhaustive ablation study and analysis under various weather conditions, it is shown that the proposed method can achieve high-accuracy vehicle counts. [ABSTRACT FROM AUTHOR]

In recent decades, special attention has been given to the adverse effects of traffic congestion. Bike-sharing systems, as a part of the broader category of shared transportation systems, are seen as viable solutions to these problems. Even if the quality of service in bike-sharing service systems were permanently improved, there would still be some issues that needed new and more efficient solutions. One of these refers to the rebalancing operations that follow the bike depletion phenomenon that affects most stations during shorter or longer time periods. Current work develops a two-step method to perform effective rebalancing operations in bike-sharing. The core elements of the method are a fuzzy logic-controlled genetic algorithm for bike station prioritization and an inference mechanism aiming to do the assignment between the stations and trucks. The solution was tested on traffic data collected from the Citi Bike New York bike-sharing system. The proposed method shows overall superior performance compared to other algorithms that are specific to capacitated vehicle routing problems: standard genetic algorithm, ant colony optimization, Tabu search algorithm, and improved performance compared to Harris Hawks optimization for some scenarios. Since the algorithm is independent of past traffic measurements, it applies to any other potential bike-sharing system. [ABSTRACT FROM AUTHOR]

Network topology is the basic for the development of traffic management and control. In a road network, bayonets with installation of surveillance facilities are key components to recognize traffic congestion from time to time. Therefore, identifying the essential bayonets in a road network becomes one of the most efficient ways to alleviate traffic congestion for traffic engineers and transport department. To do so, this paper aims to propose a novel sorting algorithm based on similarity measurements and traffic flow information to efficiently identify key bayonets in road networks. Our research results show that by analyzing the bayonet data in a fixed period of time in a medium-sized city of China, we have successfully identified the location of key bayonet points. Most of these key bayonet points are closed to residential areas and important traffic stations. The rank of these bayonet points can help the city managers better understand the topological characteristics of the road network as well as the propagation of congestion so as to make the traffic policies or control strategies for traffic congestion alleviation. [ABSTRACT FROM AUTHOR]

Identifying traffic congestion accurately is crucial for improving the expressway service level. Because the distributions of microscopic traffic quantities are highly sensitive to slight changes, the traffic congestion measurement is affected by many factors. As an essential part of the expressway, service areas should be considered when measuring the traffic state. Although existing studies pay increasing attention to service areas, the impact caused by service areas is hard to measure for evaluating traffic congestion events. By merging ETC transaction datasets and service area entrance data, this work proposes a traffic congestion measurement with the influence of expressway service areas. In this model, the traffic congestion with the influence of service areas is corrected by three modules: 1) the pause rate prediction module; 2) the fitting module for the relationship between effect and pause rate; 3) the measurement module with correction terms. Extensive experiments were conducted on the real dataset of the Fujian Expressway, and the results show that the proposed method can be applied to measure the effect caused by service areas in the absence of service area entry data. The model can also provide references for other traffic indicator measurements under the effect of the service area. [ABSTRACT FROM AUTHOR]

The waterfront in the South Bronx in New York City is used industrially and harbors the Harlem River Yards (HRY). The HRY borders an environmental justice area, which includes a mixed-use area that is separated from a densely populated residential area by interstates. Recently, development of the HRY has expanded including the 2018 opening of a large online store warehouse. The goal of this study was to evaluate trends in traffic congestion nearby the HRY between 2017 and 2019. We analyzed one-hourly time series of crowd-sensed traffic congestion maps, both at the neighborhood scale and the road stretch level. Traffic radar measurements at two locations did not indicate bias in the crowd-sensed data over the study period, i.e., changed mappings between vehicle speed and the reported congestion. In the mixed-use areas, traffic congestion increased significantly during all hours of the day, with greatest increases at night and in the morning. Congestion increased close to the entrances of the HRY and along routes used by pedestrians and bicyclists to access a nearby recreational area. In the residential area, congestion increased significantly from midnight to morning and was unchanged for the remainder of the day. On the interstates, congestion decreased during the daytime but increased at night. Neighborhood-scale traffic congestion increased in mixed-use and residential areas in an environmental justice community. Our methods can be applied globally as long as crowd-sensed traffic data can be acquired. The data enable communities to advocate for mitigating measures. • We assessed traffic congestion in an environmental justice community over 2 years. • We analyzed time series of crowd-sensed traffic maps. • Traffic increased in the South Bronx in New York City between 2017 and 2019. • The local community will use the data to advocate for mitigating measures. • Crowd-sensed traffic data allow assessing community impacts of traffic globally. [ABSTRACT FROM AUTHOR]

Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy. [ABSTRACT FROM AUTHOR]

There has been an increase in vehicles across the globe. Also, the congestion due to traffic has leapfrogged in India. The traffic flow information has been required to find out the route with minimum congestion and forecast the traffic. And this has been a part of the Intelligent Transportation System (ITS) which would help build smart cities. A lot of work has been done on the traffic measurement system. But the integration of emerging techniques such as the Internet of Things (IoT) and cloud computing has provided a lot of research scope in ITS. This paper has proposed an IoT-based method to determine the real-time traffic flow in a road section with ultrasonic sensors, Arduino, ESP8266 Wi-Fi module, and an open-source cloud. There has been an average traffic flow every five minutes to be displayed in the cloud platform. This method can be very much cost-effective with less power consumption and improved accuracy. Hence, the proposed IoT-based technique has provided the traffic flow data, and this data shall further be used for traffic predictions using machine learning algorithms. [ABSTRACT FROM AUTHOR]

Traffic congestion is caused due to an imbalance of transportation demand and supply. Traffic congestion is not only responsible for harming our health but also for not gaining full potential in our economic sector. Traffic congestion is an obstacle to the development of any country as it creates a huge amount of economic costs, discomfort cost, and alike. In the current scenario Indore city also prevails such a condition, some intersections of the city are badly affected by traffic congestion which causes potential hazard and delays so traffic congestion study is much needed at some potential road intersections. Several parameters like segmental delay, delay ratio, and relative delay ratio have been used to effectively measure traffic congestion at some potential sections taken for 6 locations. A model developed for segmental delay (Vehicle-second), segmental delay (person-second), delay rate, and relative delay Rate. [ABSTRACT FROM AUTHOR]

Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station- and bridge-cameras and manage to keep the error rate of lost vehicles under 10%. [ABSTRACT FROM AUTHOR]

To improve the operational efficiency of airport surfaces, this paper studies the air traffic congestion prediction of airport surfaces, demonstrates the limitations of traffic congestion prediction, and proposes a prediction method for airport surface traffic congestion based on decision tree. Firstly, the definition and measurement methods of traffic congestion in airport surfaces are promoted. Then, the key factors affecting traffic congestion are extracted, and a prediction model of traffic congestion is established. Finally, we verify the validity of the model based on actual operation data from Atlanta. The results show that the accuracy of the prediction is 70%. [ABSTRACT FROM AUTHOR]

Crowd-sourced traffic data potentially allow prediction of traffic-related air pollution at high temporal and spatial resolution. To examine associations (1) of radar-based traffic measurements with congestion colors displayed on crowd-sourced traffic data maps and (2) of black carbon (BC) levels with radar and crowd-sourced traffic data. At an off-ramp of an interstate and a small one-way street in a mixed-use area in New York City, we used radar devices to obtain vehicle speeds and flows (hourly counts) for cars and trucks. At these radar sites and at an additional non-radar equipped site at a 2-way street, we monitored BC levels using aethalometers in the summer and early fall of 2017. At all three sites, free-flow traffic conditions typically did not occur due to the nearby presence of traffic lights and forced turns. We also downloaded real-time traffic maps from a crowd-sourced traffic data provider and assigned an ordinal integer congestion color code CCC to the congestion colors, ranging from 1 (dark red) to 5 (gray). CCC increased with vehicle speed. Traffic flow was highest for intermediate speeds and intermediate CCC. Regression analyses showed that BC levels increased with either segregated or total vehicle flows. At the off-ramp, time-dependent BC levels can be inferred from time-dependent CCC and radar-derived mean vehicle flow data. A unit decrease in CCC for a mean traffic flow of 100 vehicles/h was associated with a mean (95% CI) increase in BC levels of 0.023 (0.028, 0.018) μg/m3. At the small 1-way and the 2-way street, BC levels were also negatively associated with CCC, though at a >0.05 significance level. Use of inexpensive crowd-sourced traffic data holds great promise in air pollution modeling and health studies. Time-dependent traffic-related primary air pollution levels may be inferred from radar-calibrated crowd-sourced traffic data, in our case radar-derived mean traffic flow and widely available CCC data. However, at some locations mean traffic flow data may already be available. • Crowd-sourced traffic data can be calibrated by traffic-radar measurements • Traffic-related black carbon levels depend on traffic volume but may also depend on vehicle speed • Traffic-related black carbon levels can be associated with congestion colors displayed on crowd-sourced traffic maps • Use of inexpensive crowd-sourced traffic data holds great promise in air pollution modeling and health studies [ABSTRACT FROM AUTHOR]

With the implementation of the freeway free policy during the holidays, traffic congestion in the freeway becomes a common phenomenon. In order to alleviate traffic pressure, traffic flow prediction during the holidays has become a problem of great concern. This paper proposes a hybrid prediction methodology combining discrete Fourier transform (DFT) with support vector regression (SVR). The common trend in the traffic flow data is extracted using DFT by setting an appropriate threshold, which is predicted by extreme extrapolation of the historical trend. The SVR method is applied to predict the residual series. The experimental results with measured data collected from the toll stations in Jiangsu province of China show that the proposed algorithm has higher accuracy compared with the traditional method, and it is an efficient method for traffic flow prediction during the holidays. [ABSTRACT FROM AUTHOR]

Adding capacity is one policy mechanism to alleviate congestion. However, the empirical evidence strongly suggests that additional capacity only makes congestion worse. This study analyzes the differential effects of additional freeway capacity versus additional arterial capacity on vehicle kilometers of travel (VKT) in metropolitan areas across the U.S. The analysis uses vehicle data and household data from the 2001 and the 2009 National Household Travel Surveys (NHTS) and includes stock and flow measures of road capacity, road congestion, commuter demand, and economic growth for metropolitan areas. Taking into account differences between metropolitan areas on each measure, the study adopts a novel multilevel model approach to estimate how additional capacity affects VKT. Results indicate that adding more arterial capacity slightly decreases VKT over a lag period from six years (1995 to 2001) to eight years (2001 to 2009), probably because adding arterials shortens routes between origins and destinations more so than adding freeways. Consistent with expectations, VKT is lower in more congested metropolitan areas, and in metropolitan areas that got more congested. Results also indicate that rebound effects (higher fuel-economy vehicles are driven much more than lower fuel-economy vehicles) will at least partially offset the demand management benefits of (gasoline) price sensitivity (higher gasoline prices decrease VKT). [ABSTRACT FROM AUTHOR]

Short period traffic counts (SPTCs) are conducted routinely to estimate the annual average daily traffic (AADT) at a particular site. This paper uses Indian traffic volume data to methodically and extensively study the effect of four aspects related to the design of SPTCs. These four aspects are: (i) for how long, (ii) on which days should SPTCs be carried out, (iii) how many times, and (iv) on which months should SPTCs be carried out? The analyses indicate that the best durations for conducting SPTCs are 3 days (starting with a Thursday) and 7 days, for total traffic and truck traffic, respectively. Further, these counts should be repeated twice a year keeping a separation of two months between the counts to obtain good estimates of AADT at minimal cost. An additional outcome of this study has been the determination of seasonal factor values for roads in developing economies, like India. [ABSTRACT FROM PUBLISHER]

To avoid bias caused by the high percentage of rear-end collisions in the generic models, only the observed sideswipe collisions on Highway I-5 in the central Puget Sound, Washington State, area are used to analyze the matched case-control logistic regression model. Considering the spatial-temporal characteristics of traffic flow and short-term variation of the sideswipe collision occurrence, a comprehensive analysis of sideswipe collision occurrence and its relationship with the freeway flow across lanes and detector locations are studied. The results imply that sideswipe collisions are more likely to occur at straight and level segments of multilane freeways in off-peak hours. High average occupancy and low average flow and speed variance upstream of collision location tend to increase the probability of sideswipe collision in congested scenarios. In contrast, high average speed and coefficient of variation in speed, low speed variance and coefficient of variation in occupancy, and high average absolute difference in speed between adjacent lanes downstream of collision location may decrease the likelihood of sideswipe collisions in uncongested conditions. Furthermore, the average absolute difference in occupancy and speed between upstream and downstream of the collisions location leads to an increase in the occurrence of sideswipe collisions, while standard deviation of absolute difference in occupancies and speeds exerts opposite effects. The sideswipe collision model proposed is validated using sideswipe collision data of SR 520 freeway, and both the transferability and stability of the proposed sideswipe collision model are statistically tested. [ABSTRACT FROM AUTHOR]

Congestion has become highly recognized as a worldwide traffic problem, as traffic demand has grown steadily over the past few decades. Variable speed limits (VSLs) are an intelligent transportation system (ITS) measure that limits mainline flow to mitigate bottleneck congestion. Currently, VSLs have become proactive based on short-term prediction. Proactive VSLs succeed in simulation evaluations, but few have been deployed in the field and their real-world effectiveness has not been proven. Various factors may lead to this limitation, such as the absence of reliable field application software, accuracy of prediction models, and high computation time for proactive control. To address this research gap, this study reports a preliminary VSL test and details its implementation results on Whitemud Drive, Edmonton, Canada. First, based on field traffic measurements before VSL control, recurrent bottleneck locations are identified. Second, the proactive control algorithm is briefly introduced. Then, a software application is designed to realize all necessary functions for VSL field implementation.With all these in hand, the preliminary field test was conducted and the VSL control performance and reliability are evaluated. Finally, the results for before and after VSL control implementation are analyzed in depth. The analysis compares average traffic speed, standard deviation of speed, total travel time, and total travel distance. The results from this study confirm that proactive VSL can relieve recurrent traffic congestion effectively. [ABSTRACT FROM AUTHOR]

This study aims to analyze and visualize the traffic performance condition of the highway network in the Mississippi Gulf Coast area using the traffic flow models to identify the congested links in a Geographic Information System (GIS). The traffic performance condition of the highway network was estimated based on each link's average daily traffic, design hourly volume, link capacity, and volume-to-capacity ratio (v/c), which were calculated using the traffic flow models in Highway Capacity Manual 2010. County polygons with geographical information were obtained from the Mississippi Automated Resource Information System (MARIS), and ArcMap GIS was used to symbolically present as geographic information the volume to capacity ratio of each link of the study area. The risk traffic area was also presented considering flood inundation in hurricane. The statistical analysis results indicated that among the total of 4,274 links in the study area, 4,231 links have v/c ratio values under 1.0 and 16 links have v/c ratio values above 1.0, and the links with high v/c ratios may indicate the highly congested locations. [ABSTRACT FROM AUTHOR]

Conditions at a freeway bottleneck are predicted with a two-dimensional stochastic differential equation (SDE) model of flow and speed. Stochastic behavior is assumed to be standard Brownian motion. To fully describe conditions observed in the field, it is necessary to couple the SDE model with a stochastic capacity model, thus forming a stochastic capacity-differential equation (SCDE) model. The model contains mechanisms to (1) trigger a breakdown event when the traffic flow reaches or exceeds the freeway capacity, and (2) identify when recovery takes place. Measures of freeway performance are derived from SCDE model predictions. They include breakdown probability, expressed as a function of traffic flow and time-of-day, and recovery time, i.e., the length of time the freeway remains in a congested state before returning to a free-flow state. To achieve dependable model forecasts, it is necessary to impose constraints when fitting the stochastic capacity model. The constraint condition consists of matching the breakdown and prebreakdown flow distributions derived from field data and stochastic capacity model forecasts. Sensitivity analyses are performed to test validity of the model-fitting procedure and to test the dependability of the SCDE model forecasts. An empirical study of recurrent workday freeway congestion on I-93 in Salem, NH is used to demonstrate the approach. The approach demonstrates the need to treat breakdown as a random process. [ABSTRACT FROM AUTHOR]

Hierarchy is an important property of a street network, which suggests that only a small number of streets are prominent. A previous empirical study of a European city has identified four levels of scale in a street network, namely the top 1%, top 20%, bottom 80%, and bottom 20%. This paper investigates such street hierarchies in a large Asian city, Wuhan, with a complicated network of streets. Based on network analysis, we find that street hierarchies in this case study are slightly different so that the fourth scale is adjusted from the initial 20 to 25%. The detected street hierarchies are further compared to the intensity of large-scale traffic flows at different time scales. We find that distributions of both daily and hourly traffic conform well to the street hierarchies. More specifically, the 20% of top streets accommodate about 98% of traffic flow, and the 1% of top streets account for more than 60% of traffic flow. Moreover, this finding indicates that the current street network of Wuhan needs to be improved because the top 20% of streets are rather overburdened leading to traffic congestion. Our study not only provides new quantitative evidence as to the emergence of street hierarchies but also highlights the possible traffic congestion. [ABSTRACT FROM AUTHOR]

A valuable consideration is inserted into car-following theory to investigate the difference of drivers' anticipation. Furthermore, a fresh macro traffic model involving the difference of the drivers' anticipation is deduced from the car-following model by applying transformation relationship from micro to macro variables. And this effect resulted from the difference of the drivers' anticipation is observed via numerical simulation. The results show that the difference of drivers' anticipation has a negligible impact on traffic flow. [ABSTRACT FROM AUTHOR]

Sound unpleasantness due to urban road traffic at crossroads was investigated through a listening test performed in a laboratory environment. Thirty-two sound sequences were created. Four factors were studied: sound level, type of crossroads, traffic density and traffic composition. The results showed that sound unpleasantness was mainly influenced by sound level. Decreasing traffic density by a factor of 2 also reduced sound unpleasantness. The roundabout was judged less unpleasant than the crossroads with traffic lights, when traffic density was high. This outcome may be explained by differences in traffic flow dynamics between the crossroads with traffic lights and the roundabout: on the one hand, for the crossroads with traffic lights, the traffic flow resembled a constant pulsed flow, with alternate cycles of acceleration/deceleration; on the other hand, for the roundabout, the traffic flow was more similar to a constant fluid flow, with less marked cycles of acceleration/deceleration. For the roundabout only, sound unpleasantness increased with the presence of heavy vehicles (or buses). Actually, sound sequences with only light vehicles were rated less unpleasant for the roundabout than for the crossroads with traffic lights. This could be due to differences in their spectral pattern. In particular, for traffic-light scenarios, higher levels within the frequency range [400, 3000] Hz occurred when vehicles passed by, in connection with higher vehicle speeds, and higher levels in the high frequencies (around 16 kHz) occurred more often when vehicles stopped, indicating the presence of braking noise with a shrill character. [ABSTRACT FROM AUTHOR]

The sensor location problem is that of locating the minimum number of traffic sensors at intersections of a road network such that the traffic flow on the entire network can be determined. In this paper, we provide a new necessary condition on the location of these sensors to enable the traffic flow throughout the network to be computed. This condition is not sufficient in general, but we show that for a large class of problem instances, the condition is sufficient. Many typical road networks are included in this category, and we show how our condition can be used to inform traffic sensor placement. [ABSTRACT FROM AUTHOR]

In this paper, we present a visual analysis system to explore sparse traffic trajectory data recorded by transportation cells. Such data contains the movements of nearly all moving vehicles on the major roads of a city. Therefore it is very suitable for macro-traffic analysis. However, the vehicle movements are recorded only when they pass through the cells. The exact tracks between two consecutive cells are unknown. To deal with such uncertainties, we first design a local animation, showing the vehicle movements only in the vicinity of cells. Besides, we ignore the micro-behaviors of individual vehicles, and focus on the macro-traffic patterns. We apply existing trajectory aggregation techniques to the dataset, studying cell status pattern and inter-cell flow pattern. Beyond that, we propose to study the correlation between these two patterns with dynamic graph visualization techniques. It allows us to check how traffic congestion on one cell is correlated with traffic flows on neighbouring links, and with route selection in its neighbourhood. Case studies show the effectiveness of our system. [ABSTRACT FROM PUBLISHER]

This paper presents a simple spatial equilibrium model for a linear monocentric city to investigate the effects of rationing and pricing on morning commuters' travel cost and modal choice behavior in each location. Under rationing and pricing, every day in the morning peak hour, each commuter is classified as either "free" or "rationed". "Free" commuters are allowed to use the highway without paying the toll, whereas "rationed" commuters can avoid the toll only if they travel by transit. Each day, a fraction of commuters are rationed in their free use of the highway, and the rationing fractions are determined systematically so that everyone is equally rationed in a given period. It is found that Pareto-improving rationing and pricing scheme might be obtained as a combination of the rationing degree and the toll associated with rationing. Extension to the rationing and pricing scheme with cordon and park-and-ride service has been made. Cordon and park-and-ride might help in improving the efficiency of rationing and pricing strategy although remains its Pareto-improving property. [ABSTRACT FROM AUTHOR]

• Weigh in motion (WIM) data are analysed for evaluating fatigue of bridges. • Model uncertainties are included through a probabilistic assessment. • Existing fatigue load models fail in representing the actual load effect. • A new fatigue load model is developed that agrees well with WIM data. • The new model can easily be calibrated with WIM data from other roads. Fatigue verification of bridge structures requires information on the loads induced by heavy vehicles, which can be obtained from weigh in motion measurement (WIM) systems. The current fatigue load models applied in Europe are based on traffic load measurements in 1986. This paper evaluates the appropriateness of these models for today's traffic by comparing their load effects with those of recent WIM databases, covering the years 2008 to 2018. A procedure is derived to determine the required size of the WIM database for a sound representation of the fatigue loads. The effects of traffic jams are evaluated and the required safety margins or partial factors are derived. As already concluded by others, it appears that the most frequently used fatigue load model is unable to represent the fatigue action effects of today's European traffic. In addition, the paper demonstrates that the other fatigue load models are also inaccurate. A new fatigue load model is proposed that is easy in use and gives a significant improvement in accuracy compared to the existing models. The parameters in this model can easily be calibrated for other WIM databases. [ABSTRACT FROM AUTHOR]

• A hybrid observer merging PWSL modeling and Kalman filter estimator is proposed • Two new kNN-based mechanisms for detecting road traffic congestion are designed. • Measurements from the four-lanes SR-60 freeway in California are used for validation. • The detection results show the superior performance of the new coupled kNN-ES mechanism. A systematic approach for monitoring road traffic congestion is developed to improve safety and traffic management. To achieve this purpose, an improved observer merging the benefits of a piecewise switched linear traffic (PWSL) modeling approach and Kalman filter (KF) is introduced. The PWSL-KF observer is utilized as a virtual sensor to emulate the traffic evolution in free-flow mode. In the proposed approach, residuals from the PWSL-KF model are used as the input to k-nearest neighbors (kNN) schemes for congestion detection. Here, kNN-based Shewhart and exponential smoothing schemes are designed for discovering the traffic congestions. The proposed detectors merge the desirable properties of kNN to appropriately separating normal from abnormal features and the capability of the monitoring schemes to better identify traffic congestions. In addition, kernel density estimation has been utilized to set nonparametric control limits of the proposed detectors and compared them with their parametric counterparts. Tests on traffic measurements from the four-lane State Route 60 in California freeways show the effectiveness of the PWSL-KF-based kNN methods in supervising traffic congestions. [ABSTRACT FROM AUTHOR]